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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 397 Documents
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Peramalan Harga Emas (XAU/USD) menggunakan metode Sigle Exponential Smoothing (SES) dan Autoregressive Integrated Moving Average (ARIMA) Aidil Adha, Balqis; Devega, Mariza
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10397

Abstract

Gold (XAU/USD) is one of the most significant global commodities, often viewed as a safe-haven asset amid economic and political uncertainty. Accurate forecasting of gold prices is crucial for investors and policymakers in formulating strategic financial decisions. This study aims to compare the performance of the Single Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting gold prices using historical datasets from Kaggle, Investing.com, and ForexSB covering the period from January 2020 to September 2024. The analysis was conducted using Python on Google Colaboratory with evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that both SES and ARIMA effectively captured the upward trend of gold prices, with SES achieving slightly better accuracy across all datasets. The lowest MAPE value of 0.62% was obtained using SES on the ForexSB dataset, indicating an excellent forecasting performance. Therefore, SES is considered more efficient and reliable for non-seasonal time series with stable trends
Pendekatan Convolutional Neural Network dalam Mendeteksi Kemiringan Tulisan Tangan Menggunakan Framework YOLO Nurlita, Anna; muhammad haviz irfani; Zaid Romegar Mair
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10406

Abstract

Despite the continuous advancement of digital technology, handwriting still plays an important role, especially in the field of education as a means of evaluating students’ writing skills. However, manual handwriting assessment tends to be subjective and inconsistent, particularly in the aspect of slant, which can reflect the clarity, legibility, and personality of the writer. Therefore, an automated method capable of accurately and objectively detecting handwriting slant is required. This study aims to develop an automated system based on a Convolutional Neural Network (CNN) using the YOLOv5 framework to detect the handwriting slant of university students. The dataset consists of 680 handwriting images annotated into three categories: upright, left-slanted, and right-slanted. The training process was conducted through four main experiments with variations in parameters such as batch size, epoch, and image size. The best model configuration was achieved with a batch size of 16, 150 epochs, and an image size of 640, resulting in an mAP@0.5 score of 0.894 and an F1-score of 0.84 on the training data. Evaluation on the training data showed that the model successfully classified left-slanted handwriting with 97% accuracy, right-slanted with 95%, and upright with 84%. On the test data, the model also demonstrated good performance with an average mAP@0.5 of 0.59, recall of 0.835, and classification accuracies of 100% for left-slanted, 93% for right-slanted, and 57% for upright handwriting. This study demonstrates that the CNN approach using YOLOv5 is effective for handwriting slant detection and has great potential for application in other related fields
Penerapan Empirical-Bayes pada Sistem Peringkat Produk E-Commerce Pratama, Chandra; Ramadhan, Fahri; Arrazi Satria, Ghibran; Setiawan, Aji
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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

Abstract

This study examines the application of Empirical Bayes (EB) smoothing for product ranking in e-commerce platforms characterized by sparse sales signals and highly skewed transaction distributions. Under these conditions, top lists tend to fluctuate when rankings rely solely on raw cumulative sales, particularly for long-tail products; therefore, a method that balances population-level information with item-level evidence is required to produce more consistent top-k rankings. The method models purchase counts using a Gamma–Poisson framework, where a global prior is estimated from the overall data and item-level posteriors are updated so that the posterior mean serves as a smoothed popularity score. Experiments are conducted on real product catalogs (smartphones and laptops) augmented with a 12-week sales simulation featuring mild seasonality and promotional noise, and EB is compared against a naive baseline that ranks items by raw cumulative units sold under a rolling, week-by-week evaluation. Results show that EB improves NDCG@5 and NDCG@10 while reducing week-to-week Top-10 churn relative to the baseline, with the most notable gains observed for low-signal and long-tail items because shrinkage dampens extreme rank swings caused by sparse observations. Overall, EB smoothing is effective in stabilizing top-k product rankings for listing interfaces and administrative dashboards, and it can be extended through time-decayed priors and the incorporation of contextual features such as price and category to further improve ranking accuracy
Analisis Sentimen Kebijakan Makan Bergizi Gratis Menggunakan IndoBERT dan Machine Learning Sulistyo, Danang Arbian; Setiadi, Erik
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10546

