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M. Miftach Fakhri
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INDONESIA
Journal of Embedded Systems, Security and Intelligent Systems
Published by Universitas Negeri Makassar
ISSN : 2745925X     EISSN : 2722273X     DOI : -
Core Subject : Science,
Computer Science & IT
The Journal of Embedded System Security and Intelligent System (JESSI), ISSN/e-ISSN 2745-925X/2722-273X covers all topics of technology in the field of embedded system, computer and network security, and intelligence system as well as innovative and productive ideas related to emerging technology and computer engineering, including but not limited to : Network Security System Security Information Security Social Network & Digital Security Cyber Crime Machine Learning Decision Support System Intelligent System Fuzzy System Evolutionary Computating Internet of Thing Micro & Nano Technology Sensor Network Renewable Energy Wearable Devices Embedded Robotics Microcontroller
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 240 Documents
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Simulation-Based Hybrid LSTM–XGBoost Framework for Early Prediction of Simulated Learning-Loss Risk Using LMS Log Features Agunawan; Ruslan; Dandi Darmadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13173

Abstract

Purpose – Learning loss remains a concern in Indonesian higher education after the pandemic, while LMS-based early warning systems remain limited for timely intervention. This study designs and evaluates a hybrid LSTM–XGBoost framework for early prediction of simulated LMS-based learning-loss risk, prioritizing architectural innovation over metric superiority. Methods – Using 6,440 synthetic LMS records structured from publicly documented Sevima EdLink log fields, early risk was predicted from weeks 1–4 features and labeled at week 6 with behavioral proxy rules. An 80:20 group-aware train–test split by student identifier used leakage-safe, training-only scaling and one-hot encoding. Models were compared as an untuned architectural benchmark with matched-feature ablations, approximate confidence intervals, calibration/threshold analysis, and feature importance inspection. Findings – The temporal-only LSTM model produced the strongest overall predictive performance, while the LSTM with static-feature fusion showed comparable results. The hybrid LSTM–XGBoost decision stage remained competitive but did not outperform the matched LSTM configurations or demonstrate a clear advantage over simpler baseline models. Ablation analysis further showed that neither static-feature integration nor the use of XGBoost as the final decision engine provided a meaningful performance improvement. Changes in engagement, feedback activity, interaction patterns, and early time-on-task emerged as the most influential simulated indicators of learning-loss risk. Research Implications – The hybrid architecture offers a replicable blueprint for LMS early-warning pipelines that separate temporal extraction (LSTM) from risk classification (XGBoost). Institutional use requires real LMS validation, recall optimization, and ethical compliance. Originality – This simulation-based late-fusion LSTM–XGBoost blueprint separates the prediction window (weeks 1–4) from the outcome window (week 6) and evaluates architectural contribution.
IT-Informed ISO/IEC 27001:2022 Readiness Assessment in a Psychiatric Hospital Ihotbaen Parulian Manalu; Gunawan Wang
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13804

Abstract

Purpose – This study assesses the readiness of a psychiatric hospital to strengthen its Information Security Management System (ISMS) using an IT-informed evaluation aligned with ISO/IEC 27001:2022, ISO/IEC 27002:2022, and Indeks KAMI, while translating assessment evidence into preliminary implementation priorities and governance artifacts. Methods – A case-based organizational readiness assessment was conducted using purposive informants from the hospital’s information technology unit. Evidence was collected through structured checklist assessment, interviews, document review, and observation. Annex A controls were evaluated through maturity scoring, aggregation sensitivity analysis, evidence-confidence assessment, and an author-developed implementation-priority heuristic. The findings were subsequently mapped into proposed standard operating procedures and a phased implementation roadmap. Findings – The assessment revealed an uneven security-readiness profile across organizational, people, physical, and technological controls. Organizational controls showed comparatively stronger institutionalization, while people-related controls represented the most substantial readiness gap. Physical and technological controls demonstrated recurring practices but remained inconsistently documented, monitored, and integrated. The findings also indicate that maturity scores alone are insufficient for determining implementation priorities because service impact, compliance urgency, control dependencies, feasibility, and evidence confidence must also be considered. Research Implications – The study provides a practical basis for hospitals to structure cross-functional ISMS improvement, although the findings remain limited by the single-site, IT-informed assessment scope and require broader organizational validation. Originality – The study contributes a transparent evidence-to-score-to-priority-to-artifact approach that links ISMS readiness assessment with preliminary SOP design, ownership, validation gates, and phased implementation planning.
Performance and Training-Time Comparison of Five Pretrained CNN Architectures for South Kalimantan Food Image Classification Ahmad Balya Al Erpat; Dwi Kartini; Fatma Indriani; Andi Farmadi; Muliadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13850

