Jurnal Nasional Teknologi Informasi dan Aplikasinya
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya), publishes original research in computer science and engineering, covering theory, practice, methodology, and innovations in technology and information systems. It is published quarterly (February, May, August, November) with a double-blind review process.
Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University
Implementasi LexRank dan BERT2GPT dalam Auto Summarization Teks Bahasa Indonesia
In Indonesia, with the rapid growth of internet and social media usage, the amount of information produced in the Indonesian language has reached significant levels. This creates challenges in managing and understanding this information quickly and efficiently. Text summarization has emerged as a potential solution to help users organize and summarize information, enabling easier and more efficient access to relevant content. This study discusses the development of an Indonesian text summarization model using the LexRank algorithm. The results show that this model can produce accurate and concise summaries, with ROUGE-L result of 0.91 and also a ROUGE-1 result of 0.31. Developing an Indonesian text summarization model is important because it can help users manage and understand information quickly and efficiently. This study provides a positive contribution to the development of Indonesian text summarization models, by providing evidence that the LexRank model can produce accurate and concise summaries.
Copyright (c) 2025 Tristan Bey Kusuma, I Made Widiartha, I Putu Gede Hendra Suputra (Author) https://creativecommons.org/licenses/by/4.0
Pengembangan Sistem Informasi Manajemen Sekolah Terintegrasi untuk Digitalisasi Administrasi
The rapid advancement of the Industrial Revolution 4.0 and Society 5.0 era has driven significant digital transformation across various sectors, including education. Schools are expected to adopt integrated information systems to improve efficiency, data accuracy, and transparency in academic administration. However, many schools, particularly in rural areas such as Tabanan Regency, Bali, still rely on manual processes involving paper records and spreadsheets. This situation often leads to inefficiencies, data inaccuracies, limited accessibility, and vulnerability to document loss or damage. SMA Negeri 1 Marga is one such school that continues to manage core administrative tasks—such as student data, grading, and report card preparation—manually. To address these challenges, this study aims to develop an Integrated School Management Information System (SIMS) that supports the digitalization of administrative processes and improves overall school management.The research adopts a Research and Development (R&D) approach using the System Development Life Cycle (SDLC) with the Prototype model to ensure iterative feedback from users. The system was implemented using PHP for the backend, MySQL for the database, and HTML, CSS, and JavaScript for the front end. Blackbox Testing was conducted to evaluate the system’s functionality, involving representative users: administrators, teachers, students, and parents. The test results show that most modules, including Login, User Data, Master Data, Teacher and Student Dashboards, Parent Dashboard, and Announcements, achieved a 100% success rate. Only minor issues were found in the Schedule module (87.5%) and Student Grades module (91.7%), resulting in an overall success rate of 97.5%. These findings indicate that the developed SIMS is highly suitable for implementation, effectively addressing the current challenges faced by the school. The system significantly improves efficiency, enhances data accuracy, and ensures transparency among all stakeholders, demonstrating the potential of digital solutions to transform school administration in similar educational environments.
Copyright (c) 2025 Surya Arnawa Ida Bagus Ketut, Ida Ayu Gede Wiwik Purnamayanti (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Metode LSTM untuk Prediksi Harga Saham PT Indofood CBP Sukses Makmur TBK
High stock price fluctuations make stock prices difficult to predict accurately. Therefore, a predictive analysis approach that utilizes historical data in addition to machine learning methods is needed to help estimate price movements more effectively. This study aims to determine the performance of the Long Short-Term Memory (LSTM) method in predicting the stock prices of PT Indofood CBP Sukses Makmur Tbk based on historical data. LSTM is a type of artificial neural network that is effective in processing time series data due to its ability to capture long term relationships between data. Historical data is used to train the LSTM model. The results show that the LSTM model is effective in predicting stock prices, with an average accuracy of 80.5%. Sukses Makmur Tbk based on historical data. LSTM is one type of artificial neural network that is effective in processing time series data due to its ability to capture long-term relationships between data points. The data used consists of ICBP stock closing prices from January 2019 to May 2025. The methods used include data cleaning, data normalization, data splitting, model design, prediction, denormalization, and evaluation using the Mean Absolute Percentage Error (MAPE) metric. The research results demonstrate that the LSTM model performs well in recognizing time series data patterns, as indicated by the lowest MAPE value of 1.43, at the combination of 100 epochs and a batch size of 32.