Abstract

Media sosial telah menjadi forum vital untuk opini publik terhadap kebijakan pemerintah, seperti program "Makan Bergizi Gratis" (MBG) di Indonesia. Memahami sentimen ini sangat penting bagi pemangku kepentingan. Penelitian ini bertujuan untuk (1) menganalisis distribusi sentimen publik terhadap kebijakan MBG dan (2) menentukan model machine learning terbaik untuk klasifikasi sentimen tersebut. Penelitian ini menggunakan 12.389 tweet yang dikumpulkan dari platform X. Metode hybrid labeling, yang mengkombinasikan leksikon berbasis domain dengan IndoBERT, diterapkan untuk melabeli data secara otomatis. Untuk klasifikasi, tiga model (Random Forest, XGBoost, dan Ensemble) dibandingkan menggunakan fitur hybrid (TF-IDF trigram, embedding IndoBERT, dan fitur leksikon) pada dataset yang telah diseimbangkan dengan SMOTE. Hasil penelitian menunjukkan bahwa sentimen publik didominasi oleh sentimen negatif (68,6%), diikuti oleh positif (19,5%) dan netral (11,9%). Model Random Forest menunjukkan kinerja tertinggi, dengan pencapaian F1-Score rata-rata 0.9383 pada K-Fold cross-validation dan 0.9363 pada test set final. Studi ini membuktikan bahwa pendekatan hybrid yang diusulkan sangat efektif untuk klasifikasi sentimen publik berbahasa Indonesia pada domain kebijakan pemerintah.
Analisis Sentimen Ulasan Pemain Genshin Impact Menggunakan Kombinasi TF-IDF, Lexicon, dan Support Vector Machine Sulistyo, Danang Arbian; Fahrudillah, Mochammad Fiqi
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10553

Abstract

The rapid growth of the digital gaming industry in Indonesia has been accompanied by a significant increase in user-generated reviews on distribution platforms such as Google Play Store. This condition necessitates automated methods capable of efficiently interpreting player perceptions on a scale. This study conducts sentiment analysis on player reviews of Genshin Impact by developing a seven-stage analytical pipeline consisting of data preparation, lexicon-based labeling, TF-IDF feature extraction, Support Vector Machine (SVM) training, multi-metric evaluation, rule-based post-processing, and automated summarization using a Large Language Model. A total of 40,000 reviews from 2023 until 2025 were collected through web scraping and processed through text cleaning, slang normalization, tokenization, stopword removal, and stemming. Initial labels were generated using an updated domain-specific sentiment lexicon and subsequently refined through a rule-patch mechanism that handles negation, contrastive expressions, and domain-specific technical cues such as lag, bug, and crash. The SVM model was trained using a TF-IDF configuration (1–3 grams) and evaluated across 10 runs with different random seeds, producing an average accuracy of 0.945, a macro-F1 of 0.900, and stable performance across iterations. Visualization of sentiment distribution and WordClouds highlights prominent thematic patterns within each class, while automated summarization using IBM Granite provides qualitative insights into player appreciation of visual and character design, alongside complaints related to performance issues and the game’s gacha system. Overall, the integration of statistical, rule-based, and LLM-driven approaches demonstrates an effective and contextually robust framework for sentiment analysis in game analytics
Kolaborasi Algoritma K-Nearest Neighbor Dan Gradient Boosting Untuk Klasifikasi Diabetes Melitus Tipe 2 Oktavian, Aloysius; Arini, Florentina Yuni; Aryaputra, Daffa Pramata; Syanjalih, Alul Hidja; Aldevis, Mohammad Farrel
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10556

Abstract

Diabetes Melitus Tipe 2 (DMT2) telah menjadi salah satu tantangan kesehatan masyarakat terbesar di Indonesia, dengan prevalensi yang terus meningkat dan sebagian besar kasus tidak terdiagnosis. Deteksi dini menjadi kunci untuk mencegah komplikasi serius. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model klasifikasi berbasis machine learning untuk prediksi DMT2. Tiga pendekatan dieksplorasi: algoritma K-Nearest Neighbor (KNN), Gradient Boosting, dan model KNN + Gradient Boosting yang mengintegrasikan keduanya melalui arsitektur stacking ensemble. Kinerja diukur menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model ensemble secara signifikan mengungguli model tunggal. Model KNN mencapai akurasi 90.92% namun dengan presisi yang rendah untuk kelas diabetik (0.48). Model Gradient Boosting menunjukkan peningkatan signifikan dengan akurasi 95.50% dan presisi 0.72. Model KNN + Gradient Boosting menunjukkan kinerja terbaik dengan akurasi keseluruhan 96.17% dan presisi tertinggi untuk kelas diabetik (0.81), yang secara efektif mengurangi tingkat alarm palsu. Temuan ini mengindikasikan bahwa model KNN + Gradient Boosting memiliki potensi besar sebagai alat bantu pendukung keputusan klinis yang andal untuk identifikasi dini individu berisiko tinggi DMT2.
Perbandingan Metode Q-Learning Dan SARSA Dalam Optimasi Prediksi Tren Saham Pada Indeks Harga Saham Gabungan (IDX) Affarel, Muhammad; Fikri Ikhsan Ramadhan; Nugroho Aldi Prayoga
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10596

