Sistem Pendukung Keputusan Penerima Beasiswa Menggunakan Metode Entropy Weighting dan TOPSIS
The manual selection of tuition fee reduction assistance recipients is time-consuming and potentially subjective. This study aims to develop a Decision Support System that provides priority recommendations for tuition assistance recipients using Entropy Weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The study uses 194 student records from Universitas Udayana based on seven economic and administrative criteria. Entropy Weighting is applied to determine objective criterion weights according to data variation, while TOPSIS calculates preference values and ranks the students. Ranking performance is evaluated using Precision@K, whereas system usability is assessed through a simulated System Usability Scale (SUS) involving five respondents. The weighting results indicate that parental income has the highest weight at 23.13%. TOPSIS produces preference values ranging from 0.1706 to 0.9213. Precision@K scores range from 75% to 82.5%, while the SUS simulation produces an average score of 81.50. These results indicate that the system can support the prioritization of tuition assistance recipients, while the final decision remains under the authority of the Posko UKT management.
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Klasifikasi Genre Musik Menggunakan Metode Hibridasi Particle Swarm Optimization dan Backpropagation
Automatic music genre classification is one of the fundamental challenges in the field of Music Information Retrieval (MIR) due to the high similarity of acoustic characteristics among music genres. This study proposes a hybrid approach that combines Mel-Frequency Cepstral Coefficients (MFCC) for acoustic feature extraction with a Backpropagation Neural Network (BPNN), whose hyperparameters are optimized using Particle Swarm Optimization (PSO). The GTZAN dataset, consisting of 1,000 audio files across 10 music genres, was used in this study. The PSO optimization process automatically searches for the optimal values of five BPNN hyperparameters, namely the number of neurons in three hidden layers, the learning rate, and the dropout rate, using a 5-fold cross-validation scheme. The evaluation of PSO control parameters (the cognitive coefficient c1, social coefficient c2, and inertia weight w) indicates that the combination of c1 = 1.2, c2 = 1.5, and w = 0.7 achieves the lowest average validation loss of 0.9463 and is therefore selected as the optimal PSO configuration. Based on this configuration, PSO identifies the optimal BPNN hyperparameters as 192 neurons in the first hidden layer, 192 neurons in the second hidden layer, 32 neurons in the third hidden layer, a learning rate of 0.000185, and a dropout rate of 0.379. The resulting PSO-BPNN model achieves an average accuracy of 84.40%, precision of 85.09%, recall of 84.40%, and F1-score of 84.34%, outperforming the baseline BPNN model, which achieves an accuracy of 82.80%, precision of 83.60%, recall of 82.80%, and F1-score of 82.80%. Among the evaluated genres, classical achieved the highest classification performance, followed by metal, blues, and jazz, whereas rock, disco, and reggae consistently exhibited the lowest classification performance due to their acoustic similarities with other genres. These findings demonstrate that PSO-based optimization effectively improves the performance of BPNN for MFCC-based music genre classification, although the overall improvement remains relatively modest in absolute terms
Redesign E-Learning Sukmabelajar Dengan Metode UX Design Thinking Dan Konsep Gamifikasi
SukmaBelajar adalah sistem e-learning berbasis website untuk mendukung pembelajaran Matematika Diskrit di Universitas Udayana. Studi pendahuluan menggunakan USE Questionnaire terhadap 30 responden menghasilkan rata-rata skor 3,31 (kategori “Cukup”), dengan permasalahan utama berupa antarmuka tidak responsif di perangkat mobile, kualitas materi yang rendah, minimnya penjelasan pembahasan soal, dan bug teknis pada fitur test. Penelitian ini melakukan redesign SukmaBelajar menggunakan metode UX Design Thinking (Empathize, Define, Ideate, Prototype, Test) dan konsep gamifikasi untuk meningkatkan motivasi belajar pengguna. Data dikumpulkan melalui qualitative usability testing terhadap 6 pengguna aktif dan USE Questionnaire secara kuantitatif. Prototype dirancang di Figma dan diuji secara iteratif, sementara SukmaBelajar 2.0 diimplementasikan menggunakan framework Laravel dengan elemen gamifikasi berupa badge, leveling, leaderboard, dan login streak. Pengujian akhir terhadap 30 responden menunjukkan peningkatan rata-rata skor USE dari 3,31 menjadi 4,32 (“Sangat Baik”), dengan peningkatan tertinggi pada dimensi Satisfaction (+51,2%), diikuti Usefulness (+31,9%), Ease of Use (+23,7%), dan Ease of Learning (+18,3%). Penelitian ini membuktikan bahwa pendekatan Design Thinking dikombinasikan dengan gamifikasi efektif meningkatkan kualitas pengalaman pengguna pada sistem e-learning SukmaBelajar.
