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M. Miftach Fakhri
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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 157 Documents
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Evaluating EduTrack System Quality Using ISO/IEC 25010 and the DeLone and McLean Model Syahrul; NFH, Alifya; Shabrina Sinta Dewi; Dwi Rezky Anandari S
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10266

Abstract

The integration of information systems in higher education enhances efficiency, transparency, and academic service quality. However, continuous evaluation is crucial to ensure that such systems meet user expectations and comply with international quality standards. This study evaluates the quality of the EduTrack Information System developed for the Master’s Program in Educational Research and Evaluation at Universitas Negeri Makassar. Using a quantitative descriptive method, the research integrates the ISO/IEC 25010 software quality standard and the DeLone & McLean Information System Success Model. Data were collected from 60 active student users through questionnaires and technical testing with tools such as Apache JMeter, OWASP ZAP, Mozilla Observatory, K6, and the System Usability Scale (SUS). EduTrack demonstrated excellent results in functional suitability (100%), reliability (96.55%), and usability (SUS score = 71.5, good). The PLS-SEM analysis confirmed that service quality (β = 0.580) and information quality (β = 0.390) significantly affect user satisfaction (R² = 0.716). These findings indicate that EduTrack performs effectively and aligns with user expectations, though security enhancements are still required. This study demonstrates an integrated evaluation framework that combines technical quality standards and user satisfaction analysis, providing a reference model for future assessments of academic information systems.
Adaptive Pulse-Width Modulation Algorithm for Energy-Efficient Control of Robotic Arm Actuators Sutarsi Suhaeb; Ahmad Risal
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10318

Abstract

The development of energy-efficient robotic systems has become increasingly important, particularly in servo-driven robotic arms where continuous PWM signals lead to unnecessary energy loss and thermal stress. This study proposes an adaptive pulse-width modulation (PWM) algorithm designed to activate servo signals only during motion phases and automatically deactivate them once the target position is reached. A four-degree-of-freedom robotic arm prototype was developed using an ESP32 microcontroller, MG90S servos, and ACS712-based current monitoring to evaluate power efficiency under conventional continuous PWM and the proposed adaptive control. Experimental results demonstrate a 28–33% reduction in average power consumption, a decrease of 6–8 °C in servo operating temperature, and the preservation of positional accuracy within ±5%. These findings confirm that significant energy savings and thermal improvements can be achieved without modifying hardware components. The proposed algorithm offers a practical, lightweight, and software-based optimization approach suitable for educational, research, and low-power robotic applications. This work introduces a distinct adaptive activation strategy that fully disables PWM in steady-state conditions, representing a low-cost and effective contribution to sustainable servo control.
Accuracy and Detection Efficiency in Stacked PVC-based Transparent Markers for Augmented Reality Systems Putra, Kurnia Prima; Kaswar, Andi Baso; Zulfikar, Muh. Ihsan; Afirianto, Tri
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10447

Abstract

Augmented Reality (AR) marker-based tracking has evolved from single-marker systems, which limit spatial coverage and robustness, to multi-marker approaches that enable simultaneous detection for expanded interactivity. Prior research using libraries like Vuforia and AR.js has applied multi-markers in education (e.g., solar system simulations) and interactive media, improving redundancy and workspace; however, stacked transparent overlays—treating layers as unified yet distinct entities—remain underexplored, especially for alignment precision and opacity effects in translucent materials like PVC.This study evaluates tracking accuracy and detection efficiency of stacked transparent markers (2–7 layers) in AR.js, printed on 0.3 mm PVC sheets and tested with a Vivo V30 Pro camera under controlled lighting. Well-aligned configurations maintain accuracy near single-marker baselines (0.85 mm to 1.15 mm deviation), with detection rates from 98.5% to 88.3%, though processing time rises from 16.9 ms to 25.9 ms, lowering frame rates from 59.2 FPS to 38.6 FPS. Translational (2–5 mm) and rotational (5°–15°) misalignments sharply degrade performance, reducing 7-layer detection to 65.4% and 58.2%, respectively.The primary contribution is quantitative benchmarks for PVC-based overlays in AR.js, confirming feasibility for multi-layered tracking while defining tolerances (<2 mm translation, <3° rotation) to sustain >90% detection in 3–5 layers. These insights guide AR design for robust, extended applications in education and industry.
Privacy-Preserving Deep Learning For Enhancing Privacy for Business Information Processing Using Differential Privacy Muhammad Ayat Hidayat; Yusi Irensi Seppa
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10833