Copyright (c) 2025 Endritha Pramudya, Dwi Retnoningsih, Diyah Ruswanti (Author) https://creativecommons.org/licenses/by/4.0
Analisis Sentimen Fenomena FoMO Gaya Hidup Sehat Menggunakan Metode Naïve Bayes Classifier
The development of social media has given rise to the Fear of Missing Out (FoMO) phenomenon which can affect healthy lifestyles among the community, especially the younger generation. This study aims to conduct a sentiment analysis of the FoMO phenomenon related to a healthy lifestyle using the Naïve Bayes Classifier method. Data were collected from Twitter social media using a crawling technique with certain keywords in the period from January 2024 to June 2025. The data was then processed through the preprocessing stage, sentiment labeling, data balancing using SMOTE, and feature weighting using TF-IDF. The model was trained using 2,364 training data and tested on 591 data. The evaluation results showed that the model achieved an accuracy of 84.09%, precision 85.66%, recall 84.09%, and F1-score 83.57%. Validation using 5-Fold Cross Validation also showed stable model performance with an average accuracy of 84.50%. This study proves that Naïve Bayes is effective in analyzing social media sentiment towards the FoMO phenomenon, with potential for development through dataset expansion and exploration of other algorithms
Copyright (c) 2025 Putu Mahdalika Intan Pratiwi, Anak Agung Istri Ngurah Eka Karyawati (Author) https://creativecommons.org/licenses/by/4.0
Analisis UX pada Aplikasi Komik Digital Menggunakan Metode Usability Testing
The shift to digital media has intensified competition among comic applications, making User Experience (UX) a critical determinant of success. This study evaluates a digital comic app's UX via Usability Testing with five users , measuring effectiveness, errors, learnability, and satisfaction.Results indicate a 100% task completion rate , yet a high error rate of 60% reveals significant usability issues. The "Change Notification" and "Download Offline" features were the primary sources of errors , scoring lowest in learnability. Interestingly, high user satisfaction contrasts with the high error rate , suggesting users encountered non-critical errors that hindered efficiency but did not spoil the overall experience. In conclusion, while functionally effective , the application requires a targeted redesign of its notification and download features to improve usability.
Copyright (c) 2025 Nanda Asmara Ramdhan Putra, I Gede Arta Wibawa (Author) https://creativecommons.org/licenses/by/4.0
Analisis Komparatif Arsitektur CNN dan VGG16 pada Klasifikasi Genre Musik
Music genre classification based on spectrogram images is an important task in music information retrieval. This study compares the performance of a custom Convolutional Neural Network (CNN) architecture and VGG-16 for classifying five music genres from the GTZAN dataset: blues, classical, hiphop, metal, and reggae. A total of 500 audio files were converted into spectrogram images for training and testing. The custom CNN was designed and trained from scratch, while VGG-16 utilized pretrained weights with fine-tuning applied to the fully connected layers. Experimental results show that the custom CNN achieved 75% test accuracy and a macro F1- score of 0.74, outperforming VGG-16 which achieved 68.75% accuracy and a macro F1-score of 0.67. These findings demonstrate the advantage of using a tailored architecture for spectrogram- based music genre classification and provide directions for future research, including full fine- tuning of pretrained models, hybrid architectures, and integration of temporal features.
Copyright (c) 2025 Komang Indra Pradnya, Made Agung Raharja (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Kematangan Tomat pada Citra Digital Menggunakan DeiT (Data-efficient Image Transformer)
This study addresses the critical need for accurate and efficient tomato ripeness classification in agriculture and agribusiness, aiming to overcome the limitations of subjective manual methods. Leveraging advances in Computer Vision, this study implements a Data-efficient Image Transformer (DeiT) model for automatic classification of digital tomato images. DeiT, a Transformer-based architecture developed by Facebook AI Research, was chosen for its superior performance on small to medium-sized datasets, leveraging knowledge distillation. The model was trained using the Kaggle dataset, instrumented to enhance visual diversity, to classify tomatoes into “ripe” and “unripe” categories. Evaluation was performed using standard classification metrics including accuracy, F1-Score, and confusion matrix. The model demonstrated high performance, achieving an overall accuracy of 0.96 on the test dataset.
