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
Developing A Web-Based Point-of-Sale Application using RAD: A Cafe Sonia Case Study
This study reports the development of a web-based Point of Sale (POS) system for Café Sonia, an MSME café setting where day-to-day transaction handling and reporting benefit from a workflow-aligned digital solution. The system was produced using Rapid Application Development (RAD) through short, stakeholder-driven iteration cycles with explicitly bounded scope and systematic feedback incorporation, resulting in an end-to-end POS flow from ordering to receipt and transaction history. Performance metrics reported in this paper are derived from a small-scale JMeter run and should be interpreted as an indicative baseline rather than evidence of peak-hour robustness. No dedicated usability evaluation was conducted; therefore, user-facing quality remains a pending empirical question that warrants task-based usability testing and standardized instruments such as the System Usability Scale (SUS) in subsequent iterations. Debates around RAD often hinge on a perceived tension between development speed and methodological accountability, particularly when systems must remain maintainable beyond the prototype stage. The Café Sonia case suggests that RAD can still yield a coherent, maintainable, and context-fit POS when iteration cycles are guided by stakeholders and the scope is clearly controlled. The novelty lies in providing case-based evidence of RAD’s practical effectiveness for delivering a customized MSME café POS, while the contribution is a traceable development pathway linking requirements capture, iterative prototyping artefacts, and scenario-based functional verification for reuse in comparable small-retail environments.
Micro, Small, and Medium Enterprises (MSMEs)
Copyright (c) 2026 Jonathan Arya Wibowo, Muharman Lubis, Rio Guntur Utomo, Kobthong Ladkoom, Rahmat Yasirandi (Author) https://creativecommons.org/licenses/by/4.0
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
Klasifikasi URL Berbahaya Menggunakan Algoritma Random Forest Berbasis Fitur Struktural
Phishing attacks remain a critical threat in the digital era, often exploiting deceptive URLs to trick users into divulging sensitive personal information. To address this issue, this study proposes a machine learning-based detection system using the Random Forest algorithm to identify phishing URLs based on structural features. The main objective of this research is to build an efficient and lightweight model that can detect phishing attempts in real-time without relying on third-party databases or content-based analysis. From the dataset used, 10 structural features were selected based on relevance and efficiency, such as the presence of IP addresses, use of HTTPS, domain age, and URL length. The model was trained and tested on a labeled dataset and evaluated using accuracy, confusion matrix, and classification report. The Random Forest model achieved a testing accuracy of 92.72%, with strong precision and recall values for both phishing and legitimate classes. The results indicate that the proposed approach is effective in distinguishing malicious URLs using only structural characteristics, making it a practical solution for enhancing cybersecurity at the URL level.
Copyright (c) 2025 I Gede Putra Wiratama, 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
Klasifikasi Instrumen Musik Menggunakan Metode Machine Learning
Music plays an important role in human life, and automatic identification of musical instruments is becoming an increasingly relevant field in the digital era. This study aims to classify musical instrument types based on acoustic features using machine learning methods, specifically Support Vector Machine (SVM). The dataset used contains audio recordings of four instruments, namely guitar, piano, drum, and violin. Each audio file goes through a preprocessing process such as sample rate standardization, duration trimming, and framing. Furthermore, feature extraction is carried out from the time domain (Zero Crossing Rate and RMS), frequency domain (Spectral Centroid, Spread, and Roll-off), and cepstral domain (MFCC). The SVM model is trained with a combination of various features and evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that the combination of all features produces the best accuracy of 68.33%. Although its performance is not optimal, these results show the potential of a feature-based approach for musical instrument classification and become the basis for further development using more complex methods such as deep learning.
Music Classification, Instrument Recognition, Audio Processing, Feature Extraction, Support Vector Machine, MFCC
Copyright (c) 2025 Theresia Margaretha Purba, I Gusti Agung Gede Arya Kadyanan (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
Perbandingan CNN dan SVM untuk Klasifikasi Citra Rempah Indonesia
The purpose of this study is to evaluate how well Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) perform while using digital image data to classify different kinds of Indonesian spices. This study's dataset, which includes a variety of spice images with different shapes, textures, and colors, was sourced from the Kaggle platform. CNN is frequently used in difficult picture classification problems and is well known for its capacity to automatically extract visual information. SVM, on the other hand, is a traditional machine learning method that has demonstrated reliable results in a range of classification tasks, especially when dealing with sparse data and well-organized features. There are several processes in the study technique, such as gathering data, preprocessing images, training the model, evaluating the model, and comparing performance. The models abilities and flaws in categorizing spice photos are thoroughly examined by evaluating them using important performance measures such as accuracy, precision, recall, and F1-score. The findings are anticipated to aid in the development of intelligent agricultural systems, particularly in automating the process of classifying and identifying spice items. Furthermore, this comparison can be used as a reference to choose suitable machine learning techniques for comparable picture classification problems including datasets linked to agriculture or food.
