Analisis Sentimen Terhadap Pembatalan Piala Dunia U-20 di Indonesia Menggunakan Algorima Naïve Bayes dan Genetika
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%.
Copyright (c) 2026 Jurnal Elektronik Ilmu Komputer Udayana
Implementasi Intrusion Prevention System (IPS) Menggunakan Signature – Based Detection Berbasis Suricata
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.
Penentuan Entitas Tokoh Pada Satua Bali Menggunakan Algoritma Conditional Random Fields
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.
Perbandingan Performa CNN Inception-V3 dan ResNet50 Dalam Klasifkasi Kematangan Buah Tomat
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.
Sistem Informasi Manajemen Keuangan Pribadi Dengan Input Suara Dan Berbasis Iot
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.
Rancangan Sistem Monitoring Suhu, Kelembaban, Kecepatan Angin Untuk Memprediksi Keamanan Jalur Pendakian
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.
mountain climbing route safety prediction
A Case-Based Reasoning with Artificial Intelligence Approach for Laptop Repair Assistant System
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).
Copyright (c) 2025 Jurnal Elektronik Ilmu Komputer Udayana
Pengamanan Data menggunakan Algoritma Enkripsi Advanced Encryption Standard (AES) dan Zigzag Cipher
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.
Optimasi Algoritma SVM dengan Ekstraksi Fitur Warna pada Klasifikasi Biji Kopi Sangrai
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.
Redesign UI/UX Aplikasi Property Management Berbasis User-Centered Design dengan Evaluasi Heuristic Evaluation dan System Usability Scale
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%).
Klasifikasi Kategori Cerita Pendek Menggunakan XGBoost dengan Seleksi Fitur Chi-Square
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.
Sistem Representasi Digital Wayang Kulit Bali Menggunakan AHP Dengan Pendekatan Ontologi
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.
Klasifikasi Mood Musik Menggunakan Support Vector Machine Berdasarkan Fitur Spektral
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.
Identifikasi Alat Musik Tradisional Nusa Tenggara Timur (NTT) Menggunakan Metode Mel Frequency Cepstral Coefficients (MFCC) dan K-Nearest Neighbor (KNN)
Traditional musical instruments such as gongs and drums from East Nusa Tenggara (NTT) are an important part of the local cultural heritage, but their existence is increasingly threatened due to lack of documentation and difficulties in the sound tuning process. This study presents a digital-based approach to classify the accuracy of the sound of these musical instruments by utilizing the Mel Frequency Cepstral Coefficients (MFCC) feature extraction technique and the K-Nearest Neighbor (KNN) classification algorithm. A total of 238 sound data were recorded and processed to remove noise. The results of the feature extraction were then grouped into three categories: "not right", "almost right", and "already right". The system managed to achieve an accuracy of 92.93% with the best parameter configuration. This solution is expected to help craftsmen in the sound tuning process and contribute to the preservation of regional culture.
Perbandingan Metode K-Nearest Neighbors Dan Support Vector Machine Pada Klasifikasi Lagu Daerah Dengan Penambahan Ekstraksi Fitur Principal Component Analysis
This study examines the application of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in music data classification based on audio features on the IRSD (Indonesian Regional Songs Dataset) dataset. The main objective of this study is to evaluate the effect of using the Principal Component Analysis (PCA) dimension reduction technique on the performance of both algorithms. The experimental results show that the use of PCA with KNN improves classification accuracy, especially for the number of PCA components between 20 and 30 components. In the SVM model, the Radial Basis Function (RBF) kernel provides the highest accuracy, reaching 79%. Although PCA can reduce dimensions and speed up execution time, its effect on SVM is not significant. This study concludes that KNN with and without PCA can provide good classification results, while SVM with the RBF kernel is more stable and accurate. These findings provide insight for further research in the use of classification algorithms for more complex music audio data.
