Jurnal Elektronik Ilmu Komputer Udayana
type: channel
language: en
publication name: Jurnal Elektronik Ilmu Komputer Udayana
items: Klasifikasi Kualitas Air Layak Minum Menggunakan Algoritma Genetika dan Random Forest
items: Rancang Bangun Algoritma ChaCha20-Poly1305 Berbasis SHA-256 Untuk Pengamanan Dokumen Penting Masyarakat
items: Penyisipan Handwriting Signature Pada Karya Seni Digital Menggunakan Metode Steganografi LSB Matching Revisited dan Huffman Coding
items: Penggunaan LSTM dan Fast-Text dalam Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi dengan Seleksi Fitur Information Gain
items: Optimasi Convolutional Neural Network pada Klasifikasi Mood Musik Berbasis Fitur Audio
items: Penyisipan Pesan Rahasia pada Citra Digital dengan Metode Steganografi dan Algoritma Least Significant Bit (LSB)
items: Klasifikasi Serangan DDoS Menggunakan Algoritma Support Vector Machine dan Correlation-Based
items: Perancangan Website Penyedia Kost Menggunakan Metode Design Thinking
items: Sistem Rekomendasi Genre Musik Menggunakan SVM dengan Pendekatan Content-Based Filtering
items: Klasifikasi Cyberbullying dengan Menggunakan Convolutional Neural Network (CNN) dan ELMo Embeddings
items: Analisis Sentimen Ulasan Produk Kecantikan Menggunakan Bi-LSTM dan Weighted FastText Embedding
items: Implementasi Ontologi OWL dan Rule-Based System CART untuk Manajemen Pengetahuan Kidung Bali
items: Perancangan Ulang Website Baliedu Tours and Travel Menggunakan Metode User Centered Design
items: Pemanfaatan Bi-LSTM dan GloVe dengan SMOTE untuk Menganalisis Sentimen Pengguna Bank Digital
items: Perancangan Aplikasi Destinasi Wisata Kabupaten Bangli Menggunakan Metode Haversine Formula
items: Sistem Rekomendasi Hotel Menggunakan Algoritma Item-Based Clustering Hybrid Method
items: Analisis dan Pengembangan Design Aplikasi EasyBill Menggunakan Pendekatan User Centered Design
items: Analisis Sentimen Terhadap Pembatalan Piala Dunia U-20 di Indonesia Menggunakan Algorima Naïve Bayes dan Genetika
items: Implementasi Intrusion Prevention System (IPS) Menggunakan Signature – Based Detection Berbasis Suricata
items: Penentuan Entitas Tokoh Pada Satua Bali Menggunakan Algoritma Conditional Random Fields
items: Perbandingan Performa CNN Inception-V3 dan ResNet50 Dalam Klasifkasi Kematangan Buah Tomat
items: Sistem Informasi Manajemen Keuangan Pribadi Dengan Input Suara Dan Berbasis Iot
items: Rancangan Sistem Monitoring Suhu, Kelembaban, Kecepatan Angin Untuk Memprediksi Keamanan Jalur Pendakian
items: A Case-Based Reasoning with Artificial Intelligence Approach for Laptop Repair Assistant System
items: Pengamanan Data menggunakan Algoritma Enkripsi Advanced Encryption Standard (AES) dan Zigzag Cipher
items: Optimasi Algoritma SVM dengan Ekstraksi Fitur Warna pada Klasifikasi Biji Kopi Sangrai
items: Redesign UI/UX Aplikasi Property Management Berbasis User-Centered Design dengan Evaluasi Heuristic Evaluation dan System Usability Scale
items: Klasifikasi Kategori Cerita Pendek Menggunakan XGBoost dengan Seleksi Fitur Chi-Square
items: Sistem Representasi Digital Wayang Kulit Bali Menggunakan AHP Dengan Pendekatan Ontologi
items: Klasifikasi Mood Musik Menggunakan Support Vector Machine Berdasarkan Fitur Spektral

Klasifikasi Kualitas Air Layak Minum Menggunakan Algoritma Genetika dan Random Forest
type: item
description: Akses terhadap air minum yang aman merupakan kebutuhan dasar yang krusial bagi kesehatan masyarakat, namun pemantauan kualitas air secara manual melalui laboratorium sering terkendala biaya dan waktu. Penelitian ini bertujuan mengimplementasikan model artificial intelligence untuk klasifikasi kualitas air layak minum (potable) dan tidak layak minum (non-potable) dengan mengoptimalkan Random Forest melalui seleksi fitur menggunakan Algoritma Genetika. Penggunaan Algoritma Genetika dimaksudkan untuk mengatasi tantangan pemilihan fitur yang relevan. Penelitian menggunakan Water Quality Dataset dengan 3.276 data dan 10 atribut. Tahapan pengolahan meliputi penanganan missing value, Winsorization, Min-Max Normalization, Random Oversampling, seleksi fitur dengan Algoritma Genetika, dan klasifikasi Random Forest. Hasil menunjukkan seleksi fitur menghasilkan subset terbaik dengan fitness 73,76%. Hasil klasifikasi ini kemudian dilakukan validasi K-Fold Cross Validation, mendapatkan hasil model dengan seleksi fitur mengalami penurunan akurasi dari 68,57% menjadi 66,58% dan presisi dari 64,86% menjadi 60,00%. Namun, recall meningkat dari 48,45% menjadi 51,66% serta F1-score dari 55,28% menjadi 55,47%, menunjukkan peningkatan kemampuan mengenali kelas minoritas meskipun berdampak pada penurunan performa pada kelas mayoritas.
subject: Articles
  topic: section
subject: random forest
  topic: keywords
subject: klasifikasi
  topic: keywords
subject: kualitas air
  topic: keywords
subject: algoritma genetika
  topic: keywords
subject: seleksi fitur
  topic: keywords
subject: machine learning
  topic: keywords
creator: Ni Made Ayu Pranasanthi Dewi
creator: I Made Widiartha
creator: I Dewa Made Bayu Atmaja Darmawan
creator: I Gusti Agung Gede Arya Kadyanan
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 633
ending page: 642
doi: 10.24843/

Rancang Bangun Algoritma ChaCha20-Poly1305 Berbasis SHA-256 Untuk Pengamanan Dokumen Penting Masyarakat
type: item
description: Data security is a critical aspect in protecting sensitive information, especially in digital environments where data is vulnerable to unauthorized access. This study aims to implement the ChaCha20-Poly1305 algorithm with a SHA-256-based key for data protection, as well as to evaluate its performance and security through several testing scenarios. The method involves implementing the ChaCha20-Poly1305 algorithm using keys generated from SHA-256 hashing, followed by testing using parameters such as Avalanche Effect, image visual analysis, MSE, PSNR, and performance comparison with the AES-256-GCM algorithm. The results show that the system successfully restores data with 100% similarity after decryption. The Avalanche Effect achieves an average value of 50.038%, indicating high sensitivity to key changes. Additionally, entropy increases from 0.7159 to 7.9993 and pixel correlation decreases from 0.7914 to -0.0085, indicating a high level of randomness in the encrypted data. The MSE value of 16,715.31 and PSNR value of 6.19 dB indicate significant differences between original and encrypted data. Based on these results, it can be concluded that the ChaCha20-Poly1305 algorithm with a SHA-256 key is capable of securing data with a high level of randomness while maintaining data integrity after the decryption process.
subject: Articles
  topic: section
subject: ChaCha20-Poly1305
  topic: keywords
subject: SHA-256
  topic: keywords
subject: Encryption
  topic: keywords
subject: Data Security
  topic: keywords
subject: Avalanche Effect
  topic: keywords
subject: Entropy
  topic: keywords
creator: I Made Chandra Widjaya
creator: I Ketut Gede Suhartana
creator: Luh Gede Astuti
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 623
ending page: 632
doi: 10.24843/