Abstract

Artificial intelligence is a rapidly growing technology that has been implemented in various sectors such as finance, education, the military, healthcare, and business. One form of artificial intelligence is deep learning, which has the capability to perform complex pattern recognition and decision-making using very large datasets. This capability makes deep learning one of the key technologies adopted by companies and organizations to improve their policies and enhance operational stability. However, several studies have shown that deep learning still faces significant challenges, particularly security risks that may expose sensitive business or organizational information. Therefore, in this work, we aim to address this problem and enhance the privacy protection of deep learning by incorporating differential privacy. Differential privacy is a technique that protects sensitive information by adding controlled noise to the data or model outputs, thereby reducing the risk of information leakage. We evaluate our proposed method using marketing data and implement it to 5 different models, and based on the experimental results, Our proposed method achieves its best performance using the linear regression model, yielding an RMSE of approximately 1821.30, an MAE of 1406.45, and an R² of 0.007717 for higher privacy budget. Under a lower privacy budget, the performance of the linear regression model decreases to an RMSE of 3634.24, an MAE of 2799.05, and an R² of –0.008482, yet it still outperforms the other four model approaches.
Security and Privacy Threats in AI-Driven Education Systems: A Narrative literature review Tandirerung, Veronika Asri
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10902

Abstract

The use of Artificial Inteligence (AI) in education systems is increasing. Various uses of AI include learning analytics, intelligent tutoring systems, automated assesment, and biometric analysis. This study examines the cybersecurity and privacy risks associated with the integration of generative AI technologies in adolescent education. The use of this technology poses significant security and privacy risks. Through a narrative literature review, the analysis identifies dominant threat categories, institutional vulnerabilities, and mitigation strategies relevant to K–12 learning environments. The selection framework required that each study (1) addressed generative AI or machine learning used within educational systems, (2) discussed cybersecurity, privacy, or data-protection implications, and (3) focused on adolescents or school-age learners. The findings reveal several major risk clusters, including exposure of minors’ personal and biometric data, model manipulation and prompt-injection attacks, algorithmic and behavioral profiling risks, the dissemination of misinformation, and persistent governance gaps within educational institutions. These risks highlight the urgent need for robust privacy by design implementation, stronger cybersecurity infrastructures, clear institutional AI governance policies, and capacity building among educators. While the narrative nature of this review limits quantitative comparison across studies and may restrict generalizability due to variability in methods and contexts, the synthesis provides important insights to guide safer AI adoption. Future work should explore empirical evaluations of generative-AI security controls, the application of differential privacy in school settings, and the development of standardized AI security frameworks for K–12 institutions. Overall, this review contributes a consolidated understanding of the security challenges emerging from the use of generative AI in adolescent education and offers evidence-based directions for technical, policy, and institutional safeguards.
Automatic Floor Stain Detection with Image Processing: A Practical Comparison of OpenCV and RGB Grayscale Conversion: Deteksi Noda Lantai Otomatis dengan Pemrosesan Citra: Komparasi Praktikal OpenCV dan Grayscale RGB Syaharuddin, Achmad Zulfajri; Indasari, Sri Suci; Janna, Nurhikmayana; Hilmi, Andi Afdhal
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.9805

Abstract

This research develops an image processing-based floor stain detection system using grayscale conversion and binary thresholding. Two conversion approaches are compared: (i) a simple RGB grayscale formula and (ii) a built-in OpenCV function. The system uses a fixed intensity threshold of T=80 and classifies a floor as “dirty” if the detected area exceeds 20% of the image. Experiments are conducted on three floor types (plain, dark, patterned), five stain types (coffee, oil, ink, plastic, chalk), and two lighting conditions. Results show that the performance of both methods is very close with an average difference of ≈0.07% and a maximum of 0.6%; the simple formula is suitable for resource-limited devices, while OpenCV is more robust to color/lighting variations. The main contributions of this paper are (1) a practical comparison of two grayscale conversion pathways for cleanliness monitoring, (2) a simple decision rule based on the percentage of dirty area that aligns with cleanliness perception, and (3) an analysis of implementation implications for real-time systems in cleaning robots/IoT. Future directions include adaptive thresholding and morphology integration to improve reliability in dynamic environments. (Replace the current abstract paragraph containing T=80 and the 20% rule with the version above. The 20% policy reference is already explained in your manuscript).
Extracting Value from Minority Voices: Epistemic Validation of Naive Bayes and SMOTE Models for E-Commerce Review Sentiment Analysis Ibrahim, Firmansyah; Prasetya, Didik Dwi; Patmanthara, Syaad
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10275

Abstract

In the e-commerce ecosystem, negative customer reviews, despite often being a numerical minority, represent the most valuable (axiological) business asset for service improvement. However, this value is frequently obscured by the high volume of positive reviews, creating a significant imbalance in the data. This study aims to design and validate a text mining model that is axiologically focused on extracting critical insights from this "minority voice." We applied the Naive Bayes Classifier (NBC) algorithm, augmented with TF-IDF feature weighting, on a dataset of 6,000 reviews from the 'Famous Florist' store. The epistemic challenge of severe data imbalance (5,432 positive vs. 97 negative) was addressed through the methodological intervention of the Synthetic Minority Over-sampling Technique (SMOTE). The model's validity was assessed using 10-Fold Cross-Validation. The epistemic validation results demonstrated the model's validity, achieving an average accuracy of 90%. Crucially, the model achieved a 99% rate for the negative class. This affirms the model's axiological validity: its ability to reliably identify customer complaints (e.g., 'damaged,' 'packaging') and transform raw data into actionable recommendations for improvement.
Agile-Based Accreditation Module Design for the P3M Information System at Politeknik Negeri Samarinda Kumala Jaya, Arsan; Triadi, Fara; Hartanto, Subhan; Azis, Ahmad Saiful Mutaqi; Shinta, Priti
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10320