The Implementation of the RSA-CRT Algorithm for Securing Banking Customer Data
Data security is a crucial aspect in the digital era, particularly in sectors that handle sensitive information such as banking. This study implements and evaluates the RSA and RSA-CRT algorithms to secure customer data, focusing on their performance in terms of decryption efficiency, memory usage, and resistance to brute-force attacks. Experimental results demonstrate that RSA-CRT is significantly more efficient than standard RSA in decryption processes. Across five test files ranging from 6.53 MB to 33.1 MB and using 2048-bit keys, RSA-CRT achieved an average time efficiency improvement of approximately 71%. In terms of memory usage, RSA-CRT consumed slightly less memory than RSA for smaller files but tended to use more memory for larger files—up to 370.84 MB compared to RSA's 340.86 MB for a 33.1 MB file. Despite the increased memory usage, this trade-off is considered acceptable given the substantial speed gains. Additionally, brute-force testing revealed that decryption time increases exponentially with key length, making attacks on 2048-bit RSA keys highly impractical. These findings affirm that RSA-CRT is a viable modern cryptographic solution for protecting sensitive data, offering both high performance and robust security, provided the system's memory capacity can support the process
Pengembangan Sistem Pencarian Buku Fiksi Berbasis Semantik Impresi dengan Pendekatan Ontologi
Fiction book search systems continue to be developed for efficiency and relevance, but keyword-based approaches are still limited to capturing subjective impressions. This research develops a semantic impression-based fiction search system using an ontology approach. The methodology used is Design Science Research Methodology (DSRM), and the ontology is built with Methontology, then extended using semantic relations based on the wheel of emotions and synonyms from WordNet. Data collection from 60 respondents resulted in 183 unique impressions that formed the basis of the ontology and testing. Verification results using reasoner revealed conflict-free ontology, and 30 SPARQL scenarios showed stable and efficient search. System performance was evaluated through testing and Blackbox Testing, showing extensive, stable, and accurate search. User evaluation based on the Technology Acceptance Model (TAM) also found the system useful and easy to use, with an average Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) above 5.70 which is measured on a Likert scale of 1-7. This research proves the ontology approach is effective for expanding the search of fiction books based on impressions.
design science research methodology (dsrm)
Sistem Rekomendasi Dan Sistem Pencarian Destinasi Wisata Dengan Pendekatan Ontologi
This research develops a recommendation and destination search system based on ontology to improve the distribution of tourist visits in Badung Regency. By using an ontology approach and the Simple Additive Weighting (SAW) method, the system can provide more relevant destination recommendations based on criteria such as price, distance, operating hours, and rating. This method enables the presentation of destination information in a more structured and accessible manner for users. The evaluation was conducted using Black Box Testing and the Technology Acceptance Model (TAM), which showed that the system is easy to use and beneficial to users. The evaluation results indicated that the system had an average Perceived Usefulness (PU) of 5.93, indicating that the system is useful in meeting user needs. Additionally, the average Perceived Ease of Use (PEOU) of 6.04 showed that the system is considered easy to use. This evaluation shows that the system can introduce alternative tourist destinations, thereby supporting more equitable and sustainable tourism development in Badung Regency.
Performance Evaluation of ResNet-50 and Inception-V3 for Diabetic Retinopathy Detection on Retinal Images
Diabetic retinopathy (DR), a leading cause of blindness among diabetics, poses significant diagnostic challenges due to subtle early-stage symptoms like microaneurysms, often undetectable through conventional manual fundus examinations, which are time-consuming and inaccessible in remote areas. This study evaluates the performance of ResNet-50 and Inception-V3 deep learning models in detecting DR using the APTOS 2019 dataset, preprocessed with Gaussian filtering to reduce noise. Binary classification (DR/No_DR) was implemented to address extreme class imbalance in the original dataset (No_DR: 1,750 images; Severe DR: 150 images). Methodology included data augmentation (rotation ±20°, 20% horizontal/vertical shifts, 20% zoom, horizontal flip) and evaluation using accuracy, precision, recall, and F1-score. Results demonstrated that Inception-V3 achieved the highest validation accuracy of 97.64% with augmentation, outperforming ResNet-50 (91.82%). Inception-V3 was also 40% more computationally efficient, requiring only 9,959.88 seconds compared to ResNet-50’s 16,673.65 seconds. Augmentation improved model generalization, particularly for ResNet-50 (+2.37% accuracy). Inception-V3 achieved an optimal F1-score of 0.9856, balancing precision (99.27%) and recall (97.85%). These findings recommend Inception-V3 as an accurate and efficient solution for automated DR screening, especially in resource-constrained settings.