Copyright (c) 2026 Komang Arjuntama Satria, I Wayan Supriana (Author) https://creativecommons.org/licenses/by/4.0
Perancangan Sistem Notifikasi Tamu Menggunakan Kombinasi Sensor PIR dan Ultrasonik
Modern lifestyles often cause homeowners to be unaware of a guest's presence due to distracting activities like working from home, gaming, or using social media. Conventional doorbells fall short in these situations, highlighting the need for a more effective, technology-based solution. This research focuses on designing and building an innovative guest notification system based on the Internet of Things (IoT) using an ESP32 microcontroller. To enhance accuracy and reduce false positives, the system integrates a dual-sensor verification mechanism. A Passive Infrared (PIR) sensor acts as the initial trigger to detect motion, which is then verified by an HC-SR04 ultrasonic sensor to confirm the object's presence within a relevant distance from the door. A notification is sent via the Telegram application only when both conditions are met. This approach effectively filters out irrelevant movements and significantly improves reliability compared to single-sensor systems. The final prototype demonstrates an accurate, affordable, and practical solution to ensure homeowners never miss a visitor.
Copyright (c) 2026 Dewa Putu Adrian Paramarta, I Made Widhi Wirawan (Author) https://creativecommons.org/licenses/by/4.0
Analisis Usability Pada Aplikasi SatuSehatMenggunakan Mixed Methods
SATUSEHAT mobile application is the official health application of the Ministry of Health of the Republic of Indonesia (Kemenkes RI). This application is a development of the previous application, PeduliLindungi, which only focused on handling the COVID-19 pandemic in 2020-2021. However, the focus of the SATUSEHAT app has been expanded. Some of its main focuses are accessing medical records, viewing vaccination status, and checking personal health status. Unfortunately, from several reviews, there are still many users who are not satisfied with their experience using this application, so this study aims to analyze the usability value using a combined method, of which two are Usability Testing and Post-Study System Usability Questionnaire. This research will involve 10 respondents, who will work on a short task, then answer a questionnaire to measure Usability Testing scores, and finally answer some questions to measure Post-Study System Usability Questionnaire scores. It is hoped that this research can help the developer of the application to see what aspects still have shortcomings, so that in the future it can be improved and developed again. Keywords: SATUSEHAT, Usability Testing, Post-Study System Usability Questionnaire
Post-Study System Usability Questionnaire
Copyright (c) 2025 Abel Gilang Saputra, Ngurah Agus Sanjaya ER, I Made Satria Bimantara (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
Implementasi Logistic Regression dan SMOTE dalam Analisis Sentimen Ulasan Wondr by BNI
Rapid innovation in Indonesia’s digital banking, evidences by digital transactions reaching Rp7,492.93 trillion by September 2024 and the launch of Wondr by BNI by PT. Bank Negara Indonesia, which has 5.3 million active users and 397 thousand reviews on Google Play Store by June 2025, presents a challenge for manual sentiment analysis of reviews due to its inefficiency. This study addresses this issue by employing a machine learning approach, utilizing the Logistic Regression algorithm for sentiment analysis. A total of 8000 review data from Kaggle were used, with sentiment labeled based on rating scores (1-3 negative, 4-5 positive). The methodology included data preprocessing, feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF), and balancing training data with Synthetic Minority Oversampling (SMOTE). The Logistic Regression model was trained after parameter optimization via grid search, yielding the optimal combination of C=1, penalty=’l2’, and solver=’newton-cg’. Evaluation using a confusion matrix revealed an overall accuracy of 93.54%. For negative sentiment, the model achieved 71.89% precision, 91.5% recall, and 80.52% F1-score, while for positive sentiment, it reached 98.48% precision, 93.89% recall, and 96.13% F1-score.
