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INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG Jalan Soekarno Hatta Rembuksari No 1A Malang
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Jurnal ilmiah teknologi informasi Asia
Published by Institut Teknologi dan Bisnis Asia Malang
ISSN : 0852730X     EISSN : 25808397     DOI : https://doi.org/10.32815/jitika
Core Subject : Science,
Computer Science & IT
Published by Institute for Research, Development and Community Service (Lembaga Penelitian, Pengembangan dan Pengabdian Masyarakat / LP2M) of High School of Information & Computer Management (Institut Teknologi dan Bisnis AsiA MALANG as a periodical publication that provides information and analysis on the science of Technology and Information. Jurnal Ilmiah Teknologi Infomasi Asia is a journal published twice a year, double-blindly reviewing enriches the understanding of past, present, and future issues relevant to Information Technology. Jurnal Ilmiah Teknologi Infomasi Asia hopes the article raises debate, controversy, new understanding, solid theory, and reflection on the topics. Focus and Scope: Manuscript in Jurnal Ilmiah Teknologi Infomasi Asia is the result of research including but not limited to: Computer Science, Informatics Engineering, Computer Systems and Information Systems. The authors are invited to submit articles that have not been published before and are not under consideration elsewhere.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 264 Documents
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Pengembangan website ujian tes TOEFL pada Lembaga Kursus “Plug-in” Wijaya, Arya Bima; Widayati, Yohana Tri; Prakoso, Satrio Agung
Jurnal Ilmiah Teknologi Informasi Asia Vol 19 No 2 (2025): Volume 19 nomor 2 2025 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v19i2.1189

Abstract

The TOEFL test is an international standard for measuring English language ability. This research developed a web-based TOEFL examination system at the “Plug-in” Training Institute using the Scrum method to address the shortcomings of the old system, such as audio control, score accuracy, and insufficient detail in recording results. The data were collected through observation, interviews, and literature study. Scrum was applied in three sprints that focused on the listening section navigation, score calculation, and answer logging. Features for single audio playback, a timer, automatic score calculation, and a detailed results display were successfully implemented. Testing showed the system to be stable and accurate according to TOEFL standards. This system improves efficiency, fairness, and user experience in digital TOEFL examinations. Suggestions for development include broader testing, UI/UX optimization, a data export feature, and more detailed answer logging for analysis.
Analisis optimasi multi-objektif prestasi mahasiswa dengan algoritma NSGA-II Rochman, Apriatur; Suryanto, Andik Adi; Suprapto, Suprapto
Jurnal Ilmiah Teknologi Informasi Asia Vol 19 No 2 (2025): Volume 19 nomor 2 2025 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v19i2.1201

Abstract

This study investigates the application of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing multiple conflicting objectives related to student academic performance. Using the Student Performance dataset from the UCI Machine Learning Repository, which contains demographic, behavioral, and academic information of 395 secondary school students, the research aimed to maximize final grades (G3), minimize absenteeism, and maximize study time. The study began with exploratory data analysis, which revealed wide variability in academic outcomes, low average absenteeism, and moderate study time, justifying the selection of these three objectives. NSGA-II was then implemented with a population of 100 individuals across 200 generations, employing crossover and mutation operators to generate Pareto-optimal solutions. The results demonstrated diverse non-dominated solutions, illustrating trade-offs between academic achievement, attendance, and study time. Absenteeism emerged as the most significant negative factor, while study time and school support were positively associated with better outcomes. Unlike conventional regression or classification methods that produce a single prediction, NSGA-II provided a spectrum of optimal alternatives, offering flexibility in policy and decision-making. These findings highlight the relevance of multi-objective optimization in education and emphasize the importance of integrating behavioral, social, and digital dimensions to design adaptive strategies for improving student performance.
Model ADDIE dan Waterfall sebagai framework pengembangan media pembelajaran (studi kasus: media pembelajaran sistem peredaran darah) Rustandi, Andi; Darmawati , Darmawati
Jurnal Ilmiah Teknologi Informasi Asia Vol 19 No 2 (2025): Volume 19 nomor 2 2025 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v19i2.1203

Abstract

The ADDIE model, while effective for instructional design, often lacks a structured approach to the technical aspects of software development. This study introduces a combined ADDIE-Waterfall framework to create learning media that are both pedagogically effective and technically sound. Using a Research and Development (R&D) method, a learning application for the high school biology topic of the circulatory system was created. The product was evaluated for feasibility through expert validation, practicality through user response questionnaires (n=150), and effectiveness using pre-test and post-test scores to calculate a normalized gain (N-Gain) score. The results demonstrated high quality, with an expert validation score of 92% (“Very Feasible”) and a user response score of 95% (“Very Good”). The learning media were also effective, showing a significant improvement in student learning outcomes with an N-Gain score of 0.63, indicating moderate effectiveness. The findings confirm that integrating the ADDIE and Waterfall models provides a comprehensive framework for developing high-quality, effective educational software. This combined approach successfully addresses both instructional and technical requirements, resulting in a product that is well-received by users and improves learning.
Implementation of Feature Selection to Improve the Accuracy of Gender Classification Based on Voice Data with Random Forest Suhardiyanto, Suhardiyanto; Amaluddin, Fitroh; Wijayanti, Aris
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (In Progress)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1204

Abstract

Voice-based gender recognition has gained increasing importance in biometrics, security, forensics, and human–computer interaction. While humans can easily distinguish male and female voices, automatic classification remains challenging due to variability and high-dimensional acoustic data. This study investigates the role of feature selection in enhancing the performance and efficiency of Random Forest for gender classification. The dataset, obtained from Kaggle, consists of 3,168 balanced voice samples with 23 acoustic features. Using Pearson’s correlation analysis, five features with the strongest associations to the target variable were selected. Random Forest classification was then conducted using both the full set of 22 features and the reduced set of 5 features. Results suggest that although the accuracy gain was marginal (98% to 99%), computation time decreased substantially from 0.3 to 0.1 seconds, representing a 66% efficiency improvement. These findings suggest that lightweight correlation-based feature selection can simplify models and enable faster real-time applications without compromising predictive performance. The study emphasizes efficiency rather than accuracy as the main contribution, providing a methodological insight for designing scalable and inclusive voice-based gender recognition systems.

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