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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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Pembangunan Multi-Domain Human-Labelled Dataset untuk Konversi Bahasa Alami ke SQL pada Bahasa Indonesia Firlanda, Fiorella Asyfa; Prasetya, Agung
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.v16i2.12020

Abstract

This study develops a multi-domain, human-labelled Text-to-SQL dataset in Indonesian to address the absence of such resources for the language. The dataset was constructed through a corpus-based approach comprising four stages: corpus planning, corpus construction, corpus validation, and model evaluation. Database schemas represented as Entity Relationship Diagrams (ERD) were used as the structural foundation for generating pairs of Indonesian natural language questions and SELECT-type SQL queries, all created through manual annotation. Validation was performed through four sequential mechanisms: SQL syntax checking, schema conformity verification, query execution testing, and semantic alignment assessment between questions and SQL queries. The resulting dataset is Spider-compatible in JSON format, covering 40 databases, 27 domains, 1,524 questions, and 1,355 unique SQL queries distributed across four difficulty levels. Preliminary evaluation using SQLNet and TypeSQL baseline models under example split and database split scenarios confirms that the dataset provides a representative and challenging evaluation environment for Indonesian Text-to-SQL experiments, though model performance remains limited on complex queries and previously unseen database schemas. The dataset is publicly available and is intended to support future development and evaluation of Indonesian Text-to-SQL models..
Implementasi Metode K-Means Clustering dan Association Rules Apriori untuk Pengelolaan Stok Obat Fajriah, Nursyamsiah; Tassia, Shelvi Eka
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.v16i2.12088

Abstract

Drug inventory management at Klinik Pratama Kencana was previously performed through manual or semi-manual recording, making stock monitoring and procurement decisions less effective. This study develops a web-based drug inventory management system by integrating K-Means Clustering and Apriori Association Rules. Historical stock and drug-out transaction data from January to March 2026 were processed using a descriptive quantitative applied-research approach. K-Means used initial stock, incoming stock, outgoing stock, and remaining stock attributes after Min-Max normalization to classify 64 drugs into fast-moving, medium-moving, and slow-moving groups. Apriori analyzed 597 transaction baskets with a minimum support of 5% and a minimum confidence of 40%. The clustering produced 15 fast-moving, 27 medium-moving, and 22 slow-moving drugs. Apriori generated several strong rules; the rule Acetylcysteine to Ambroxol achieved 11.06% support, 85.71% confidence, and a lift ratio of 5.39. System calculations matched Microsoft Excel validation results, all tested functions were valid, and five users gave a 96% acceptance score. The integrated system provides stock status, restock recommendations, monthly reports, and co-occurrence patterns to support more structured inventory decisions. By combining stock movement classification with drug co-occurrence patterns, the system helps administrators prioritize restocking together with related medicines, thereby reducing manual checking and improving inventory monitoring efficiency.
The Implementasi Paralel K-Means Clustering dan Decision Tree untuk Deteksi Risiko Kredit Macet Maar, Halili; Nurjanah, Elin; Suraya, Gina; Khoirunnisa, Dea
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.v16i2.12095

Abstract

Non-Performing Loan (NPL) pose a major threat to the stability of financial institutions. This study implements parallel K-Means Clustering and Decision Tree methods in RapidMiner 2026.0.5 to identify potential credit default risks using the Credit Risk Dataset. The dataset consists of 32,576 borrower records with 12 attributes after filtering. Preprocessing includes handling missing values, dummy coding, converting loan_status into a binary variable, and applying Z-Transformation normalization. The Multiply operator enables K-Means (k=3) and Decision Tree to run simultaneously. The Clustering results show that Cluster 0 (33.10%) is a low-risk group dominated by homeowners with mortgages and an A loan rating; Cluster 1 (32.07%) is a medium-risk group dominated by tenant borrowers with a loan rating of B, and Cluster 2 (34.83%) is a high-risk group dominated by a history of delinquency, loan ratings of C–G, and the highest interest rates. The Decision Tree model achieved an Accuracy of 89.26%, Precision of 76.30%, Recall of 73.05%, and an F-Measure of 74.58%, making it effective as an early detection system for nonperforming loans.
Pengembangan Model Klasifikasi Code Smells Pada Backend Python Menggunakan Algoritma Random Forest (Studi Kasus Proyek Open Source Github) Ar-Hammar, Aqilla; Maisura, Mira
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.v16i2.12107