Copyright (c) 2025 Komang Krisna Jaya Nova Antara, Ngurah Agus Sanjaya ER (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Pipeline CI/CD dengan Github Actions dalam Mengurangi Downtime pada Aplikasi Berbasis Web
This research focuses on optimizing the application deployment process to minimize prolonged downtime, which is often caused by the architecture that don’t supports automation of program code to the deployment server. To overcome this problem, Continuous Integration/Continuous Deployment (CI/CD) pipeline using GitHub Workflow is implemented by involving three main components: local development server, GitHub (repository and actions), and deployment server. The pipeline design includes three types of branches in the GitHub repository: dev, staging, and master. The workflow process starts from local development to the dev branch, then integrated to staging for integration testing, and finally to master which triggers automatic deployment actions to the server using the FTP protocol. From the results of the system testing, the metrics used are downtime and deployment time. The results show that the implementation of the pipeline successfully achieved zero downtime (0 seconds) both in the initial deployment and the deployment of changes. However, the deployment time is still relatively long due to the use of the FTP protocol. Although the application does not experience downtime, the long deployment time has the potential to make the application not optimally usable during the deployment process.
Copyright (c) 2025 I Komang Wahyu Pranata, I Wayan Santiyasa (Author) https://creativecommons.org/licenses/by/4.0
Evaluasi ICA dan NMF pada Pemisahan Sinyal Audio Menggunakan BSS Metrics dan MFCC
Source separation is a crucial challenge in audio signal processing, particularly for stereo data. This study compares the performance of Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF) in separating mixed audio signals. ICA operates directly on stereo signals, while NMF is applied to mono versions derived from stereo mixtures. Three pairs of audio data with diverse natural sound combinations were used. Evaluation metrics include Blind Source Separation indicators (SDR, SIR, SAR), spectral similarity based on Mel-Frequency Cepstral Coefficients (MFCC), and robustness tests by adding noise at 10 dB and 5 dB SNR levels. The results show that ICA consistently yields higher SDR and SIR scores and lower Euclidean distances in MFCC compared to NMF. In contrast, NMF performs poorly due to its mono-only limitation and inability to exploit spatial information. This study highlights ICA's superiority in separation accuracy and noise robustness, and emphasizes the importance of spectral analysis as a complementary evaluation method.
Copyright (c) 2026 Ni Ketut Sukardiasih, I Gede Arta Wibawa (Author) https://creativecommons.org/licenses/by/4.0
Implementasi Model Poisson Laplace untuk IR pada E-Skripsi Universitas Udayana
At Udayana University, a digital repository system is available to store books and student theses. However, the current repository system only categorizes documents based on the year of publication, without any grouping based on topics, fields of science, or other data. Although a search feature is available, the function has not been optimized. To overcome this problem, research was conducted on the Probabilistic Information Retrieval Model, namely the Poisson Model with Laplace Smoothing and Normalization 2. Research was conducted on student thesis title data in 2024 as many as 4,671 titles and evaluation using 10 queries. The research resulted in a Mean Average Precision value, normalized Discounted Cumulative Gain, and recall of 0.715 and a precision value of 0.1421. From this value, further experiments need to be held because the precision value is far different from the recall, indicating the number of False Positive values.
Copyright (c) 2026 I Putu Andhika Ardianta Putra, Cokorda Pramartha (Author) https://creativecommons.org/licenses/by/4.0
Model Penerjemah Bahasa Isyarat SIBI Statis Berbasis Convolutional Neural Network
This study addresses the communication gap between deaf and hearing communities by developing an optimal sign language recognition system for Indonesian Sign Language System (SIBI) static gestures. A comprehensive comparative analysis was conducted on four VGG architecture variants (VGG-11, VGG-13, VGG-16, and VGG-19) using a dataset across 10 SIBI word classes. The research employed systematic methodology including data extraction from video sources, preprocessing with augmentation techniques, model training over 25 epochs, and comprehensive evaluation using accuracy, precision, recall, and F1-score metrics. Results demonstrate that VGG-16 achieves superior performance with 83.4% accuracy, 85.2% precision, 83.4% recall, and 82.9% F1-score, establishing optimal balance between model complexity and generalization capability. The study reveals diminishing returns phenomenon in VGG-19 despite increased architectural complexity. Computational efficiency analysis shows VGG-11 provides highest efficiency score (10.46 GFLOPs) while VGG-16 maintains optimal accuracy-efficiency trade-off. These findings provide crucial insights for developing effective assistive technology solutions that bridge communication barriers for the Indonesian deaf community.