Abstract

Purpose - This study analyzes the classification performance and computational efficiency of five pretrained Convolutional Neural Network (CNN) architectures for identifying South Kalimantan traditional food images as an expanded benchmark. Design/methods/approach - The models were trained on a curated traditional food image dataset using a two-stage transfer learning strategy consisting of linear probing and full fine-tuning with frozen Batch Normalization, supported by a multi-technique data augmentation pipeline. Evaluation was conducted under a fixed stratified data-splitting scenario across repeated runs with different random seeds to assess model stability and reproducibility. Findings - EfficientNetV2B0 achieved the strongest overall performance among the evaluated architectures and provided the most favorable balance between classification accuracy and computational efficiency. Its performance was comparable to other high-performing CNN architectures while requiring substantially lower training time than deeper residual and hybrid networks. The results indicate that greater architectural complexity does not necessarily translate into better recognition performance for a relatively small traditional food image dataset. Research implications/limitations - The findings provide practical guidance for selecting efficient CNN architectures for traditional food recognition. However, the evaluation was conducted on a curated dataset under controlled conditions, and the absence of an ablation study prevents isolating the individual effects of data augmentation and two-stage fine-tuning. Originality/value - This expanded benchmark highlights the critical trade-off between reliable classification performance and computational cost, offering practical guidance for selecting efficient deep learning models to support the digital preservation of culinary heritage.
Advances in Mixed-Type Data Clustering: A Systematic Review of Algorithms, Similarity Measures, and Validation Strategies Hendi Setiawan; Ema Utami; Alva Hendi Muhammad; Hanif Al Fatta
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.14574

Abstract

Purpose - This study reviews recent research on clustering mixed-type data and examines how clustering algorithms, distance or similarity measures, validation methods, and application domains are combined, with particular attention to education and special education. Design/methods/approach - A systematic literature review with descriptive evidence mapping was conducted using Scopus-indexed studies published from 2020 to 2025. The search identified 2,065 records, and the documented selection process resulted in 57 included studies. Each study was mapped by clustering algorithm, distance or similarity function, internal validation, external validation, and application domain. Findings - K-Means was the most frequently reported algorithm (18 studies), followed by HDBSCAN (10) and DBSCAN (9). Euclidean distance appeared in 49 studies, while Gower distance appeared in one. Internal validation was not reported in 35 studies and external validation was not reported in 36. When validation was reported, the Silhouette Score and Accuracy were the most common measures, while DBCV and Adjusted Rand Index (ARI) were uncommon. Research implications/limitations - The findings show a recurring gap between heterogeneous data structures and methods that are mainly designed for numerical or compact cluster structures. Future studies should test mixed-type distance measures, density-based clustering, and compatible internal and external validation. The review is limited to Scopus, English-language publications, the 2020-2025 period, accessible full text under the review protocol, and the information available in the review record. Originality/value - The review connects similarity representation, clustering structure, and validation instead of discussing each component separately. It identifies a weighted Gower-HDBSCAN-DBCV-ARI configuration as a testable research direction. This configuration is not presented as an empirically validated or superior method.
Performance and Security Evaluation of Selected Indonesian Mosque Websites Toto Andri Puspito; Suyono
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.14599

Abstract

Purpose – Mosque websites increasingly serve as public digital service platforms for information dissemination, administrative services, donations, and community engagement. However, previous studies have mainly focused on system development, functionality, and usability, while integrated evaluations of technical performance and externally observable security remain limited. This study evaluates the performance and basic security posture of selected Indonesian mosque websites through a multidimensional technical audit framework. Design/methods/approach – A quantitative non-invasive comparative design was applied to seven purposively selected operational mosque websites. Data were collected through repeated Google Lighthouse measurements, PageSpeed Insights field data, Core Web Vitals analysis, HTTP security-header evaluation, and Transport Layer Security (TLS) assessment. The framework considers performance, user experience indicators, browser-level security controls, and transport security as complementary dimensions. Findings – Desktop performance was significantly better than mobile performance, particularly in loading-related metrics, while Accessibility, Best Practices, and Search Engine Optimization (SEO) remained relatively consistent across devices. Security evaluation revealed uneven browser-level security implementation, showing that header presence does not always indicate effective configuration. TLS protection was generally stronger than browser-level controls. Research implications/limitations – The findings demonstrate the importance of multidimensional website assessment. However, the study is limited to seven purposively selected websites concentrated in Java and represents comparative case evidence rather than national assessment. Originality/value – This study contributes an integrated evaluation framework combining performance, user experience, HTTP security configuration, and TLS assessment for mosque websites.
Quantum Exposure Assessment of Public-Sector TLS Infrastructure Using a Composite Cryptographic Index Rudolf Sinaga; Frangky; Danang
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.12876