Copyright (c) 2025 I Gede Made Widi Anditya, Gst. Ayu Vida Mastrika Giri (Author) https://creativecommons.org/licenses/by/4.0
Analisis Kualitas Air PAM Layak Minum dengan Metode Random Forest dan Decision Tree
Water is an important source of life for living things including humans. Human needs for water include water that is suitable for use in cooking, washing, and bathing activities that are clean and healthy, as well as water that is safe to drink. Drinking Water Companies (PAM) have a vital role in providing water that meets the standards of consumption eligibility. This study aims to analyze the quality of PAM water by utilizing the Random Forest method as a classification method. The data used include physical, chemical, and microbiological parameters of water. The use of the random forest method was chosen because of its ability to handle complex data and produce accurate predictions. The results of the study showed that the random forest model was able to classify water quality with a high level of accuracy and identify the parameters that most influence the eligibility of drinking water. This study is expected to help related parties in monitoring and improving the quality of PAM water so that it is in accordance with the established health standards.
Copyright (c) 2025 Stefani Kelin Martha Ampak, Anak Agung Istri Ngurah Eka Karyawati, I Komang Arya Ganda Wiguna (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Customer Churn Menggunakan XGBoost dengan Optimasi GridSearchCV Berbasis Shapley Additive Explanations
Customer churn is a significant challenge in the banking sector, often leading to revenue loss and requiring predictive strategies to enhance customer retention. This study implements the Extreme Gradient Boosting (XGBoost) algorithm for churn classification, with hyperparameter optimization using the GridSearchCV technique to improve model performance. The dataset comprises 10,000 banking customers with 9 features and 1 target label. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Prior to tuning, the XGBoost model achieved an accuracy of 80.8%. After applying optimal parameters, the model's performance improved to 81.5%, along with higher precision and recall values, indicating improved robustness and consistency. For model interpretability, Shapley Additive Explanations (SHAP) were used and visualized through a beeswarm Plot. The analysis identified age, customer activity status, and number of products owned as the most influential features in predicting churn. Based on these findings, this study proposes business recommendations including age-based customer segmentation, enhancing active customer engagement, and optimizing product offerings as strategies to reduce churn.
Copyright (c) 2025 I Gusti Ayu Riyana Astarani, Luh Arida Ayu Rahning Putri (Author) https://creativecommons.org/licenses/by/4.0
Segmentasi Pelanggan Berbasis RFMT Menggunakan K-Means dan Hierarchical Clustering
The rapid growth of online retail has generated vast transactional data, creating significant opportunities for advanced customer segmentation. While the standard RFM (Recency, Frequency, Monetary) model is widely used in Customer Relationship Management (CRM), it possesses a key limitation by not capturing the temporal dynamics between customer purchases. This research addresses that gap by proposing an RFM-T model, which enhances the traditional framework with Interpurchase Time (IPT) to provide a more holistic view of customer behavior. Using a dual-clustering methodology on an online retail dataset, the K-Means algorithm is first applied for broad segmentation, followed by Hierarchical Clustering to explore deeper sub-segments within high-value groups. The process yielded four primary clusters, and the model's robustness was systematically validated through a strong Silhouette Score, a low Davies-Bouldin Index, and a high Calinski-Harabasz Index. This detailed analysis successfully identified distinct customer personas, such as 'Consistent Loyalists' (low IPT) and 'Periodic Premium Buyers' (high monetary value), which are crucial for developing targeted strategies. The findings demonstrate that this integrated RFM-T framework provides a quantitatively validated with Silhouette Score 0.410, Davies-Bouldin Index 0.720, and Calinski-Harabasz Index 1365.14 this score show actionable model for personalized marketing and effective customer retention.