Analisis Sentimen Review Toko Menggunakan Naive Bayes dengan Penanganan Negasi dan Mutual Information
Sentiment analysis refers to a technique within natural language processing for extracting and interpreting sentiments or opinions regarding a particular topic, such as product reviews. The model in this study was built through a series of stages, including preprocessing, feature extraction using TF-IDF, feature selection using Mutual Information, and sentiment classification with Multinomial Naive Bayes algorithm. The preprocessing stage consists of several processes, namely cleaning, case folding, tokenization, normalization, stopwords removal, stemming, and handling of negations and intensifiers. This study was tested using four scenarios. In the first scenario, classification was performed using the Multinomial Naive Bayes algorithm as a baseline approach. The second scenario involved classification with the handling of negations and intensifiers during the preprocessing stage. The third scenario involved classification with feature selection using Mutual Information. The fourth scenario combined the handling of negations and intensifiers with feature selection using Mutual Information. Based on the results, the classification using Multinomial Naive Bayes with the combination of negation and intensifier handling and feature selection using Mutual Information delivered the highest performance, attaining 74.08% accuracy, 74.32% precision, 74.19% recall, and an F1-Score of 74.04% with 60% of the features selected.
Sistem Pendeteksi Penyakit Daun Tanaman Jeruk Kintamani menggunakan Metode Naive Bayes dan Ekstraksi Fitur HOG
Kintamani citrus is one of Bali’s native plants that offers many benefits, especially from its leaves, which are often used in traditional medicine. However, the potential of these leaves is often not fully utilized due to the lack of public awareness in recognizing disease symptoms on the leaves. Currently, the detection of diseases in Kintamani citrus leaves is mostly done manually, which can be less efficient and less accurate. This research aims to build a classification system for detecting diseases in Kintamani citrus leaves using digital images, by applying the Histogram of Oriented Gradients (HOG) for feature extraction and the Naive Bayes method for classification. A total of 100 leaf images collected from the field were used as the dataset. The process includes image preprocessing, feature extraction using HOG, and classification using Naive Bayes. The results show an accuracy of 90%, indicating that this method can be considered fairly effective, although there is still room for improvement. This research is expected to contribute to the development of a more efficient and automated system for detecting plant diseases, especially in the agricultural sector.
Sistem Kompresi Data Logging pada Gateway Orange Pi 3 Menggunakan Metode GZIP
Smart farming is a breakthrough developed to increase rice production. Smart farming refers to the use of IoT technology to log environmental condition in agricultural area. Through smart farming, data can be stored in the Orange Pi 3 gateway, then sent directly to the central government. However, smart farming produces increasingly large data over time. Due to time-series nature, 302,4 MB of data from 25 nodes can be generated over 1 week. The increasing data size causes wastage of bandwidth and increases of latency in sending the data logging to the warehouse. System implements GZIP before the logging data dump is sent to the warehouse. The GZIP method is compared with BZIP2 to determine its effectiveness. As the result, GZIP has an average compression ratio of 5.164, lower than BZIP2’s 7.426. The average percentage of storage saving of GZIP method is 86,5%, lower than BZIP2’s 86.5%. BZIP2 excels in the transfer time parameter. BZIP2 reduces the transfer time to 39.53 seconds. 30.6% lower than the GZIP treatment and 82.8% lower than the uncompressed treatment. Finally, system testing using load testing shows that the built system can handle up to 100 virtual users with HTTP request durations below 1 second. CPU usage is successfully maintained below 60%.