Abstract

Purpose – This study develops and empirically evaluates a Quantum Exposure Index (QEI) for characterizing quantum-related exposure observable from public-sector Transport Layer Security (TLS) infrastructures and supporting post-quantum migration prioritization. Methods – The framework integrates TLS protocol version, certificate public-key algorithm, classical key-strength equivalence, and Perfect Forward Secrecy into a normalized composite index. Component weights were derived through the Analytical Hierarchy Process, while non-intrusive TLS measurements from publicly accessible government infrastructures provided the empirical inputs. Sensitivity and sector-level analyses were conducted to examine the robustness and interpretability of the resulting exposure profiles. Findings – The evaluated infrastructures remain dependent on classical public-key cryptography and therefore retain exposure to future quantum-capable adversaries despite widespread adoption of modern TLS configurations and forward secrecy. The analysis further shows that cryptographic key strength and protocol configuration differentiate exposure profiles, whereas the continued reliance on quantum-vulnerable public-key primitives establishes a common exposure baseline. Classification outcomes are sensitive to interpretive thresholds, reinforcing the importance of continuous index values over categorical labels. Research Implications – The QEI provides a reproducible infrastructure-level baseline for prioritizing cryptographic inventories, monitoring migration progress, and supporting evidence-based post-quantum transition planning. Originality – This study combines operational TLS measurement, standardized cryptographic-strength mapping, and multicriteria weighting within a unified quantitative framework for assessing observable quantum exposure in public-sector infrastructures.
Ensemble Learning for Android Privacy-Risk Flow Pre-Screening Using Permission and Metadata Features Tri Wahyuni; Muhammad Faisal; Titin Wahyuni; Nurnawaty; Rio Prasetyo Lukodono; Titik Khawa Abd Rahman
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13180

Abstract

Purpose - This study develops a privacy-oriented pre-screening framework for identifying Android applications with potential Sensitive Data Exposure by combining lightweight permission and metadata features with ensemble learning. Design/methods/approach - Android applications obtained from the AndroZoo repository were analyzed using FlowDroid to construct reference labels based on sensitive source–sink flows. Privacy-oriented features were derived from permissions, application metadata, source and sink indicators, and interaction patterns. An Ensemble Stacking model integrating Random Forest, Support Vector Machine, and Extreme Gradient Boosting with Logistic Regression as the meta-classifier was evaluated under class imbalance. Additional circularity, ablation, repeated validation, and clean-feature experiments were conducted to assess robustness and deployment feasibility. Findings - The proposed framework demonstrated strong capability in distinguishing applications containing FlowDroid-defined potential privacy-risk flows. FlowDroid-derived source and sink indicators were highly discriminative, while permission-only features were less effective. Importantly, the clean-feature configuration retained strong discriminatory capability without requiring FlowDroid at inference time, supporting its use as a lightweight first-stage screening mechanism before more computationally intensive taint analysis. Research implications/limitations - The framework can support developers, security auditors, and platform administrators in prioritizing applications for deeper privacy inspection. However, the study relies on static analysis, a single application repository, and FlowDroid-derived reference labels. Originality/value - This study contributes a two-stage Android privacy-risk screening framework that combines lightweight deployable features with targeted static taint analysis while explicitly addressing label-feature circularity and inference-time feasibility.
Forecasting User Perception of Steam Game Reviews across Multiple Genres: A BERT Sentiment-Topic Framework with Topic Attribution Muhammad Pramudito Priambodo; Afrizal Doewes; Arif Rohmadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13290