Abstract

Deadline pressure in software development often drives coding shortcuts, leading to internal quality degradation known as code smells. These structural anomalies contribute to technical debt accumulation and complicate system maintenance over time. This study develops an automated classification model to detect code smell contamination in the Python backend ecosystem. The methodology uses the Random Forest ensemble algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing. Data mining on GitHub with high-reputation criteria extracted 137,728 code samples from 7 large-scale repositories using the Radon multi-metric tool. To simulate human error in real-world scenarios, 5% random noise was inserted into the labeling data. Testing using the confusion matrix shows the proposed model achieves highly stable and balanced performance, with average precision, recall, and f1-score of 0.95 in both macro and weighted averages. Ablation study analysis proves that SMOTE intervention effectively maintains detection consistency in minority class categories. Feature importance ranking identifies the Logical Lines of Code (LLOC) metric as the most crucial indicator with 37.65% influence weight, followed by LOC and Blank metrics. This research provides an automated quality assurance system for developers to detect code refactoring opportunities at an early stage.
Analisis Komparatif Model ARIMA, LSTM, dan GRU dalam Forecasting Harga Bitcoin Berbasis Streamlit Nurardian, Ridwana Septian; Riyandi, Albert
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.v16i2.12126

Abstract

Bitcoin is a cryptocurrency asset characterized by high volatility and non-linear movement patterns that trigger sudden market risks. Considering that the majority of previous studies have solely focused on mathematical metric evaluations without providing practical solutions, this study aims to compare the performance of the conventional statistical model Autoregressive Integrated Moving Average (ARIMA) with Deep Learning architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and to implement the best-performing model into an interactive dashboard using Streamlit. The dataset used encompasses daily Bitcoin closing prices for the 2020–2026 period. The methodology includes data preprocessing, MinMaxScaler normalization, sequential data generation using a 30-day sliding window technique, chronological data splitting (80:20), and evaluation using MAE, RMSE, MAPE, Accuracy, and R² Score. Experimental results prove that ARIMA fails to adapt to non-stationary data, whereas GRU outperforms LSTM in terms of architectural efficiency and precision. The GRU model achieved the best performance with an MAE of IDR 34,773,098.88, an RMSE of IDR 46,684,832.94, a MAPE of 2.26%, an Accuracy of 97.74%, and an R² Score of 0.9661. The GRU model was then successfully implemented into a Streamlit web dashboard that facilitates the real-time visualization of historical and predicted prices. In conclusion, the GRU architecture is the most effective and efficient approach for Bitcoin price forecasting, successfully bridging the gap between theoretical analysis and the availability of practical applications.
Identifikasi Tanaman Obat Indonesia dengan Vision Transformer dan Augmentasi Adaptif Tristanti, Novi; Sunardi, Sunardi; Murinto, Murinto
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.v16i2.12326

