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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 272 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 (8)
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.
Implementation of PCC Load Balancing and Failover Using Mikrotik CHR on Virtual GNS3 Abraara, Novellza Arfedin; Islamiyah, Mufidatul
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1198

Abstract

The rapid development of network technology demands a reliable, efficient system capable of providing continuous, uninterrupted service. One important aspect of achieving this goal is implementing a load-balancing method that distributes network traffic evenly. In this study, a PCC (Per Connection Classifier) was designed and integrated with a failover mechanism to improve network performance and availability. The PCC method is used to dynamically divide traffic based on connections, while the failover mechanism functions as an automatic backup system in the event of disruption to the main path. The design results indicate that combining PCC and failover can increase reliability, improve load distribution, and minimize downtime in the network structure. This implementation is expected to serve as a reference for the development of a more stable and efficient network infrastructure. Test results show that applying the PCC and failover method can increase average throughput by up to 35%, reduce latency by 20%, and accelerate connection recovery, with an average failover time of under 3 seconds. Thus, this design is proven effective in improving network reliability and internet service quality in multi-gateway systems
Utilizing Long Short-Term Memory (LSTM) Networks for Predicting Seismic-Induced Building Damage: A Bawean Region Case Study Zarkoni, Ahmad; Almais, Agung Teguh Wibowo; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Pagalay, Usman; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1212

Abstract

This study examines the feasibility of employing Long Short-Term Memory (LSTM) networks to estimate earthquake-induced building damage using a focused dataset derived from the continuous 8-day mainshock–aftershock sequence that occurred in March 2024. A total of 483 events were analyzed, utilizing three readily available source parameters: magnitude, depth, and epicentral distance to predict the corresponding EMS-98 damage grade. The motivation for using an LSTM architecture stems from its capacity to model temporal dependencies within sequential seismic activity, despite the limited size of the dataset. The best-performing single-split model (B4) achieved a test R^2 of 0.5738 and an RMSE of 0.2997 on the held-out set. However, to obtain a more robust assessment of the model’s generalizability, a 5-fold TimeSeriesSplit cross-validation was conducted. The cross-validation procedure yielded a mean R^2 of 0.49 with a standard deviation of 0.27, and a mean RMSE of 0.33 with a standard deviation of 0.16. These results demonstrate that the LSTM model provides a credible baseline model for exploratory damage estimation, although a substantial portion of the variance remains unexplained due to the absence of geotechnical, soil amplification, and structural fragility information. The findings highlight the potential of sequence-based modeling for rapid damage estimation and underscore the need for integrating site-specific and structural variables in future work to enhance predictive accuracy.
A hybrid GoogLeNet–GLCM feature extraction framework for textural representation of post-disaster building damage imagery Amani, Holidiyatul; Almais, Agung Teguh Wibowo; Abidin, Zainal; Nugroho, Fresy; Kurniawan, Fachrul; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1214

Abstract

Accurate representation of visual characteristics in post-disaster building imagery is crucial for downstream analytical tasks such as damage interpretation, retrieval, and automated assessment. This study presents a focused investigation of feature extraction using a hybrid approach that integrates deep semantic representations from the GoogLeNet architecture with statistical texture descriptors inspired by the Gray-Level Co-Occurrence Matrix (GLCM). The objective of this work is limited strictly to the generation and analysis of semantic–textural feature vectors rather than the development or evaluation of any classification or prediction model. High-level feature maps are obtained from a selected convolutional layer of GoogLeNet, after which statistical texture properties—contrast, energy, and homogeneity—are computed per channel. A representative set of feature channels is analyzed to demonstrate the capabilities of the proposed hybrid extraction pipeline. The results demonstrate the potential of semantic–textural descriptors to provide interpretable feature characteristics in building-damage imagery. This study provides a methodological foundation and analytical insight for future works that may incorporate these feature representations into classification, clustering, or decision-support frameworks.
Implementation and verification of a SAW-based decision support system for culinary MSME ranking Nugroho, Muhammad Fariz; Supriyanto, Edy
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1221

Abstract

This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to determine the most preferred culinary Micro, Small, and Medium Enterprises (MSMEs) in the Dracik Campus area of Batang. The system incorporates four primary criteria—price, location, service, and food quality—each supported by structured sub-criteria and weights derived from customer assessments. Data were collected via questionnaires administered to MSME customers, and the results were normalized and weighted to generate objective rankings. The system architecture was developed using the SDLC model, with MySQL utilized for database management and automated SAW computation. The results indicate that the system successfully generated rankings, and the preference values produced by the automated SAW computation were identical to those from manual SAW calculations, with no numerical deviation, proving the algorithmic correctness of the system. The study confirms that DSS with SAW enhances transparency, reduces subjective bias, and provides actionable insights for MSME development. Overall, this study validates the successful functional implementation of a SAW-based DSS and demonstrates that the system can be used as a reliable computational tool for multi-criteria decision-making.
Implementation of the Rapid Application Development (RAD) method for guidance and counseling: a case study at SMPN 3 Pakuhaji Yulianto, Muhammad Arief; Khoirunnisya, Khoirunnisya
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1222

Abstract

This study presents the development and evaluation of a web-based Guidance and Counseling Information System at SMPN 3 Pakuhaji using the Rapid Application Development (RAD) method. Prior to system implementation, counseling services relied on manual documentation and face-to-face reporting, resulting in delayed case processing, limited monitoring, and inefficient data management. The RAD approach was applied through iterative prototyping cycles involving continuous feedback from counselors and teachers to ensure alignment with user requirements. The system was developed using PHP and MySQL, supported by UML modeling and database design. System evaluation was conducted using black-box testing to verify functional correctness and performance measurement to assess operational efficiency. The results indicate that counseling case processing time was reduced from approximately 2–3 working days to 3–5 hours, representing an efficiency improvement of about 85%. In addition, a user satisfaction survey involving 45 respondents yielded a 84.1% score, categorized as “Strongly Agree.” These findings demonstrate that the RAD-based system effectively improves the efficiency, accuracy, and accessibility of counseling services. The study confirms that iterative user-centered development not only accelerates system delivery but also enhances system usability and practical effectiveness in educational counseling environments.
Ethical Challenges in Primary vs. Secondary Datasets: A Systematic Review of Manipulation and Transparency Riska, Suastika Yulia; Widiyaningtyas, Triyanna; Elmunsyah, Hakkun; Sendari, Siti
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.1227

Abstract

The swift advancements in Artificial Intelligence and Machine Learning have rendered datasets essential; nonetheless, their heightened utilization has engendered intricate ethical dilemmas that are frequently neglected. This study seeks to delineate and highlight ethical concerns associated with the collection of primary data and the reutilization of secondary datasets in computer science research. We employed a Systematic Literature Review (SLR) methodology in accordance with the PRISMA 2020 guidelines, examining 72 publications sourced from five esteemed academic databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Google Scholar) published from 2021 to 2025. The study results indicate that ethical difficulties emerge uniformly in both primary and secondary datasets. Primary datasets primarily face challenges related to privacy threats, anonymization, and Informed Consent, whereas secondary datasets are more susceptible to licensing infringements, dataset repurposing, and insufficient preparation transparency. The three domains that predominantly encountered these challenges were Machine Learning, Computer Vision, and Natural Language Processing. Moreover, practices of data manipulation, including cherry-picking and concealed preparation, were identified as detrimental to scientific integrity. This study's findings underscore the need for enhanced ethical standards for datasets and greater transparency in preparation documentation to ensure the repeatability of data-driven research.

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