Penyisipan Handwriting Signature Pada Karya Seni Digital Menggunakan Metode Steganografi LSB Matching Revisited dan Huffman Coding
type: item
description: Perlindungan hak cipta pada karya seni digital merupakan hal yang krusial. Penelitian ini bertujuan melindungi hak cipta karya seni digital melalui penyisipan handwriting signature menggunakan metode steganografi Least Significant Bit Matching Revisited (LSBMR) dan Huffman Coding. Informasi yang bertindak sebagai invisible watermark merupakan citra Handwriting signature dengan format PNG; citra cover memiliki dimensi 3000 x 3000 piksel. Hasil evaluasi menunjukan bahwa seluruh citra stegano melampaui batas bawah Peak Signal-to-Noise Ratio (PSNR) sebesar 30 dB. Kualitas tertinggi dicapai oleh citra RGBA dengan nilai PSNR 79.34 dan Mean Square Error (MSE) terendah 0.0008. Sebaliknya, citra Grayscale menghasilkan PSNR terendah (73.39 dB) dan MSE tertinggi (0.0030), namun angka ini membuktikan citra tetap mampu mempertahankan kualitas visualnya dengan sangat baik. Adapun citra RGB dan Blue Monochromic memperoleh hasil yang identik (PSNR 78.09 dB ; MSE 0.0010). Proses ekstraksi juga berhasil mengembalikan citra tanda tangan secara sempurna tanpa kerusakan sedikitpun dengan nilai Bit Error Ratio sebesar 0.
subject: Articles
  topic: section
creator: Putu Arya Dharma Kesuma
creator: I Ketut Gede Suhartana
creator: I Gusti Agung Gede Arya Kadyanan
creator: I Gusti Ngurah Anom Cahyadi Putra
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
doi: 10.24843/

Penggunaan LSTM dan Fast-Text dalam Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi dengan Seleksi Fitur Information Gain
type: item
description: This study aims to perform aspect-based sentiment analysis on user reviews of the GoTube application from the Google Play Store. The large volume of user reviews makes manual analysis inefficient, requiring an automated approach to extract meaningful insights. Data were collected through web scraping, resulting in over 280,000 reviews, which were further processed through cleaning, labeling, and data balancing. To improve label consistency, a pseudo-labeling approach using IndoBERT was applied. The proposed method combines Information Gain for feature selection, FastText for word representation, and Long Short-Term Memory (LSTM) for sentiment classification. In addition, Latent Dirichlet Allocation (LDA) was used for aspect extraction. The experimental results show that the sentiment classification model achieved an accuracy of 95.37% and F1-score of 0.9537 using an optimal threshold of 0.59, with balanced precision (0.9539) and recall (0.9534). Meanwhile, the aspect classification model achieved an accuracy of 87.12% with a macro F1-score of 0.8697. These findings indicate that the combination of feature selection, subword-based representation, and sequential modeling is effective in producing accurate and informative aspect-based sentiment analysis
subject: Articles
  topic: section
subject: sentiment analysis
  topic: keywords
subject: aspect-based sentiment analysis
  topic: keywords
subject: LSTM
  topic: keywords
subject: FastText
  topic: keywords
subject: information gain
  topic: keywords
creator: Maedelien Tiffany Kariesta Simatupang
creator: I Made Widiartha
creator: Luh Gede Astuti
creator: I Gede Santi Astawa
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 599
ending page: 611
doi: 10.24843/

Optimasi Convolutional Neural Network pada Klasifikasi Mood Musik Berbasis Fitur Audio
type: item
description: This study develops an audio-based music mood classification system using a Convolutional Neural Network (CNN) implemented independently without deep learning frameworks in the core architecture and model training process. This approach was chosen so that the model learning process, from forward propagation to parameter updates, can be understood more thoroughly. The GTZAN dataset is used, comprising 999 audio files. Each audio sample is processed at a sampling rate of 22050 Hz, converted to mono, and standardized into a 30-second segment. Audio representation is constructed as a 71-dimensional feature vector consisting of MFCC, spectral features, spectral contrast, chroma features, tonnetz, energy-related features, and tempo, and then standardized using StandardScaler. Class labels are defined into three mood categories, namely positive, negative, and neutral. This study evaluates three CNN architecture variants (5 layer, 7 layer, and 9 layer architectures) and applies training parameter settings including class weighting, label smoothing, Gaussian noise, and early stopping by monitoring the validation Macro F1-score. Experimental results indicate that hyperparameter tuning improves validation performance compared to the baseline, while fine-tuning provides performance improvement under certain configurations. The best model is obtained from the 7 layer architecture after fine-tuning, achieving an accuracy of 0,97 and a Macro F1-score of 0,97. These results indicate that the extracted audio features are capable of representing music mood characteristics effectively. Furthermore, architectural variation has a positive impact on classification performance; however, increasing network depth does not necessarily lead to consistently better performance. The system is integrated into a Flask-based web application for inference and result presentation.
subject: Articles
  topic: section
subject: Music Mood Classification
  topic: keywords
subject: Audio Features
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Model Optimization
  topic: keywords
subject: Music Information Retrieval
  topic: keywords
creator: Merry Royanti Manalu
creator: Made Agung Raharja
creator: Ngurah Agus Sanjaya ER
creator: I Dewa Made Bayu Atmaja Darmawan
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 588
ending page: 598
doi: 10.24843/

Penyisipan Pesan Rahasia pada Citra Digital dengan Metode Steganografi dan Algoritma Least Significant Bit (LSB)
type: item
description: This study discusses the implementation of the Least Significant Bit (LSB) steganography method to insert secret messages into digital images by adding the Caesar Cipher algorithm as an additional security layer. The secret message is first encrypted using the Caesar Cipher before being embedded into the least significant bit of digital image pixels. This approach ensures that the message is not only visually hidden but also protected in terms of content. The system is developed as a web-based application using HTML, CSS, and JavaScript, enabling users to perform message insertion and extraction directly through a browser without installing additional software. Image quality evaluation is conducted using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The experimental results show that the LSB method produces low MSE values and high PSNR values, indicating minimal visual distortion. The combination of LSB steganography and Caesar Cipher encryption proves effective in enhancing information security on digital image media.
subject: Articles
  topic: section
subject: Steganography
  topic: keywords
subject: Least Significant Bit (LSB
  topic: keywords
subject: Caesar Cipher
  topic: keywords
subject: Digital Image
  topic: keywords
subject: Information Security
  topic: keywords
subject: PSNR
  topic: keywords
subject: MSE
  topic: keywords
creator: Made Dhandy Satria Mahagangga
creator: Luh Gede Astuti
creator: Luh Arida Ayu Rahning Putri
creator: I Gede Surya Rahayuda
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 578
ending page: 587
doi: 10.24843/

