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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 448 Documents
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Perbandingan Model Deep Learning LSTM, GRU, dan Bi-LSTM untuk Prediksi Hujan Harian Australia Menggunakan Teknik SMOTE Risnanto, Ari; Nugroho, Bayu Tri; Hermawan, Arief; Avianto, Donny
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11691

Abstract

Climate change causing increasingly erratic rainfall patterns, triggering an increase in hydrometeorological disasters such as floods, droughts, and declining agricultural productivity. Therefore, accurate rainfall prediction is crucial for mitigation and decision-making. However, previous research often focuses solely on accuracy metrics without evaluating the model's computational burden, and often ignores the problem of class imbalance in weather datasets. This study evaluates the performance and computational efficiency of LSTM, GRU, and Bi-LSTM deep learning models for daily rainfall prediction using the historical Australian meteorological dataset weatherAus. The novelty of this study lies in the comprehensive mapping between predictive quality and resource efficiency after dataset balancing. The preprocessing stage includes handling missing values, categorical data transformation, data leakage prevention, data sharing, and the application of SMOTE oversampling. The results of the area under the curve (AUC-ROC) evaluation show that the GRU model is superior with a value of 0.85, surpassing LSTM and Bi-LSTM, respectively, at 0.84. In the rain class recall metric, GRU again leads (0.70), compared to LSTM (0.67), and Bi-LSTM (0.57). Computational evaluation, GRU is significantly more efficient with the fastest training time (1,306.26 seconds), followed by LSTM (2,259.12 seconds), and Bi-LSTM (13,348.43 seconds). Peak RAM usage relatively comparable, GRU (2,053.77 MB), LSTM (1,971.47 MB), and the highest Bi-LSTM (2,242.60 MB). These findings conclude that GRU is recommended as the most optimal model that balances accuracy and efficiency, LSTM as an alternative, while Bi-LSTM is considered less effective. Future research recommended to explore hybrid architectures or ensemble learning to capture more complex spatiotemporal patterns.
Evaluasi Kritis Random Forest dalam Prediksi Kegagalan CI/CD Pipeline pada Dataset Log Sintetis Rachma, Rizqia Fauziah; Gata, Windu; Ginanjar, Farizal; Arief Nadhofa, Muhammad
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11756

Abstract

Continuous Integration and Continuous Deployment (CI/CD) pipelines are crucial in modern DevOps environments; however, pipeline failures often hinder software delivery. This study aims to implement Machine Learning specifically the Random Forest algorithm to predict the specific stage of failure (Build, Test, or Deploy phases) using execution logs. The research methodology encompasses data preprocessing, the extraction of three key dynamic features (execution duration, CPU usage, and memory consumption), model training using an 80:20 train-test split, and performance evaluation via a confusion matrix. Evaluation based on 9,000 test samples representing 20% ​​of the total 45,000 synthetic failure logs yielded an overall accuracy of 33.22%, with F1-scores of 0.33 for the Build class, 0.35 for Deploy, and 0.32 for Test. These results indicate that the Random Forest model possesses very limited generalization capability because the synthetic dataset lacked naturally robust failure trace patterns, as evidenced by an accuracy level approaching that of random guessing. It is concluded that the use of a synthetic log dataset constitutes the primary limitation and the root cause of the model's generalization failure in this study. Therefore, future research is recommended to utilize actual operational logs from real-world CI/CD platforms, such as GitHub Actions or Jenkins, to enable the model to demonstrate more valid causal relationships.
Perbandingan Algoritma LSTM dan GRU dalam Prediksi Hasil Panen Padi Pulau Sumatera Hadi Riono, Sapto; Fathimah Ahmad, Risalatul; Af Fathir, Ridho
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11765

Abstract

Rice production is a strategic commodity that plays a vital role in maintaining food security in Indonesia. Rice production data, being time-series in nature, is influenced by trends, seasonal patterns, and weather and climate factors; therefore, a prediction model capable of accurately capturing temporal patterns is required. This study aims to compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting rice production on the island of Sumatra. The study utilizes secondary data obtained from Statistics Indonesia (BPS), the Meteorology, Climatology, and Geophysics Agency (BMKG), and Kaggle. Preprocessing stages include handling missing data, data normalization, and transformation into a time-series format. The data is then split into training and testing sets using an 80:20 ratio. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the GRU model outperforms the LSTM model, yielding an MAE of 0.0505, MSE of 0.0075, and RMSE of 0.0868, whereas the LSTM model produces an MAE of 0.0522, MSE of 0.0082, and RMSE of 0.0917. Thus, the GRU model is more effective at capturing the temporal patterns of rice production data and generating more accurate predictions compared to the LSTM model. These findings are expected to serve as a reference for selecting rice production prediction models to support data-driven decision-making in the agricultural sector.
Arsitektur Hibrida Ekstraksi Fitur Lokal Edge Computing: Tinjauan Literatur Sistematis Zhilal Manafi, Wildan; Fatin, Nabhani; Bastian, Ade
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11806

