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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : https://doi.org/10.15294/sji.vxxix.xxxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. The SJI publishes 4 issues in a calendar year (February, May, August, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 190 Documents
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Adoption of Artificial Intelligence in Chatbot Recommendation Systems for Complex Customer Preferences: A Case Study of Shopee E-Commerce Apriyanti Sijabat; Dana Indra Sensuse
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.48989

Abstract

Purpose: This study identifies and analyzes factors influencing AI chatbot recommendation system adoption and proposes optimization strategies based on user perceptions and organizational decisions. Methods: A qualitative case study was conducted with Shopee as the unit of analysis. Nine participants three internal Shopee personnel and six active users were interviewed and selected via purposive sampling. Thematic analysis followed three stages (open, axial, and selective coding) guided by the TOE–TAM framework. Trustworthiness was ensured through member checking, peer debriefing, and four criteria: credibility, transferability, dependability, and confirmability. Result: Shopee's AI chatbot delivers personalized recommendations but users frequently experience information overload from irrelevant results. Three TOE dimensions technology readiness, organizational readiness, and external pressure, were found to drive adoption, while four TAM factors perceived usefulness, ease of use, trust, and satisfaction, shape user acceptance. Five strategic recommendations are proposed: algorithm enhancement, data quality improvement, adaptive personalization, deeper customer profiling, and information overload reduction. Novelty: Prior studies examine organizational adoption (TOE) or user acceptance (TAM) of AI chatbots in isolation, leaving a gap in understanding how macro-level institutional readiness interacts with micro-level user cognitive barriers. This study addresses that gap by integrating TOE and TAM as a dual-perspective lens, explaining how institutional readiness spanning technology, organization, and environment directly reduces cognitive barriers during automated recommendations. The study further foregrounds the "Complex Customer Preferences vs. Information Overload" paradox as a central challenge: AI chatbots deployed to manage complex preferences often generate overload that undermines user trust and satisfaction, a tension prior TOE–TAM integrations have not addressed.
Stacking Ensemble with Hybrid Balancing for Aquaculture Water Quality Prediction Ari Nugroho Putro; Much Aziz Muslim
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.49383

Abstract

Purpose: This study aims to improve the accuracy of water quality classification models by addressing class imbalance and data noise. Aquaculture water quality monitoring is essential to support sustainable aquaculture production and maintain aquatic organism health. As aquaculture systems become more intensive, accurate predictive models are needed for effective monitoring and decision-making. Therefore, this study proposes a stacking ensemble model optimized using SMOTEENN hybrid balancing to improve classification performance on aquaculture water quality datasets. Methods: The proposed approach combines SMOTEENN hybrid balancing with a meta stacking ensemble framework. First, Synthetic Minority Oversampling Technique (SMOTE) was applied to balance minority classes by generating synthetic samples. Next, Edited Nearest Neighbor (ENN) was used to remove noisy data from both original and synthetic datasets. After preprocessing, the classification process employed a meta stacking ensemble model consisting of Extra Trees (ET) and Random Forest (RF) as base learners, while Logistic Regression (LR) served as the meta learner. The model was evaluated using the Aquaculture Water Quality (AWQ) dataset. Result: Experimental results show that the proposed model achieves the highest performance under the SMOTEENN scenario, reaching an accuracy of 99.88%, outperforming SMOTE 99.20%, ENN 99.78%, and the original imbalanced data 99.18%. The results indicate that combining class balancing and noise reduction significantly improves classification performance. Novelty: This study presents a novel integration of SMOTEENN hybrid balancing and meta stacking ensemble learning, offering an effective solution for handling imbalanced and noisy environmental datasets in water quality classification
Boundary-Aware Learning for Glioma Detection in MRI Using YOLOv8 Segmentation Supervision Muhammad Nurbaitullah
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.42307

