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Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Published by Universitas Muhammadiyah Riau
ISSN : 20893353     EISSN : 28089162     DOI : https://doi.org/10.37859/jf.v11i3.2781
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) is expected to be a media of scientific study of research result, a thought and a study criticial analysis to a System engineering research, Informatics Engineering, Information Technology, Computer Engineering, Informatics Management, and Information System. We accept research papers which focused to these following topics: System Engineering Expert System Decision Support System Data Mining Artificial Intelligent Computer engineering Digital Image Processing Computer Graphic Computer Vision Genetic Algorithm Machine Learning Deep Learning Information System Design Business Intelligence and Knowledge Management Database System Big Data IOT Enterprise Computing ICT and Islam Technology Management and other relevant topics to field of Information Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 417 Documents
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H-ASICS: Desain Intrusion Detection System Adaptif Berbasis Hybrid Deep Learning untuk Infrastruktur Kritis Andri Yudha Pratama; Ujianto, Erik IH; Rianto, Rianto
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11006

Abstract

The digital transformation of critical infrastructure, particularly Smart Grid and SCADA systems, has exposed new vulnerabilities to complex cyber-attacks such as False Data Injection (FDI), necessitating proactive defense mechanisms that transcend conventional approaches. Through a Systematic Literature Review (SLR) of 51 state-of-the-art studies (2022–2026), this research confirms a paradigm shift from static Deep Learning models toward adaptive, transparent, and decentralized detection ecosystems. Addressing the critical trade-off between high accuracy and operational latency, this study proposes the conceptual framework of H-ASICS (Hybrid Adaptive System for Infrastructure Critical Security). Based on a closed-loop MAPE-K architecture, H-ASICS dynamically selects the most optimal detection algorithms switching between Hybrid CNN-LSTM for complex spatial-temporal patterns and LightGBM for edge computing efficiency. Addressing the critical trade-off between high accuracy and operational latency, this study proposes the conceptual framework of H-ASICS (Hybrid Adaptive System for Infrastructure Critical Security). Based on a closed-loop MAPE-K architecture, H-ASICS dynamically selects the most optimal detection algorithms switching between Hybrid CNN-LSTM for complex spatial-temporal patterns (yielding up to 99.81% detection accuracy) and LightGBM for edge computing efficiency (reducing operational latency to under 10 ms). The superiority of H-ASICS is further reinforced by the integration of Explainable AI (XAI) and blockchain technology to guarantee the transparency of mitigation decisions and the immutability of cyber forensic data. This proposed architecture provides a strategic roadmap for next-generation security systems that are not only accurate and resilient but also highly accountable.
Deteksi Bahasa Isyarat Menggunakan Arsitektur YOLOv8 Berbasis Website Sulistyo, Danang Arbian; Rabbani, Muhammad Faruqi
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11070

Abstract

Communication difficulties between the general public and people with hearing impairments due to limited access to real-time detection tools are the primary urgency of this research. This research aims to develop a cross-platform and easily accessible website-based sign language detection system, while implementing the YOLOv8 variant to remain accurate on devices with limited computing resources. The method used is Research and Development (R&D) with the AI Project Cycle framework, which includes data collection, preprocessing, modeling using the YOLOv8n variant, and implementation. The data used is sourced from the Roboflow platform, consisting of hand gesture images divided into 70% training data, 20% validation, and 10% testing. The results show that the YOLOv8n model provides high performance with a precision of 0.932, recall of 0.997, and mAP50 value of 0.995. Additionally, the model achieves an efficient inference speed averaging 2.1 ms. In conclusion, the implementation of YOLOv8 on a website-based successfully creates an accurate and responsive sign language detection system, making it suitable for assisting communication in real-world scenarios
Implementasi Metode Design Science Research (DSR) pada Monitoring Server Voip Berbasis Web dengan Pendekatan Agile untuk Optimalisasi Kinerja Sistem (Studi Kasus: PT. XYZ) Dimas, Dimas Adjie; Zakaria, Hadi
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11071

Abstract

XYZ is a telecommunications technology company that has been providing cloud-based communication services, such as Cloud PBX, VoIP Telephony, Cloud Call Centers, and integrated Omni-Channel CRM systems, since 2013. The scope of this research is focused on infrastructure optimization through the development of a centralized monitoring system to manage thousands of call history records and server performance data. The background of this study is driven by the increasing number of clients and system complexity, which has rendered manual monitoring methods ineffective. This inefficiency leads to risks such as limited performance visibility, potential human error, and undetected security threats. Consequently, this research aims to build an innovative artifact in the form of a web-based VoIP server monitoring system capable of providing automated, centralized, and real-time monitoring. The methodology employed is Design Science Research (DSR) combined with an Agile approach to create a system that is adaptive to the company's changing needs. The system was developed using the Laravel framework, Python for backend processing, Chart.js for interactive visualization, and MySQL for telemetry data storage. The collected monitoring data includes CPU, memory, and disk utilization, connectivity status, and essential port openness, gathered by agents every minute. Implementation results indicate that the system successfully presents current server conditions, detects resource usage anomalies, and provides automated notifications during service disruptions. In conclusion, this system significantly improves operational efficiency, service security, and the quality of VoIP server monitoring, while strengthening the company's position as a reliable and trusted cloud-based communication solution provider in Indonesia.
Implementasi Algoritma Random Forest untuk Menentukan Tingkat Keberhasilan Proyek pada Sistem Work Order (Studi Kasus: PT XYZ) Utomo, Unggul Prasetyo; Zakaria, Hadi
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11115