Klasifikasi Serangan DDoS Menggunakan Algoritma Support Vector Machine dan Correlation-Based
type: item
description: Distributed Denial-of-Service (DDoS) that attacks targeting server resources are increasing every year, making early detection crucial. While network traffic analysis can aids detection, high dimensionality data and noise make manual efforts more difficult. This research implements a Support Vector Machine (SVM) with Correlation-Based Feature Selection (CFS) to reduce dimensionality, comparing it against a full-feature SVM model. Both use Radial Basis Function (RBF) kernel with hyperparameter C = 97 and gamma = 0.74. The CFS-SVM model will implemented in real-time. Performance is evaluated using confusion matrix (recall, precision, F1-Score, and accuracy) and computational time needed. Results show the CFS SVM model achieves 99.55% recall, 97.28% precision, 98.40% F1-Score, and 98.40% accuracy, needing 310.73s for training and 0.21s for testing. In Contrast, the full-feature model yields 98.89% for recall, 99.95% precision, 99.42% F1-Score, and 99.42% accuracy, but taking 438.96s for training proses and 0.29s for testing. Although the full-feature model exhibits superior metrics, applying CFS significantly reduces computational time and the computational load of feature extraction. Therefore, the CFS-SVM approach has proven to be more suitable for real-time detection systems.
subject: Articles
  topic: section
subject: Classification
  topic: keywords
subject: network security
  topic: keywords
subject: SVM
  topic: keywords
subject: Distributed Denial-of-Service
  topic: keywords
subject: Correlation-Based Feature Selection
  topic: keywords
creator: I Nyoman Arista Wisnawa
creator: I Made Widiartha
creator: I Made Widhi Wirawan
creator: Made Agung Raharja
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 566
ending page: 577
doi: 10.24843/

Perancangan Website Penyedia Kost Menggunakan Metode Design Thinking
type: item
description: This research was initiated by the evaluation of an existing boarding house provider website, which obtained a System Usability Scale (SUS) score of 68,18 from 15 respondents who had used the website to search for boarding house information. To address this, the study employs the Design Thinking method to redesign the boarding house website. The primary objectives are to measure the functional levels of the website following the Design Thinking process and to compare its functionality before and after the redesign. Additionally, the System Usability Scale (SUS) was utilized for testing, while the TF-IDF algorithm and Cosine Similarity were implemented to calculate document similarity for the recommendation feature. The developed website prototypes achieved successive SUS scores of 72 and 75,5. The final score met the system's target for providing an above-average user experience, categorized as “acceptable” in the acceptability ranges, “C” on the grade scale, and “excellent” in the adjective ratings. The application of these methods significantly improved the website's usability, as evidenced by the substantial increase in the System Usability Scale (SUS) scores.
subject: Articles
  topic: section
subject: Design Thinking
  topic: keywords
subject: System Usability Scale
  topic: keywords
subject: Redesign
  topic: keywords
creator: I Kadek Agus Wijaya Kusuma Wijaya
creator: Gst. Ayu Vida Mastrika Giri
creator: I Made Widhi Wirawan
creator: I Gede Santi Astawa
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 557
ending page: 565
doi: 10.24843/

Sistem Rekomendasi Genre Musik Menggunakan SVM dengan Pendekatan Content-Based Filtering
type: item
description: Pertumbuhan industri musik digital memunculkan fenomena information overload, menyulitkan pengguna mengeksplorasi lagu yang sesuai preferensi. Sistem rekomendasi konvensional seringkali menghasilkan rekomendasi homogen dan rentan terhadap masalah cold-start. Oleh karena itu, penelitian ini bertujuan membangun sistem rekomendasi musik berbasis web dengan pendekatan hibrida yang mengintegrasikan Support Vector Machine (SVM) dan Content-Based Filtering (CBF). Model SVM multikelas dengan Kernel Radial Basis Function (RBF) berfungsi sebagai penyaring makro untuk memprediksi 7 kelas genre berdasarkan 8 fitur audio. Selanjutnya, model CBF beroperasi di tingkat mikro menggunakan Euclidean Distance untuk mengukur kemiripan fitur antara profil riwayat pengguna dan basis data lagu. Hasil pengujian menunjukkan model SVM mencapai akurasi keseluruhan sebesar 55,39%, dengan pengenalan terbaik pada genre Classical dan HipHop. Implementasi Dynamic Hard Filtering yang membatasi ruang pencarian Euclidean Distance hanya pada genre tervalidasi terbukti efektif mereduksi anomali lintas genre. Secara keseluruhan, sistem ini berhasil menyajikan 10 rekomendasi teratas yang relevan secara musikal dan terpersonalisasi secara optimal.
subject: Articles
  topic: section
subject: Sistem Rekomendasi
  topic: keywords
subject: Genre Musik
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: Content-Based Filtering
  topic: keywords
subject: Content-Based Filtering, Euclidean Distance
  topic: keywords
creator: I Gusti Agung Istri Agrivina Shyta Devi
creator: I Made Widiartha
creator: Luh Gede Astuti
creator: I Gusti Agung Gede Arya Kadyanan
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 545
ending page: 556
doi: 10.24843/

Klasifikasi Cyberbullying dengan Menggunakan Convolutional Neural Network (CNN) dan ELMo Embeddings
type: item
description: The rapid development of social media in the digital era has led to various negative impacts. One of them is cyberbullying, which can cause serious psychological effects on victims. This study aims to analyze the best combination of hyperparameter tuning for a cyberbullying classification model using a Convolutional Neural Network (CNN) with Embedding from Language Model (ELMo) word representation, as well as to examine the effect of using stemming in the preprocessing stage on accuracy. The data used consists of English-language cyberbullying data obtained from the Kaggle website, with five classification categories: not_cyberbullying, gender, religion, age, and ethnicity. The results show that hyperparameter tuning produces the same best configuration for both conditions—without stemming and with stemming—namely 256 filters and a learning rate of 0.0001. The testing accuracy achieved is 0.8957 for the model without stemming and 0.9017 for the model with stemming. The testing loss is 0.2891 for the model without stemming and 0.2923 for the model with stemming. The application of stemming does not provide a significant improvement in accuracy and even increases the model loss, considering that ELMo as a contextual embedding is already capable of capturing morphological variations of words contextually, making the contribution of stemming limited.
subject: Articles
  topic: section
subject: Cyberbullying
  topic: keywords
subject: Text Classification
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: ELMo Embeddings
  topic: keywords
subject: Stemming
  topic: keywords
subject: Hyperparameter Tuning
  topic: keywords
creator: Anak Agung Istri Intan Permata Sari
creator: I Ketut Gede Suhartana
creator: I Gede Surya Rahayuda
creator: I Made Widhi Wirawan
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-05-31
publication date: 2026-05-31
starting page: 643
ending page: 654
doi: 10.24843/

Analisis Sentimen Ulasan Produk Kecantikan Menggunakan Bi-LSTM dan Weighted FastText Embedding
type: item
description: The rapid expansion of the beauty product sector in Indonesia has contributed to a growing volume of consumer reviews, which hold significant potential for extracting insights into user perceptions toward products. This study seeks to conduct sentiment analysis on beauty product reviews by employing the Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm, coupled with FastText word embeddings weighted through an Inverse Document Frequency (IDF) scheme. The data utilized in this research were sourced from the Female Daily platform. The research process encompasses text preprocessing, word representation using FastText, IDF-based word weighting, and Bi-LSTM model training with hyperparameter tuning involving the number of LSTM units, dropout rate, and learning rate. The evaluation results on the testing data reveal that the model achieved an accuracy of 85.96%. The optimal hyperparameter configuration consisted of 96 LSTM units, a dropout value of 0.3, and a learning rate of 0.0008. The best-performing model was subsequently deployed into a web-based system for automated sentiment analysis of beauty product reviews.
subject: Articles
  topic: section
subject: Sentiment Analysis
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: FastText
  topic: keywords
subject: IDF
  topic: keywords
subject: Beauty Products
  topic: keywords
creator: Shelomita Putrinda Culio
creator: Luh Gede Astuti
creator: Ida Ayu Gde Suwiprabayanti Putra
creator: I Made Widiartha
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 534
ending page: 544
doi: 10.24843/

