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All Journal Jupiter Jurnal Simetris Jurnal Informatika JSI: Jurnal Sistem Informasi (E-Journal) International Conference on Engineering and Technology Development (ICETD) Jurnal Ilmu Komputer dan Agri-Informatika Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Sisforma: Journal of Information Systems Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) International Journal of Artificial Intelligence Research Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Sains dan Informatika Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mantik Journal of Information Systems and Informatics Jurnal Abdi Insani Jurnal Teknologi Informatika dan Komputer Journal of Software Engineering Ampera Djtechno: Jurnal Teknologi Informasi Jurnal Informatika: Jurnal Pengembangan IT Indonesia Bergerak: Jurnal Hasil Kegiatan Pengabdian Masyarakat
Usman Ependi
Universitas Bina Darma
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Other

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Journal : Journal of Information Systems and Informatics

 1 
OPTIMIZATION OF WIRELESS NETWORK PERFORMANCE USING THE HIERARCHICAL TOKEN BUCKET (CASE STUDY: MUHAMMADIYAH UNIVERSITY OF PALEMBANG) Yukos Pratama; Usman Ependi; Heri Suroyo
Journal of Information System and Informatics Vol 1 No 1 (2019): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/journalisi.v1i1.4

Abstract

Computer networks have penetrated into various fields including education for the learning process which is used as a medium for delivering scientific concepts to be more attractive and easily accepted. The Muhammadiyah University of Palembang currently has very high mobility, both used for browsing information, downloading data and using other facilities. For the need for bandwidth management to manage each passing data so that the distribution of bandwidth becomes evenly distributed by using the queue tree method that is applied to the proxy. To evaluate internet bandwidth analyze QoS (Quality of Service) using typhoid standardization in terms of measurement of throughput, delay, and packet loss. The results of this study show that the quality of the network with the hierarchical token bucket method is more optimal, this is because the bandwidth will be divided according to the rules applied to bandwidth management and does not cause clients to fight over bandwidth
ANALYSIS OF THE USE OF CELLULAR OPERATORS USING THE ANALYTIC HIERARCHY PROCESS METHOD Ari Muzakir; Usman Ependi
Journal of Information System and Informatics Vol 1 No 1 (2019): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/journalisi.v1i1.5

Abstract

The development of technology in the telecommunications sector is now easier to be enjoyed by the community. Almost all regions have been able to enjoy the ease of access to information through telecommunications networks. Telecommunications service providers certainly have competed in such a way as to be able to hold users to remain loyal to the provider used. From several surveys conducted, there are still many users who cannot be loyal to use the services of these providers. The increasing number of communication network providers makes users more choices. Various ways are carried out by communication network providers to maintain their users such as giving bonuses, providing cheap rates, increasing services to the regions. In this study will focus on the level of customer satisfaction with cellular operators with a variety of criteria that have been prepared which will be a consideration for users in using these cellular operators. This research will use the AHP method (Analytic Hierarchy Process) to find out which cellular operators are the most superior based on the judgment of the user with various criteria each. So that in the end it will be known which cellular operators will be selected and purchased by consumers according to their respective needs. The results of this study show that based on the bonus, IM3 is the best with a weight of 0.21712. Then based on the price rate, a tri card with a weight of 0.16565. Furthermore, the service criteria show a better Sympathy card with a weight of 0.21311
Deep Learning Model Analysis and Web-Based Implementation of Cryptocurrency Prediction Gege Ardiyansyah; Ferdiansyah Ferdiansyah; Usman Ependi
Journal of Information System and Informatics Vol 4 No 4 (2022): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v4i4.365

