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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 423 Documents
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Komparasi Performa REST API Laravel 11 dan CodeIgniter 4 Menggunakan Metode Eksperimental valentina, Putri Eka; Dede Handayani; Surya Rizky Maulana Ibrahim; Nanang, Nanang; Kusumah Putra, Faris Maulana
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.v16i1.11274

Abstract

This study evaluates the performance of Laravel 11 and CodeIgniter 4 REST API frameworks using an experimental method with controlled variables. Both frameworks were built with identical CRUD endpoints and stress-tested using Apache JMeter 5.6 at concurrency levels of 100, 500, and 1,000 users, with 10 replications each. Key metrics were response time, throughput (RPS), and server memory usage. Results show CodeIgniter 4 consistently outperforms Laravel 11 in raw speed: 70 ms vs. 100 ms at 100 users; 310 ms vs. 480 ms at 1,000 users — a 35–55% advantage. Throughput ratio reached 2.09:1 in favor of CodeIgniter 4 (1,420 vs. 680 RPS at low load), while memory consumption was 66% lower (10 MB vs. 30 MB per request). Analysis of ORM impact shows Eloquent adds a 24% penalty over Query Builder (385 ms vs. 310 ms for 1,000-record fetches). However, applying route caching, config caching, and OPcache boosted Laravel 11 throughput by 75% (reaching 1,180 RPS) and narrowed response time to 85 ms. These findings provide empirical guidance: CodeIgniter 4 suits lightweight microservices with limited resources, while Laravel 11 is preferable for complex enterprise systems demanding security, maintainability, and team productivity.
Pemodelan Dataset On-chain pada BiLSTM untuk Prediksi Harga Bitcoin Malik, Gamar Ramadhani; Parlika, Rizky; Kartini, Kartini
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.v16i1.11275

Abstract

Bitcoin is a crypto asset for investment. It can give high profit, but it also has high risk because the price changes very fast and is not stable. To reduce the risk of loss, we need a prediction system that can read price changes well. This research aims to model and predict the closing price of Bitcoin using network activity data (on-chain metrics). The method used is Deep Learning with the BiLSTM algorithm. This method is chosen because it can process data in two directions (forward and backward), so it can learn patterns better than standard LSTM. The dataset is taken from the public Blockchain network using BigQuery, from August 18, 2011, to February 6, 2026, with 5,287 daily data. The model uses the main input active_spending_addresses and two volatility indicators: Percent of Top Range (PTR) and Percent Low Range (PLR). Before modeling, the data is processed using a sliding window of 60 days, with 90% training data and 10% testing data. The results show that the BiLSTM model has high accuracy, with MAE 2.958, RMSE 3.905, and MAPE 3.22%. The comparison shows that BiLSTM is better than other models. LSTM has MAPE 29.06%, and MLP has MAPE 4.01%. In conclusion, BiLSTM can handle extreme crypto market changes very well, so it gives stable and accurate Bitcoin price predictions.
Prediksi Lead Scoring untuk Optimasi Penjualan Menggunakan Random Forest dan Teknik SMOTE Pratama Putra, Daffa; Agil Kusuma, Dimas; Al Akbar, M. Rizki; Ibrahim, Ali; Fathoni, Fathoni
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.v16i1.11292

Abstract

Accurate lead scoring systems have become a strategic necessity for organizations operating in data-driven marketing environments, as they enable systematic identification of high-value customer prospects to maximize sales conversion efficiency. A fundamental challenge confronting conventional classification models is the class imbalance inherent in real-world marketing data, which induces majority-class bias and substantially reduces sensitivity toward minority-class prospects. This study proposes a Random Forest (RF)-based lead scoring prediction model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this limitation systematically. The dataset employed is the Lead Scoring Dataset from Kaggle, comprising 9,240 customer prospect records from an educational company with a class imbalance ratio of 1.59:1. Preprocessing included missing value treatment, removal of attributes exceeding 40% data loss, mode-based imputation, and categorical feature encoding. Following an 80:20 stratified split, SMOTE was applied exclusively to the training set to produce a balanced class distribution and prevent data leakage. The RF model was configured with n_estimators = 100, max_features = 'sqrt', and class_weight = 'balanced'. The proposed RF+SMOTE model achieved accuracy of 88.80%, precision of 86.44%, recall of 84.13%, F1-Score of 85.27%, and AUC-ROC of 0.9453, outperforming the baseline across four of five evaluation metrics. The most notable improvement was observed in recall, with a gain of 1.26 percentage points. Stratified 5-Fold Cross-Validation confirmed robust generalization capability, with AUC-ROC values consistently ranging between 94% and 95%. These findings demonstrate that the hybrid RF+SMOTE approach effectively enhances high-potential prospect detection while maintaining overall model stability for real-world Customer Relationship Management (CRM) deployment.
Implementasi Extremely Randomized Trees dengan Optimasi Hyperparameter Accelerated Particle Swarm Optimization untuk Klasifikasi Subtipe Anemia Adelia, Adelia; Trimono, Trimono; Idhom, Mohammad
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.v16i1.11295

