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All Journal JURTEKSI Jurnal Computer Science and Information Technology (CoSciTech)
Tamaza, Muhammad Abyanda
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Computer Science & IT

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Implementasi Naïve Bayes dalam M-Series 4 Mobile Legends untuk Prediksi Kemenangan Tamaza, Muhammad Abyanda; Defit, Sarjon; Sumijan, Sumijan
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6707

Abstract

Mobile Legends is a game made by a developer from China called Moontoon which implements the Multiplayer Online Battle Arena (MOBA) system which is currently popular. The popularity of this game is proven by the holding of low, middle and high level tournaments. Recently a high level or international tournament called the M-Series World Championship was held in Indonesia. This game is played by two teams consisting of five players with the aim of destroying enemy targets in the form of towers. The problem in this game is winning and losing. One of the factors that determines victory or defeat is the choice of hero. The wrong hero composition during the draft pick stage can make it difficult for your team to play and lead to unexpected results. This research aims to predict the percentage level of Mobile Legends wins based on the drafted heroes. Prediction is the process of minimizing errors in systematically estimating the future based on past information. The technique used in this research is the Naïve Bayes algorithm. The Naïve Bayes algorithm is a classification method based on probability. This method consists of four stages, namely data understanding, data preparation, data analysis, and results analysis. This research dataset is provided by Youtube MPL Indonesia. The dataset consists of 880 training data and 90 test data for M-Series 4 Mobile Legends. The results of this research provide a percentage value in the form of prediction of 96.67%, precision of 95.65% and recall of 97.78%. The results of an accuracy rate of 96.67% using the Naïve Bayes algorithm show that predictions using the Naïve Bayes algorithm can be applied to predict win ratios in M-Series 4 Mobile Legends.
COUNSELING MODEL BASED ON BACKWARD CHAINING OF STUDENT BEHAVIOR AT SMK 10 MUHAMMADIYAH KISARAN Amin, Muhammad; Supriyanto, Boby; Tamaza, Muhammad Abyanda; Asy’ari, Ilham; Fadillah, Riszki
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 1 (2023): Desember 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2811

Abstract

Student development includes conduct as a key component. Student behavior becomes crucial in deciding how successful students will be in different spheres of life. The variety of student behavior can hinder the learning process and personal development of students. Through the development of an expert system-based counseling model based on backward chaining, this study seeks to discover trends in student behavior. The research process starts with problem analysis, goal setting, literature study, data collection, system design and implementation, and results analysis. It then moves on to counseling model development and implementation in the school setting. To determine the reasons for the unruly behavior of the kids, data were analyzed using a backward chaining methodology. UML Usecase diagrams are used in system design to define the roles of actors and users. The established counseling model, which consists of 14 behaviors, 67 phenomena/symptoms, and 14 rules, focuses on goals and methods to modify student behavior. Three students underwent system testing based on previously achieved goals from therapy. The findings revealed "Smoking," "Emotional Problems," and "Fighting" among the student behaviors. When the Backward Chaining-based counseling model is used, it is simpler for homeroom teachers to gather information about students' conduct from them and to offer remedies based on the transfer of professional knowledge without having to wait for the counselor guidance procedure
Co-Authors Asy’ari, Ilham Fadillah, Riszki Muhammad Amin Sarjon Defit Sumijan Sumijan Supriyanto, Boby