Abstract

Purpose: Since glioma is the most aggressive and infiltrative type of brain tumor, its detection in magnetic resonance imaging (MRI) is especially difficult. Despite the excellent overall accuracy for brain tumor detection with YOLOv8-based object detection, the glioma-specific performance is limited owing to ambiguity of tumor boundaries. This work seeks to elucidate if boundary-aware learning can enhance glioma detection beyond typical bounding box–based approaches. Methods/Study design/approach: This study focuses exclusively on glioma detection using the Cheng brain tumor MRI dataset. YOLOv8 is used as the baseline detector, and boundary-aware learning is implemented through segmentation supervision using YOLOv8-Seg by leveraging pixel-level tumor masks. All the experiments are done in a standardized training environment to allow fair and unbiased comparison. Result/Findings: Experimental performance demonstrates the saturation of detection with YOLOv8 on glioma, irrespective of architectural and loss-level optimizations. Nonetheless, supervision on segmentation results in better modeling of glioma’s boundaries and leads to more informative localization responses, especially for infiltrative tumor areas. Novelty/Originality/Value: Unlike the other works that are based on augmentation of the data and performance of better detection, this work has devised a glioma-centric design, and shows bounding box-based detection is insufficient. This work highlights the need for considering boundary aware learning applying the supervision of segmentation in the automated glioma detection system, which can improve the reliability and interpretability of the system.
Good Governance Practices of COBIT 2019 and ITIL v4 for Sustainable Service Management in Museum Electronic-Based Government System Fauzia Dhiyaa' Farros; Dinar Mutiara Kusumo Nugraheni
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.47112

Abstract

Purpose: Good governance practices in transparency, accountability, responsibility, fairness, and stakeholder participation are core principles that ensure governance mechanisms are not merely procedural, but also creates a sustainable service management environment that aligns governance conditions with public interests. Methods/Study design/approach: Through the integration of COBIT 2019 and ITIL v4 frameworks, along with structured staff interviews, service performance analysis, and direct operational observations, several important domains emerged, indicating the need for management development. Result/Findings: The results show that several COBIT 2019 domains, APO07 (Managed Human Resources), DSS01 (Managed Operations), BAI03 (Managed Solutions Identification and Build), EDM02 (Ensured Benefits Delivery), and MEA01 (Managed Performance and Conformance Monitoring) and ITIL v4 domains, Information Security Management, Organizational Change Management & Workforce, Continual Improvement require immediate improvement with the Capability Index  scored 46.875% classified as Partially Achieved (15–49%) and classified in Level 2 (Managed Process). COBIT 2019 Design Factors show urgent areas for improvement in DF-4 (IT Related Issues), DF-6 (Compliance Requirements), and DF-7 (Role of IT). Balanced Scorecard (BSC) was also evaluated governance performance from four perspectives, financial, customer, internal process, and learning & growth, scoring 69,4% and classified as moderate. Key Performance Indicator (KPI) recommendations were also proposed, e.g., staff training increasement, operational IT services availability, user-based IT solutions, IT process measurement, security accidents reporting, effective workforce, and user-oriented IT developments. Novelty/Originality/Value: This study proposes an integrated COBIT 2019 and ITIL v4 approach, embedding good governance principles for sustainable service management in Museum Electronic-Based Government System (SPBE) and addressing the gap between governance conditions and public interests.
Contrast Enhancement or Noise Reduction? On Improving Cervical Cancer Classification Ach Khozaimi; Ulfatun Nahdhiyah
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.48099

Abstract

Purpose: To evaluate the impact of image preprocessing techniques, specifically contrast enhancement and noise reduction, on improving the CNN performance on Pap smear image classification for early cervical cancer detection. Methods: Three CNN architectures (ResNet34, DenseNet121, and MobileNet-V2) were trained and evaluated on the SIPaKMeD dataset. Two preprocessing techniques were applied: Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement and Perona-Malik Diffusion (PMD) filter for noise reduction. Model performance was assessed using a confusion matrix. Results: Preprocessing improved classification performance across all models. CLAHE significantly increased the accuracy of ResNet34 from 76.73% to 84.16% and DenseNet121 from 83.17% to 84.16%, while also providing modest improvement for MobileNet-V2. In contrast, PMD filtering yielded limited improvement and, in some cases, slightly reduced model performance. Novelty: This study provides a systematic comparison of contrast enhancement and noise reduction techniques across multiple CNN architectures. This study demonstrates that contrast enhancement is more effective than noise reduction in improving CNN performance. The study provides new pipelines for improving cervical cancer classification.
Comparison of Tree-Based Survival Models for Predicting Graduate Study Duration and Risk Stratification Ahmad Syauqi; Anwar Fitrianto; Hari Wijayanto
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.48890