Abstract

PT XYZ is a company focused on technological innovation to provide modern, effective, and efficient solutions across various aspects of life. As a pioneer in the technology evolution industry, PT XYZ combines expertise in software development and the latest technologies to create positive transformation for society and businesses. In project implementation, PT XYZ faces challenges in determining project success levels objectively and measurably, particularly within the context of the work order system. This condition leads to less optimal strategic decision-making, increased risk of losses, and difficulties in conducting comprehensive, data-driven project evaluations. To address these issues, this study develops a web-based project success prediction system within the work order system by implementing the Random Forest algorithm and the Agile development approach. The Random Forest algorithm is developed using the Python programming language to classify project success levels based on several historical parameters, such as completion duration, budget, and profit percentage. The system is equipped with a user interface developed using PHP with the Laravel framework and a MySQL database, enabling efficient and integrated data processing and visualization. The results show that the implementation of the Random Forest algorithm improves prediction accuracy and provides recommendations that can support management in decision-making. The Agile approach also offers high flexibility in adapting the system to user requirements. Through this system, PT XYZ is expected to optimize work order management and proactively minimize the risk of project failure in a data-driven manner.
Penerapan Dekomposisi Matriks untuk Reduksi Kompleksitas Komputasi pada Algoritma Machine Learning Setiyani, Safira Hasna; Rahma, Yusiana
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11166

Abstract

The increasing complexity of machine learning algorithms is often accompanied by higher computational costs particularly when dealing with high-dimensional data. This condition poses significant challenges in terms of computational efficiency and resource utilization. One mathematical approach that can address this issue is the application of linear algebra concepts, specifically matrix decomposition techniques. This study aims to apply matrix decomposition methods to reduce computational complexity in machine learning algorithms without significantly degrading model performance. The proposed approach employs matrix decomposition, such as Singular Value Decomposition (SVD), during the data preprocessing and model training stages. The performance of the algorithms is evaluated by comparing their behavior before and after the application of matrix decomposition in terms of computational time, accuracy, and memory efficiency. The experimental results demonstrate that matrix decomposition can significantly reduce computational complexity and improve learning efficiency, while maintaining stable or only slightly reduced accuracy. These findings indicate that matrix decomposition is an effective and practical approach for optimizing machine learning algorithms, particularly for large-scale and high-dimensional datasets.
Analisis Sentimen Ulasan Game Stardew Valley pada Steam dan Google Play Tampubolon, Surya Viari; Sulistyo, Danang Arbian
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11217

Abstract

The large number of user reviews on Steam and Google Play platforms makes manual analysis difficult and prone to subjective bias. This study aims to analyze and compare user sentiment toward Stardew Valley game reviews on both platforms using a text mining approach. The data used consist of 25,099 Steam reviews and 25,594 Google Play reviews. The text preprocessing stage includes case folding, cleansing (removal of punctuation and non-alphabetic characters), tokenization, stopword removal, and lemmatization to produce more structured data. Sentiment labeling is performed using the VADER method, followed by feature extraction using TF-IDF and classification using the Multinomial "Naïve Bayes" algorithm. Model evaluation is conducted using 5-Fold Cross Validation with accuracy, precision, recall, and F1-score as evaluation metrics. The results show that most reviews on both platforms have positive sentiment. The classification model achieves an average accuracy of 0.8151 on Steam and 0.8382 on Google Play. In addition, the model obtains an average F1-score (macro average) of 0.55 on Steam and 0.40 on Google Play. These results indicate that the model performs adequately in sentiment classification, although it still has limitations in identifying minority sentiment classes such as negative and neutral.
Perbandingan Fuzzy Mamdani dan Sugeno dalam Optimasi Trading Bitcoin Berbasis Indikator Teknikal Dwi Rahmadewi, Cynthia; Parlika, Rizky; Maulana, Hendra
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11235

