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All Journal JURIKOM (Jurnal Riset Komputer) Journal Computer Science and Informatic Systems : J-Cosys
Sumarto, Marco Alfan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Klastering pada Karakteristik Karakter Pahlawan Mobile Legends: Bang Bang (MLBB) Menggunakan Algoritma Simple K-Means Sumarto, Marco Alfan; Firya, Muhammad Zulfikri
Journal Computer Science and Information Systems : J-Cosys Vol 3, No 1 (2023): J-Cosys - Maret
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/jco.v3i1.381

Abstract

Penelitian ini bertujuan untuk melakukan klastering karakter pahlawan pada permainan Mobile Legends: Bang Bang (MLBB) menggunakan metode Simple K-Means pada aplikasi Weka. Data karakter pahlawan diperoleh dari 103 karakter pahlawan MLBB dengan 19 atribut, di antaranya nama karakter pahlawan, role, overall defense, overall offense, overall skill effect, overall difficulty, movement speed, magic defense, mana, hp regen, physical attack, physical defense, health point, attack speed, mana regen, win rate, pick rate, ban rate, dan tahun rilis. Metode Simple K-Means digunakan untuk membagi karakter pahlawan MLBB menjadi dua klaster berdasarkan atribut yang dimiliki. Hasil klastering menunjukkan bahwa karakter pahlawan MLBB pada klaster 0 memiliki nilai pertahanan, serangan, efek skill dan tingkat kesulitan yang lebih tinggi dibandingkan dengan hero pada klaster 1. Selain itu, hero pada klaster 0 juga memiliki pick rate yang lebih rendah dibandingkan dengan karakter pahlawan pada klaster 1. Hasil penelitian ini dapat digunakan sebagai referensi bagi pemain MLBB untuk memilih karakter pahlawan yang sesuai dengan kebutuhan dan strategi permainan mereka
Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Quantitative Analysis of Training Completion Using Multivariate Linear Regression Devianto, Yudo; Dwiasnati, Saruni; Gunawan, Wawan; Sumarto, Marco Alfan; Saputra, Dony Ramadhan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8851

Abstract

The urgency of this research stems from the strategic need to monitor and evaluate the achievements of digital training implemented by various academies under government coordination, including VSGA, FGA, DEA, TA, and GTA. In the context of the national digital transformation program, the availability of an analytical model that can predict the success of participants in completing training is critically crucial to support the achievement of the Ministry’s Key Performance Indicators (KPIs). The purpose of this study is to develop a predictive model based on multivariate linear regression that combines two main variables, the percentage of participants accepted and the percentage of participants who participate in onboarding, to project the level of training completion. This model is expected to provide a quantitative and objective assessment of the effectiveness of digital training implementation in each academy. The targeted outputs of this study include the development of a predictive model with performance validation through the calculation of R², which yielded a value of 0.9448, as well as the provision of technical reports and data-driven recommendations for enhancing digital training governance. The Technology Readiness Level (TKT) of this study is at TKT 3, and there is evidence of conceptual validation of the predictive model based on real data collected from the implementation of the training. This stage marks the readiness of the research to continue developing the system model and implementing it on the training evaluation platform in the next stage.
Co-Authors Dwiasnati, Saruni Firya, Muhammad Zulfikri Saputra, Dony Ramadhan Wawan Gunawan Yudo Devianto