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I Gede Surya Rahayuda
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Sekretariat JNATIA Gedung FMIPA Lantai 1, Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
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Bali
INDONESIA
Jurnal Nasional Teknologi Informasi dan Aplikasinya
Published by Universitas Udayana
ISSN : 29863929     EISSN : 30321948     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) adalah jurnal yang berfokus pada teori, praktik, dan metodologi semua aspek teknologi di bidang ilmu komputer, informatika dan teknik, serta ide-ide produktif dan inovatif terkait teknologi baru dan teknologi informasi. Jurnal ini memuat makalah penelitian asli yang belum pernah diterbitkan. JNATIA (Jurnal Teknologi Informasi dan Aplikasinya) diterbitkan empat kali setahun (Februari, Mei, Agustus, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 49 Documents
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Implementasi Sistem E-Voting Berbasis Blockchain Menggunakan Teknologi Internet Computer Protocol (ICP) Aditya Premana Putra; Ngurah Agus Sanjaya ER; I Gusti Agung Gede Arya Kadyanan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p21

Abstract

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.   
Segmentasi Pelanggan Berbasis RFMT Menggunakan K-Means dan Hierarchical Clustering I Komang Yosua Triantara; Made Agung Raharja; Ida Bagus Gede Sarasvananda
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p22

Abstract

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.
Klasifikasi Customer Churn Menggunakan XGBoost dengan Optimasi GridSearchCV Berbasis Shapley Additive Explanations I Gusti Ayu Riyana Astarani; Luh Arida Ayu Rahning Putri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p01

Abstract

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.
Analisis Usability Pada Aplikasi SatuSehatMenggunakan Mixed Methods Abel Gilang Saputra; Ngurah Agus Sanjaya ER; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p23

Abstract

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
Analisis Kualitas Air PAM Layak Minum dengan Metode Random Forest dan Decision Tree Stefani Kelin Martha Ampak; Anak Agung Istri Ngurah Eka Karyawati; I Komang Arya Ganda Wiguna
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p23

Abstract

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.
Klasifikasi Instrumen Musik Menggunakan Metode Machine Learning Theresia Margaretha Purba; I Gusti Agung Gede Arya Kadyanan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p24

Abstract

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.
Implementasi Metode LSTM untuk Prediksi Harga Saham PT Indofood CBP Sukses Makmur TBK Endritha Pramudya; Dwi Retnoningsih; Diyah Ruswanti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p24

Abstract

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.
Pengembangan Sistem Informasi Manajemen Sekolah Terintegrasi untuk Digitalisasi Administrasi Surya Arnawa Ida Bagus Ketut; Ida Ayu Gede Wiwik Purnamayanti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p25

Abstract

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.
Implementasi LexRank dan BERT2GPT dalam Auto Summarization Teks Bahasa Indonesia Tristan Bey Kusuma; I Made Widiartha; I Putu Gede Hendra Suputra
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p03

Abstract

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.

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