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IJISCS (International Journal of Information System and Computer Science)

ijiscs
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Published by Institut Bakti Nusantara

ISSN : 25980793 EISSN : 2598246X DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

The IJISCS (International Journal of Information System and Computer Science) is a publication for researchers and developers to share ideas and results of software engineering and technologies. These journal publish some types of papers such as research papers reporting original research results, technology trend surveys reviewing an area of research in software engineering and technologies, survey articles surveying a broad area in software engineering and technologies. The scope covers all areas of software engineering methods and practices, object-oriented systems, rapid prototyping, software reuse, cleanroom software engineering, stepwise refinement/enhancement, ambiguity in software development, impact of CASE on software development life cycle, knowledge engineering methods and practices, formal methods of specification, deductive database systems,logic programming, reverse engineering in software design, expert systems, knowledge-based systems, distributed knowledge-based systems, knowledge representations, knowledge-based systems in language translation & processing, software and knowledge-ware maintenance, Software Specification and Modeling, Embedded and Real-time Software (ERTS), and applications in various domains of interest.

Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi

Articles 6 Documents

Search results for , issue "Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)" : 6 Documents clear

SENTIMENT ANALYSIS OF THE GOPAY APPLICATION USING THE NAIVE BAYES METHOD BASED ON USER REVIEWS Gumanti, Miswan; Aprianto, Rudi; Zahra, Fista Anisa; Muslihudin, Muhamad
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1865

The development of financial technology, particularly digital wallet applications, has made significant progress in Indonesia. One of the most widely used applications is GoPay, which offers various conveniences in conducting financial transactions. However, despite GoPay 's many advantages, user responses to this application are not always positive. Some users provide negative comments reflecting their less than satisfactory experiences. In this context, this study aims to analyze sentiment from user reviews of the GoPay application using the Naive Bayes method, which is known to be effective in text classification. This method was chosen because of its ability to classify data well, even in large and diverse datasets. This study involved data collection from platforms Using Kaggle, 1,000 reviews were randomly selected for further analysis. The analysis revealed that positive comments predominated among the reviews. This indicates that the majority of users had a positive experience with the GoPay app, although there were also a number of negative reviews worth noting.

SINGLE-LABEL LEARNING STYLE CLASSIFICATION USING MACHINE LEARNING WITH GRIDSEARCH-BASED HYPERPARAMETER TUNING ON LMS BEHAVIORAL DATA Lestari, Uning; Salam, Sazilah; Choo, Yun Huoy
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1876

The rapid growth of online learning environments has increased the importance of Learning Management Systems (LMS) as a rich source of behavioral data for learning analytics. One learner characteristic that strongly influences learning effectiveness is learning style; however, traditional questionnaire based identification approaches suffer from subjectivity, limited scalability, and static representation. To address these limitations, this study proposes a machine learning-based approach for automatic learning style classification using LMS behavioral data grounded in the Felder–Silverman Learning Style Model (FSLSM). This study utilizes LMS activity log data collected from Universitas Siber Asia over three academic years (2022–2024). The dataset consists of 5,633 student interaction records with 72 raw behavioral attributes, which were preprocessed, aggregated, and transformed into 12 representative behavioral features reflecting students’ interactions with learning materials, assessments, discussions, multimedia resources, and navigation patterns. A rule-based FSLSM mapping mechanism was applied to generate 16 learning style profiles, which were treated as targets in a single-label classification setting. Support Vector Machine (SVM) and Gradient Boosting (GB) classifiers were implemented and optimized using feature selection and GridSearch-based hyperparameter tuning. The dataset was divided into 75% training data and 25% testing data using a stratified split to preserve class distribution. Experimental results show that Gradient Boosting consistently outperforms SVM across all evaluation metrics. The GB model achieved an accuracy of 0.84 and a macro F1-score of 0.79, demonstrating strong generalization capability and robustness to class imbalance. In contrast, SVM exhibited lower and less stable performance, particularly on minority learning style classes. These findings confirm that ensemble-based methods such as Gradient Boosting are more effective for LMS-based single-label learning style classification and support the feasibility of automatic FSLSM-based learning style detection for data-driven adaptive learning systems.

DEVELOPMENT OF AN EYE-CONTROLLED MOBILE ROBOT SYSTEM USING EOG SIGNALS Triloka, Joko; Fauzi, Adi Ahmad
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1859

The development of an eye-controlled mobile robot system using Electrooculography (EOG) signals is presented in this study. The proposed system enables robot motion control through eye movement detection, providing an alternative interaction method for individuals with limited physical mobility. The EOG sensor captures eye movement potentials, which are processed by a microcontroller to generate motion commands. A threshold-based detection algorithm was implemented to classify eye movements into four directional commands: left, right, forward, and backward. The system was tested to evaluate movement accuracy and response time. Experimental results show that the proposed system achieved an average directional detection accuracy of 88.3% and an average response time of 218 milliseconds, indicating reliable and real-time performance. The findings demonstrate that EOG-based control provides a feasible and responsive approach for human–robot interaction. Future improvements may involve noise filtering techniques and machine learning models to enhance signal stability and classification precision.