Abstract

Purpose: This study aims to develop and evaluate a tree-based survival machine learning framework for predicting graduate study duration and identifying students at risk of delayed graduation. The study addresses the growing need for accurate educational time-to-event prediction to support academic monitoring and data-driven decision-making in higher education institutions, including IPB University. Methods: A quantitative predictive analysis was conducted using data from 3,417 master students from the 2020–2022 cohorts. Four tree-based survival models were evaluated, namely Survival Tree (ST), Extremely Randomized Survival Tree (EST), Random Survival Forest (RSF), and Gradient Boosting Survival (GBS). The analysis used right-censored survival data with a 42-month observation period. Model evaluation was conducted using repeated stratified random split validation (10 repetitions) with Concordance Index (C-index) and Integrated Brier Score (IBS) metrics. Risk stratification was subsequently performed using the best-performing model based on predicted survival probabilities at a 24-month time horizon. Result: GBS achieved the best overall predictive performance with the highest mean C-index (0.659) and the lowest mean IBS (0.194), indicating superior discrimination and prediction accuracy compared to ST, EST, and RSF. The repeated evaluation results also demonstrated stable predictive performance across data partitions. Risk stratification successfully separated students into low-, medium-, and high-risk groups with significantly different survival patterns. High-risk students generally tended to be older, have lower undergraduate GPA, were more often male, and more frequently originate from private undergraduate institutions. Novelty: This study provides a comparative evaluation of multiple tree-based survival machine learning models within an educational time-to-event framework. The integration of repeated survival model evaluation with practical student risk stratification offers both methodological and applied contributions for academic monitoring and early intervention strategies in higher education.
An Evaluation of the Implementation of Electronic Medical Records on Healthcare Service Performance Using SEM-PLS Arie Gunawan; Asrul Sani; Harun Al Fathih Gunawan
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.49387

Abstract

Purpose: The study aims to explore the factors that determine the successful implementation of Electronic Medical Records in community health centers of Indonesia in which System Quality, Information Quality, and Perceived Ease of Use are proposed to influence User Satisfaction, and then the impact of User Satisfaction on Healthcare Service Performance is measured. Methods: A quantitative cross-sectional survey was conducted involving 180 healthcare professionals, namely physicians, nurses, administrative staff, and information system operators who routinely use EMR systems. The data were collected through a five-point Likert-scale questionnaire. The proposed model is the DeLone and McLean Information Systems Success Model and Technology Acceptance Model (TAM) integrated. Data analysis was performed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 4. Result: The results show that System Quality, Information Quality, and Perceived Ease of Use significantly and positively affect User Satisfaction, which then significantly positively influences Healthcare Service Performance. The results further show that User Satisfaction mediates the relationship between technical system characteristics and organizational performance, highlighting the importance of system reliability, information quality, and usability for improved delivery of healthcare services. Novelty: This paper presents an SEM-PLS model that integrates the DeLone and McLean Information Systems Success Model with TAM in explaining healthcare service performance through User Satisfaction. The proposed framework not only offers a holistic approach to evaluate EMR implementation but also provides practical insight to enable the sustainability of digital health transformation in Indonesian community health centers.
An Integrated Cybersecurity Governance Ecosystem for Healthcare: A Quality-Appraised Systematic Review of Governance, Risk, and Compliance Frameworks for AI, Cloud, and Connected Health Technologies Chipo Miranda Mukwaira; Yvonne Chigariro; Belinda Ndlovu
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.49854

