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All Journal SAMUDERA Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) INFORMAL: Informatics Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JISKa (Jurnal Informatika Sunan Kalijaga) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Informasi dan Teknologi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Multica Science and Technology jeti TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi International Journal of Information System & Innovative Technology Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Scientific Journal of Informatics International Journal of Information System and Innovative Technology Smatika Jurnal : STIKI Informatika Jurnal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
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Implementasi Metode Fisher-Yates Shuffle Dan Metode Finite State Machine Pada Game Edukasi Untuk Meningkatkan Minat Belajar Siswa Anak Sekolah Dasar Rizal, Muhammad; Rozzi Kesuma Dinata; Zahratul Fitri
Jurnal Elektronika dan Teknologi Informasi Vol 7 No 1 (2026): Maret 2026
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v7i1.582

Abstract

The use of educational games as interactive learning media is one solution to increase learning motivation and understanding among elementary school students. This study aims to implement the Fisher–Yates Shuffle (FYS) and Finite State Machine (FSM) methods in the development of a Unity-based educational game for Natural Sciences (IPA) and Social Sciences (IPS), and to evaluate its effectiveness in improving students’ learning outcomes. The system was developed using the Multimedia Development Life Cycle (MDLC), which consists of concept, design, assembly, testing, and distribution stages. FYS was applied to randomize quiz questions and answer options, while FSM was used to manage game flow and scene transitions in a structured manner. System testing was conducted using black-box testing, and learning effectiveness was evaluated through pre-test and post-test involving grade III and IV elementary school students. The results indicate an increase in students’ average scores after using the educational game, with improvement percentages ranging from 22% to 25%. In addition, teacher questionnaire results show that the game is feasible, easy to use, and beneficial as a supporting learning medium. Therefore, the developed Unity-based educational game is effective in enhancing students’ understanding of IPA and IPS subjects
Comparison of the K-Nearest Neighbor and Random Forest Methods in Classifying the Best Selling Medicines at Khan Pharmacy Matang Glumpang Dua Putri, Anya Regina; Rozzi Kesuma Dinata; Maryana
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25183

Abstract

Khan Matang Glumpang Dua Pharmacy faces difficulties in analyzing drug sales patterns that affect inventory efficiency and customer satisfaction. The need to anticipate demand and reduce the risk of stockouts or excess stock requires an effective classification system for best-selling drugs. This study aims to test the K-Nearest Neighbor (KNN) and Random Forest methods to perform and find the best classification model. The data used in this study consisted of 382 data points. This study compared two classification models on pharmacy sales data. The K-Nearest Neighbor (KNN) model was tested using the parameter k=3, while the Random Forest model was tested with 100 trees and a max depth of 5. The results showed that the KNN and Random Forest (RF) algorithms. The Random Forest (RF) model outperformed KNN on all metrics: RF achieved an Accuracy and F1-Score of 94.81%, while KNN recorded an Accuracy of 93.51% and an F1-Score of 93.44%.