Analysis of the Effect of Data Augmentation Methods on CCTV Image Face Recognition Attendance Systems Using CNN
Teknologi pengenalan wajah (face recognition) berbasis Convolutional Neural Network (CNN) menjadi salah satu pendekatan efektif untuk sistem presensi otomatis. Namun, implementasi real-time masih menghadapi tantangan terkait efisiensi komputasi, biaya perangkat keras, dan variasi kondisi lingkungan. Penelitian ini bertujuan mengimplementasikan sistem presensi otomatis berbasis face recognition menggunakan arsitektur MobileNetV2 yang ringan. Sistem memanfaatkan smartphone sebagai kamera melalui aplikasi iVCam dan antarmuka web interaktif berbasis Streamlit. Penelitian juga menganalisis pengaruh augmentasi data terhadap performa dan ketahanan model dengan membandingkan model tanpa augmentasi dan model dengan augmentasi berupa rotasi, pergeseran, serta variasi kecerahan. Hasil pengujian menunjukkan MobileNetV2 mampu mengklasifikasikan identitas mahasiswa dengan akurasi pengujian sebesar 98%. Model dengan augmentasi menghasilkan validation accuracy sebesar 98,43% dan validation loss sebesar 0,1192, lebih baik dibandingkan model tanpa augmentasi yang memperoleh 97,64% dan 0,1518. Teknik augmentasi terbukti meningkatkan ketahanan model terhadap variasi posisi wajah dan pencahayaan rendah. Integrasi timer 3 detik dan frame skipping pada Streamlit berhasil menghasilkan sistem presensi real-time berbasis iVCam yang stabil, transparan, dan bebas lag.
Implementasi Teknologi Face Recognition Menggunakan Metode CNN Dan Multi-Factor Authentication Untuk Meningkatkan Keamanan Login Pada E-Learning
Platform E-Learning menjadi salah satu media utama dalam mendukung proses pembelajaran digital. Namun, mekanisme autentikasi yang hanya mengandalkan username dan password masih rentan terhadap berbagai ancaman keamanan, seperti Brute Force attack. Penelitian ini bertujuan untuk merancang, mengimplementasikan, dan mengevaluasi sistem Multi-Factor Authentication (MFA) berbasis face recognition menggunakan Convolutional Neural Network (CNN) dengan arsitektur VGG16 untuk meningkatkan keamanan autentikasi pada platform E-Learning. Tahapan penelitian meliputi pengumpulan dan pengolahan data wajah, pelatihan model, implementasi MFA, evaluasi model menggunakan confusion matrix, serta pengujian keamanan melalui simulasi Spoofing attack dan Brute Force attack. Hasil penelitian menunjukkan bahwa model VGG16 memperoleh accuracy sebesar 97,95%. Pengujian Spoofing attack menunjukkan bahwa sistem berhasil menolak 9 dari 10 percobaan menggunakan foto. Pada pengujian Brute Force attack, sistem tanpa MFA berhasil mengidentifikasi kredensial valid dalam 10 menit 11 detik 852 milidetik, sedangkan setelah penerapan MFA, seluruh 1.000 wordlist selesai diuji dalam 1 jam 8 menit 20 detik 621 milidetik tanpa berhasil memperoleh akses ke sistem.