Abstract

Cryptocurrency is a digital asset designed by cryptography, such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). Cryptocurrency uses Blockchain technology to ensure security, transparency, ease of locating, and unchangeability. This makes cryptocurrency very popular in many sectors, especially in the financial industry. Although, the uncertainty and the dynamic change of cryptocurrency price make the risk for investment in this digital asset high. This is the reason why studies about cryptocurrency price prediction became popular globally. This study intended to predict cryptocurrency prices using hybrid GRU LSTM than setting up the epoch to get the most accurate prediction model. The researcher would make a web-based application that can be used by the public, especially those involved in cryptocurrency investment. The result was a web-based application that could predict the price of cryptocurrency for the next few days, which had been validated using data from the previous 7 days, 14 days, 30 days, 60 days, and 90 days.
Machine Learning Models for DDoS Detection in Software-Defined Networking: A Comparative Analysis Ferdiansyah, Ferdiansyah; Antoni, Darius; Valdo, Muhammad; Mikko, Mikko; Mukmin, Chairul; Ependi, Usman
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.864

Abstract

In today's digital age, Software-Defined Networking (SDN) has become a pivotal technology that improves network control and flexibility. Despite its advantages, the centralized nature of SDN also makes it susceptible to threats such as Distributed Denial of Service (DDoS) attacks. This study compares the effectiveness of three machine learning models Random Forest, Naive Bayes, and Linear Support Vector Classification (LinearSVC) using the 'DDoS SDN dataset' from Kaggle, which contains 104,345 records and 23 features. An equal 70/30 ratio was used on model. The models were then assessed using measures such as accuracy, precision, recall, and F1-score, and ROC curves. Among the models, Random Forest outperformed the others with a 97% accuracy, precision values of 1.00 (benign traffic) and 0.94 (malicious traffic), and an ROC AUC score of 1.00. In contrast, Naive Bayes and LinearSVC recorded lower accuracies of 63% and 66%, respectively. These findings underscore Random Forest's effectiveness in detecting DDoS attacks within SDN environments.
Advanced Techniques for Anomaly Detection in Blockchain: Leveraging Clustering and Machine Learning Ferdiansyah, Ferdiansyah; Ependi, Usman; Tasmi, Tasmi; Haikal, Muhammad; Mikko, Mikko
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1047

Abstract

Blockchain technology has revolutionized data security and transaction transparency across various industries. However, the increasing complexity of blockchain networks has led to anomalies that require further investigation. This study aims to analyze anomalies in blockchain systems using machine learning approaches. Various anomaly detection techniques, including supervised and unsupervised methods, are evaluated for their effectiveness in identifying irregularities. The results indicate that machine learning models can detect anomalies with high accuracy, providing insights into potential threats and system vulnerabilities. The findings of this research contribute to improving blockchain security and developing more robust monitoring systems.
Predicting Bitcoin and Ethereum Prices Using the Long Short- Term Memory (LSTM) Model Aswadi, M; Ependi, Usman
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1228

Abstract

Cryptocurrency is a highly volatile digital asset, necessitating accurate and adaptive forecasting methods. This study implements a Long Short-Term Memory (LSTM) model to predict the daily closing prices of two leading cryptocurrencies Bitcoin (BTC) and Ethereum (ETH) using historical data from Yahoo Finance and Binance. To enhance data richness and model robustness, datasets from both sources were vertically merged. The methodological framework included data preprocessing, Min–Max normalization, formation of 24-day sliding input windows, and training across three data split ratios (70:30, 80:20, and 90:10). Model performance was evaluated using the Root Mean Squared Error (RMSE). Results indicate that the LSTM model achieved high prediction accuracy, with the lowest RMSE values of 0.0137 for BTC and 0.0152 for ETH using the combined dataset with a 90:10 split. Beyond modeling, a web-based application was developed using Streamlit, enabling users to perform real-time predictions and export results. This study contributes to the field of cryptocurrency forecasting by demonstrating that multi-source data integration significantly improves predictive accuracy and model generalization. The proposed framework offers both theoretical insights and practical tools for researchers and investors in financial technology.
Predicting Accounts Receivable of the Social Security Administration for Employment Using LSTM Algorithm Khansa, Ainna; Ependi, Usman
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1274

