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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization JURIKOM (Jurnal Riset Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Bimas Islam Southeast Asia Journal on Open and Distance Learning
Diena Rauda Ramdania
UIN Sunan Gunung Djati Bandung
Religion Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mathematics Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games Yudi Widhiyasana; Maisevli Harika; Fahmi Faturahman Nul Hakim; Fitri Diani; Kokoy Siti Komariah; Diena Rauda Ramdania
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.832

Abstract

Controlling each member of the soldiers to carry out battle with Non-Playable Characters (NPC) is one of the secrets to winning Real-Time Strategy games. The game could be more complicated and offer a more engaging experience if every NPC acts like humans rather than machines with patterned behavior. Like people during a war, each army member's command requires rapid reflexes and direction to strike or evade attacks. An intelligent opponent based on ANN as NPC can react quickly to their opponents. The accuracy of ANN could be enhanced by weight modifications using a Genetic Algorithm (GA). The crossover and mutation rates significantly impact GA's performance as an ANN setup. This research aims to find the best crossover and mutation rates in GA as a weight adjustment in ANN. Experiments were conducted using an RTS game simulator using 20 scenarios on a maximum of 4000 iterations. The initial setup of each troop is random, with a seven-unit type available. In this research, the troops won because their men were subjected to fewer attacks than the opposing forces. The GA optimal crossover and mutation rates are determined using troop victories as a baseline. According to the findings, the best crossover rate for GA as an ANN weight adjustment is 0.6, whereas the specific mutation rate is 0.09. The crossover rate of 0.6 has the highest average win value and tends to increase every generation. As for the mutation rate of 0.09, it has the highest average win value. Thus, this preliminary study can develop NPC more humanly.
Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study Sapdi, Rohmat Mulyana; Maylawati, Dian Sa'adillah; Ramdania, Diena Rauda; Budiman, Ichsan; Al-Amin, Muhammad Insan; Fuadi, Mi'raj
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1823

Abstract

This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history. The study's central aim is to discern learning loss in IRE in Islamic schools, utilizing the Gradient Boosting Classifier as its primary analytical tool. Various classification algorithms, including the Cat Boost Classifier, Light Gradient Boosting Machine, Extreme Gradient Boosting, and others, were tested. The study engaged a sample of 38,326 Islamic Elementary school students, 29,350 Islamic Junior High school students, and 13,474 Islamic High school students across Indonesia. The findings revealed that the Light Gradient Boosting Machine was the most effective model for Islamic Elementary and High school data, while the Cat Boost Classifier excelled for Islamic Junior High school data. These results highlight the extent of learning loss in IRE and offer invaluable perspectives for education stakeholders. Future studies are encouraged to further explore the root causes of this learning loss and devise specific interventions to tackle these issues effectively.
Co-Authors Aaz Muhammad Hafidz Azis Agung Wahana Agus Sulaeman Aldy Rialdy Atmadja Ayu Puji Rahayu Busro, B. Cepy Slamet Deden Suparman, Deden Dhanis Al Ghifari Fahmi Faturahman Nul Hakim Faiz M. Kaffah Fazhar Restu Fauzi Ferry Feirizal Fitri Diani Gina Giftia Azmiana Delilah Handi Pradana Ichsan Budiman Irfan Safrudin Khaerul Manaf Kokoy Siti Komariah Maisevli Harika Maisevli Harika Maylawati, Dian Sa'adillah Mi’raj Fuadi Mohamad Irfan Muhammad Ali Ramdhani Muhammad Fasya Adzkia Muhammad Insan Al-Amin Rifaldo Sukma Hidayat Rohmat Mulyana Sapdi Safira Oktarini Salma Nuralisa Habsah Sandi Rahmadika Sandi Rahmadika Taufik, Ichsan Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yudi Widhiyasana