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All Journal Jurnal Radioisotop dan Radiofarmaka Jurnal Khatulistiwa Informatika INTEGER: Journal of Information Technology Jurnal Teknologi Dan Sistem Informasi Bisnis JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Telcomatics Journal of Information System and Technology (JOINT)
Sabariman Sabariman
Pusat Pengembangan Radioisotop dan Radiofarmaka-BATAN, serpong.
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Medicine & Pharmacology

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PENGEMBANGAN SISTEM FIFO (FIRST IN FIRST OUT) PADA ODOO 13 Sabariman, Sabariman; Yulianto, Andik; Lie, Steven
Journal of Information System Management (JOISM) Vol. 5 No. 2 (2024): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2024v5i2.1382

Abstract

Odoo merupakan sebuah layanan ERP yang digunakan untuk meningkatkan manajemen kerja. Namun, Odoo saat ini belum memiliki sistem FIFO yang komprehensif untuk mengelola persediaan sehingga sulit untuk melacak inventaris berdasarkan gudang, nomor seri, dan tanggal masuk barang. Penelitian ini berfokus pada pengembangan sistem First In First Out (FIFO) di Odoo 13. Metode penelitian yang digunakan dalam penelitian ini adalah metode waterfall, yang melibatkan langkah-langkah sistematis dan berurutan. Penelitian ini mengumpulkan data tentang persyaratan untuk sistem FIFO, merancang struktur sistem dan diagram, mengimplementasikan sistem menggunakan bahasa pemrograman Python, mengintegrasikan dan menguji sistem. Hasil penelitian meliputi pengembangan sistem FIFO dalam Odoo 13, yang meliputi pembuatan basis data relasional, pemrograman backend dan implementasi model stock_move_line, stock_quant, dan product_product.
Analisis Pengaruh Game Online Dan Motivasi Bermain Terhadap Minat Belajar Siswa SD di Kota Batam Sama, Hendi; Syahputra, Bayu; Siahaan, Mangapul; Zulkarnain, Zulkarnain; Sabariman, Sabariman; Eryc, Eryc
Journal of Information System and Technology (JOINT) Vol. 6 No. 3 (2025): Journal of Information System and Technology (JOINT)
Publisher : Program Sarjana Sistem Informasi, Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/joint.v6i3.11501

Abstract

This study aims to determine the effect of online games and playing motivation on the learning interest of elementary school students in Batam City. The research method used is a qualitative and quantitative method with variables Online Games (X1), Motivation to Play (X2) and Interest in Learning (Y). The qualitative method took 30 samples of elementary school students, and the quantitative method took 392 samples of elementary school students with the population set at 20,000, because there is no specific number. Qualitative data collection techniques were carried out by interviewing respondents, while quantitative data collection, it was done by distributing questionnaires and measurement using a Likert scale. Data analysis in both methods was carried out using the SPSS application, specifically for the qualitative method was codified first. From the results of the qualitative and quantitative analysis, it can be concluded that the variables Online Games and Motivation to Play partially and simultaneously have a significant effect on the variable Interest in Learning. It can also be concluded that the variable Interest in Learning is influenced by the variables Online Games and Motivation to Play by 59.9% while 40.1% is influenced by other variables not examined in this study.
Random Forest Classifier Approach for Accurate Malicious URL Identification Haeruddin, Haeruddin; Elvert; Yulianto, Andik; Sabariman, Sabariman
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11173

Abstract

Internet users currently face significant risks from malicious URLs that facilitate phishing attacks, malware distribution, and data theft. Traditional blacklisting methods have become ineffective against evolving cyberattack techniques. This study proposes a Random Forest classification approach for more accurate malicious URL detection, focusing on critical URL features including URL length, presence of special keywords, subdomain structure, and special character usage. these features train the Random Forest model to distinguish between safe and malicious URLs. We evaluate model effectiveness using accuracy, precision, and recall metrics. This research aims to develop a Random Forest-based malicious URL detection system that is more accurate and adaptive than conventional methods. The study examines both the advantages and limitations of this approach, along with its potential as a reliable detection solution for dynamic digital environments. Evaluation results demonstrate an overall accuracy of 94%, weighted average F1-score of 0.94, and macro average F1-score of 0.94.
Co-Authors Alexander Alexander, Alexander Andik Yulianto Andreas, Willy Celvin, Celvin Daya Agung Sarwono Dicky Tri Jatmoko Elvert Erlina Erlina Eryc, Eryc Francisco, Leo Haeruddin Haeruddin Julyanto, Julyanto Lau, Eric Lee, Deven Lie, Steven Mungkap Mangapul Siahaan Nimatul Mamuriyah Prasetyo, Stefanus Eko Puteri, Vier Adinda Rustandi, Jeffrey Saifudin Nafis Sak, Erwin Sama, Hendi Su, Vincent Syahputra, Bayu Trimurni Trimurni Zulkarnain Zulkarnain