Abstract

This study compares Mamdani and Sugeno fuzzy inference systems for Bitcoin trading using historical BTC/USDT data. In highly volatile and non-linear cryptocurrency markets, especially during bear markets, conventional methods struggle to interpret ambiguous signals, making fuzzy logic suitable for adaptive decision-making. The dataset was collected from the Binance API for the period 20 November 2021 to 31 December 2022 and consists of 9,746 candlestick records. This period corresponds to a bear market phase, characterized by a significant downward trend in Bitcoin prices, which provides a challenging environment for evaluating trading strategies. Four technical indicators, Bollinger Bands, RSI, ADX, and PSAR, were used as input variables. The data were split into 70% training and 30% testing using a time-based approach. Performance evaluation was conducted through long-only backtesting using Total Profit, Win Rate, Maximum Drawdown, Sharpe Ratio, and Sortino Ratio. The results show that Mamdani achieved better profitability than Sugeno, with total profit of -34.17% on training data and -2.45% on testing data, while Sugeno produced -53.91% and -3.04%, respectively. Although both methods resulted in negative returns due to the bearish market conditions, their performance was better than the buy-and-hold strategy, which recorded losses of -65.78% on training data and -17.49% on testing data. This indicates that both fuzzy approaches were effective in reducing losses and improving risk management under extreme market conditions. However, Sugeno showed better risk control on testing data with a lower maximum drawdown of 18.72% compared to 25.01% for Mamdani. Overall, Mamdani is more suitable for return-oriented strategies, while Sugeno is more appropriate for risk management under bearish conditions.
Pemodelan Deteksi dan Klasifikasi Fraktur Tulang pada Radiografi X-Ray Menggunakan YOLOv8 dan Preprocessing CLAHE HIDAYAT, JOSE JULIAN; Anshor, Abdul Halim; Anwar, M. Syaibani
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11241

Abstract

This study aims to develop a model for detecting and classifying bone fractures in digital X-ray radiography images using the You Only Look Once version 8 (YOLOv8) architecture with the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing method. The CLAHE method is used to improve contrast quality and clarify bone structure details, thereby facilitating the feature extraction process by the detection model. The research dataset comprises 641 X-ray and MRI images divided into ten classes consisting of various types of bone fractures, namely Comminuted, Greenstick, Linear, Oblique, Oblique Displaced, Segmental, Spiral, Transverse, and Transverse Displaced, as well as the Healthy class as a comparison. Model training was conducted for 100 epochs using YOLOv8n with CLAHE-based augmentation to improve the visibility of the fracture area. The best results were obtained from the YOLOv8-CLAHE (balanced) model with a mAP@0.5 of 0.933 to 0.941, precision of 0.939 to 0.965, and recall of 0.877 to 0.901. The Segmental and Comminuted classes showed the highest performance, while classes with limited data such as Greenstick and Linear still had relatively low accuracy.  The model's inference speed reached 8.3 milliseconds per image, demonstrating the potential application of this system for real-time fracture detection in clinical settings. The results of this study show that the application of the CLAHE method in the image pre-processing stage can improve the detection and classification performance of YOLOv8, and has the potential to support the development of automated diagnosis systems in the field of orthopedic radiology.
Implementasi Metode TOPSIS pada Sistem Pendukung Keputusan Penentuan Prioritas Penerima Bantuan Sosial Berbasis Aplikasi Desktop Ramadhan, Azhyka Rizki; Naya, Candra; AG, Abdillah
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11256

Abstract

The accurate distribution of social assistance remains a major challenge in improving community welfare. The process of determining eligible beneficiaries is often carried out manually, which can lead to subjectivity and inaccuracies in decision-making. Therefore, a decision support system is needed to assist the selection process by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), enabling a more structured and objective evaluation. This study aims to implement the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in determining the priority of social assistance recipients through a desktop-based application. The strength of TOPSIS lies in its ability to rank options based on their proximity to positive ideal solutions and to avoid negative optimal solutions. The criteria used in this study include monthly income, number of dependents, housing conditions, employment status, and productive assets. The system is developed as a desktop application equipped with features for data management, criteria weighting, and automated TOPSIS calculations to generate rankings of potential beneficiaries. The results of Black Box testing indicate that all system features function in accordance with the specified requirements, achieving a 100% success rate, thereby supporting a fast, accurate, and objective decision-making process. Therefore, this application is expected to enhance the effectiveness and transparency of social assistance distribution.
Klasifikasi Rating Film Berdasarkan Genre Menggunakan XGBoost dan LightGBM serta Analisis SHAP Roiqoh, Aprinia Salsabila; Parlika, Rizky; Aditiawan, Firza Prima
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11273

Abstract

Movie rating is often used as an indicator of film quality and audience satisfaction. With the large availability of movie data on online platforms, machine learning techniques can be used to analyze the relationship between film characteristics and rating patterns. One important attribute that can influence movie ratings is genre. This study aims to classify movie ratings based on genre using the XGBoost and LightGBM algorithms and to analyze the contribution of each genre using SHAP (SHapley Additive Explanations). Movie data were collected from The Movie Database (TMDB) API and processed through several preprocessing stages including genre separation, data cleaning, one-hot encoding, and rating categorization. The dataset was then divided into training and testing data with a ratio of 70:30. The classification results show that XGBoost achieved an accuracy of 0.53, slightly higher than LightGBM with an accuracy of 0.52. Further analysis using SHAP indicates that genres such as Horror, Drama, Action, and Comedy have the highest global importance in the classification model. Meanwhile, the analysis of high-rating class predictions shows that Drama has the largest contribution to predicting movies with high ratings. The findings indicate that movie genres have a measurable influence on rating classification, although the importance of genres in the machine learning model does not always align with their average rating values.

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