Implementasi Ontologi OWL dan Rule-Based System CART untuk Manajemen Pengetahuan Kidung Bali
type: item
description: Kidung Bali merupakan warisan budaya lisan yang sarat nilai religius, filosofis, dan estetika dalam kehidupan masyarakat Hindu Bali, namun dokumentasinya masih bersifat tersebar dan minim aksesibilitas digital. Penelitian ini mengembangkan sistem manajemen pengetahuan bernama SariKidung berbasis ontologi OWL yang diintegrasikan dengan Rule-Based System menggunakan algoritma Decision Tree CART dengan kriteria Entropy (Information Gain), serta dilengkapi asisten percakapan berbasis Large Language Model (LLM) melalui API Groq. Sistem dibangun menggunakan framework Flask dengan Python 3.10+, memanfaatkan OWL sebagai basis pengetahuan yang dikonstruksi melalui metode Methontology. Ontologi yang dihasilkan mencakup 7 kelas utama, 36 subkelas, 80 individu KidungPancaYadnya, 15 data properties, dan 7 object properties, yang telah divalidasi menggunakan HermiT Reasoner pada Protégé 5.5. Mesin inferensi Decision Tree CART dilatih menggunakan 3 fitur kategoris dari Object Properties ontologi dan menghasilkan tingkat ketepatan rekomendasi sebesar 100% berdasarkan perbandingan dengan sumber referensi primer pada 20 skenario pengujian. Evaluasi penerimaan pengguna menggunakan Technology Acceptance Model (TAM) dengan 41 responden dan instrumen 18 butir pernyataan skala Likert 7 poin menunjukkan persentase penerimaan Perceived Usefulness (PU) sebesar 95,33%, Perceived Ease of Use (PEoU) sebesar 94,19%, Attitude Toward Using (ATU) sebesar 95,30%, dan Behavioral Intention to Use (BI) sebesar 92,45%, mengindikasikan penerimaan sistem yang sangat tinggi.
subject: Articles
  topic: section
subject: Kidung Bali
  topic: keywords
subject: Sistem Manajemen Pengetahuan
  topic: keywords
subject: Ontologi OWL
  topic: keywords
subject: Rule-Based System
  topic: keywords
subject: Decision Tree CART
  topic: keywords
subject: Methontology
  topic: keywords
subject: Technology Acceptance Model
  topic: keywords
creator: Putu Ardi Sudarmika Sudarmika
creator: Ngurah Agus Sanjaya ER
creator: I Gede Arta Wibawa
creator: Luh Arida Ayu Rahning Putri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 523
ending page: 533
doi: 10.24843/

Perancangan Ulang Website Baliedu Tours and Travel Menggunakan Metode User Centered Design
type: item
description: The Baliedu Tours and Travel website is a platform that provides information about tour packages and tourism services in Bali. However, an initial evaluation using the User Experience Questionnaire (UEQ) indicated that the website had a bad user experience, highlighting the need for improvements in interface design and system functionality. Therefore, this study aims to redesign the website using the User Centered Design (UCD) method. The redesign process followed the stages of UCD, including plan the human centered design, understand and specify context of use, specify user and organizational requirement, product design solutions, and evaluate design against requirement. To enhance the functionality of the website, a tour package recommendation system was implemented using the Content Based Filtering method. The results show that the redesigned website improved user experience across all UEQ scales, categorized as good to excellent. Black box testing also showed that the system functionalities operated according to the functional requirements. Furthermore, the recommendation system achieved a precision value of 83.3%, indicating that most recommendations were relevant.
subject: Articles
  topic: section
subject: User Centered Design
  topic: keywords
subject: User Experience Questionnaire
  topic: keywords
subject: Content Based Filtering
  topic: keywords
subject: Tourism Website
  topic: keywords
subject: Redesign
  topic: keywords
creator: Ni Putu Meita Kartika Dewi
creator: Ida Ayu Gde Suwiprabayanti Putra
creator: I Gusti Ngurah Anom Cahyadi Putra
creator: Luh Arida Ayu Rahning Putri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 514
ending page: 522
doi: 10.24843/

Pemanfaatan Bi-LSTM dan GloVe dengan SMOTE untuk Menganalisis Sentimen Pengguna Bank Digital
type: item
description: The rapid growth of digital banking in Indonesia has generated a large volume of user reviews on platforms such as Google Play Store, which contain valuable feedback regarding service quality. This study aims to perform sentiment analysis on digital banking user reviews, specifically the Bank Jago application, using the Bidirectional Long Short-Term Memory (Bi-LSTM) method combined with Global Vectors for Word Representation (GloVe) Word Embedding and Synthetic Minority Over-sampling Technique (SMOTE). A total of 5,590 reviews were collected through web scraping from February 1 to September 29, 2023. Text preprocessing included cleaning, case folding, tokenization, normalization, stopword removal, and stemming. SMOTE was applied to balance the training data, resulting in a 50:50 class distribution. Hyperparameter tuning using Grid Search identified the optimal configuration of 32 hidden units and 0.0 dropout rate. The model achieved 89.09% validation accuracy and an average validation accuracy of 89.13% using 5-Fold Cross Validation. Evaluation on the test set produced an accuracy of 88.55%, demonstrating that the proposed approach effectively classifies sentiment in digital banking reviews.
subject: Articles
  topic: section
subject: Bi-LSTM
  topic: keywords
subject: GloVe Word Embedding
  topic: keywords
subject: Hyperparameter Tuning
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: SMOTE
  topic: keywords
creator: Ni Komang Ayu Juliana
creator: Made Agung Raharja
creator: Ngurah Agus Sanjaya ER
creator: Luh Arida Ayu Rahning Putri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-05-31
publication date: 2026-05-31
starting page: 498
ending page: 506
doi: 10.24843/

Perancangan Aplikasi Destinasi Wisata Kabupaten Bangli Menggunakan Metode Haversine Formula
type: item
description: Kabupaten Bangli merupakan salah satu daerah di Provinsi Bali yang memiliki potensi wisata alam, budaya, dan sejarah yang cukup besar. Namun, keterbatasan informasi yang terintegrasi serta belum tersedianya sistem pencarian lokasi wisata berbasis jarak terdekat menyebabkan wisatawan mengalami kesulitan dalam menentukan destinasi yang ingin dikunjungi. Penelitian ini bertujuan untuk merancang dan membangun aplikasi destinasi wisata Kabupaten Bangli berbasis lokasi dengan menerapkan metode Haversine Formula untuk menghitung jarak antara pengguna dan objek wisata, serta menguji tingkat akurasi dan usability aplikasi yang dikembangkan. Metode penelitian yang digunakan meliputi tahap analisis kebutuhan sistem, perancangan sistem, implementasi aplikasi, serta pengujian sistem. Metode Haversine Formula diterapkan untuk menghitung jarak berdasarkan koordinat lintang dan bujur antara posisi pengguna dan lokasi objek wisata. Pengujian sistem dilakukan menggunakan Black Box Testing untuk menguji fungsionalitas aplikasi, pengujian akurasi dengan membandingkan hasil perhitungan jarak dengan Google Maps, serta pengujian usability menggunakan kuesioner yang dianalisis melalui uji validitas dan reliabilitas. Hasil penelitian menunjukkan bahwa aplikasi berhasil dibangun dengan fitur utama Explore, Kategori, Nearest, dan Settings. Penerapan metode Haversine Formula menghasilkan perhitungan jarak yang akurat dengan selisih yang relatif kecil dibandingkan dengan Google Maps dengan akurasi 99.86%. Hasil pengujian usability memperoleh nilai rata-rata sebesar 85,85% yang termasuk dalam kategori Sangat Baik, serta seluruh instrumen pengujian dinyatakan valid dan reliabel.
subject: Articles
  topic: section
subject: Aplikasi Destinasi Wisata
  topic: keywords
subject: Bangli
  topic: keywords
subject: Haversine Formula
  topic: keywords
subject: Google Maps
  topic: keywords
subject: Usability
  topic: keywords
creator: Sang Ayu Putu Eka Trisna Andriani
creator: I Gusti Agung Gede Arya Kadyanan
creator: I Made Widiartha
creator: I Gusti Ngurah Anom Cahyadi Putra
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-05-31
publication date: 2026-05-31
starting page: 655
ending page: 663
doi: 10.24843/