Twitter Sentiment Analysis Regarding the Influence of Political Figures Using the Distance – Weighted K – Nearest Neighbor Method
Social media such as Twitter has become an important platform for voicing public opinion, including in the political context. This study aims to classify public sentiment towards political figures on Twitter using the K – Nearest Neighbor (KNN) algorithm and its development, Distance – Weighted K – Nearest Neighbor (DWKNN). KNN is a nearest neighbor – based classification algorithm, but its performance is greatly influenced by the selection of the k value and does not consider the weight of the distance between neighbors. To overcome these limitations, DWKNN is applied by giving weights based on the distance of each neighbor to the test data. This study goes through the stages of data collection, data preprocessing, feature extraction, cross validation, algorithm implementation, and evaluation using three data balancing scenarios, namely baseline, oversampling, and undersampling. The evaluation results show that DWKNN provides the best performance in the baseline scenario with an accuracy of 68%, precision 52%, recall 47%, and F1 – score 48%, compared to KNN with an accuracy of 66%, precision 47%, recall 45%, and F1 – score 45%. These findings indicate that DWKNN is more effective in classifying public sentiment towards political figures than KNN.
Representasi Pengetahuan pada Web Semantik untuk Sistem Rekomendasi Menu Diet Berbasis Ontologi
Unhealthy dietary habits and generalized nutritional recommendations remain major contributors to chronic diseases, despite increasing public awareness of healthy lifestyles. To address the limitations of conventional diet recommendation systems, this research proposes an ontology-based diet menu recommendation system built on Semantic Web principles. The system utilizes structured knowledge representation using ontologies to model food, nutrients, diet types, and user preferences. It employs a hybrid filtering method combining content-based and collaborative filtering to generate contextual and personalized recommendations. The ontology was developed using the Methontology framework and implemented in Protégé, while the system was built using Next.js and integrated with Apache Jena Fuseki for SPARQL-based reasoning. System evaluation covers ontology verification and validation, functional testing via black-box method, recommendation performance using Precision@10, and user acceptance measured by the Technology Acceptance Model (TAM). Results show high accuracy in recommendations (average Precision@10 = 0.72), and users found the system both useful and easy to use, validating the effectiveness of semantic technologies in personalized diet planning.
Analisis dan Visualisasi Sentimen Analisis Data Twitter Menggunakan Support Vector Machine dan Social Network Analysis
The dissemination of information in today's era is extremely rapid due to the development of various social media platforms. However, the information circulating is sometimes inaccurate or invalid. One of the platforms commonly used to spread information is Twitter. Among the vast amount of circulating information, some content is deliberately spread—either positive or negative. This sparked the idea to develop a sentiment analysis program that can be visualized using Support Vector Machine (SVM) and Social Network Analysis (SNA). The model was built using a Twitter dataset and achieved an accuracy of 86.8%, a precision of 86.2%, a recall of 87%, and an F1-score of 86.6%. The model effectively classifies sentiment in Twitter data into appropriate categories. The visualization is presented by displaying nodes and edges interconnected to form a graph. Each node varies in size and color to represent the relationships found in the data.