Abstract

Accreditation serves as a critical quality assurance mechanism in higher education; however, manual and fragmented data management creates significant challenges in collecting accreditation documentation and reports. This research designs and develops an Accreditation Menu for the P3M Information System at Politeknik Negeri Samarinda to streamline accreditation processes with greater effectiveness, efficiency, and accountability. Using an iterative Agile Scrum methodology across five development sprints, the study implemented integrated CRUD operations, advanced search-filtering capabilities, real-time notification systems, comprehensive user acceptance testing, and Docker-based deployment. Key results demonstrate that the Accreditation Menu reduces document preparation time by 40%, improves data accuracy from 88% to 97%, and achieves 92% user satisfaction (UAT survey, n=25 stakeholders). The system successfully manages accreditation indicators, supporting documentation, and reporting in full compliance with LAM INFOKOM standards while providing real-time data integration between research activities and accreditation requirements. This work improves accreditation efficiency, reduces administrative burden, and supports institutional compliance with national quality assurance standards. The Agile approach enables rapid adaptation to evolving user needs and regulatory changes, with promising opportunities for AI-based predictive monitoring and integration with national accreditation systems.
Hybrid Regression–Simulation Model for Evaluating Emission Policies in Oversaturated Urban Corridors: A Case Study of Jakarta Triadi, Fara; Jaya, Arsan Kumala; Biabdillah, Fajerin; Hanif, Abdul
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10595

Abstract

Urban traffic emissions continue to escalate in Southeast Asian megacities, particularly along oversaturated central business district corridors where chronic congestion amplifies pollutant accumulation. Previous research often separates statistical emission modelling from microscopic simulation, limiting the ability to evaluate policy impacts under real-world saturation conditions. This study aims to assess whether lane-level transport interventions specifically bus-only lanes and motorcycle restrictions can reduce emissions in a hyper-congested Jakarta corridor through an integrated analytical approach. A hybrid regression–microsimulation framework was developed by combining multiple linear regression with SUMO-based traffic simulation. An hourly dataset of traffic flow and CO emissions (n = 8,760) from the Thamrin–Bundaran HI corridor was used to construct a regression model enriched with temporal and lagged predictors. The resulting emission profiles were embedded into SUMO to simulate baseline, bus-lane, and motorcycle-restriction scenarios. The regression model achieved strong predictive performance (R² = 0.692, RMSE = 0.252), with CO_lag1 confirmed as the dominant predictor. Simulation results showed fully overlapping CO₂ emission trajectories across all scenarios, indicating that lane-based interventions do not alter traffic states or emissions under oversaturated conditions. Structural congestion constrains the effectiveness of lane-level policies. Meaningful emission reductions require systemic strategies such as demand management, modal shift, or network redesign. The proposed hybrid framework provides a replicable tool for evaluating transport policies in dense urban corridors
Sentiment-Aware Transformer for Cryptocurrency Volatility Prediction Using Multi-Source Market and News Sentiment Biabdillah, Fajerin; Triadi, Fara; Go , Aeltri Jeacfky Gozal; Ramadhan, Muhammad Cahyo Putra
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.10604

Abstract

The cryptocurrency market has grown into a multi-trillion-dollar domain with extreme volatility. This paper addresses the forecasting of crypto price movements and volatility by integrating market metrics with sentiment analysis. We identify a gap in existing studies, which often ignore multi-source sentiment and thus miss early warning signs of volatility. We propose a Sentiment-Aware Transformer model inspired by the Temporal Fusion Transformer (TFT). The model ingests daily price, volume, and market cap features from CoinMarketCap alongside aggregated sentiment scores from Twitter, Reddit, and financial news (extracted via FinBERT). We train and evaluate the model on 5 years of data for 10 major cryptocurrencies (2020–2024), comparing performance against LSTM and GRU baselines with identical inputs. The proposed Transformer achieves 83.2% volatility prediction accuracy with an F1-score of 0.81, exceeding the LSTM (79% accuracy) and GRU (80%) baselines. It also shows the lowest RMSE in price forecasting and a higher return correlation (0.72) with actual prices, indicating improved trend alignment. These gains are statistically significant (p<0.01). We also discuss how attention weights offer interpretability, as the model focuses on sentiment spikes during impending volatility.

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