Sistem Rekomendasi Hotel Menggunakan Algoritma Item-Based Clustering Hybrid Method
type: item
description: Penelitian ini dilaksanakan dengan tujuan untuk membangun sistem rekomendasi hotel berbasis algoritma Hybrid Recommendation Sistem (ICHM) yang menggabungkan metode Content Based Filtering dan Collaborative Filtering untuk membantu pengguna dalam menemukan hotel sesuai preferensi mereka. Metode Content Based Filtering menggunakan teknik TF-IDF dan Cosine Similarity yang berfungsi untuk menganalisis fitur konten hotel. Sedangkan metode Collaborative Filtering menggunakan matriks rating pengguna dengan proses mean centering dan Cosine Similarity yang berfungsi untuk mengelompokkan hotel berdasarkan kemiripan karakteristik data. K-Means Clustering diterapkan untuk mengelompokkan hotel berdasarkan kemiripan karakteristik data. Evaluasi menghasilkan akurasi model sebesar 0,882, MAE sebesar 0,625, precision sebesar 0,907, recall sebesar 0,790, dan F1-score sebesar 0,845 yang menunjukkan bahwa model dapat memberikan rekomendasi hotel yang relevan dan akurat serta dapat diimplementasikan ke sistem rekomendasi hotel yang memungkinkan pengguna untuk melakukan rekomendasi hotel berdasarkan preferensi masing-masing
subject: Articles
  topic: section
subject: Sistem Rekomendasi
  topic: keywords
subject: Hybrid Recommendation Sistem
  topic: keywords
subject: Content Based Filtering
  topic: keywords
subject: Collaborative Filtering
  topic: keywords
subject: K-Means Clustering
  topic: keywords
creator: Ngakan Putu Bagus Ananta Wijaya
creator: Ida Ayu Gde Suwiprabayanti Putra
creator: I Gusti Ngurah Anom Cahyadi Putra
creator: Putu Praba Santika
creator: I Putu Gede Hendra Suputra
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-05-31
publication date: 2026-05-31
starting page: 507
ending page: 513
doi: 10.24843/

Analisis dan Pengembangan Design Aplikasi EasyBill Menggunakan Pendekatan User Centered Design
type: item
description: Aktivitas sosial seperti makan bersama, perjalanan kelompok, maupun kegiatan komunitas sering melibatkan pengeluaran bersama yang perlu dibagi secara adil. Namun, proses pembagian biaya (split bill) masih banyak dilakukan secara manual sehingga berpotensi menimbulkan kesalahan perhitungan, ketidakjelasan kontribusi, serta kurangnya transparansi dalam pengelolaan pengeluaran bersama. Penelitian ini bertujuan untuk menganalisis kebutuhan pengguna serta mengembangkan desain aplikasi EasyBill berbasis mobile menggunakan pendekatan User Centered Design (UCD). Metode UCD digunakan karena berfokus pada keterlibatan pengguna dalam setiap tahap pengembangan, mulai dari identifikasi kebutuhan pengguna, perancangan solusi desain, hingga evaluasi kegunaan (usability evaluation). Proses pengembangan desain dilakukan secara iteratif melalui dua tahap iterasi dan dievaluasi menggunakan metode Usability Testing terhadap 30 responden berusia 19–25 tahun. Hasil evaluasi menunjukkan bahwa pada iterasi pertama aplikasi EasyBill telah memperoleh nilai usability yang lebih tinggi dibandingkan aplikasi sejenis pada seluruh aspek, yaitu learnability (86,83%), memorability (85,66%), efficiency (86,66%), error (85,66%), dan satisfaction (89,86%). Setelah dilakukan perbaikan desain pada iterasi kedua, seluruh aspek mengalami peningkatan nilai menjadi learnability (92,33%), memorability (92,00%), efficiency (93,33%), error (91,33%), dan satisfaction (93,73%). Hasil tersebut menunjukkan bahwa penerapan pendekatan User Centered Design mampu meningkatkan kualitas usability aplikasi serta menghasilkan desain yang lebih sesuai dengan kebutuhan pengguna.
subject: Articles
  topic: section
subject: User Centered Design
  topic: keywords
subject: Usability Testing
  topic: keywords
subject: User Interface
  topic: keywords
subject: Split bill
  topic: keywords
subject: Blackbox Testing
  topic: keywords
creator: Ni Luh Gede Cahaya Putri Mahadewi
creator: I Ketut Gede Suhartana
creator: I Dewa Made Bayu Atmaja Darmawan
creator: Anak Agung Istri Ngurah Eka Karyawati
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-06-12
publication date: 2026-06-12
starting page: 489
ending page: 497
doi: 10.24843/

Analisis Sentimen Terhadap Pembatalan Piala Dunia U-20 di Indonesia Menggunakan Algorima Naïve Bayes dan Genetika
type: item
description: Pembatalan Piala Dunia U-20 di Indonesia menimbulkan berbagai reaksi masyarakat di media sosial, khususnya Twitter. Sebagian masyarakat menyayangkan keputusan tersebut karena dianggap merugikan Indonesia dalam kesempatan tampil di level dunia. Sebaliknya ada pihak yang mendukung pembatalan dengan alasan tertentu, seperti faktor politik atau prinsip nasional. Hal ini dapat dilakukan analisis sentimen terhadap permasalahan tersebut. Penelitian ini bertujuan untuk mengklasifikasikan sentimen masyarakat terhadap pembatalan Piala Dunia U-20 dengan menerapkan algoritma Naïve Bayes dengan seleksi fitur Algoritma Genetika. Data penelitian diperoleh melalui proses crawling di Twitter pada periode 30 Maret hingga 5 April 2023 dengan kata kunci “Pembatalan Piala Dunia U-20 Indonesia” dan memperoleh data sebanyak 14.807 tweet. Penelitian ini menggunakan data sebanyak 2.000 dan dilakukan pelabelan data data tersebut bisa digunakan untuk pelatihan model klasifikasi. Tahap preprocessing meliputi case folding, filtering, tokenizing dan stemming. Tahap pembobotan data menggunakan TF-IDF mengukur tingkat kepentingan atau bobot suatu kata dalam sebuah dokumen. Penelitian ini menggunakan seleksi fitur genetika untuk memilih fitur (kata) terbaik dan mengurangi kata yang kurang relevan. Naive bayes merupakan algoritma klasifikasi berbasis probabilitas untuk memprediksi sentimen positif atau negatif. Proses klasifikasi dilakukan dengan dua skenario, yaitu tanpa seleksi fitur dan dengan seleksi fitur menggunakan Algoritma Genetika. Hasil penelitian menunjukkan bahwa model Naïve Bayes tanpa seleksi fitur memperoleh akurasi sebesar 71,5% dan mengalami peningkatan menjadi 76,8% menggunakan seleksi fitur genetika. Peningkatan juga terjadi pada nilai presisi, recall, dan F1-score, masing-masing mencapai sekitar 75,99%, 76%, dan 75,98%.
subject: Articles
  topic: section
subject: klasifikasi
  topic: keywords
subject: naive bayes
  topic: keywords
subject: genetika
  topic: keywords
subject: piala dunia
  topic: keywords
subject: twitter
  topic: keywords
creator: I Ketut Oning Pusparama
creator: I Putu Gede Hendra Suputra
creator: Gst. Ayu Vida Mastrika Giri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 469
ending page: 476