Abstract

Anemia is a health problem that negatively affects both medical outcomes and social well-being, highlighting the need for accurate early detection. This study applies a machine learning approach to classify anemia subtypes to support clinical intervention and further examination. The Extra Trees method employs a hierarchical decision-tree structure with extreme randomization, making it robust to overfitting and capable of good generalization on small to medium datasets. Accelerated Particle Swarm Optimization (APSO) is utilized as an efficient optimization technique to improve classification performance. The novelty of this study lies in integrating Extra Trees with APSO to optimize anemia subtype classification. The dataset consists of 385 records collected from a regional hospital in East Java, Indonesia, covering four classes: thalassemia, iron deficiency anemia, anemia of chronic disease, and non-anemia. The features include patient initials, gender, age, and hematological parameters (Hb, HCT, RBC, MCV, MCH, MCHC, RDW). The optimized model achieved 85% accuracy, 87% precision, 85% recall, 85% F1-score, 95% specificity, and 94% AUC, outperforming the non-optimized model. These results indicate that the proposed approach is effective for anemia subtype classification.
Perancangan Sistem Pemesanan Online E-Kopma Mahasiswa Berbasis Web Widjayani, Alifya Aisya; Bastio, Aldrik; Pandiangan, Daniel; Niska, Debi Yandra
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.v16i1.11338

Abstract

The development of information technology is now increasingly driving digital transformation in various fields, except for student cooperatives. Currently, KOPMA FMIPA Universitas Negeri Medan still relies on traditional ordering methods that often result in long queues, slow service, plus suboptimal data management. Therefore, this research focuses on creating and developing an online ordering system via the web (E-KOPMA) to overcome these problems. The way it works uses the Waterfall model to create the software, starting from analyzing what is needed, system design, coding, to testing. The system is built with PHP and MySQL, plus the illustration uses UML. For testing, Black Box Testing is used so that all features work as expected. From the results, this system really helps ease the process of ordering goods, reduce the number of queues, and make service smoother. Key features such as login, managing product stock, shopping carts, and payment processes, all work well and pass the test. So, E-KOPMA can be the right answer to improve service quality plus manage buying and selling data faster and more precisely
Analisis Klasifikasi Kekeruhan Air Berbasis Citra Dengan K-NN Pada Variasi Pencahayaan M. Fatuhrahman; Gasim, Gasim; Mair, Zaid Romegar
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.v16i1.11339

Abstract

Clean and high-quality water is an essential requirement for public health and the continuity of industrial processes, including at PT Pupuk Sriwidjaja Palembang. One of the main parameters of water quality is turbidity, which is related to the presence of suspended particles such as mud, organic matter, and microorganisms. This study aims to analyze the effect of lighting intensity variations on the performance of water turbidity classification based on digital image processing using the K-Nearest Neighbor (K-NN) algorithm. The experiment was conducted under five lighting intensity levels: 10, 30, 50, 80, and 100 lux. The research stages included image acquisition, pre-processing (resizing, color conversion, and normalization), feature extraction of color and texture using mean, standard deviation, and Gray Level Co-occurrence Matrix (GLCM), followed by classification using the K-NN algorithm. The value of k = 5 was selected because it provides a balance between sensitivity to noise and classification stability, and preliminary testing showed more consistent performance compared to smaller or larger k values. System performance evaluation was carried out using accuracy, precision, F1-score, and confusion matrix. The results showed that the best performance was achieved at 100 lux lighting intensity with an accuracy of 91.67%, precision of 93.33%, and F1-score of 91.53%, while the lowest performance occurred at 10 lux with an accuracy of 61.54%. These findings indicate that lighting intensity significantly affects turbidity classification performance, with optimal conditions found in the range of 80–100 lux. This study proves that proper lighting adjustment can improve the reliability of digital image-based classification systems for automatic water quality monitoring.
Analisis Pemerataan Pendidikan di Indonesia Menggunakan Reduksi Dimensi PCA dan Klasterisasi K-Means Kusuma, Erwin Arry; Dharmawati, Adani
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.v16i1.11349