Abstract

Purpose: The growing use of digital technologies in healthcare has significantly increased exposure to cybersecurity risks, creating a need for governance approaches that go beyond traditional control-based security models. This study reviews how Governance, Risk, and Compliance (GRC) frameworks are applied in healthcare cybersecurity, focusing on their effectiveness, implementation challenges, and integration into organizational governance. Methods: Using the PRISMA methodology, literature was collected from PubMed, IEEE Xplore, and ScienceDirect, with 20 studies included in the final analysis and individually quality-appraised against six criteria. Findings: The findings indicate a shift from traditional frameworks to more flexible, integrated governance approaches that account for emerging technologies, including artificial intelligence (AI), cloud computing, and interconnected healthcare systems. While GRC frameworks help improve governance structures, strengthen cybersecurity, and support regulatory compliance, their effectiveness is often limited by factors such as skills shortages, resource constraints, regulatory complexity, and legacy systems. The study also identifies key enablers of successful implementation, including leadership involvement, cross-departmental collaboration, and continuous monitoring. Novelty: Based on these findings, an integrated healthcare cybersecurity governance framework is proposed that aligns regulatory, organizational, and technological dimensions within a unified model and provides practical insights to strengthen resilience in modern healthcare systems. Unlike prior reviews that examine GRC frameworks in isolation, this study also compares quality-appraised evidence across frameworks to show which approaches work best in different organizational contexts, including those governing AI, cloud computing, and other connected health technologies. For example, control-catalog frameworks such as ISO 27001 and COBIT are best suited to larger, well-resourced organizations, whereas AI governance frameworks succeed only once regulatory uncertainty is resolved internally.
Gibbs-BERTopic for Topic Modeling of Short Indonesian Social Media Texts on Artificial Intelligence Issues Nur'aini; Budi Susetyo; Cici Suhaeni
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.50254

Abstract

Purpose: This study aims to evaluate whether integrating Gibbs Sampling into BERTopic improves topic modeling of short Indonesian social media texts concerning Artificial Intelligence. Baseline BERTopic is used as the primary comparator, while LDA and Top2Vec are included as external baselines representing probabilistic and embedding-based topic-modeling approaches. Methods: A corpus of 9,585 public posts from platform X, collected from January 2024 to May 2025, was modeled using Latent Dirichlet Allocation (LDA), Top2Vec, Baseline BERTopic, and Gibbs-BERTopic. Model performance was assessed using topic coherence, topic diversity, topic uniqueness, number of topics, and document coverage. The number of outliers was compared specifically between Baseline BERTopic and Gibbs-BERTopic because LDA and Top2Vec do not use an equivalent HDBSCAN-based outlier mechanism. Result: Baseline BERTopic generated 58 topics and 4,674 outliers, with coherence, diversity, and uniqueness scores of 0.480, 0.460, and 0.380. Gibbs-BERTopic generated 15 topics without outliers and achieved the highest corresponding scores of 0.510, 0.880, and 0.870. LDA and Top2Vec produced lower scores, with 58 and 84 topics, respectively. These findings indicate that, within the corpus and configuration examined, Gibbs-BERTopic provided a more coherent and distinctive topic representation, broader document coverage, and a more compact topic structure. Novelty: This study extends the evaluation of Gibbs-BERTopic to short Indonesian social media texts and compares it with probabilistic, embedding-based, and BERTopic-based models. The results highlight the importance of evaluating topic models using multiple complementary metrics rather than relying on a single performance measure.
Real-Time Multi-Class DoS Attack Detection on Proxmox VMs UsingLightGBM with MikroTik Integration Danu Candra Saputra; Bambang Agus Herlambang; Noora Qotrun Nada
Scientific Journal of Informatics Vol. 13 No. 3: August 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.57873

Abstract

Purpose: The adoption of virtualization increases the dependence on service availability, making Denial of Service(DoS) attacks a serious threat, while rule-based detection is poorly adaptive to evolving attacks. Many previous studies also rely on outdated public datasets, are evaluated offline, rarely measure inference latency, and lack automatic mitigation. This study aims to build a real-time multi-class DoS detection system on Proxmox virtual machines using LightGBM integrated with MikroTik.Methods: A controlled testbed based on Proxmox and MikroTik was built to generate normal and attack traffic. The dataset was collected from the real infrastructure at a one-second granularity and labeled into six classes, namely the normal condition and five DoS attacks. LightGBM was proposed as the detection model, while XGBoost, Random Forest, Decision Tree, and SVM served as baselines, compared using a temporal holdout to prevent data leakage, with SMOTE applied only to the training data.Findings: LightGBM was selected as the best model with an accuracy of 96.22%, a macro F1-score of 96.25%, and an inference latency of 1.369 ms. The four flooding attacks were detected almost perfectly, whereas Slowloris was the hardest class because it resembles normal traffic. Its PR-AUC dropped to 0.9271, and the system performed automatic mitigation at a median latency of 56.2 ms.Originality: This study integrates lightweight real-time detection with automatic firewall-based mitigation in a closed loop on real infrastructure, emphasizing the balance between accuracy and efficiency rather than the highest accuracy alone. Future work can extend it to distributed (DDoS) attacks.

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