Implementasi Intrusion Prevention System (IPS) Menggunakan Signature – Based Detection Berbasis Suricata
type: item
description: Network security is a crucial aspect of maintaining the integrity, confidentiality, and availability of data. One solution to strengthen network security is the implementation of an Intrusion Prevention System (IPS). This research implements a hybrid IPS that integrates Suricata as a signature-based detection engine with the Aho-Corasick algorithm for secondary log analysis and pattern matching. The system, implemented on a Windows operating system, is designed to automatically detect and block various types of attacks, such as Port Scanning, DDoS (SYN Flood), and Protocol-Specific Attacks. Testing was conducted through end-to-end attack scenarios to measure the system's effectiveness and response time. The results demonstrate that the proposed architecture is highly effective, achieving an average detection rate (recall) of 86.67% and a precision rate of 81.25%, which yields an F1-Score of 83.87%. Response time analysis revealed that Suricata detects threats within an average of 1-3 seconds, while the Aho-Corasick layer analyzes the resulting logs in a matter of milliseconds. This implementation proves that the combination of Suricata and Aho-Corasick provides a security solution that is not only effective at detecting known threats but also efficient at analyzing the results.
subject: Articles
  topic: section
subject: network security
  topic: keywords
subject: intrusion prevention system
  topic: keywords
subject: suricata
  topic: keywords
subject: signature-based detection
  topic: keywords
subject: attack detection
  topic: keywords
creator: I Wayan Dimas Wirahadi
creator: I Gede Santi Astawa
creator: Made Agung Raharja
creator: I Made Widiartha
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 449
ending page: 458

Penentuan Entitas Tokoh Pada Satua Bali Menggunakan Algoritma Conditional Random Fields
type: item
description: Pengenalan Entitas Bernama (NER) adalah proses untuk mengidentifikasi dan mengklasifikasi Entitas Bernama (NEs) pada suatu teks. Penelitian sebelumnya melakukan pengenalan entitas bernama pada bahasa bali menggunakan metode rule-based yang berfokus pada kelas person memberikan akurasi yang kurang memadai, karena data yang digunakan berfokus kepada fitur semantik dari kata pada kalimat(Kurniadi & ER, 2021). Tujuan dari penelitian ini adalah menerapkan fitur gramatikal dari kata berupa label part-of-speech, dan juga fitur lainnya untuk meningkatkan akurasi pada proses pengenalan entitas bernama yang berfokus pada entitas person. Metode Conditional Random Fields merupakan metode diskriminatif yang hanya berfokus kepada persebaran probabilitas bersyarat pada fitur ke label. Penelitian ini menggunakan 4 fitur, diantaranya: fitur semantik, fitur gramatikal, fitur transisi, serta fitur representasi embedding. Penelitian ini menggunakan data dari penelitian (Bimantara et al ea, 2024) yang telah dilabeli dengan label part-of-speech dan entitas bernama dengan skema labeling beginning-inside-outside (BIO). Penelitian ini menghasilkan accuracy 0.96 dengan precision sebesar 0.96, recall sebesar 0.96, dan total f1-score sebesar 0.96.
subject: Articles
  topic: section
subject: conditional random fields
  topic: keywords
subject: pengenalan entitas bernama
  topic: keywords
subject: bahasa bali
  topic: keywords
subject: regularisasi
  topic: keywords
subject: satua bali
  topic: keywords
creator: I Made Widi Arsa Ari Saputra
creator: I Ketut Gede Suhartana
creator: I Gusti Ngurah Anom Cahyadi Putra
creator: I Wayan Supriana
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 439
ending page: 448

Perbandingan Performa CNN Inception-V3 dan ResNet50 Dalam Klasifkasi Kematangan Buah Tomat
type: item
description: Buah tomat memiliki durasi mencapai kematangan yang singkat. Sehingga jika melewati durasi kematangannya, maka buah tomat akan seketika membusuk. Para petani buah tomat melakukan proses klasifikasi kematangan buah tomat secara manual. Proses klasifikasi secara manual dapat mengakibatkan hasil klasifikasi yang tidak konsisten, yang disebabkan karena proses tersebut sangat menguras tenaga manusia jika dilakukan dalam kuantitas yang banyak. Oleh karena itu, penulis merancang sistem klasifikasi tingkat kematangan buah tomat dengan algoritma Convolutional Neural Network (CNN). Metode yang digunakan dalam penelitian ini adalah model CNN dengan arsitektur Inception-V3 dan arsitektur ResNet50. Penulis juga melakukan perbandingan performa klasifikasi dari kedua arsitektur tersebut dan membandingkan arsitektur mana yang memiliki performa klasifikasi yang lebih baik. Berdasarkan hasil pengujian yang dilakukan, nilai akurasi terbaik diperoleh ketika menggunakan model CNN dengan arsitektur ResNet50 dengan tingkat akurasi sebesar 95%. Lalu, berdasarkan pengujian menggunakan data uji menunjukkan kedua model CNN tersebut dapat melakukan klasifikasi citra dengan optimal dan tanpa kesalahan.
subject: Articles
  topic: section
subject: sistem klasifikasi
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: inception-v3
  topic: keywords
subject: resnet50
  topic: keywords
subject: tomat
  topic: keywords
creator: I Putu Hanggara Diatha Putra
creator: I Wayan Supriana
creator: I Ketut Gede Suhartana
creator: I Gusti Ngurah Anom Cahyadi Putra
creator: Farhan Akmala Putra
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 429
ending page: 438

Sistem Informasi Manajemen Keuangan Pribadi Dengan Input Suara Dan Berbasis Iot
type: item
description: The increasing need for practical personal financial management, especially among students and young professionals, highlights the challenges of recording small daily expenses such as parking or food transactions. Manual recording often leads to inconsistencies due to forgetfulness or inconvenience. To address this issue, this study develops an Internet of Things (IoT) based personal financial management system integrating smartwatch and smartphone devices with voice input features. Voice commands are transcribed into text using Google Speech-to-Text, while transaction information such as type, description, and nominal value is extracted using the Boyer-Moore string matching algorithm. The system was implemented using Android technology with real-time synchronization through Firebase Firestore. Functional testing through blackbox methods showed that all features, including voice transcription and transaction synchronization, operated as expected. Performance testing of the Boyer-Moore algorithm was conducted on input lengths ranging from 30 to 120 characters. To minimize JVM-related bias, each experiment was repeated 100 times, and the average execution time was calculated. Regression analysis of the results produced the equation y = 1034x + 5192 with a coefficient of determination R² = 0.992. Furthermore, user acceptance testing using the Technology Acceptance Model (TAM) yielded positive results, with average scores above 4.00 on a 5-point scale. These results indicate that integrating IoT and voice-based input significantly improves the efficiency and user acceptance of personal financial management applications.
subject: Articles
  topic: section
subject: speech-to-text
  topic: keywords
subject: string matching
  topic: keywords
subject: bouyer-moore
  topic: keywords
subject: internet of things
  topic: keywords
subject: personal finance
  topic: keywords
creator: I Dewa Gede Partha Wijaya
creator: I Made Widiartha
creator: I Gede Santi Astawa
creator: Gst. Ayu Vida Mastrika Giri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 419
ending page: 428