Abstract

Purpose – This study develops an interpretable framework for monitoring and forecasting user perception of Steam games by integrating sentiment analysis, topic modelling, short-horizon forecasting, and topic-level attribution. Methods – English-language Steam reviews from fifteen games across five genres were analysed over a twelve-month period. BERT was used to generate review-level sentiment ratings, which were aggregated into a weekly User Perception Score (UPS). BERTopic identified discussion themes, while an expanding-window moving-average model forecast UPS over a four-week horizon. Forecast performance was evaluated through walk-forward cross-validation against naive and damped linear-regression baselines, and topic-level attribution was used to explain recent changes in perception. Findings – The sentiment model showed strong agreement with Steam’s binary voting signal, while the expanding-window forecaster generally produced lower prediction error than the comparison baselines. Genre-level patterns indicated more favourable perception for Simulation, Role-Playing, and Action-Adventure titles, whereas First-Person Shooter and Strategy titles showed more mixed perception. Topic attribution further revealed that changes in both topic sentiment and topic prevalence contributed to shifts in weekly UPS. Research Implications – The framework provides developers and publishers with an interpretable monitoring approach for identifying perception trends and the discussion themes associated with them. Originality – The study combines weekly perception forecasting with a decomposition of topic-level contribution, enabling dynamic and interpretable analysis beyond static sentiment or topic summaries.
Benchmarking Indonesian Transformer Models and Explainable AI for Disaster-Related Sentiment Analysis Muhammad Saifuddin Eka Nugraha; Afrizal Doewes; Herdito Ibnu Dewangkoro
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13295

Abstract

Purpose – This study evaluates the comparative performance of Indonesian transformer models for disaster-related sentiment analysis and examines the faithfulness of explainable artificial intelligence methods applied to the best-performing model. Methods – YouTube comments related to the 2025 Sumatra flood were processed using a hybrid labeling approach combining automatic classification and expert annotation. After preprocessing and class balancing, IndoBERT, IndoBERTweet, and IndoRoBERTa were fine-tuned using Optuna-based hyperparameter optimization. Model performance was assessed using accuracy, precision, recall, and F1-score. Integrated Gradients (IG), Local Interpretable Model-Agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP) were subsequently evaluated using the Area Under the Threshold-Performance Curve (AUC-TP) to quantify explanation faithfulness. Findings – IndoRoBERTa achieved the strongest overall classification performance among the evaluated models. Faithfulness analysis showed that IG provided the strongest overall explanation performance and performed particularly well for negative and positive sentiment, whereas LIME showed better performance for neutral sentiment. SHAP produced comparatively weaker faithfulness under the applied evaluation protocol. Research Implications – The findings demonstrate the potential of Indonesian transformer models and quantitative XAI evaluation for analyzing disaster-related social media discourse. However, the results should be interpreted cautiously because automatic labeling, confidence-based undersampling, and the absence of inferential significance testing may affect generalizability. Originality – This study integrates comparative benchmarking of Indonesian transformer models with systematic deletion-based faithfulness evaluation of multiple XAI methods, extending explainable sentiment analysis beyond predominantly qualitative interpretation.
Day–Ahead Sulfuric Acid Production Forecasting Using Optuna–Tuned Machine Learning Models: An Industrial Case Study Fransiska Prihatini Sihotang; Daniel Udjulawa; Intan Cahya Sucita
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13845

Abstract

Purpose – This study develops and evaluates a day-ahead forecasting framework for sulfuric acid production in a continuous chemical manufacturing process and examines whether machine-learning models provide meaningful predictive value beyond simple persistence forecasting. Methods – Five years of operational production data were processed through a validated ETL pipeline and transformed using calendar, lag, and rolling-window features. Six regression algorithms were screened using chronological time-series validation, followed by equal-budget Optuna hyperparameter optimization of selected candidates. The final model was evaluated on an untouched holdout period and through repeated walk-forward validation against persistence, seasonal-naive, and rolling-mean baselines. Permutation importance was used to examine predictor contributions. Findings – Random Forest achieved the strongest development-stage performance and produced accurate day-ahead forecasts. However, its advantage over persistence was marginal on the final holdout and inconsistent across repeated temporal evaluations. Current-day production overwhelmingly dominated predictor importance, indicating strong persistence in the underlying industrial process. Performance also deteriorated during maintenance-related shutdown conditions. Research Implications – Industrial forecasting systems should prioritize robust temporal validation and comparison with simple operational baselines before adopting more complex machine-learning models. Originality – The study provides a leakage-aware forecasting and evaluation pipeline that combines baseline benchmarking, model interpretation, and deployment within a prototype decision-support system for continuous chemical production.

Page 24 of 24 | Total Record : 240

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