Copyright (c) 2025 I Komang Yosua Triantara, Made Agung Raharja, Ida Bagus Gede Sarasvananda (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Sistem E-Voting Berbasis Blockchain Menggunakan Teknologi Internet Computer Protocol (ICP)
This research develops and implements a blockchain e-voting system using the Internet Computer Protocol (ICP), addressing challenges in conventional voting such as data manipulation, security, and transparency. The study explores e-voting and blockchain technologies, particulary ICP, to enhance secure voting processes. The system lavarages blockchain’s decentralization, transparency, and immutability, ensuring transparent and permanent vote recording. Smartcontracts automate vote data recording and security within the blockchain. Initial testing demonstrates the system’s stability, effective voter authentication, and accurate vote data reporting.
Copyright (c) 2025 Aditya Premana Putra, Ngurah Agus Sanjaya ER, I Gusti Agung Gede Arya Kadyanan (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Berita Berdasarkan Kategori Menggunakan Convolutional Neural Network dengan IndoBERT
The advancement of technology information has led to a significant increased the volume of digital news, that makes needs for automatic news classification. This study aims to design a model capable of caterogizing Indonesian language news articles into six predefined categories, such as News, Money, Bola, Health, Tekno, and Tren. To achieve this goal, the method used combines IndoBERT as the embedding technique with Convolutional Neural Network (CNN) as the classification algorithm. The dataset consists of 3.000 news articles collected from Kompas.com and is divided into training data and testing data using four different data split ratios: 60:40, 70:30 80:20, and 90:10 . The evaluation results show that the best performance was achieved using the 80:20 ratio, where the model reached an accuracy of 91%, along with high precision, recall, and F-1 Score These result prove that the combination of IndoBERT and CNN is effective for the automatic classification of Indonesian new texts.
Copyright (c) 2025 Jonathan Federico Tantoro, I Dewa Made Bayu Atmaja Darmawan (Author) https://creativecommons.org/licenses/by/4.0
Optimasi Metode Support Vector Machine (SVM) Mengunakan Particle Swarm Optimization pada Permasalahan Klasifikasi Diabetes
Diabetes mellitus is a chronic disease that requires accurate early detection. This study presents a diabetes classification system by integrating Support Vector Machine (SVM) with Particle Swarm Optimization (PSO) to automatically optimize model parameters. The dataset used was obtained from Kaggle, consisting of 100,000 entries and nine medical attributes. Data preprocessing included cleaning, encoding, Min-Max normalization, and undersampling to balance class distribution. Model performance was evaluated using 5-Fold Cross Validation. The results showed that the SVM- PSO achieved an average accuracy of 83.60% which is higher than the conventional SVM with 83.39% accuracy. These findings demonstrate that PSO effectively enhances the classification performance of SVM and is recommended for machine learning-based medical diagnosis, especially in diabetes prediction.
Copyright (c) 2025 Anak Agung Gde Agung Pranandita, I Made Widiartha (Author) https://creativecommons.org/licenses/by/4.0
Rancang Bangun Sistem Informasi Pemilihan Fasilitas Kesehatan Berbasis PCA dan Fuzzy AHP
This paper discusses a proposed development process of an information system for the placement of health facilities in Bali by utilizing a fuzzy analytical hierarchy process and principal component analysis to reduce the dimensionality of the various datasets used in the decision making process. Fuzzy AHP is used to determine the weights of nine distinct criteria, including distance to population centers, service quality, infrastructure availability, population density, and healthcare accessibility, among others. Principal Component Analysis (PCA) helps in extracting the most influential variables from socio-economic, geospatial, and satellite-based datasets, simplifying the input without significant loss of information. The system is built using the Laravel framework and offers functionalities for both users seeking facility recommendations and authorities proposing new locations. Manual Fuzzy AHP calculations showed a consistency index of 0.23, indicating the need for improved consistency in pairwise comparisons. Overall, the system successfully automates the recommendation process, combining expert judgment and data-driven analysis to support equitable and effective placement of health services.