Rancangan Sistem Monitoring Suhu, Kelembaban, Kecepatan Angin Untuk Memprediksi Keamanan Jalur Pendakian
type: item
description: Mountain climbing activities pose risks to climbers because they can cause serious injuries and often result in death, so weather information in mountainous areas is essential for predicting safety on climbing routes. In this study, the Internet of Things with a Decision Tree algorithm was used to build a real-time weather monitoring system and obtain data on weather conditions on mountain climbing routes. This study aims to measure the accuracy of the Decision Tree algorithm in classifying safety conditions and to develop a web-based application capable of displaying real-time data on temperature, humidity, and wind speed. The results of this study indicate that the use of the Decision Tree algorithm for classifying safety on hiking trails demonstrates very high accuracy, with an accuracy rate of 99.27%. Thus, the classification model built is highly effective in distinguishing between safe and hazardous conditions based on these three weather parameters. The very high accuracy indicates that the Decision Tree model used is highly suitable for application in prediction or monitoring systems, as developed in this study.
subject: Articles
  topic: section
subject: decision tree
  topic: keywords
subject: internet of things
  topic: keywords
subject: mountain climbing route safety prediction
  topic: keywords
creator: I Wayan Wikananda Adikara
creator: Gst. Ayu Vida Mastrika Giri
creator: I Wayan Supriana
creator: I Gede Surya Rahayuda
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 407
ending page: 418

A Case-Based Reasoning with Artificial Intelligence Approach for Laptop Repair Assistant System
type: item
description: This study presents a Case-Based Reasoning (CBR) approach for a Laptop Repair Assistant System, leveraging past repair cases to diagnose and resolve new technical issues efficiently. The system integrates Sentence-BERT (SBERT) for semantic feature extraction and LLaMA-3 (via Groq API) to dynamically revise solutions for partially matched cases. A dataset of 8,000 solved laptop issues was collected through web scraping, processed via text cleaning and SBERT embedding, and structured into a case base. The CBR cycle (Retrieve, Reuse, Revise, Retain) was implemented with cosine similarity thresholds: >90% for direct reuse, 60–90% for LLM-based revision, and <60% for user feedback requests. Evaluation through black-box testing confirmed system accuracy in exact matches, adaptive revisions, and handling novel cases. The system demonstrates scalability through automated case retention and user-friendly interfaces (Streamlit).
subject: Articles
  topic: section
subject: Case-Based Reasoning
  topic: keywords
subject: Laptop Repair
  topic: keywords
subject: Sentence-BERT
  topic: keywords
subject: Semantic Similarity
  topic: keywords
subject: Large Language Model (LLM)
  topic: keywords
creator: I Wayan Supriana
creator: Kadek Belvanatha Gargita Satwikananda
creator: Putu Yuki Parmawati
creator: Amsal Hamonangan Butarbutar
rights: Copyright (c) 2025 Jurnal Elektronik Ilmu Komputer Udayana
date: 2025-11-28
publication date: 2025-11-28
starting page: 349
ending page: 354

Pengamanan Data menggunakan Algoritma Enkripsi Advanced Encryption Standard (AES) dan Zigzag Cipher
type: item
description: Masalah keamanan dan kerahasiaan data merupakan aspek penting dalam komunikasi modern. Kriptografi adalah metode utama untuk mengamankan informasi dari suatu data dengan mengubahnya menjadi data yang tidak dapat dibaca menggunakan suatu algoritma enkripsi. Penelitian ini bertujuan untuk meningkatkan keamanan enkripsi data, khususnya dalam format video, dengan memodifikasi algoritma Advanced Encryption Standard (AES-256) menggunakan penambahan langkah enkripsi Zigzag Cipher. Penelitian dilakukan dengan menguji performa algoritma zigzag cipher, AES-256, AES-256 + zigzag pada input data, AES-256 + zigzag cipher pada output data, dan AES-256 + zigzag cipher pada input password, digunakan untuk memproses 30 file video yang dipotong ke durasi tertentu dari 3 video sumber dan 1 file video dengan tampilan monolitik (1 warna), dengan menggunakan 4 password per algoritma. Pengujian diterapkan menggunakan bahasa pemrograman Python 3.8.10. Hasil penelitian menunjukkan bahwa penambahan Zigzag Cipher pada AES-256 dapat meningkatkan kompleksitas dan keamanan enkripsi. Algoritma zigzag sendiri diketahui lebih cepat sekitar 20 kali dibandingkan AES atau variasinya. Meskipun penambahan zigzag cipher menyebabkan penambahan waktu pemrosesan, peningkatan ini relatif kecil. Algoritma AES Modifikasi 1 (Zigzag pada plaintext AES) secara konsisten mencapai nilai entropy yang lebih tinggi, sementara semua algoritma yang diuji mampu mencapai koefisiensi korelasi yang rendah, mendekati 0.
subject: Articles
  topic: section
subject: video
  topic: keywords
subject: encryption
  topic: keywords
subject: python
  topic: keywords
subject: AES-256
  topic: keywords
subject: zigzag cipher
  topic: keywords
subject: cryptography
  topic: keywords
creator: Bagus Arimbawa I Gusti Ngurah
creator: I Made Widiartha
creator: I Gusti Ngurah Anom Cahyadi Putra
creator: I Ketut Gede Suhartana
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 477
ending page: 488

Optimasi Algoritma SVM dengan Ekstraksi Fitur Warna pada Klasifikasi Biji Kopi Sangrai
type: item
description: Manual classification of roasted coffee beans is often hampered by subjectivity and inconsistency. This study aims to compare the performance of the Support Vector Machine (SVM) algorithm in classifying the ripeness of roasted coffee beans using two different feature approaches: raw pixel features and extracted hybrid features. The research focuses on finding the best hyperparameter combination for each approach and determining which method yields superior classification performance. The dataset used consists of 900 augmented coffee bean images, evenly distributed across three classes (Dark, Light, Medium). In the first approach, an SVM model was trained directly on flattened raw pixel data. In the second approach, an SVM model was trained using combined features extracted via a Convolutional Neural Network (CNN) and Color Histogram. The experimental results show a significant performance difference. The SVM model using raw pixel features achieved a maximum accuracy of 88.33% with the best parameters {kernel: 'rbf', C: 10, gamma: 0.01}. Meanwhile, the hybrid model utilizing feature extraction from the deeper_wider CNN architecture and color histograms drastically improved performance, reaching an accuracy of 98.33% with parameters {kernel: 'rbf', C: 10, gamma: 0.1}. These results demonstrate that employing high-level feature extraction through CNN is significantly superior to using raw pixels for the task of roasted coffee bean classification.
subject: Articles
  topic: section
subject: support vector machine
  topic: keywords
subject: image classification
  topic: keywords
subject: roasted coffee beans
  topic: keywords
subject: feature extraction
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: color histogram
  topic: keywords
creator: Gde Krishna Sankya Yogeswara
creator: I Ketut Gede Suhartana
creator: I Gusti Ngurah Anom Cahyadi Putra
creator: I Wayan Supriana
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 397
ending page: 406

