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All Journal Jurnal Teknik Komputer AMIK BSI Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Infortech Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Pengabdian UNDIKMA Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro IMTechno: Journal of Industrial Management and Technology BEES: Bulletin of Electrical and Electronics Engineering
Hanggoro Aji Al Kautsar
Universitas Bina Sarana Infomatika
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Computer Science (CO-SCIENCE)

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Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization Heristian, Sujiliani; Anwar, Rian Septian; Kautsar, Hanggoro Aji Al
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.4433

Abstract

Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.
Implementasi Firewall Mikrotik dalam Pembatasan Akses Situs Terlarang di RT/RW Net Sastra, Ricki; Al Kautsar, Hanggoro Aji
Computer Science (CO-SCIENCE) Vol. 5 No. 2 (2025): Juli 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i2.8897

Abstract

The increasing use of the Internet in RT/RW Net environments has brought both positive and negative impacts. One issue is the unrestricted access to inappropriate content, especially among students, such as online games, pornography, and gambling websites. This study aims to restrict internet user access rights in RT/RW Net networks by implementing firewall filters on MikroTik routers. The method used is firewall filtering configuration based on IP, port, and domain restrictions. The results show that the implementation of the firewall filter on MikroTik routers effectively blocks access to restricted sites and improves network security. Tests were carried out before and after configuration, showing the successful blocking of unwanted content. standards.
Klasifikasi Perilaku Pemain Game Online Menggunakan Naïve Bayes Berbasis Particle Swarm Optimization Heristian, Sujiliani; Anwar, Rian Septian; Kautsar, Hanggoro Aji Al; Sujiliani, Sujiliani Heristian; A
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.4433

Abstract

Much research has been conducted to understand player behavior as a result of the rapid growth of online gaming. In this research, we use the Naive Bayes method optimized using Particle Swarm Optimization (PSO) to analyze the behavior classification of online game players. The classification accuracy value of the baseline method is 75.09% and the Area Under the Curve (AUC) value is 0.798. We use PSO-based optimization on Naïve Bayes to improve model performance. The results showed that the combination of Naïve Bayes and PSO increased classification accuracy to 95.28% with an AUC value of 0.990. This is a major advance that shows that combining the PSO algorithm with Naive Bayes can enable better classification of online game player behavior. These findings will make a significant contribution to the process of making plans that can improve the gaming experience.
Co-Authors ., Alfani A Ade Setiawan Agus Tri Purnomo Ardisetya, Alfhian Asep Sayfulloh Astuti, Rachmawati Darma Badariatul Lailiah Ehon Abdulmanan Eka Kusuma Pratama Fadlilah, Fikri Fauzi Ahmad Muda Febriansah, Rifqi Dzaky Firmansyah Firmansyah Firmansyah Maulana, Andry Novanto, Noufal Dwi Nugraha, Mara Putri Fitri Astuti Putri, Destiana Rabiatus Sa’adah Sa’adah Rachmat Adi Purnama Rian Septian Anwar Ricki Sastra Sampurna, Adam Mulya Sari Dewi Sri Watmah Sujiliani Heristian Suryadithia, Rachmat Suryanto Suryanto