Abstract

The proliferation of Internet of Things (IoT) devices and the demand for real-time processing have positioned edge computing as critical infrastructure for deploying deep learning models near the data source. Hardware constraints, including limited memory, narrow computational budgets, and strict power limits, challenge deployment of large-parameter neural architectures. This review examines hybrid architectures integrating convolutional neural networks (CNN) with attention mechanisms for local feature extraction on resource-constrained edge devices. Following the PRISMA 2020 protocol, a multi-stage search was conducted exclusively on Scopus, yielding 3,571 records, screened until 121 high-quality studies were included. The review addresses five research questions covering architectural trends, efficiency strategies, performance trade-offs, application domains, and federated learning for privacy-preserving deployment. Findings show that hybrid CNN-Attention architectures outperform pure CNN and Transformer baselines, with 3.2% average accuracy improvement while maintaining competitive inference latency on platforms such as NVIDIA Jetson and Raspberry Pi. Depthwise separable convolution, efficient channel attention, and token aggregation emerged as dominant compression strategies, with health imaging and fault diagnosis as leading domains. The review concludes that hybrid architectures represent the state of the art for edge-oriented local feature extraction, with future directions toward heterogeneous node generalization and multi-modal sensor fusion for distributed IoT networks.
Pengembangan E-Registcam Dengan Penerapan Preprocessing Imagick Untuk Pengenalan Citra KTP Berbasis OCR Riyandi, Renaldi; Yuliana, Yuliana
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11833

Abstract

Identity card data entry is commonly performed manually and may result in human errors during the registration process. Optical Character Recognition (OCR) technology can be utilized to automate the extraction of textual information from identity card images. This study aims to develop the E-Registcam system by applying Imagick-based image preprocessing for OCR-based Indonesian identity card (KTP) recognition. The preprocessing stages consist of grayscale conversion, resizing, contrast enhancement, noise removal, sharpening, and cropping. OCR processing is performed using Tesseract OCR to extract the name field from KTP images. The study utilized 30 KTP image samples and evaluated recognition performance using the Character Accuracy Rate (CAR) method. Experimental results showed that OCR with preprocessing successfully recognized 23 images and failed on 7 images, achieving an average CAR value of 74.13%. Meanwhile, OCR without preprocessing successfully recognized 26 images and failed on 4 images, with an average CAR value of 81.05%. The results indicate that the proposed system is capable of extracting textual information from KTP images; however, the applied preprocessing stages did not consistently improve OCR recognition accuracy. Factors such as lighting conditions, image quality, and background complexity affected the recognition performance.
Algoritma K Means dan TF-IDF untuk Pengelompokkan Opini Publik terhadap Program Makan Bergizi Gratis pada Komentar TikTok Sugianti, Devi; Putra, Ari; Syaifudin, Anas; Wijonarko, Rizqi
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11846

Abstract

In Indonesia, 14% of children suffer from stunting due to malnutrition. To address this issue, the government launched the Free Nutritious Meal (MBG) Program, which has generated diverse public opinions on social media, particularly TikTok. This study aims to cluster public opinions regarding the MBG program using the K-Means Clustering algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) without requiring manual labeling. A total of 57,362 comments were collected, of which 53,204 valid comments remained after preprocessing. Truncated Singular Value Decomposition (SVD) was applied for dimensionality reduction, while the optimal number of clusters (K = 5) was determined using the Elbow Method and Silhouette Score. The clustering results identified five main discussion themes: general program discussion (28.4%), child nutrition and school access (7.9%), spontaneous reactive responses (45.2%), criticism and rejection of the program (15.0%), and support for public figures (3.6%). The model achieved a Silhouette Score of 0.0385 and a Davies–Bouldin Index of 4.9507, reflecting the characteristics of short and informal social media text. The findings demonstrate that the unsupervised clustering approach effectively maps public opinion into meaningful thematic groups and provides valuable insights for the National Nutrition Agency to improve menu quality, budget transparency, and distribution standards of the MBG program.
Analisis Segmentasi Perilaku Pembeli Marketplace Shopee Menggunakan Model RFM dan Algoritma K-Means Clustering pada Toko Retail Online XYZ di Yogyakarta Amymaztura, Rezky; Pratama, Irfan
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11903