Abstract

Manual identification of medicinal plants faces serious challenges due to morphological similarities between species, variations in lighting, and limited availability of botanists in the field. This study proposes an identification system for 100 types of Indonesian medicinal plants using the Vision Transformer (ViT) architecture with a stepwise fine-tuning approach and adaptive image augmentation. The model used is vit_base_patch16_224 initialized with ImageNet-1k pretrained weights, equipped with a classifier head consisting of a series of LayerNorm, Dropout (0.3), and Linear (768→100). The training strategy integrates stepwise freezing (freeze-unfreeze) in the first three epochs, the AdamW optimizer with a weight decay of 0.05, label smoothing (ε=0.1), and cosine-based learning rate scheduling to ensure stable convergence on medium-scale datasets. The dataset used consists of 10,000 images divided using a non-stratified random split with a ratio of 70% training data, 15% validation data, and 15% test data. The evaluation results show that the model achieved an accuracy of 97.3% on the test data, with a macro precision of 0.974, a macro recall of 0.975, and a macro F1-score of 0.973. The macro values ​​were calculated by summing the metric values ​​for each class separately, then dividing by the number of classes without considering the number of samples in each class. The training process lasted for 16 epochs before being terminated by the early stopping mechanism with the best validation accuracy of 97.53% at the 11th epoch. These results demonstrate that the ViT stepwise fine-tuning approach is able to address the challenges of multi-class scale classification on medium-sized datasets effectively and reproducibly.
Prediksi Keberhasilan Akademik Siswa Berbasis Fitur Kategorikal Na-tive dengan Explainable AI (SHAP) menggunakan CatBoost vs LightGBM Anugerah Putra, Bayu; Soni, Soni; Firdaus, Rahmad; Putri, Ayunda; Dwi Sanggar Wati, Anisa
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.v16i2.12360

Abstract

Prediction of students academic success is important to support decision-making in education. Educational datasets are generally dominated by categorical variables that require encoding before modeling, which may cause information loss and reduce accuracy. This study applies the CatBoost algorithm, which processes categorical variables natively without additional encoding, to predict students' Exam Score on the Student Performance Factors dataset from Kaggle, with LightGBM used as a comparison model. Evaluation was carried out under three data-split schemes (70:30, 80:20, 90:10) using k-fold cross-validation and three regression metrics (R², MAE, RMSE), followed by model interpretation using Shapley Additive Explanations (SHAP). The results show that CatBoost consistently outperforms LightGBM across all schemes, with the best performance obtained under the 90:10 scheme (CatBoost: R² = 0.851, MAE = 0.475, RMSE = 1.414; LightGBM: R² = 0.809, MAE = 0.758, RMSE = 1.599). SHAP analysis identifies Attendance, Hours_Studied, and Previous_Scores as the most influential features in the prediction. These findings confirm that combining CatBoost with SHAP produces an academic prediction model that is both accurate and transparen.
Ablasi Kelompok Fitur Multi-View pada Random Forest untuk Deteksi Intrusi IIoT Amien, Januar Al; Anugrah Putra, Bayu; Azim, Fauzan; Medikawati Taufiq, Reny; Syahril, Syahril
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.v16i2.12438

Abstract

Internet of Things (IIoT) systems generate heterogeneous multisource telemetry data, including network traffic, host resource usage, and security logs. Intrusion detection studies commonly combine all available feature sources based on the assumption that incorporating more sources (multi-view) will always improve detection performance. This study examines this assumption using the X-IIoTID dataset through two experiments. First, feature selection based on Random Forest Gini importance was evaluated using five feature sizes (K = 10, 20, 30, 45, and 61), with cross-algorithm robustness assessed using Decision Tree, Logistic Regression, and K-Nearest Neighbors. Second, a systematic ablation study was conducted on seven combinations of three feature groups: Network (N), Host (H), and Log (L), with Timestamp excluded from the Network group to ensure consistent feature treatment. Using 299,999 samples, comprising 239,999 training and 60,000 test samples across 19 attack classes and a normal class, the results show that multiclass performance increased with the number of features, achieving an F1-macro of 0.876 at K = 10 and 0.912 at K = 61. The ablation study showed that the Full MultiView (N+H+L) achieved the best performance (F1-macro = 0.912), followed by N+H (0.905) and N+L (0.879). The Log group alone yielded low performance (0.098) but provided additional value when combined with Network features. These findings demonstrate that the effectiveness of multi-view intrusion detection depends on feature-source combinations rather than merely the number of sources, highlighting the importance of feature-group ablation in designing IIoT intrusion detection systems.

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