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All Journal Journal of System and Computer Engineering Jurnal Penelitian Teknologi Informasi dan Sains
Angger Binuko Paksi
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Early Warning System Berbasis Machine Learning Untuk Deteksi Pola Aktivitas Anomali Server Linux Berdasarkan Log Sistem Operasi Mochamat Bayu Aji; Angger Binuko Paksi; Bintang Raka Putra; Tiyan Ganang Wicaksono
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 4 No. 2 (2026): Juni: JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS (JPTIS)
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v4i2.3929

Abstract

The increase in activity and security threats on Ubuntu server causes the volume of system logs to become very large and difficult to analyze manually. This condition potentially leads administrators to experience delays in detecting abnormal activities, such as repeated login attempts and web access patterns related to online gambling promotions. Therefore, this research aims to develop a machine learning-based Early Warning System capable of automatically detecting anomalous activities. The system is developed using the Python programming language and runs on an Ubuntu server by utilizing authentication logs and web access logs as the main data sources. The anomaly detection model is trained using normal activity data collected directly from the Ubuntu server logs to learn standard system behavior patterns. During the operational phase, the system reads server logs in real-time, extracts activity features, and analyzes them using the Isolation Forest algorithm. Activities detected as anomalies trigger alert notifications via Telegram to the administrator without performing automatic blocking. The results show that the system is able to provide early warnings for suspicious activities, thereby helping to improve server security more effectively.
Stability and Significance Analysis of Daily Food Price Forecasting Models Gus Nanang Syaifuddiin; Imam Junaedi; Angger Binuko Paksi; Hendrik Kusbandono
Journal of System and Computer Engineering Vol 7 No 4 (2026): JSCE: October 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i4.2903

Abstract

Food price forecasting has been widely studied using machine learning, yet most published work reports accuracy from a single data split without a baseline comparison or a significance test. This study evaluates the stability and statistical significance of such comparisons. Eleven models comprising four baselines, two statistical models, and five machine learning algorithms were tested on eight strategic food commodities in East Java Province using 2,380 daily observations from April 2017 to September 2026 obtained from the Strategic Food Price Information Centre of Bank Indonesia. Evaluation employed six-fold expanding window validation with a thirty-step horizon, using a scaled error measure and a skill score relative to a naive benchmark as primary metrics, followed by Friedman and Nemenyi testing. No model proved significantly superior to the naive benchmark, which itself ranked third among the eleven models. Rankings proved highly unstable: the standard deviation across folds reached 35 to 106 percent of mean values, the best model changed for half of the commodities after cleaning only 0.1 percent of observations, and percentage error correlated negatively with the skill score. These findings indicate that claims of model superiority require baseline comparison, multi-fold validation, and significance testing.
Co-Authors Bintang Raka Putra Gus Nanang Syaifuddiin Hendrik Kusbandono Imam Junaedi Mochamat Bayu Aji Tiyan Ganang Wicaksono