Abstract

Perkembangan Marketplace Shopee di Indonesia membuat salah satu Toko Retail Online XYZ yang bergerak di bidang fashion di Yogakarta memerlukan strategi pemasaran yang efektif agar tepat sasaran. Data transaksi shopee toko tersebut hanya berakhir menjadi arsip penjualan dan belum diolah secara optimal sehingga mengalami kesulitan dalam mengelompokkan dan memahami perilaku pelanggan. Penelitian ditujukan untuk melakukan segmentasi perilaku pelanggan menggunakan pendekatan Customer Relationship Management (CRM) analitik dengan model RFM (Recency, Frequency, Monetary) dan Algoritma K-Means Clustering. Penelitian menggunakan 85.781 data transaksi periode 2025, yang kemudian dilakukan tahap preprocessing dan pembersihan outlier dengan Interquatile Range (IQR) sehingga menjadi 43.036 data. Evaluasi gabungan dilakukan untuk menentukan klaster optimal menggunakan metode Elbow, Silhouette Score dan Davies-Bouldin Index. Penelitian menetapkan hasil k=3 sebagai jumlah klaster optimal dengan titik siku pada grafik yang ditunjukkan pada grafik Elbow serta divalidasi menggunakan Silhouette Score dengan nilai paling tinggi 0,4949 dan nilai minimum Davies-Bouldin Index 0,7429. Mayoritas pelanggan memiliki karakteristik one-time buyers, didasari oleh nilai Frequency yang relatif sama sehingga nilai variabel Recency dan Monetary lebih mempengaruhi segmentasi. Segmen yang dihasilkan adalah Klaster 0 (New Customer / Potential), Klaster 1 (At Risk Customer), Klaster 2 (Lost Customer). Penelitian berhasil mengubah ribuan data transaksi menjadi informasi yang strategis dengan menggambarkan karakteristik setiap segmen pelanggan sehingga perusahaan dapat menerapkan strategi pemasaran dengan tepat.
Sistem Penggajian Terintegrasi Berbasis API pada SMART UMRI Prambadi, Heru; Ari Wibowo, Agus Urip; Putra, Emansa Hasri
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11968

Abstract

Digital transformation in higher education requires integrated information systems that support operational efficiency, data consistency, and accountable governance. Payroll management is a critical administrative process because it involves employee master data, compensation components, taxation, and social security contributions. At Universitas Muhammadiyah Riau, payroll processing previously relied on separated data flows that increased the risk of duplicate entry, inconsistent employee data, delayed payroll generation, and calculation errors. This study aims to examine whether an API-based integrated payroll system connected to SMART UMRI can resolve these problems, using employee and payroll data sourced from the SMART UMRI database. The system was developed using Rapid Application Development and implemented as a RESTful API architecture with HMAC authentication, HTTPS, IP allowlist, role-based access control, and audit trail. Evaluation covered functional testing, API integration, payroll validation, security assessment, and performance testing with Apache JMeter. The results show successful synchronization of employee master data, payroll calculation accuracy with zero difference against manual validation, and stable API performance with 0% error rate in operational testing, confirming that the problems identified before the integration have been resolved. The system improves payroll transparency through digital payslips and traceable logs. The model is feasible for deployment in higher education institutions.
Rancang Bangun Smart Squeeze Cage Berbasis Internet of Things untuk Monitoring Bobot dan Rekomendasi Pakan Ternak Rania, Ghina; Rifki Munawar, Muhammad; Bonardo Marpaung, Ilham; Tiftazani, Hafiz; Nasir, Muhammad
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11980

Abstract

The development of precision livestock farming requires robust and automated data collection tools to minimize animal stress and improve farm efficiency. Traditional livestock weighing methods often lack immediate data access and do not support dynamic resource planning. This study designs and implements a Smart Squeeze Cage based on the Internet of Things (IoT) integrated with a Random Forest Regressor algorithm for real-time livestock weight monitoring and feed optimization. The system integrates four load cell sensors connected in parallel, an HX711 amplifier, and an ESP32 microcontroller embedded within a customized Squeeze Cage structure. Weight data is transmitted via wireless protocol to a centralized cloud database using Supabase and PostgreSQL. Based on historical data, the Random Forest model automatically predicts livestock weight trends and calculates daily feed requirements to provide intelligent recommendations. Testing results indicate high sensor precision with an accuracy of 97% (error tolerance of 0,5 kg), data transmission latency of 1.2 seconds, and a successful data delivery rate of 99.1%. This system offers a seamless, non-invasive solution for data-driven livestock management in modern farming environments.
Penerapan Metode TOPSIS dalam Penentuan Kelayakan Penerima Beasiswa KIP Handayani, Fitri; Azim, Fauzan; Hakim, Mursyalina
JURNAL FASILKOM Vol. 16 No. 2 (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.v16i2.11999

Abstract

The selection of Kartu Indonesia Pintar (KIP) scholarship recipients requires an objective and systematic decision-making process because it involves multiple social and economic criteria. Manual selection may cause inaccurate targeting due to subjective assessment. This study applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the eligibility of KIP scholarship recipients at Universitas Muhammadiyah Pringsewu and evaluates the conformity of the ranking results with actual recipient data using Spearman Rank Correlation. The data consist of 940 KIP scholarship applicants that were cleaned, encoded, and transformed into sixteen decision criteria. Entropy weighting was used to obtain objective criterion weights, followed by TOPSIS calculation to produce preference values and rankings. The results show that TOPSIS recommended 524 of 540 actual recipients, with a decision conformity percentage of 97.04%. The Spearman correlation coefficient reached 0.8479 with a very small p-value, indicating a very strong and significant relationship between TOPSIS rankings and actual data. These findings indicate that TOPSIS is reliable for supporting targeted KIP scholarship selection.

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