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Journal of System and Computer Engineering
Published by Universitas Pancasakti Makassar
ISSN : -     EISSN : 27231240     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Programming Languages Algorithms and Theory Computer Architecture and Systems Artificial Intelligence Computer Vision Machine Learning Systems Analysis Data Communications Cloud Computing Object Oriented Systems Analysis and Design Computer and Network Security Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 117 Documents
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Bayesian-Optimized Prophet for Tourism-Based Regional Government Revenue Forecasting Adha, Muhammad Sofwan; Karuru, Sakti Swarno; Angel, Feby; Joling, Jesika
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2373

Abstract

Accurate hotel tax revenue forecasting is critical for supporting proactive fiscal planning in tourism-dependent local governments . Hotel tax revenues in these regions exhibit high volatility influenced by seasonal tourism patterns, visitor preferences, economic conditions, and external shocks such as the COVID-19 pandemic . Traditional time series forecasting methods such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing struggle to capture complex seasonal patterns and accommodate multiple external factors . Recent advances in time series forecasting—particularly Facebook's Prophet framework—offer automatic decomposition of trend, seasonality, and holiday effects, plus the ability to integrate external regressors . However, Prophet's performance is highly sensitive to hyperparameter configurations, and default settings often produce suboptimal results on volatile data . Bayesian Optimization has emerged as an efficient technique for hyperparameter tuning, achieving convergence with significantly fewer iterations compared to exhaustive grid search . This study develops and validates a Bayesian-Optimized Prophet Framework for forecasting monthly hotel tax revenue in Kabupaten Tana Toraja, a cultural tourism destination in Indonesia, over 60 months (January 2020–December 2024) encompassing normal conditions, pandemic disruption, and recovery phases. The optimized model achieved Mean Absolute Percentage Error (MAPE) of 9.59% compared to baseline Prophet's 33.72%—a 71.55% improvement in forecasting accuracy. Mean Absolute Error (MAE) reduced from Rp 11.76 million to Rp 3.34 million per month. Robustness testing during COVID-19 pandemic demonstrated model stability with MAPE ≤15% despite >60% revenue decline. The framework provides 24-month forecasts (2025–2026) with 95% confidence intervals and decision-support capability with lead-time advantage of 3–6 months for early revenue shortfall detection. This research contributes a reproducible, efficient methodology for hyperparameter tuning in time series forecasting within fiscal planning domain, applicable to other tourism-dependent regions and tax categories.
Energy Efficient IoT-Based Forest Fire Detection Using LoRaWAN and AI Syafaat, Muhammad; A Suyuti, Muh Zulfadli; Alfiansyah, A
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2381

Abstract

Forest fires remain a global problem that has a major impact on the economy and health. Indonesia suffered losses of up to Rp. 72.95 trillion due to forest fires in 2019. Internet of Things (IoT) technology can be used for early detection of forest fires, but is constrained by limited network infrastructure and high energy consumption. This study aims to design a smart mitigation device and application for early detection of forest fires using LoRaWAN technology, which does not require an internet connection from the node to the gateway. In addition, an Artificial Intelligence method with adaptive sampling is applied, namely adaptive sampling threshold modeling and reinforcement Q-learning on the gateway to optimize energy use. The method used is Research and Development (R&D), with testing of the effectiveness of the design and descriptive statistical analysis to compare the energy efficiency between LoRaWAN devices with AI and conventional smart mitigation devices. The results of the study show that LoRa-based mitigation devices can cover the entire Jompie Botanical Garden area with a transmission distance of up to 3 kilometers and are 105% more energy efficient than conventional mitigation devices.
Peramalan Beban Listrik Harian di Kota Ternate Menggunakan ELM Ilyas, Andi Muhammad; Rahman, Muhammad Natsir; Aswat, Aldi; Syamsuddin, Faris; Suparman, Suparman; Ashad, Bayu Adrian; Siswanto, Agus
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2465

Abstract

The continuously increasing growth of electricity demand necessitates accurate and systematic planning of electric power systems to ensure power flow quality and system reliability. Ternate City, as one of the major activity centers in North Maluku Province, has experienced a substantial rise in electricity consumption, thereby requiring an effective and reliable load forecasting approach. This study aims to predict the daily electricity load in Ternate City using the Extreme Learning Machine (ELM) method. The analysis is conducted using historical electricity load data, which are processed through data preprocessing stages, dataset partitioning into training and testing sets, and ELM-based modeling. The performance of the proposed model is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the MAPE values for the training dataset range from 5.84% to 13.63%, corresponding to very good to good performance categories. Meanwhile, the testing dataset yields MAPE values ranging from 13.45% to 33.09%, which fall within the good to sufficient performance categories. Furthermore, the prediction results are able to accurately capture daily electricity load fluctuation patterns from Monday to Sunday, including peak load periods. Based on these findings, the ELM method demonstrates strong potential as a reliable approach to support electric power system planning and to enhance the quality and reliability of electricity supply in Ternate City.
Performance Optimization of Image Cryptography for Copyright Protection on High-Resolution Images Using the Hill Cipher with Flexible Matrix Keys Rohman, Miftakhul; Fauzan, Abd. Charis; Mafula, Veradella Yuelisa
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2471