Sistem Rekomendasi Parfum Berbasis Ontologi Sebagai Domain Pengetahuan dengan Pendekatan Content-Based Filtering
Pemilihan parfum yang sesuai dengan preferensi pengguna seringkali menjadi tantangan karena banyaknya variasi parfum yang tersedia di pasaran. Pengguna mengalami kesulitan dalam menemukan parfum yang sesuai dengan preferensi pribadi tanpa mencoba secara langsung. Penelitian ini bertujuan membangun sistem rekomendasi parfum berbasis web untuk menghasilkan rekomendasi yang sesuai dengan preferensi pengguna. Data parfum diperoleh dari berbagai situs e-commerce dan direpresentasikan dalam bentuk ontologi. Proses rekomendasi dilakukan menggunakan TF-IDF dan cosine similarity untuk menghitung kemiripan atribut parfum. Evaluasi sistem dilakukan pada empat skenario pengujian berdasarkan variasi atribut serta tiga nilai Top-N (10, 15, dan 20) menggunakan metrik precision, recall, f1-score, dan akurasi. Hasil evaluasi menunjukkan bahwa skenario terbaik diperoleh pada penggunaan tiga dan empat atribut pada Top-20 rekomendasi dengan nilai precision sebesar 80%, recall 61,5%, f1-score 66,7%, dan akurasi 86,8%. Hasil tersebut menunjukkan bahwa pendekatan ontologi dan Content-Based Filtering efektif dalam menghasilkan rekomendasi parfum yang relevan serta mendukung proses pencarian parfum sesuai referensi pengguna.
Pengembangan Sistem Berbasis Ontologi dan Mesin Rekomendasi untuk Dokumentasi Peninggalan Kolonial Bali
Peninggalan sejarah masa kolonial di Bali rentan terhadap kerusakan fisik dan terhambat proses penetapan status cagar budaya yang panjang, sementara dokumentasi konvensional belum menjamin aksesibilitas publik maupun keterkaitan semantik antarobjek. Penelitian ini mengembangkan Bali Archief, sistem manajemen pengetahuan berbasis ontologi yang berkontribusi pada pelestarian peninggalan kolonial di Bali melalui dokumentasi terstruktur dan akses publik yang tidak bergantung pada penetapan hukum formal. Ontologi dibangun menggunakan Protégé dengan kerangka kerja Methontology, mencakup 29 kelas, 13 object property, 12 data property, dan 96 individu peninggalan dari periode Kolonial Belanda dan Pendudukan Jepang. Sistem dikembangkan menggunakan Flask dengan mesin rekomendasi yang menggabungkan Wu-Palmer Similarity dan Jaccard Similarity berbobot pada enam dimensi properti ontologi. Evaluasi ontologi berbasis kueri SPARQL terhadap 20 Competency Questions menghasilkan pass rate 95 persen. Evaluasi mesin rekomendasi menggunakan Normalized Discounted Cumulative Gain (NDCG@5) terhadap 95 query menghasilkan Mean NDCG@5 sebesar 0,9411. Evaluasi penerimaan pengguna melalui Technology Acceptance Model (TAM) menghasilkan Perceived Usefulness 5,76 dan Perceived Ease of Use 6,14. Hasil ini membuktikan ontologi merepresentasikan pengetahuan domain secara semantik dan diterima baik oleh pengguna sebagai instrumen dokumentasi pelestarian.
Pendekatan Berbasis GLCM untuk Optimasi Citra Sampul pada Steganografi LSB dengan Keamanan RSA-2048
Least Significant Bit (LSB) steganography usually treats every image as an equally good carrier, ignoring that cover texture governs how visible and how detectable the embedded payload becomes. This work proposes a Gray-Level Co-occurrence Matrix (GLCM) approach to rank traditional Indonesian fabric motifs as covers, with the secret first encrypted under RSA-2048 with OAEP padding. Four Haralick features were extracted from 165 grayscale images across 15 motifs, then each image carried multi-bit LSB payloads over a full 3x3 factorial of bit modes and load percentages, yielding 1,485 stego images. Pearson correlation links each feature to quality (MSE, PSNR, SSIM) and to detectability under Chi-square Attack and RS Analysis. Homogeneity is the single strongest predictor, tracking SSIM (r = -0.763 to -0.846) and the RS Gap (r = 0.668 to 0.878), whereas PSNR and MSE capture no texture effect. RS Analysis separates stego from cover far more sharply than Chi-square (AUC 0.986 to 1.000 versus 0.788 to 0.939). A min-max composite score ranks Bali Barong first (0.9394); the worst-case RS Gap between best and worst motif differs by 84.5 percent, confirming that cover choice materially affects security.