Copyright (c) 2025 Aditya Chandra Nugraha, I Wayan Supriana, I Made Satria Bimantara (Author) https://creativecommons.org/licenses/by/4.0
Perancangan Model Ontologi Domain Tanaman Upakara Hutan Yadnya Monkey Forest PadangTegal
Upakara, which is frequently constructed from a variety of materials and given with symbolic roles and profound religious philosophical connotations, is an essential tool in Balinese Hindu religious events. "Tanaman Upakara" refers to plants that are offered. The kinds of upakara plants and their ceremonial applications are likewise unknown to most Balinese Hindus. Additionally, traditional, unstructured data on upakara flora is still recorded in the Yadnya Forest of Monkey Forest Padangtegal, a conservation site. This study uses ontology technology to try to solve these problems. As a formal representation of knowledge, ontology can improve data management effectiveness and information system interoperability. The Methontology method was used to create the ontology model for the Upakara Plant domain in the Yadnya Forest of Monkey Forest PadangTegal. The developed ontology model includes 17 classes, 8 object properties, 2 data properties, and 282 individuals. Evaluation was performed using SPARQL queries to test the ontology's correctness and functionality.
Copyright (c) 2025 I Kadek Dwika Pradnyana, I Putu Gede Hendra Suputra (Author) https://creativecommons.org/licenses/by/4.0
Analisis Klasifikasi Tweet Berdasarkan Topik Sosial Menggunakan SVM
Social media platforms, including Twitter (now X), produce a constant flow of user-generated text that reflects public discourse in real time. However, the informal and unstructured nature of these short messages poses challenges for manual topic classification, especially when handling large volumes. This study aims to categorize Indonesian-language tweets into three topics: Politics, Entertainment, and Others, using a supervised machine learning approach. A total of 1,478 tweets were collected through keyword-based scraping and manually labeled according to predefined guidelines. The preprocessing stage included text cleaning, tokenization, stopword removal, stemming, and label encoding. TF-IDF was employed to convert the cleaned text into numerical features, while classification was performed using the Support Vector Machine (SVM) algorithm with a One-vs-Rest strategy for multi-class classification. The model reached an overall accuracy of 84 percent, with particularly high performance in the Politics and Entertainment categories. These results indicate that the combination of TF-IDF and SVM is effective for classifying short Indonesian-language tweets and can be applied to support the organization and filtering of topical content in social media analytics.
Copyright (c) 2025 Abdurrazik, I Made Widhi Wirawan (Author) https://creativecommons.org/licenses/by/4.0
Pengenalan Tulisan Tangan Aksara Bali Menggunakan Algoritma CRNN
This research proposes the development of a handwritten Balinese script recognition system using the Convolutional Recurrent Neural Network (CRNN) algorithm as a vital step in preserving this intangible cultural heritage from the threat of obsolescence in the digital era. Given the inherent complexity and variability of handwritten Balinese script, which distinguishes it from printed text, a deep learning approach is essential. CRNN was chosen for its ability to integrate the strengths of CNN in extracting spatial visual features with the power of RNN (specifically BiLSTM) in modeling sequential dependencies. Primary handwritten data was meticulously validated by a Balinese language expert, then processed through grayscale conversion, pixel normalization, and resizing for standardization. The model was constructed with convolutional layers, recurrent BiLSTM layers, and a Connectionist Temporal Classification (CTC) transcription layer, which is effective in translating sequential features into character labels. Performance evaluation of the model using the Character Error Rate (CER) on separate test data showed an average accuracy of 89.9%. These results significantly affirm the great potential of CRNN in supporting the digitalization efforts of Balinese script, as well as facilitating its broader integration into modern environments.
Copyright (c) 2025 I Kadek Bisma Yoga, Cokorda Pramartha (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Genre Buku Berdasarkan Sinopsis Menggunakan Naïve Bayes dan Logistic Regression
Genre is an important element in book categorization based on specific content characteristics or themes. However, manual classification processes are no longer efficient due to the increasing volume of literature. This study aims to compare the performance of Naïve Bayes and Logistic Regression algorithms in book genre classification based on synopses. The dataset used is secondary data obtained from Kaggle. The dataset consists of 4,535 samples after the preprocessing stage, with feature representation using the TF-IDF method. To address class distribution imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that Logistic Regression achieved the best performance with 75.19% accuracy and 75.16% F1-score, while Naïve Bayes achieved 72.22% accuracy and 72.11% F1-score. Based on this evaluation, Logistic Regression is considered more effective in classifying book genres from synopsis text.