Abstract

The increasing use of high-resolution digital images has raised serious concerns regarding copyright protection and unauthorized distribution. Image cryptography is one of the effective approaches to safeguard visual data by transforming images into unintelligible forms. The Hill Cipher algorithm, which is based on matrix operations, has potential for image encryption; however, its application to high-resolution images often suffers from high computational cost. This study proposes a performance optimization of image cryptography for copyright protection by exploiting the flexibility of matrix key sizes in the Hill Cipher algorithm. The optimization focuses on improving computational efficiency without modifying the fundamental cryptographic mechanism. Experiments were conducted on high-resolution images using different matrix key sizes (2×2, 3×3, and 4×4). Performance was evaluated in terms of encryption and decryption time, while security robustness was assessed using Entropy, Number of Pixel Change Rate (NPCR), and Unified Average Changing Intensity (UACI). The experimental results demonstrate that increasing the matrix key size significantly reduces the total computation time, achieving up to nearly 50% performance improvement, while maintaining high security levels. The encrypted images exhibit entropy values close to the ideal level, NPCR values above 99%, and stable UACI values, indicating strong randomness and diffusion properties. These findings confirm that the proposed optimization improves computational performance without compromising cryptographic security. Therefore, the optimized Hill Cipher remains effective and suitable for copyright protection of high-resolution images.
Augmented Reality and Virtual Reality in English Learning: Bibliometric Analysis of Research Trends, Citation Patterns, and Future Directions Tamra, Tamra; Wisda, Wisda; H, Muhammad Rizal; Wanita, First; Mursalim, Mursalim
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2472

Abstract

This study conducts a comprehensive bibliometric analysis to map the development of research on Augmented Reality (AR) and Virtual Reality (VR) in English language learning (ELL) from 2010 to 2025. Using 386 Scopus-indexed documents, the analysis examines publication growth, citation performance, influential authors and countries, core sources, and the thematic evolution of immersive learning research. The findings show a sharp increase in scientific production after 2020, reflecting the global rise of digital and immersive technologies in education. China, Korea, and Malaysia emerge as dominant contributors, demonstrating Asia’s leading role in AR/VR-driven language innovation. Citation trends reveal the coexistence of foundational highly cited works and rapidly influential recent publications. Source impact analysis confirms the interdisciplinary character of the field, spanning educational technology, linguistics, psychology, and computer science. Trend-topic analysis indicates a shift from general pedagogical themes toward AI-enhanced AR applications, deep learning, virtual reality environments, and interactive vocabulary learning systems. Despite significant growth, gaps remain in long-term studies, cross-country collaboration, and research on advanced language competencies. Overall, the study provides a data-driven understanding of how AR and VR have evolved as transformative tools for English language learning and offers strategic insights for guiding future research agendas in immersive educational technologies.
Implementation of Decision Support System for Illegal Cosmetic Detection in Papua Based Machine Learning Burdam, Christian Victor; Arafah, Muhammad Nur; Burdam, Bernadeta Yusi Rosvilda
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Regulatory enforcement against illicit cosmetics in the Papua Special Autonomy regions remains constrained by profound geographical complexity and systemic data fragmentation. This study develops a robust Decision Support System (DSS) that bridges the gap between digital surveillance and legal accountability at BBPOM in Jayapura. By integrating the Simple Multi-Attribute Rating Technique (SMART) with a hybrid machine learning framework, the system operationalizes parameters derived from the Indonesian Health Law (No. 17 of 2023) and the Papua Special Autonomy Law (No. 2 of 2021). A high-fidelity dataset of 1,324 multi-source inspection records was utilized to train a hybrid ensemble architecture. Empirical results demonstrate that the Artificial Neural Network (ANN) achieves near-optimal performance, yielding a 99.62% accuracy and 99.90% F1-score, with a marginal error rate of 0.38%. The inclusion of PostGIS-driven spatial intelligence further enables real-time vulnerability mapping within a scalable Service-Oriented Architecture (SOA). Beyond its technical efficacy, this research contributes a novel paradigm for localized law enforcement, successfully unifying regional regulatory mandates with advanced predictive analytics to safeguard public health in marginalized frontiers.
Sentiment Analysis of Government Policies Using LSTM: The Role of the Indonesian Language in Shaping Public Opinions Delilah, Eva; Syam, Rahmat Fuady; Aziz, Firman
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2574

Abstract

Social media has become the primary arena for the public to express opinions on government policies. This study aims to analyze public sentiment toward government policies using the Long Short-Term Memory (LSTM) model, while also examining the role of language in shaping public opinion. Data were collected from social media posts related to economic, social, and health policies, followed by preprocessing stages including text cleaning, tokenization, stopword removal, and word embedding with Word2Vec. The LSTM model was compared with Support Vector Machine (SVM) and Naïve Bayes to evaluate accuracy and performance. The results indicate that public opinion is dominated by negative sentiment (45%), particularly regarding economic policies. The LSTM model outperformed the benchmarks with an accuracy of 86.9%, surpassing SVM and Naïve Bayes. Linguistic analysis revealed the frequent use of emotional diction, sarcasm, and economic burden narratives that reinforced public resistance, while colloquial language was found to be an effective tool for engaging younger generations. This study contributes to the advancement of sentiment analysis in the Indonesian language using deep learning and provides practical recommendations for policymakers to design more persuasive and participatory communication strategies.

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