Analisis Komparasi Pengamanan Kunci Pemulihan Wallet Cryptocurrency Menggunakan Metode LSB Random dan Randomized Adaptive Edge-Based Steganography
Keamanan wallet aset kripto tanpa kustodian (non-custodial) bertumpu sepenuhnya pada perlindungan Frasa Pemulihan (Recovery Phrase). Kegagalan dalam mengamankan frasa ini pada medium yang rentan kerap memicu risiko peretasan dan hilangnya aset secara permanen. Sebagai solusi mitigasi, riset ini mengajukan mekanisme pengamanan alternatif menggunakan steganografi untuk menanamkan 12 kata Kunci Pemulihan berstandar BIP39 ke dalam gambar digital. Studi ini menguji dan membandingkan performa dua algoritma steganografi, yaitu Least Significant Bit (LSB) Random dan Randomized Adaptive Edge-Based. Evaluasi eksperimental memperlihatkan bahwa kedua algoritma secara meyakinkan mampu menyembunyikan data mikroskopis sebesar 132-bit. Metode Edge-Based terbukti unggul dalam menjaga presisi struktural visual (SSIM 0,999992), memiliki durasi komputasi yang jauh lebih ringkas (0,022 detik), serta lebih kebal terhadap pemindaian Chi-Square Attack (P-Value 0,7428). Pada sisi lain, metode LSB Random menunjukkan imunitas mutlak dengan PSNR yang stabil di kisaran 82 dB meski gambar penampung mengalami degradasi pencahayaan maupun kontras yang ekstrem. Kesimpulan menegaskan bahwa metode Edge-Based sangat direkomendasikan untuk keamanan visual dan statistik, sementara LSB Random ideal difungsikan sebagai metode cadangan pada media citra berkualitas rendah.
Keamanan Seed Phrase dalam Wallet Cryptocurrency Menggunakan Algoritma Elliptic Curve Cryptography (ECC)
Penyimpanan seed phrase pada dompet mata uang kripto seringkali menjadi titik lemah keamanan utama karena umumnya disimpan dalam format teks terang (plain text) yang rentan terhadap akses tidak sah. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem pengamanan seed phrase menggunakan algoritma Elliptic Curve Cryptography (ECC) dengan standar kurva SECP256k1. Untuk memperkuat lapisan perlindungan, sistem ini juga mengintegrasikan metode Key Wrapping berbasis PBKDF2 dengan penambahan salt guna melindungi kunci privat dari upaya peretasan. Metodologi pengujian yang digunakan mencakup analisis performa komputasi serta simulasi serangan brute force pada lapisan autentikasi sistem. Hasil penelitian menunjukkan bahwa implementasi algoritma ECC sangat efisien dengan waktu eksekusi rata-rata untuk proses enkripsi dan dekripsi di bawah 0,1 milidetik, sehingga tidak membebani performa perangkat. Selain itu, hasil pengujian ketahanan terhadap serangan brute force selama 24 jam menunjukkan status gagal (sistem aman). Hal ini dikarenakan penggunaan kata sandi kompleks menghasilkan nilai entropi sebesar 4,75 x 1023 kombinasi, yang secara matematis mustahil untuk ditembus dalam rentang waktu yang rasional menggunakan teknologi komputasi saat ini. Simpulan dari penelitian ini adalah penggabungan algoritma ECC dan kebijakan kata sandi yang kuat terbukti efektif dalam memberikan perlindungan tingkat tinggi bagi aset digital pengguna dari berbagai skenario serangan siber.
Pengaruh Augmentasi Teks pada Klasifikasi Berita Menggunakan BERT Embedding dan LSTM
According to the 2023 Digital News Report, 84% of Indonesians access or consume news online, reflecting how central digital platforms have become to public information consumption. Furthermore, 2024 data from the Indonesian Press Council recorded 1,015 online news platforms operating in Indonesia. With such a diverse range of providers, it is estimated that thousands or even millions of news articles are published annually, making consistent and efficient content organization increasingly important. News category labels serve as a universal variable that helps both online news platform providers and the general public navigate this volume of information more easily. This study therefore performs automatic news topic classification by combining BERT embedding with Long Short-Term Memory (LSTM), incorporating back-translation as a data augmentation technique to mitigate overfitting. Grid search hyperparameter tuning was employed to determine the optimal configuration, resulting in a dropout rate of 0.2 and a learning rate of 0.0001 for both the non-augmented and 30%-augmented models. The non-augmented model achieved 96% accuracy, while the 30%-augmented model achieved 98% accuracy, establishing it as the best-performing approach in this study.