Copyright (c) 2025 Anak Agung Anom Witaradiani, I Gede Arta Wibawa, Putu Praba Santika (Author) https://creativecommons.org/licenses/by/4.0
Sistem Rekognisi Akor Instrumen Musik SecaraOtomatis Menggunakan PCP dan SVM
This study presents a system for automatic chord recognition from audio recordings using the Pitch Class Profile (PCP) and Support Vector Machine (SVM). PCP was chosen as the primary feature extraction method because it can represent the standard 12 pitch classes in music accurately. SVM was selected as the classification model because of its proven success in previous chord recognition studies, offering high accuracy while remaining efficient. Using the Piano Triads Wavset dataset, which contains 432 triad chords across 12 root notes and three chord types such as major, minor, and diminished, the model was trained and tested in an experiment. The audio data were processed to extract PCP features and normalized before being classified using SVM. Evaluation was carried out using both a default SVM configuration and GridSearchCV optimization. Results show that the optimized model achieved up to 82% accuracy across all chord classes, indicating that the proposed approach can recognize chords reliably even without using deep learning or additional features. The final system also includes real-time prediction by user audio input, using Python and streamlit framework.
Copyright (c) 2025 Gede Nicholas Tejasukmana Putra, Ngurah Agus Sanjaya ER (Author) https://creativecommons.org/licenses/by/4.0
Identifikasi dan Klasifikasi Suara Vokal Menggunakan Metode Fast Fourier Transform
This research develops a vocal range identification and classification system using frequency spectrum analysis and Fast Fourier Transform (FFT) algorithm. The system addresses the need for an accessible vocal range identification tool for amateur singers and the general public without formal musical training. The proposed system combines real-time audio recording capabilities with audio file processing, implementing pitch identification through FFT analysis and windowing functions. The system features two input methods: real-time recording and audio file upload supporting various formats (MP3, WAV, FLAC, AAC, OGG, M4A). Using PyAudio for real-time processing and Librosa for file analysis, the system accurately identifies fundamental frequencies within the human vocal range (80-1100 Hz) and automatically classifies voice types (Bass, Baritone, Tenor, Alto, Mezzo-soprano, Soprano). Testing demonstrates effective frequency identification with pitch conversion accuracy ranging from 95.7% to 98.8% and voice type classification achieving 81.2% accuracy. The system provides an efficient solution for vocal range analysis with low computational complexity and real-time processing capabilities.
Copyright (c) 2025 I Ketut Manik Ambarawan, I Gusti Agung Gede Arya Kadyanan (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Cuaca Berbasis Citra Menggunakan ConvNeXt
Weather image classification plays a crucial role in many sectors, such as transportation, marine, and agriculture, where automated weather recognition can support decision-making and safety. This study proposes the use of the ConvNeXt architecture with transfer learning for weather classification using image data. The dataset, sourced from Kaggle, comprises 768 images labeled into three categories: cloudy, rain, and shine. Several preprocessing steps were conducted, including noise filtering, resizing, normalization, and augmentation to enhance model performance. Furthermore, a hyperparameter tuning process was applied using six different combinations of learning rates and batch sizes to identify the most optimal configuration. The ConvNeXt model achieved perfect evaluation scores of 100% on validation sets for two hyperparameter combinations and testing sets for a hyperparameter combinations, outperforming models from previous studies such as InceptionV3 and DenseNet169. The evaluation metrics used were accuracy, precision, recall, F1-score, and confusion matrix. The results demonstrate the model’s robustness and effectiveness in classifying weather conditions based on image data. This study shows that ConvNeXt is a highly capable architecture for visual weather classification tasks.