Abstract

This study explores the use of Long Short-Term Memory (LSTM) networks for predicting outstanding contributions from employers to the BPJS Ketenagakerjaan, Indonesia’s social security agency. The research aims to address the challenges BPJS faces due to delayed or unpaid contributions, which impact the institution's operational stability and financial health. The LSTM model, a deep learning technique well-suited for time-series prediction, was applied to historical data from BPJS Ketenagakerjaan to predict overdue contributions across three different training-validation splits: 70:30, 80:20, and 90:10. The results demonstrate that the 80:20 split achieved the highest validation accuracy of 84.71%, offering the optimal balance between training data and model generalization. The model's ability to predict overdue contributions with high accuracy could significantly improve BPJS's receivables management, allowing for more proactive financial planning and risk mitigation. The study also highlights the integration of an attention mechanism within the LSTM model, enhancing its predictive capabilities by focusing on the most relevant historical data. This research contributes to the field of predictive analytics in public sector financial management, showcasing the potential of machine learning in enhancing the efficiency and effectiveness of social security programs.
Oil and Gas Production Forecasting Based on LSTM Model: A Case Study of PT Pertamina Hulu Rokan Zone 4 Billan, Angel Caroline; Ependi, Usman
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1285

Abstract

This study addresses the critical need for accurate oil and gas production forecasting to support strategic decision-making in Indonesia’s energy sector. PT Pertamina Hulu Rokan Zone 4 (PHR Zona 4), a key player in national energy production, frequently encounters technical and external operational challenges. To tackle these issues, this research proposes a deep learning-based predictive model using the Long Short-Term Memory (LSTM) architecture, structured in an encoder-decoder format and enhanced with an attention mechanism. The model was trained and tested on historical oil and gas production data from PHR Zona 4, evaluated under two data-splitting scenarios: 80:20 and 90:10. Model performance was assessed using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results from the 80:20 scenario showed RMSE of 5.83, MAE of 5.54, MAPE of 1.71%, and R² of -1.97, suggesting difficulties in capturing extreme data fluctuations. However, the 90:10 scenario demonstrated significantly improved performance with RMSE of 0.42, MAE of 0.36, MAPE of 0.11%, and R² of 0.00, indicating better trend prediction stability. The novelty of this study lies in the integration of attention mechanisms within the LSTM encoder-decoder framework for oil and gas time series forecasting, offering enhanced accuracy and robustness. This research provides a valuable foundation for future improvements in predictive analytics and operational efficiency in the oil and gas industry.
Co-Authors Abel Arisandi Ade Putra Ade Putra Ahmad, Nahdatul Akma Antoni, Darius Ari Muzakir Ari Wibowo Arjun, Jennifer Aswadi, M Berlianti, Marutha Billan, Angel Caroline dedi irawan Edi Surya Negara Evi Yulianingsih Febriyanti Panjaitan Febriyanti Panjaitan Ferdiansyah Ferdiansyah Firamon Syakti Firamon Syakti, Firamon Fitri Purwaningtias Gege Ardiyansyah Haidar Mirza, A. Haidar Mirza, Ahmad Hamim Tezar Rizandi Heri Suroyo Hutrianto Irwansyah Irwansyah Khansa, Ainna Kisworo, Marsudi Wahyu Kurniawan, Tri Basuki M. Octa Prasetya Mikko, Mikko Muhammad Hafid Alfarisi Muhammad Haikal Muhammad Izman Herdiansyah Muhammad Ridho Kurniawan Mukmin, Chairul Novaria Kunang, Yesi Novifika, Seva Nurwijayanti Nyimas Sopiah Oktaviani, Nia Oktaviani, Nia Oktaviani, Nia pratama, rianda Purwaningtias, Fitri Qoriani Widayati Sabeli Aliya Suyanto Suyanto Suyanto Suyanto Tata Sutabri Tri Basuki Kurniawan Valdo, Muhammad Wahyu Caesarendra Widya Cholil Wulandari, Intan Fitriana Yesi Novaria Kunang Yukos Pratama Zaid Amin, Zaid