Klasifikasi Spesies Jamur Menggunakan Metode Hibrida DenseNet-121 dan Support Vector Machine
The public’s inability to distinguish between the visual characteristics of poisonous and edible mushrooms contributes to a high risk of wild mushroom poisoning. This study developed a mushroom image classification system using a hybrid method combining a Convolutional Neural Network (CNN) based on the DenseNet-121 architecture and a Support Vector Machine (SVM), which was tested through four comparative modeling schemes. The four schemes include: (1) training DenseNet-121 from scratch, (2) integrating DenseNet-121 with SVM without Transfer Learning, (3) Transfer Learning of DenseNet-121 using ImageNet weights, and (4) Transfer Learning + Fine-Tuning + SVM. Each scheme was evaluated using three batch size variations (4, 8, 16) and two optimizers (Adam and RMSProp). The results show that Scheme 4, using the Adam optimizer and a batch size of 4, achieved the highest average accuracy of 92.37%, far surpassing the baseline model in Scheme 1, which reached only 82.40%. The best model was then converted to the TensorFlow Lite format and implemented in an Android application. Testing on 180 new images (unseen data) recorded an accuracy of 94.44% with a total inference time of only 0.50 seconds per image. This study proves that the DenseNet-121 + SVM hybrid architecture with a fine-tuning strategy excels in mushroom classification and is suitable as a mobile-based educational tool for preventing poisoning.
Implementasi Variational Autoencoder dan Regularized Singular Value Decomposition pada Sistem Rekomendasi Film
The abundance of available films presents a challenge for viewers in finding content that matches their preferences. This study proposes a hybrid ensemble movie recommendation system combining a Variational Autoencoder (VAE) with KL annealing and Regularized Singular Value Decomposition (RSVD) using a weighted average approach on the MovieLens 100K dataset. Evaluation is conducted using 10-fold cross-validation, final testing on a hold-out test set, and statistical significance testing via paired t-test. Results show that the hybrid ensemble with weights alpha = 0.25 for VAE and beta = 0.75 for RSVD achieves an RMSE of 0.9307 ± 0.0043 and MAE of 0.7338 ± 0.0034 on cross-validation, and RMSE of 0.9365 and MAE of 0.7401 on the test set, consistently outperforming standalone RSVD (RMSE 0.9474) and standalone VAE (RMSE 1.0926). All performance differences are confirmed statistically significant by paired t-test with p-values well below 0.05.
Pengembangan Aplikasi Pengukur Kualitas Air Berbasis IoT Menggunakan Metode Support Vector Machine (SVM)
Manual monitoring of bore well water quality requires considerable time and is unable to provide real-time information, thereby posing potential risks to public health. This study develops Waterlytics, an Internet of Things (IoT)-based bore well water quality monitoring system that integrates Support Vector Machine (SVM) classification with a Radial Basis Function (RBF) kernel. The system utilizes a NodeMCU ESP8266 microcontroller equipped with a TDS Meter V1.0 sensor, Turbidity Sensor, and DS18B20 temperature sensor connected through an ADS1115 module. Sensor data are transmitted every 10 seconds to a Flask REST API server, stored in a MySQL database, and automatically classified into three water quality categories based on the Indonesian Ministry of Health Regulation No. 2 of 2023 standards. The SVM model parameters were optimized using Grid Search Cross-Validation with k-fold=5. Classification results are visualized through an interactive Flask-based web dashboard and delivered as automatic notifications via a Telegram Bot. The results indicate that the SVM model achieved an accuracy of 93.94% with optimal parameters of C=10 and gamma=10, exceeding the minimum target of 85%. Sensor validation against laboratory reference values from the Bali Provincial Environmental and Forestry Agency (DKLH) showed deviations below 2% for all measured parameters. Furthermore, both Black Box testing (25 scenarios) and White Box testing (19 logic cases) were successfully completed.