Copyright (c) 2025 Bagus Ajie Satria, Ida Ayu Gde Suwiprabayanti Putra (Author) https://creativecommons.org/licenses/by/4.0
Analisis Kinerja XGBoost Menggunakan Bayesian Optimization dalam Prediksi Harga Ethereum
Cryptocurrency is a digital innovation in the financial sector that has revolutionized the global transaction system through blockchain technology. One of the main challenges in the crypto domain today is determining the price of cryptocurrencies, which are highly volatile. Ethereum, one of the largest cryptocurrencies, exhibits complex volatility patterns that require a robust predictive system. This study aims to compare the performance of the standard XGBoost algorithm with XGBoost optimized using Bayesian Optimization in predicting daily Ethereum prices based on time series data from 2016 to June 2025. The dataset includes price-related features such as open, high, low, volume, and percentage price change. The modeling process consists of several stages including feature engineering, time series-based data splitting, and model training. Model performance was evaluated using three primary metrics: MAE, RMSE, and R² Score. The evaluation results show that the standard XGBoost model achieved an MAE of 80.8926 (3.12%), RMSE of 114.1457 (4.40%), and an R² Score of 0.9723. Meanwhile, the optimized model using Bayesian Optimization achieved an MAE of 70.7241 (2.73%), RMSE of 102.5334 (3.96%), and an R² Score of 0.9777. These results indicate that Bayesian Optimization helps improve the model's prediction accuracy. This study concludes that the XGBoost model with a Bayesian optimization approach yields superior and more effective performance in forecasting Ethereum prices based on time series data.
Copyright (c) 2025 Christian Valentino, Luh Arida Ayu Rahning Putri (Author) https://creativecommons.org/licenses/by/4.0
Penerapan Multinomial Naïve Bayes dan Chi-Square pada Analisis Sentimen Makan Bergizi Gratis
The Free Nutritious Meal Program (MBG) is a policy initiated by the government in order to create a healthy superior generation. This policy has generated various responses from the public, both positive, negative, and neutral. This study aims to analyze public sentiment towards the program by utilizing 2.165 comments from YouTube. Data were analyzed using the Multinomial Naïve Bayes algorithm after going through the labeling, text preprocessing, and TF-IDF representation stages. Feature selection was carried out with 10 threshold values ​​of the Chi-Square method to evaluate the effect of the number of features on model performance. Hyperparameter tuning with alpha and fit_prior was carried out through Grid Search with 5-fold cross validation. The results showed that the best performance was achieved when the threshold Chi-Square was 100% or when all features were used, which resulted in an accuracy of 83.48% and a macro F1 score of 83.42%. Visualization of the sentiment distribution shows the dominance of negative sentiment at 35.6%, followed by positive at 34.9%, and neutral at 29.5%, indicating public dissatisfaction with the policy.
Copyright (c) 2025 Ni Putu Alya Noviyanti, I Gusti Ngurah Anom Cahyadi Putra (Author) https://creativecommons.org/licenses/by/4.0
Penerapan Support Vector Machine untuk Klasifikasi Tingkat Risiko Kebakaran Hutan
Classifying forest fire risk levels is a critical step for disaster mitigation, yet it poses significant challenges due to data complexity and class imbalance. This study systematically applies and evaluates the performance of the Support Vector Machine (SVM) algorithm for the multi-class classification of fire risk (‘Low’,’Medium’,’High’) using the standard UCI Forest Fires dataset. The methodology involved a comprehensive preprocessing imbalance and hyperparameter optimization of C and gamma using GridSearchCV with cross-validation. Experimental results show that the final,optimized SVM model only achieved an accuracy of 50% and a macro-average F1-Score of 40% on the test set. This limited performance, particularly the model’s failure to reliably identify the ‘High’ risk class, indicates that the standard meteorological features within the dataset possess insufficient predictive power for the complex task of classifying fire severity, highlighting that model success is fundamentally dependent on feature richness over algorithmic optimization.
Copyright (c) 2025 I Komang Galih Agustan, I Gede Santi Astawa (Author) https://creativecommons.org/licenses/by/4.0
Evaluasi UX E‑Perpus UNUD Menggunakan UEQ (User Experience Questionnaire)
The e-Perpus UNUD system serves as a digital library platform for managing academic documents at Udayana University. Previous studies using WebQual 3.0 and 4.0 methods reported suboptimal results, particularly in interface and user interaction quality. To provide a deeper and more emotional perspective on user experience, this study adopts the User Experience Questionnaire (UEQ) to evaluate the system across six UX dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Data were collected from 24 student respondents using the official UEQ format, and results were analyzed using the UEQ Data Analysis Tool. The findings show that Perspicuity and Dependability indicated slightly positive perceptions, while the remaining dimensions, especially Novelty, indicated negative perceptions. This study suggests a user interface redesign focusing on improving innovation, visual appeal, efficiency, and user stimulation, with future research encouraged to increase the sample size for more stable results.