Copyright (c) 2025 Ni Made Wipra Ranum Ratnayu, I Dewa Made Bayu Atmaja Darmawan, I Putu Gede Hendra Suputra (Author) https://creativecommons.org/licenses/by/4.0
Optimasi Algoritma KNN Menggunakan Metode PSO dalam Klasifikasi Kanker Paru-Paru
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with early detection being critical for improving patient outcomes. However, conventional diagnostic methods often require substantial time and resources. This study implements and evaluates the integration of Particle Swarm Optimization (PSO) with the K-Nearest Neighbors (KNN) algorithm for lung cancer risk classification using a lung cancer dataset consisting of 20,000 samples and 16 predictive features. The study addresses KNN's limitation in handling irrelevant or redundant features, which can reduce classification accuracy. PSO, a population-based optimization algorithm inspired by the social behavior of bird flocks, is employed to perform feature selection, identifying the most relevant subset of features to enhance model performance. The results show that PSO successfully reduces the number of features from 16 to 4 with improve accuracy of 87,61%, over the baseline KNN model. This reduction improves computational efficiency and facilitates model interpretability without compromising performance, supporting the application of KNN-PSO as a decision support system for early lung cancer detection in clinical settings.
Copyright (c) 2026 Made Arief Budi Dharma, I Made Widiartha (Author) https://creativecommons.org/licenses/by/4.0
Analisis Sentimen Kebijakan Insentif Mobil Listrik Menggunakan TF-IDF dan Naïve Bayes
This study aims to analyze public sentiment regarding the Indonesian government’s electric vehicle (EV) incentive policy using YouTube comments as the data source. The research applies text preprocessing steps including cleaning, normalization, stopword removal, tokenization, and stemming to prepare the textual data. The cleaned data is transformed into numerical representation using the Term Frequency-Inverse Document Frequency (TF-IDF) method and classified using the Multinomial Naïve Bayes algorithm. To address class imbalance in the dataset, Synthetic Minority Over-sampling Techique (SMOTE) is applied. The model evaluation metrics include accuracy, precision, recall, and F1-score. Based on the evaluation, the model achieves an accuracy of 71%. The model performs better in classifying negative comments, as shown by a higher recall and F1-score in the negative class compared to the positive class. These findings indicate that public responses to the EV incentive policy tend to be more critical. This study provides insights into public opinion that can serve as a valuable reference for policymakers in designing more effective and well-communicated incentive strategies for promoting electric vehicle adoption in Indonesia.
Copyright (c) 2025 Yande Pramana Yustika Pradeva, I Made Widiartha, I Putu Satwika (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
Klasifikasi Berita pada Sektor Ekonomi Indonesia dari Bisnis.com Menggunakan SVM dan TF-IDF
The growth of digital news in Indonesia has seen a significant surge, especially in the economic sector. However, the abundance of information makes it difficult for users to find relevant news. This study aims to classify economic news using the Support Vector Machine (SVM) method and Term Frequency-Inverse Document Frequency (TF-IDF). The process begins with collecting economic news data, preprocessing the text, feature extraction using TF-IDF, and classification using SVM. Evaluation results show that the model achieved a training accuracy of 81.33% , a test accuracy of 76.57% , and an average 5-fold cross-validation accuracy of 77.3%. This indicates high accuracy and stable generalization in classifying news into appropriate categories. This study is expected to assist readers and information systems in efficiently filtering news content.
Copyright (c) 2026 I Ketut Tangkas Agus Sucita, Made Agung Raharja (Author) https://creativecommons.org/licenses/by/4.0
Analisis Sentimen Pengguna TikTok Terhadap Progres Pembangunan IKN Menggunakan LSTM dan FastText
The development of the Capital City of the Archipelago (IKN) is one of the national strategic projects that has generated various reactions and public opinions. Social media, especially TikTok, has become an important platform for people to voice their views through video content and interactive comments. This research aims to analyze the sentiment of TikTok users towards the progress of IKN construction using a deep learning approach, namely the Long Short-Term Memory (LSTM) algorithm. Sentiment data was collected from Kaggle as many as 1472 Indonesian comments, then processed through the stages of normalization, tokenization, and conversion into word embeddings. The LSTM model was designed and trained to classify sentiment into positive, negative, and neutral. The results of the analysis are expected to provide a comprehensive picture of public perception towards IKN, identify critical issues that are often discussed, and measure the level of public acceptance or rejection of this project. This research is expected to contribute to a better understanding of the dynamics of public opinion in the digital era.