Redesign UI/UX Aplikasi Property Management Berbasis User-Centered Design dengan Evaluasi Heuristic Evaluation dan System Usability Scale
type: item
description: Perusahaan property management menjadi organisasi yang memiliki peran krusial untuk membantu para investor lokal maupun asing yang berinvestasi di bidang properti. Untuk memperlancar proses manajemen, maka perusahaan memanfaatkan aplikasi property management. Tentunya, aplikasi ini perlu dirancang agar memiliki UI/UX yang baik. Dalam penelitian ini, penulis melakukan redesign aplikasi property management berbasis UCD menggunakan metode evaluasi HE, SUS, dan pengujian 5 aspek usability yang masing-masing dilakukan pada tahap awal (sebelum redesign) dan akhir (setelah redesign). Terhitung ada 64 bobot masalah pada pengujian HE tahap awal. Sedangkan, skor rata-rata SUS tahap awal adalah sebesar 71. Setelah dilakukan redesign berdasarkan masukan dari para evaluator, maka total bobot masalah pada HE tahap akhir mengalami penurunan ke angka 30 bobot masalah. Sedangkan, skor rata-rata SUS mengalami peningkatan ke angka 79,25. Untuk 5 aspek usability mengalami peningkatan dengan hasil learnability (64% menjadi 86%), memorability (64% menjadi 88%), efficiency (61% menjadi 89%), error (tingkat kejarangan mengalami error meningkat dari 54% menjadi 77%), satisfaction (61% menjadi 86%).
subject: Articles
  topic: section
subject: property management
  topic: keywords
subject: UI/UX
  topic: keywords
subject: heuristic evaluation
  topic: keywords
subject: system usability scale
  topic: keywords
subject: redesign
  topic: keywords
creator: Matthew Novan Sidharta
creator: Luh Arida Ayu Rahning Putri
creator: I Ketut Gede Suhartana
creator: I Gusti Ngurah Anom Cahyadi Putra
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 385
ending page: 396

Klasifikasi Kategori Cerita Pendek Menggunakan XGBoost dengan Seleksi Fitur Chi-Square
type: item
description: Text classification is one of the major challenges in the field of natural language processing, particularly in categorizing texts by genre. This study aims to develop a classification system for Indonesian short stories into three genre categories: romance, horror, and religion. Two ensemble-based machine learning algorithms, XGBoost and Random Forest, are employed in the experiments. Prior to model training, the short story data undergo text preprocessing and feature extraction using the TF-IDF method. To enhance feature relevance, Chi-Square feature selection is applied. The models are trained using various hyperparameter combinations and validated using 5-Fold Cross Validation. Experimental results show that Chi-Square feature selection improves model accuracy. Final evaluation is performed on test data using the best hyperparameter configuration. XGBoost achieves the best performance with an F1-Score of 89%, while Random Forest achieves an F1-Score of 86%. These results indicate that XGBoost generalizes better to unseen data, despite using fewer trees than Random Forest.
subject: Articles
  topic: section
subject: text classification
  topic: keywords
subject: short stories
  topic: keywords
subject: xgboost
  topic: keywords
subject: random forest
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subject: chi-square
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subject: ensemble learning
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creator: M. Faisal Afandi
creator: Ngurah Agus Sanjaya ER
creator: Putu Gede Hendra Suputra
creator: Luh Arida Ayu Rahning Putri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 375
ending page: 384

Sistem Representasi Digital Wayang Kulit Bali Menggunakan AHP Dengan Pendekatan Ontologi
type: item
description: This study aims to develop a digital documentation system for Balinese Wayang Kulit using the Analytical Hierarchy Process (AHP) combined with an ontology-based approach. The system is designed to organize and present information about Wayang Kulit Bali, including characters, stories, and cultural values, in a structured and meaningful way. The ontology serves as the foundational framework for representing the relationships between various entities in the domain of Balinese Wayang. Additionally, the AHP method is applied to provide story recommendations based on user preferences, considering criteria such as moral values, character traits, and story themes. The system is evaluated through usability testing, and the results indicate good performance in terms of ease of use and user satisfaction. However, improvements in system efficiency and error handling are identified. This digital platform is expected to enhance access to and understanding of Balinese Wayang Kulit, contributing to its preservation and promotion in the digital age.
subject: Articles
  topic: section
subject: balinese wayang kulit
  topic: keywords
subject: analytical hierarchy process
  topic: keywords
subject: ontology
  topic: keywords
subject: digital documentation
  topic: keywords
subject: system evaluation
  topic: keywords
subject: cultural heritage
  topic: keywords
creator: I Komang Widia Pratama
creator: Made Agung Raharja
creator: Cokorda Pramartha
creator: I Ketut Gede Suhartana
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 365
ending page: 374
doi: 10.24843/

Klasifikasi Mood Musik Menggunakan Support Vector Machine Berdasarkan Fitur Spektral
type: item
description: Klasifikasi musik merupakan salah satu topik MIR (Music Information Retrieval) yang berkembang pesat. Berbagai aspek musik telah menjadi topik penelitian seputar klasifikasi musik mulai dari genre, instrumen, hingga mood. Salah satu teori taksonomi mood musik yang cukup terkenal adalah circumplex model yang memetekan mood musik berdasarkan tingkat arousal dan valence .Arousal untuk tingkat intensitas dari mood tersebut. Sedangkan valence untuk kondisi emosional positif atau negatifnya suatu musik. Penelitian mengklasifikasikan 4 kelas kuadran mood musik berdasarkan circumplex model menggunakan model pembelajaran mesin SVM (Support Vector Machine). Penelitian menggunakan jenis fitur spektral yang merupakan distribusi fitur musik pada domain frekuensi untuk mengklasifikasikan 4 kelas kuadran musik. Klasifikasi dilakukan dengan beberapa pendekatan berbeda seperti mengklasifiksikan 4 kelas secara langsung serta kombinasi model arousal dan valence terpisah. Hal ini dilakukan untuk menilai performa fitur spektral dalam klasifikasi mood musik berdasarkan 4 kelas kuadran dan melihat apakah ada kecenderungan fitur spektral lebih cocok dalam klasifikasi arousal atau valence. Penelitian juga mengujicobakan beberapa tahap lain seperti tuning parameter SVM dan mengaplikasikan seleksi fitur hingga reduksi dimensi fitur. Hasil penelitian diperoleh model SVM dengan akurasi terbaik untuk pendekatan klasifiksai 4 kelas yang memperoleh akurasi sebesar 66,67%. Hasil terbaik diperoleh setelah mengaplikasikan seleksi fitur dan reduksi dimensi fitur. Selain itu hasil menunjukkan bahwa fitur spektral cenderung mampu mengklasifikasikan arousal lebih baik dibandingkan valence dalam konteks circumplex model untuk mood musik.
subject: Articles
  topic: section
subject: mood musik
  topic: keywords
subject: circumplex model
  topic: keywords
subject: fitur spektral
  topic: keywords
subject: klasifikasi
  topic: keywords
subject: support vector machine
  topic: keywords
creator: I Gusti Agung Ngurah Diputra Wiraguna
creator: Luh Arida Ayu Rahning Putri
creator: I Ketut Gede Suhartana
creator: Gst. Ayu Vida Mastrika Giri
rights: Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
date: 2026-02-02
publication date: 2026-02-02
starting page: 355
ending page: 364