Copyright (c) 2025 Komang Andika Putra, I Wayan Santiyasa (Author) https://creativecommons.org/licenses/by/4.0
Evaluasi Usability Protoype Aplikasi Jemput Sampah Online Menggunakan Metode System Usability Scale
In Indonesia, waste management is a big problem for the environment since not enough people are involved and there aren't any integrated systems. To solve this problem, a prototype mobile app called JEMPOL (Jemput Sampah Online Langsung) was made. It offers an online waste collecting service and instructional functions. The goal of this study was to evaluate the application of JEMPOL prototype. This stufy used the Design Thinking process to make sure that the application was user-centered. The System Usability Scale (SUS) approach was used to evaluate the application with 20 people who are the intended users. The average SUS score was 70.38, which puts it in the "Acceptable" range on the Acceptability Range of SUS, gives it a "C" grade, and is rated "OK" to "Good" on the Adjective Rating scale. These results show that users generally like and can use the JEMPOL prototype, although the score differences suggest that some parts of the interface may be better. The application has a lot of promise for being used more to get more people involved in digital waste management.
Copyright (c) 2025 Anak Agung Bagus Yoga Udiana, I Ketut Gede Suhartana, I Putu Satwika (Author) https://creativecommons.org/licenses/by/4.0
Analisis UX pada Aplikasi Pemutar Video Animasi dengan Metode Usability Testing
A popular animation streaming platform requires a structured User Experience (UX) evaluation to ensure its ease of use. This study aims to analyze the usability level of an animation video player application using a mixed-method Usability Testing approach. The test was conducted on 5 active users, where performance data (success rate, time efficiency, error rate) and perceptual data (satisfaction level) were collected through task-based observation sessions and a post-test questionnaire. The results indicate a perfect task success rate (100%) and a high user satisfaction level (88%), but also reveal a significant error rate of 60%. The primary source of errors was identified as the ambiguity of iconography without text labels on certain features. In conclusion, the application is proven to be functional and satisfying, but it is recommended to improve the clarity of the interface design to minimize user errors and enhance the overall UX.
Copyright (c) 2025 I Gusti Ngurah Esa Nandana, I Gede Arta Wibawa (Author) https://creativecommons.org/licenses/by/4.0
Analisis Perbandingan XGBoost dan LightGBM dalam Prediksi Penjualan Ritel Walmart Store Sales
Sales prediction is a crucial aspect in the retail industry for optimizing business strategies and inventory management. As a global retail company with a large-scale operation, Walmart faces significant challenges in efficiently managing its supply chain and inventory. This study conducts a comparative analysis between the Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) algorithms in the context of retail sales prediction using the Walmart Store Sales dataset. The dataset consists of 6,436 records with 8 attributes. The research methodology implements a comprehensive machine learning approach, including data preprocessing, feature selection, dataset splitting (80:20), model training, and evaluation using standard metrics. The analysis results show that LightGBM provides superior prediction performance, with an MSE of 0.0341, MAE of 0.1120, RMSE of 0.1847, and R² of 0.9663. In comparison, XGBoost yields an MSE of 0.0408, MAE of 0.1194, RMSE of 0.2021, and R² of 0.9596. The consistent superiority of LightGBM across all evaluation metrics indicates that this algorithm is more optimal for the Walmart sales prediction case. Additionally, feature analysis shows that the variable Store contributes the most to the predictive model, while Fuel Price has a relatively minor impact. This study emphasizes that selecting the appropriate machine learning algorithm significantly affects optimal prediction outcomes, particularly in a complex, data-driven retail industry.
Copyright (c) 2025 Jurnal Nasional Teknologi Informasi dan Aplikasinya https://creativecommons.org/licenses/by/4.0