Implementasi Sistem Iot Monitoring Anggrek Berbasis Arduino Dan Fuzzy Pada Mobile
Orchid growth is strongly influenced by environmental factors such as temperature, humidity, and light intensity. Manual monitoring of these parameters is inefficient and may cause suboptimal growth. This study designs and implements an Internet of Things (IoT) system based on Arduino, integrated with a mobile application and Fuzzy Logic algorithm to monitor the growth conditions of orchids. The system collects temperature, humidity, and light data through sensors, processes them via Fuzzy Logic using Mamdani inference, and classifies the environment into growth suitability levels. Data is transmitted in real time to the Supabase cloud database and displayed in a mobile application developed using React Native. The application features condition logs, graphical trends, and automatic notifications when values are outside optimal thresholds. Testing shows the system performs reliably in real-time monitoring and successfully categorizes environmental conditions into four categories: Very Poor, Poor, Moderate, and Optimal. This solution improves orchid care efficiency and supports smart agriculture practices.
Pengamanan Pesan Menggunakan Algoritma ElGamal dengan Public Key Infrastructure Berbasis Website
In today’s digital era, ensuring the security of data and messages is crucial. Sensitive information transmitted over the internet is vulnerable to interception and manipulation by unauthorized parties. This study implements the elgamal cryptographic algorithm in combination with a public key infrastructure (PKI) to secure message exchanges over a web-based platform. The system enables users to perform end-to-end encryption and decryption, ensuring that only authorized recipients can access the messages. The developed website supports key generation, message encryption and decryption, and digital certificate management. Security testing was carried out using black box testing and penetration tests, including SQL Injection and cross-site scripting (XSS). The evaluation results show that the system effectively maintains message confidentiality and performs encryption and decryption accurately.
Peningkatan UI/UX Platform Edukasi Budaya Gahita.com Menggunakan Pendekatan User Centred Design dan Rekomendasi Konten Berbasis TF-IDF
This study focuses on the development of the User Interface and User Experience of the Gahita.com website using the User-Centered Design approach. The objective of this research is to address usability issues identified during the initial evaluation, which resulted in a System Usability Scale score of 56.3%. Through an iterative redesign and testing process, the study implements various improvements such as simplified navigation, enhanced visual design, and the addition of a new feature called “Ruang Literasi” a cultural article section equipped with a TF-IDF-based recommendation system. Post-redesign usability testing with 10 respondents demonstrated a significant improvement, with the SUS score increasing to 91.7%. Furthermore, User Acceptance Testing of the TF-IDF-based article recommendation feature indicated a user acceptance rate of 98%. The findings confirm that the UCD approach effectively enhances usability across the five key indicators: Learnability, Memorability, Efficiency, Error Prevention, and Satisfaction — with notable improvements in Efficiency 90–100%, Error Prevention 97.5–100%, and User Satisfaction 97.5–100%. This research contributes to the field of UI/UX design by presenting a case study on the application of UCD and TF-IDF in a cultural education platform. It also highlights the importance of user feedback and iterative testing in creating an intuitive and engaging digital experience.
Integrasi Algoritma RSA dan Teknologi Kriptografi Kuantum dalam Keamanan Aplikasi Web Chatting
Abstract In today’s digital era, ensuring the security of data and messages is crucial, especially on web-based chatting platforms that are widely used for personal and professional communication. Sensitive information transmitted over the internet remains vulnerable to interception and manipulation by unauthorized parties. This study implements a hybrid security scheme that integrates the RSA public-key algorithm with simulated Quantum Key Distribution (QKD) using the BB84 protocol to secure key exchange and end-to-end encryption in a web chatting application. RSA-OAEP is used to wrap AES-256 session keys for each chat, while the BB84 module supplies a quantum-derived pad and QBER-based security signal to detect potential eavesdropping on the key exchange process. Private RSA keys are stored entirely on the client side, and all messages are encrypted using AES-GCM in the browser so that the server never accesses plaintext content. The developed system supports user registration, contact management, chat initialization, secure key provisioning, encrypted message exchange, and automatic key rotation when QBER exceeds a defined threshold. Security evaluation, including brute-force and factorization analysis of RSA key sizes, QKD simulation experiments, and black box testing of the application workflow, shows that the prototype effectively maintains message confidentiality, detects simulated interference on the key channel, and performs encryption–decryption accurately with acceptable latency for practical use.