Copyright (c) 2026 Gusti Agus Sakah Aditia, Luh Arida Ayu Rahning Putri (Author) https://creativecommons.org/licenses/by/4.0
Klasifikasi Cuaca Menggunakan Algoritma Fuzzy Mamdani dan CART
Weather prediction plays a vital role in sectors such as agriculture, transportation, and disaster mitigation. Extreme weather conditions can lead to unpredictable deviations that may cause significant harm to society. This study aims to predict weather conditions using the Fuzzy Mamdani algorithm and CART (Classification and Regression Tree). A total of 1,461 daily historical weather records from Seattle, United States, were obtained from the Kaggle website “Weather Prediction.” The fuzzy system was applied to convert numerical weather parameters—precipitation, maximum temperature, minimum temperature, and wind speed—into representative scores based on expert-defined rules. These fuzzy scores were then used as additional features in training the CART model to enhance weather classification accuracy. The dataset was split into 80% training data (1,168 records) and 20% testing data (293 records). Evaluation results show that the integration of Fuzzy and CART achieved an accuracy of 81.23% on the testing set, with high precision, recall, and F1 score for dominant categories such as sun and rain. This study demonstrates that the combination of fuzzy logic and decision trees is effective for weather classification based on historical data.
Copyright (c) 2026 I Dewa Ayu Agung Rai Ratna Karang, Ida Ayu Gde Suwiprabayanti Putra (Author) https://creativecommons.org/licenses/by/4.0
Analisis Perbandingan K-Means++, Mini Batch K-Means, dan Fuzzy C-Means pada Segmentasi Pelanggan
Customer segmentation is a crucial process for optimizing marketing strategies This study aims to implement and compare three clustering algorithms on customer transaction data using RFMT (Recency, Frequency, Monetary, and Tenure) features. The dataset, obtained from the UCI Machine Learning Repository, underwent several preprocessing stages, including data cleaning, feature extraction, outlier handling, and normalization. Optimal cluster numbers were determined using the elbow method and validated using silhouette score and davies-bouldin index. The results show that mini batch k-means outperforms the other algorithms with the highest silhouette score of 0.4011 and the lowest davies-bouldin index of 0.9521. K-means++ demonstrated better computation time but slightly lower clustering quality, while fuzzy c-means produced less distinct segmentation
Copyright (c) 2025 I Putu Satria Dharma Wibawa, Made Agung Raharja (Author) https://creativecommons.org/licenses/by/4.0
Perbandingan Metode Clustering K-Means, GMM, dan DBSCAN Berbasis Fitur RFM
In the digital banking era, understanding customer behavior has become essential for delivering relevant services and maintaining competitiveness. This study aims to develop and evaluate customer segmentation models by leveraging an extended RFM (Recency, Frequency, Monetary) model, incorporating both behavioral and demographic attributes. Preprocessing, feature engineering, handling outliers, and standardization were done on the data using a dataset of 100,000 bank transaction records from Kaggle. DBSCAN, Gaussian Mixture Model (GMM), and K-Means were the three clustering techniques that were employed and contrasted. The clustering performance was evaluated using the Silhouette Score, Calinski-Harabasz Index (CHI), and Davies-Bouldin Index (DBI). The output of DBSCAN was too noisy to be useful in the business world, despite having the highest scores on Silhouette: 0,667 and lowest score on DBI: 0,396. K-Means offered the most interpretable segmentation with five ideal clusters (Silhouette: 0,308; DBI: 0,957; CHI: 6191), identifying customer groups ranging from highly active to potentially inactive. The findings highlight the synergy between transactional features and clustering algorithms in generating actionable insights for banking strategy.
Copyright (c) 2026 Putu Nadya Putri Astina, I Wayan Supriana, I Made Satria Bimantara (Author) https://creativecommons.org/licenses/by/4.0