Segmentasi Pelanggan dengan Model LRFM Menggunakan Particle Swarm Optimization dan Fuzzy C-Means
Increasing business competition encourages companies to utilize e-commerce and better understand customer behavior through customer segmentation. This study combines the LRFM model with Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO), where PSO optimizes the initial cluster centers to improve FCM clustering performance. The research process includes LRFM calculation, data normalization, PSO parameter tuning, clustering, and evaluation using the Partition Coefficient (PC), Partition Entropy (PE), and Davies–Bouldin Index (DBI). The best PSO parameters were 50 particles, an inertia weight of 0.7, C₁ = 1.5, and C₂ = 1.0. The optimal result was obtained with two clusters, achieving PC = 0.845, PE = 0.267, and DBI = 0.613. The clustering identified lost customers with low activity and transaction value, recommended for a let-go strategy, and core customers with high activity and contribution, recommended for an enforced strategy.
Implementasi Fuzzy Sugeno Berbasis IoT untuk Monitoring Energi Listrik pada Gedung Lembaga Pelatihan Kerja Panji Sakti
The increase in electrical energy consumption in buildings requires a monitoring system that accurately and in real time presents electricity usage information. This research aims to implement an Internet of Things (IoT)-based electrical energy monitoring system using the Zero-Order Sugeno Fuzzy method to classify the condition of electrical loads at the Panji Sakti Vocational Training Center Building. The system was developed using an ESP32 microcontroller integrated with an SCT-013 current sensor and a ZMPT101B voltage sensor. The measurement data is sent to a database and displayed through a website for real-time monitoring. Testing was conducted using 30 data samples, which were compared with the Habotest HT206D multimeter as the reference instrument. The test results showed an average error of 1.9% for voltage measurements and 2% for current measurements, with accuracy rates of 98% and 97%, respectively. Furthermore, the Zero-Order Sugeno Fuzzy method successfully classified the electrical load conditions into underload, normal, and overload categories with 100% accuracy compared to manual classification. The research results indicate that the developed system effectively monitors and classifies electrical loads to support building energy management.
Sistem Prediksi Penjualan Toko Kelontong Berbasis Website Dengan Metode Holt-Winters Exponential Smoothing
The development of information technology provides opportunities for business owners to improve the effectiveness of business management, including inventory management. Grocery stores often face problems related to overstock and stock shortages due to the absence of a sales forecasting system capable of predicting future product demand. This study aims to develop a web-based sales forecasting system for grocery stores using the Holt-Winters Exponential Smoothing method and to determine the accuracy level of the resulting forecasting model. The system was developed using PHP and Python programming languages with MySQL as the database management system. PHP was used for interface and database management, while Python was used for the forecasting computation process. The Holt-Winters Exponential Smoothing method was applied because it is capable of handling time series data with trend and seasonal patterns. The alpha (α), beta (β), and gamma (γ) parameters were automatically optimized using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS-B) optimization algorithm. Model accuracy was evaluated using Mean Absolute Percentage Error (MAPE), while system functionality testing was conducted using the Black Box Testing method. The results of this study indicate that the system successfully manages product and sales data, as well as displays sales forecasting results in the form of tables and interactive charts. Based on Black Box Testing, all system features functioned according to the specified functional requirements. Furthermore, the accuracy evaluation showed that the Holt-Winters Exponential Smoothing method was able to produce MAPE values categorized as good to very good for several tested products. Therefore, the developed system can assist store owners in forecasting sales and support decision-making related to inventory management
Implementasi Sistem Monitoring Kualitas Air Berbasis Internet of Things dengan Multisensor
Continuous monitoring of household borewell water quality is essential for the early detection of changes in water conditions. This study develops and evaluates a laboratory-scale prototype of an Internet of Things (IoT)-based monitoring system for early water condition indicators, utilizing a TDS sensor (SEN0244) and a temperature sensor (DS18B20) connected to an ESP8266 microcontroller. The prototype was tested using a plastic bucket filled with borewell water to simulate a household water storage system. Measurement data were transmitted via the HTTP protocol to a Flask server, stored in an SQLite database, and displayed on a web-based dashboard in near real-time. The Sugeno fuzzy logic method was implemented to provide a more stable and gradual interpretation of water conditions based on TDS values as the primary indicator and temperature as a supporting parameter, in accordance with the Indonesian Ministry of Health Regulation (Permenkes RI) Number 2 of 2023. The experimental results showed an average accuracy of 97.76% for the TDS sensor and 98.59% for the temperature sensor. Black-box testing achieved a 100% success rate (19/19 test scenarios), while data transmission was successful in 100% of cases under normal network conditions. One-day monitoring produced 180 valid data records, with TDS values ranging from 106.47 to 116.51 mg/L, all of which complied with the water quality standards specified in Permenkes RI Number 2 of 2023. These findings are consistent with the characteristics of borewell water in rural areas of Tabanan, which were not exposed to significant contamination during the observation period. This study provides empirical evidence of the feasibility of integrating Sugeno fuzzy logic into an IoT-based monitoring system architecture using TDS and temperature measurements as a foundation for the development of similar systems at a full implementation scale.