Abstract

Educational equity in Indonesia continues to face substantial challenges due to significant disparities in achievement across provinces. This study aims to map these gaps by combining Principal Component Analysis (PCA) for dimensionality reduction and K-Means Clustering for regional grouping. Utilizing 2023 data from the Indonesian Central Bureau of Statistics (BPS) with eight key indicators, the analysis reveals that three principal components effectively capture 91.85% of the data variance. The clustering procedure successfully categorizes provinces into two distinct groups: 36 provinces in the high-achievement cluster and two provinces that lag significantly (Central Papua and Papua Mountains). A Silhouette Score of 0.782 confirms the high validity and consistency of the clustering results. These findings serve as a critical alert for policymakers to implement targeted interventions in underperforming regions to prevent further widening of the educational gap.
Analisis Sentimen Isu Artificial Intelligence di Twitter dengan SVM dan Random Forest Navidkya, Abriel; Yusuf, Mohamad
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.v16i1.10037

Abstract

Artificial Intelligence (AI) has become a widely discussed topic on social media, particularly Twitter, as public opinions about this technology grow. This study aims to analyze the sentiment of Twitter posts related to AI issues using two classification algorithms: Support Vector Machine (SVM) and Random Forest (RF). The research method involves data collection via the Twitter API, followed by text preprocessing steps including case folding, tokenization, stopword removal, and stemming. The data is then manually or semi-automatically labeled with sentiments (positive, negative, neutral) to support supervised learning. Vectorization using TF-IDF is applied before training and testing the SVM and RF models to compare their classification performance. Results indicate that SVM outperforms RF in accuracy and class balance across sentiments. The application of Synthetic Minority Oversampling Technique (SMOTE) enhances performance, especially in detecting the less frequent negative sentiment. Post-SMOTE, SVM achieves an accuracy of 89.12% and an F1-score of 0.7122 for the negative class, demonstrating its ability to handle data imbalance. Although RF also improves after SMOTE, its performance remains below SVM. This study is expected to contribute significantly to public opinion monitoring and serve as a foundation for decision-making regarding AI-based technology development. 
Sistem Berbasis Logika Fuzzy Mamdani Untuk Klasifikasi Status Gizi Anak Febrien, Fransiskus; Fitria, Vivi Aida
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.v16i1.10933

Abstract

Malnutrition among children under five remains a critical public health challenge, particularly in primary healthcare settings where assessment is often conducted manually and relies on a single anthropometric index. This study proposes a Mamdani fuzzy logic-based classification system designed to assess children’s nutritional status at Puskesmas Nanu by simultaneously incorporating four anthropometric parameters: age (months), height, weight, and mid-upper arm circumference (MUAC). Unlike previous studies that typically employ one or two indicators, this system constructs a comprehensive inference framework consisting of 135 IF-THEN rules derived from all possible combinations of fuzzy input sets. Triangular and trapezoidal membership functions are applied to each variable to capture the gradual transitions inherent in children’s growth conditions. The inference engine employs the MIN operator for rule activation and MAX for aggregation, while centroid defuzzification converts the aggregated fuzzy output into a deterministic crisp value. The system was evaluated against 20 anthropometric records from the facility and compared with the conventional Z-score method used by healthcare workers. Results show that 15 out of 20 cases were classified consistently, yielding an accuracy rate of 75%. In a representative case of a 59-month-old child, the system produced a crisp output of −0.25, corresponding to the normal nutritional status category. These findings demonstrate that the proposed system offers a more holistic and objective approach to nutritional assessment. Limitations include the relatively small sample size and membership function domains derived from local data rather than standardized WHO references. Future work should focus on expanding the dataset, aligning parameters with national anthropometric standards, and implementing the system as a web-based or mobile application integrated into primary healthcare information systems.
Analisis Sentimen Opini Masyarakat di Platrfom X (Twitter) terhadap Program Makanan Bergizi Gratis Menggunakan Metode Suport Vector Machine (SVM) Adedio, Rangga; Muhammad Husni Rifqo; Darmi, Yulia; Wijaya, Ardi
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.v16i1.11184

Abstract

The increasing use of social media as a public space has encouraged the emergence of various opinions on government policies, including the Free Nutritious Food Program which is widely discussed on Platform X (Twitter). However, unstructured text data and diverse user perspectives pose challenges in accurately identifying sentiment. This study aims to analyze public sentiment using the Support Vector Machine (SVM) method with Term Frequency – Inverse Document Frequency (TF-IDF) weighting. Data were collected through web scraping from August to November 2025 totaling 4,002 tweets, which were then processed through labeling and preprocessing to obtain 3,129 data. Testing was carried out with three classification scenarios, namely three classes, two classes, and positive and non-positive. The results show that the highest accuracy obtained in the positive vs. non-positive scenario is 90.57%, followed by two classes at 90.34%, and three classes at 80.67%. These findings indicate that simplifying the number of classes can improve model performance. The SVM method with TF-IDF has proven effective in sentiment analysis on social media data.

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