Abstract

Penelitian ini mengevaluasi kinerja algoritma Reinforcement Learning Q-Learning dan SARSA dalam menghasilkan strategi trading otomatis pada lima saham Bursa Efek Indonesia (BBCA, BBRI, TLKM, UNVR, dan ASII) melalui simulasi 1.000 episode. Analisis dilakukan berdasarkan pola reward, equity curve, dan statistik performa akhir untuk mengukur efektivitas pembelajaran pada kondisi pasar yang berbeda. Hasil penelitian menunjukkan bahwa Q-Learning lebih unggul pada saham dengan momentum harga kuat karena sifat eksplorasinya yang lebih agresif, sedangkan SARSA memberikan performa yang lebih stabil pada pasar dengan volatilitas tinggi karena pendekatannya yang konservatif dan on-policy. Secara keseluruhan, kedua metode tidak menunjukkan dominasi absolut, namun menawarkan keunggulan berbeda sesuai karakteristik saham dan profil risiko strategi. Temuan ini menegaskan potensi RL untuk pengembangan algorithmic trading di pasar Indonesia dan membuka peluang eksplorasi model lanjutan yang lebih adaptif
Implementasi YOLOv8 untuk Deteksi Pelat Nomor dan Validasi Pajak Kendaraan Pralega, Muhammad Wesya; Ningrum, Witta Listiya
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

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

Abstract

The increase in the number of vehicles in Indonesia requires a more efficient administrative system for vehicle validity validation. Manual verification processes such as checking vehicle registration certificates and license plates by officers in the field are considered ineffective, prone to error, and time-consuming, especially when dealing with high volumes of vehicles. This study aims to develop a computer vision-based automated system capable of detecting vehicle license plates and independently validating tax status. The method used is the CRISP-DM method, which includes understanding requirements, data processing, modeling, evaluation, and implementation. The model used is YOLOv8 to detect the license plate area, and EasyOCR is used for alphanumeric character recognition. The research dataset consists of 587 secondary images and 15 primary images. The secondary data was divided into 70% training data, 20% validation data, and 10% test data. The YOLOv8 model was trained using the best combination of hyperparameters, namely 200 epochs, batch size 16, and learning rate 0.01, which produced a box loss value of 0.38. The tax status validation process is divided into four categories: active, expired, invalid, and no tax information available. Thus, this research can contribute to the development of an effective vehicle tax validation automation system that has the potential to be implemented in public administration services.
Klasterisasi Topik Khotbah Pendeta Di GBI MPI Palembang Dengan Metode DBSCAN Nando, Kristian Fernando; Hafiz, Hafiz Irsyad
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10641

Abstract

Comprehensive evaluation of the teaching curriculum proportion at GBI Rayon 15 Musi Palem Indah (MPI) Palembang is a fundamental element in ensuring the doctrinal health of the congregation. However, the current evaluation process is inefficient due to reliance on manual mapping of ever-growing sermon archives. This conventional method carries a high risk of subjectivity bias, making it difficult for church leadership to objectively observe teaching theme trends. This study addresses this issue by developing an automated document clustering system based on Text Mining to process 406 sermon summary documents from the 2023-2025 period. The methodology includes preprocessing, Term Frequency-Inverse Document Frequency (TF-IDF) weighting to highlight distinctive theological terms, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN was specifically selected for its superiority in handling data with varying densities and its ability to isolate outliers without requiring a static cluster count parameter. Test results indicate an optimal configuration at Epsilon 0.3 and MinPts 3, yielding very high internal validity with a Silhouette Coefficient of 0.8888 and forming 32 core topic clusters. Significant findings reveal a high noise ratio (71%), which effectively separates incidental topics, such as holiday celebrations, from regular material. Practically, these results serve as an early warning system mechanism for the church to detect doctrinal imbalances or material gaps, providing a strategic data-driven foundation for holistic curriculum improvement.
Prediksi Dropout Mahasiswa: Early-Warning Berbasis Enrollment dengan Machine Learning putra, Febri andika; Mirajdandi, Syahisro; Nandra; Okmarizal, Bisma; Mulyanda, Sandy
JURNAL FASILKOM Vol. 15 No. 3 (2025): 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.v15i3.10714

Abstract

Dropout among university students remains a major challenge in higher education because it affects study continuity, institutional performance, and the efficiency of academic service planning. This study develops a machine learning–based Early Warning System (EWS) that leverages data available at enrollment and is updated after the first semester. Using the public dataset “Predict Students’ Dropout and Academic Success” (n = 4,424), the original three-class outcome (Dropout, Enrolled, Graduate) is simplified into a binary target, with dropout treated as the positive class. Two feature scenarios are evaluated: (1) enrollment-only for pre-entry screening and (2) enrollment plus first-semester indicators to update risk scores. Three models are compared: class-balanced Logistic Regression, class-balanced Random Forest, and Gradient Boosting. Model performance is assessed using accuracy, precision/recall/F1score for the dropout class, balanced accuracy, and ROC-AUC. Under the enrollment-only setting, Logistic Regression achieves the best early-warning performance (recall = 0.697; F1 score = 0.651). After incorporating first-semester features, performance improves (recall = 0.792; F1score = 0.779). Beyond model comparison, this study adds an operational perspective through confusion-matrix simulation and probability-threshold analysis to balance missed at-risk cases and follow-up workload.

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