DIAGNOSTIC ACCURACY OF DEEP NEURAL NETWORKS FOR PNEUMONIA AND COVID-19 DETECTION ON MEDICAL IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS Oluwagbemi, Johnson Bisi; Akinbo, Racheal Shade; Mesioye, Ayobami Emmanuel
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1857

Pneumonia and COVID-19 remain leading causes of universal morbidity and mortality, with timely and precise diagnosis essential for effective patient management. This systematic review and meta-analysis assessed the diagnostic accuracy of deep neural networks in detecting pneumonia and COVID-19 across main medical imaging modalities. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore and Cochrane Library identified 80 eligible studies published between 2017 and 2025. Included studies used chest X-ray (CXR), computed tomography (CT) and lung ultrasound (LUS) images analyzed through convolutional neural networks, transformer-based and hybrid deep models. Pooled diagnostic performance was synthesized using a bivariate random-effects model and hierarchical summary receiver operating characteristic analysis. Overall pooled sensitivity and specificity were 0.88 (95% CI: 0.84-0.91) and 0.90 (95% CI: 0.86-0.92), respectively, with an area under the curve of 0.93, indicating high discriminative capability. Subgroup analyses revealed CT-based models outperformed CXR and LUS, while transformer architectures marginally exceeded CNNs. In addition, external validation studies steadily reported lower accuracy than internal validations, reflecting limited model generalizability. Risk of bias assessment using QUADAS-2 emphasized concerns related to patient selection, data leakage and non-standardized reference criteria. Despite moderate heterogeneity (I² = 39-52%) and potential publication bias, findings confirm the substantial potential of DNNs as decision-support tools for fast, scalable and reliable respiratory disease diagnosis. However, broader clinical adoption demands multicenter validation, transparency and adherence to ethical AI standards. This study provides evidence-based insights into the current performance and translational readiness of AI-driven diagnostic imaging for pneumonia and COVID-19.

ANALYSIS OF HUMAN RESOURCE EMPOWERMENT STRATEGY BASED ON ANALYTICAL HIERARCHY PROCESS (AHP) FOR LOCAL CREATIVE ECONOMY DEVELOPMENT Bangsawan, Laksamana; Rohmah, Aida; Susanto, Ferry
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1874

This study examines effective human resource (HR) empowerment strategies to improve the local creative economy. The urgency of this research is based on the fact that the creative economy, despite being a driver of economic growth, has not reached its optimal potential due to the low capacity of local HR. This research is important to identify effective HR empowerment strategies to increase the productivity, innovation, and competitiveness of creative economy actors at the local level. The results are expected to serve as a reference for the government and stakeholders in formulating HR-based policies. The purpose of this study is to analyze the inhibiting and supporting factors of HR empowerment in the local creative economy sector, formulate strategies that are appropriate to the characteristics and needs of creative economy actors, and provide policy recommendations to increase this sector's contribution to local development. The method used is a qualitative approach with literature studies, in-depth interviews, and Focus Group Discussions (FGDs). SWOT analysis will be used to evaluate strengths, weaknesses, opportunities, and challenges, while AHP (Analytic Hierarchy Process) analysis will be used to prioritize strategies based on weighted criteria. The results of this study indicate a significant relationship between HR empowerment strategies and the improvement of the local creative economy. Through the analysis, the most effective strategy was identified as a combination of increasing technology access and business mentoring. The formulated empowerment model is applicable and evidence-based, providing policy recommendations for the government and stakeholders. Thus, this research contributes to the development.

DESIGN OF SAVINGS AND LOAN APPLICATIONS FOR ANDROID-BASED WOMEN'S GROUPS Fauzi, Fauzi; Nuraini, Sephia; Gunawan, Sahrul
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1875

Many savings and loan records in farmer women groups are still done manually using books, which can risk causing errors in recording. This is the background for the development of an offline-based android application that can help the VSLA (Village Saving and Loan Association) savings and loan recording process. The purpose of this research is to build an easy-to-use savings and loan recording application, with a simple appearance and according to the needs of users at the rural community level. Application development is carried out using the kodular platform by utilizing TinyDB local storage so that applications can be used offline. The prototype method used in this study allows the development of the system to be carried out in stages according to user input and requirements. The system is designed using the Unified Modeling Language (UML) approach, which consists of a use case diagram, class diagram, and activity diagram. Group financial management becomes easier and more efficient. With the system created offline-based, the application has proven to be relevant for use in areas that have limited internet connection issues. It is hoped that this research can contribute to efforts to digitize the community financial system, especially for farmer women groups.

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Muhamad Muslihudin

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ijiscs@ftikomibn.ac.id

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+6272922240

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ijiscs@ftikomibn.ac.id

Editorial Address
Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location
Kab. pringsewu,

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INDONESIA

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Muhamad Muslihudin

Contact Email ijiscs@ftikomibn.ac.id

Phone +6272922240

Journal Mail Official ijiscs@ftikomibn.ac.id

Editorial Address Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location Kab. pringsewu, Lampung INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Muhamad Muslihudin

Contact Email
ijiscs@ftikomibn.ac.id

Phone
+6272922240

Journal Mail Official
ijiscs@ftikomibn.ac.id

Editorial Address
Editor IJISCS (International Journal of Information System and Computer Science) Bakti Nusantara Institute Street Wisma Rini No.09 Pringsewu, Lampung Phone: 0729-22240

Location
Kab. pringsewu,

Lampung

INDONESIA