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All Journal Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Teknik Informatika (JUTIF)
Nugroho, Lustiyono Prasetyo
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Computer Science & IT

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IMPLEMENTASI APLIKASI RPG MAKER VX ACE SEBAGAI SARANA GAME EDUKASI TIGA RAGAM BAHASA DI SMP NEGERI 2 PURBALINGGA Nugroho, Lustiyono Prasetyo; Imron, Mohammad
Jurnal Sistem Informasi Kaputama (JSIK) Vol. 5 No. 1 (2021): Volume 5, Nomor 1, Januari 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jsik.v5i1.721

Abstract

School is a place for teaching and learning activities that are being carried out by each teacher to students, including theoretical and practical reviews. In the learning process the teacher is recommended to provide good quality teaching and learning activities, and to further refine these activities, a new media is needed in the learning process. Because based on the results of interviews with several teachers, it can be concluded that in the learning process students are less motivated, especially in the extra hours or evening hours and the lack of interaction makes students respond less and less. For this reason, in an effort to realize the quality of learning, new learning media are created that educate students such as educational games, where students can experience learning while playing. This can be used as a modern example, especially in the learning process of SMP Negeri 2 Purbalingga students. In accordance with the description above, this research takes the title "IMPLEMENTATION OF THE RPG MAKER VX ACE APPLICATION AS A THREE DIVERSE LANGUAGE EDUCATIONAL GAME MODELS IN SMP NEGERI 2 PURBALINGGA". The purpose of this study was to develop educational game learning media with 3 different languages to help teachers deliver material in the classroom and increase the motivation of students. So that later it can trigger interactions between teachers and students. With the hope that students will be able to understand the material presented by the teacher through the educational game, because based on the assessment of the student questionnaire, the educational game application results in an average index of 88.26% and a teacher representative questionnaire of 94.99%. So it can be concluded that this application is feasible to use and shows that this educational game learning media is very useful for use by students of SMP Negeri 2 Purbalingga.
Performance Comparison Of Xgboost Lightgbm And Lstm For E-Commerce Repeat Buyer Prediction Nugroho, Lustiyono Prasetyo; Saputro, Rujianto Eko; Utomo, Fandy Setyo
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5746

Abstract

Repeat buyer behavior is a critical indicator of customer retention success in e-commerce platforms. However, accurately predicting repeat buyers remains a challenging problem due to the complexity of user behavior patterns and the temporal characteristics embedded in interaction data. Existing studies often focus on single modeling approaches or limited sequence exploration, resulting in insufficient comparative insight between ensemble-based machine learning and sequence-based deep learning models. Therefore, this study aims to systematically compare the performance of tree-based ensemble models (XGBoost and LightGBM) and a sequence-based deep learning model (LSTM) in predicting repeat buyers using user behavior data. To ensure fair evaluation, data preprocessing and feature engineering were carefully designed to prevent data leakage by utilizing user behavior prior to the first purchase. Model performance was evaluated using Accuracy, F1-score, and ROC–AUC metrics. Experimental results show that XGBoost and LightGBM achieve stable classification performance with accuracy values of 86.11% and 85.84%, respectively, while the LSTM model attains the highest ROC–AUC value of 0.937, indicating superior capability in capturing temporal behavioral patterns. This study provides valuable insights for e-commerce platforms seeking to optimize predictive models for repeat buyers, contributing to more effective customer retention strategies.
Co-Authors Fandy Setyo Utomo Mohammad Imron Rujianto Eko Saputro