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All Journal OPSI JISKa (Jurnal Informatika Sunan Kalijaga)
Sari, Nur Fitriyah Ayu Tunjung
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Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science

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Utilizing the game design factor questionnaire to develop engaging games for adaptive learning in the serious educational game: the Ma'had Sari, Nur Fitriyah Ayu Tunjung; Kusumawati, Ririen; Karami, Ahmad Fahmi; A, Miftahul Hikmah Putri Samudera
OPSI Vol 17, No 1 (2024): ISSN 1693-2102
Publisher : Jurusan Teknik Industri Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v17i1.11322

Abstract

This study explores the unique mandatory residence, Ma'had Sunan Ampel Al Alyi (MSAA), at the State Islamic University (UIN) Maulana Malik Ibrahim Malang, aiming to develop Quranic reading competence in new students. The varying educational backgrounds of admitted students lead to differences in their Quranic reading capabilities, highlighting the need for adaptive learning. In response to this diversity, adaptive learning using artificial intelligence is employed, implemented through the serious education game "The Ma'had." Survey results from expert individuals using a Game Design Factor Questionnaire reveal the game's substantial potential. The results show high agreement (100%) on clear goals, engaging gameplay, and a sense of freedom, with 67% strongly agreeing on improved understanding. Challenges are motivating, and the game successfully sparks curiosity. "The Ma’had" Game proves effective, but further research is recommended to explore variations in player engagement and compare results with expert test subjects, employing alternative quantitative testing methods for a comprehensive analysis.
Analisis dan Optimalisasi Performa Algoritma Gaussian Naive Bayes pada Prediksi Metabolic Syndrome Menggunakan SMOTE Fauziyah, Nadiyah Jihan; Rahmania, Fadilla; Daniyal, Muhammad; Sari, Nur Fitriyah Ayu Tunjung
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.112-122

Abstract

Metabolic syndrome is a complex global health problem, with symptoms such as abdominal obesity, insulin resistance, high blood pressure, high blood sugar, and abnormal blood lipids. With this global challenge, several studies have attempted to predict these diseases using machine learning methods. However, often, predictions about a disease result in data imbalance where minority classes are underrepresented. To balance the class proportions, the Synthetic Minority Over-sampling Technique (SMOTE) method replicates the minority class samples. In this research, the technique applied to predict is the Gaussian Naive Bayes (GNB) algorithm. The results show an increase in prediction accuracy by 0.2 from 0.81 to 0.83. This study confirms the critical role of the SMOTE oversampling method in machine learning using the Gaussian Naive Bayes (GNB) algorithm in Metabolic Syndrome prediction and its positive impact on diagnostic efficiency and public health.
Co-Authors A, Miftahul Hikmah Putri Samudera Daniyal, Muhammad Fauziyah, Nadiyah Jihan Karami, Ahmad Fahmi Rahmania, Fadilla Ririen Kusumawati