Pengaruh Attention Pooling dan Metode Ekstraksi Fitur Terhadap Performa LSTM dalam Klasifikasi Emosi Musik Piano
Music serves as a medium for emotional expression through acoustic elements such as pitch, rhythm, and timbre. This study focuses on music emotion classification based on solo piano instruments without vocal elements in the field of Music Emotion Recognition (MER). The objective of this research is to analyze the effect of pooling mechanisms and to compare the performance of MFCC and Log-Mel Spectrogram feature extraction methods on an LSTM model in classifying music emotions into four categories based on Thayer's quadrant model. The results demonstrate that the LSTM model with Attention Pooling consistently outperforms the LSTM model with Mean Pooling across all evaluation metrics, with accuracy improving from 63.97% to 66.91%, macro precision from 64.03% to 68.24%, macro recall from 64.53% to 66.65%, and macro F1-score from 64.14% to 66.81%. Furthermore, a comparison of feature extraction methods on the LSTM architecture with Attention Pooling reveals that the model based MFCC features outperforms the model based on Log-Mel Spectrogram features across all evaluation metrics, achieving a macro F1-score of 66.81% compared to 60.54%, with an accuracy gap of 5.15%.
Klasifikasi Kualitas Air Layak Minum Menggunakan Algoritma Genetika dan Random Forest
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.
Rancang Bangun Algoritma ChaCha20-Poly1305 Berbasis SHA-256 Untuk Pengamanan Dokumen Penting Masyarakat
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.
Penyisipan Handwriting Signature Pada Karya Seni Digital Menggunakan Metode Steganografi LSB Matching Revisited dan Huffman Coding
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.
Penggunaan LSTM dan Fast-Text dalam Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi dengan Seleksi Fitur Information Gain
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
Optimasi Convolutional Neural Network pada Klasifikasi Mood Musik Berbasis Fitur Audio
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.
Klasifikasi Tumor Otak Berbasis Data Mri dengan Algoritma Convolutional Neural Network (CNN)
Brain tumors require accurate and timely diagnosis to support appropriate medical treatment. This study proposes a brain tumor classification system based on Magnetic Resonance Imaging (MRI) using a Convolutional Neural Network (CNN). The dataset consists of 5,266 MRI images categorized into three classes: glioma, meningioma, and no tumor. Prior to classification, image preprocessing was performed through image resizing, normalization, and Local Binary Pattern (LBP) feature extraction. The dataset was divided into 3,403 training images, 852 validation images, and 1,011 testing images. The proposed CNN architecture consists of three convolutional layers, max pooling layers, global average pooling, a dense layer, dropout, and a softmax output layer. Experimental results show that the model achieved training, validation, and testing accuracies of 98.53%, 94.24%, and 92.28%, respectively. These results indicate that CNN can effectively classify brain MRI images and provide good generalization performance for unseen data.
Penyisipan Pesan Rahasia pada Citra Digital dengan Metode Steganografi dan Algoritma Least Significant Bit (LSB)
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.
Klasifikasi Serangan DDoS Menggunakan Algoritma Support Vector Machine dan Correlation-Based
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.
Perancangan Website Penyedia Kost Menggunakan Metode Design Thinking
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.
Sistem Rekomendasi Genre Musik Menggunakan SVM dengan Pendekatan Content-Based Filtering
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.
Klasifikasi Cyberbullying dengan Menggunakan Convolutional Neural Network (CNN) dan ELMo Embeddings
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.