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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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Prediksi Dropout Mahasiswa: Early-Warning Berbasis Enrollment dengan Machine Learning Andika Putra, Febri; Mirajdandi, Syahisro; Nandra, 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.
Analisis Penerapan Metode WASPAS untuk Penentuan Pola Belajar Mahasiswa Berdasarkan Gaya Belajar Ester, Ria; Yuniarti, Dian Tri; Valentina, Putri Eka; Kusumah Putra, Faris Maulana
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.v15i3.10768

Abstract

Higher education in the digital era requires learning approaches that are able to adapt to individual student characteristics, including differences in learning styles. This study aims to develop a model for assessing students’ learning patterns and to provide more personalized learning recommendations using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The data used are secondary data obtained from 1,000 students with seven learning criteria, namely academic score, course participation, attendance rate, physical activity, emotional engagement, device usage, and feedback score. The WASPAS method is applied through two main stages, namely the calculation of the Weighted Sum Model (WSM) and the Weighted Product Model (WPM), which are then aggregated to produce a composite WASPAS score for each student. Manual calculations are demonstrated using five student samples, while computations for the entire dataset are performed using Python in the Jupyter Notebook environment. The results show that students’ WASPAS scores range from 0.2815 to 0.9914 with a distribution that tends to be normal. Most students fall into the “fair” to “very good” learning pattern categories, while a small proportion are classified as “very high” and “requiring special attention.” Analysis based on visual, auditory, and kinesthetic learning styles indicates differences in average WASPAS scores across groups, supporting the effectiveness of the WASPAS method in integrating multiple learning criteria simultaneously. These findings demonstrate that WASPAS can be used as a decision support tool to map student learning profiles and assist in designing more adaptive, targeted, and personalized learning strategies in higher education
Analisis Pola Lalu Lintas Kapal Selat Bali Berbasis AIS, K-Means, Traffic Flow Adi Setyawan, Deny; Purwatiningsih, Agustina; Sulistyo Budi, Febrian
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.v15i3.10782

Abstract

The Bali Strait is one of the busiest sea crossing routes in Indonesia, characterized by high intensity of ship movements and dynamic traffic patterns throughout the day. These conditions require comprehensive analysis to understand the characteristics of vessel movements, identify density zones, and determine peak periods that potentially increase navigation risks. This study aims to analyze ship traffic patterns in the Bali Strait using a combination of K-Means Clustering and Traffic Flow Model based on Automatic Identification System (AIS) data. The dataset consists of 790 AIS records collected during the period of 20–26 June 2025. The research stages include data preprocessing, determination of the optimal number of clusters using the Elbow method, classification of vessel movement behavior using the K-Means algorithm, and analysis of traffic parameters comprising volume, speed, density, and traffic flow. The results reveal the formation of three main clusters: low-speed vessels concentrated around port areas, medium-speed vessels operating on main trajectories, and high-speed vessels dominating the crossing lanes. Evaluation of clustering quality using the Silhouette Coefficient produced a value of 0.3040, indicating a reasonably good level of cluster separation. Furthermore, a consistent peak hour pattern was identified at 12:00, along with two high-density zones located near Ketapang Port and Gilimanuk Port. These findings demonstrate that AIS-based analysis is capable of providing measurable representation of the dynamics of ship traffic in the Bali Strait and has the potential to support operational optimization, enhancement of navigation safety, and consideration for the implementation of a Traffic Separation Scheme (TSS)
Klasifikasi Mahasiswa Calon Penerima Beasiswa KIP Menggunakan Algoritma Naive Bayes di Universitas Tomakaka Mamuju Hidayat, Hidayat; KH, Musliadi; Kusmanto, Indar; Kadir, Munawirah; Kristian, Kristian
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.v15i3.10784

Abstract

Education is a fundamental aspect of national development that demands equal access to quality education for all. The Smart Indonesia Card (KIP) program is a government initiative aimed at supporting education for underprivileged communities. Tomakaka University, Mamuju, as one of the universities in West Sulawesi, plays an active role in distributing KIP scholarships to students who meet certain criteria. However, the selection process for prospective scholarship recipients has been carried out manually, which may lead to inefficiencies and inaccurate targeting. This study aims to apply the Naïve Bayes algorithm to classify prospective KIP scholarship recipients to make the selection process more objective, fast, and accurate. The research method uses a data mining approach with stages of data preprocessing, dividing training and test data, model training, and testing using the Python programming language on the Google Colab platform. The dataset used is 171 student data, with a division of 75% training data and 25% test data. The test results showed that the Naïve Bayes model achieved an accuracy of 95.35%, with a precision of 97%, a recall of 97%, and a loss of 4.65%, indicating excellent classification performance. Thus, this research contributes to improving administrative efficiency and targeting of KIP scholarship distribution at Tomakaka University, Mamuju.
Analisis Perbandingan Metode DBSCAN dan Mean Shift Dalam Mengelompokkan Data IPM Kabupaten/Kota se-Indonesia Fasihullisan Damopolii, Muhamad Rizki; Zahrotun, Lisna; Soleliza Jones, Anna Hendri
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.v15i3.10797

Abstract

This study extends previous research that clustered the 2019 Human Development Index (HDI) data of regencies and cities in Indonesia using K-Means, K-Medoids, and Agglomerative Hierarchical Clustering (AHC). HDI is an important indicator for describing the level of regional development; therefore, clustering analysis of HDI data is needed to support more targeted development policy formulation. However, these conventional clustering methods have limitations, including the requirement to predefine the number of clusters and their limited ability to handle noise. Therefore, this study applies and compares two density-based clustering algorithms, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Mean Shift, which are capable of forming clusters automatically without specifying the number of clusters in advance and can effectively handle noise. The determination of optimal parameters for each method is conducted using the Sw/Sb Ratio metric, which measures the ratio between within-cluster and between-cluster standard deviations. The results show that Mean Shift with an optimal bandwidth parameter of 1 achieves an Sw/Sb Ratio value of 0.3609, which is better than DBSCAN with a value of 0.3739, and also outperforms the clustering methods used in previous studies, which produced a value of 0.51. These findings indicate that density-based clustering algorithms, particularly Mean Shift, provide more representative clustering results for HDI data and may serve as a more effective alternative method for analyzing human development data in Indonesia.
Klasifikasi Algoritma Kriptografi pada Pesan Terenkripsi menggunakan Support Vector Machine (SVM) Fatma, Yulia; Gunawan, Rahmad; Fitri, Nurkhairi; Firdaus, Rahmad; Hayami, Regiolina; Soni, Soni
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.v15i3.10843

Abstract

Data protection has become a highly critical aspect, particularly in addressing ransomware threats that illegally encrypt data. This study is important to evaluate the capability of machine learning techniques in identifying encryption algorithms used in encrypted data, especially in ransomware attacks. This work represents an initial step that can assist cybersecurity practitioners in more rapidly understanding attack patterns, determining appropriate response strategies, and enhancing proactive mitigation and response efforts to protect data against increasingly complex cyber threats. The machine learning algorithm employed in this study is the Support Vector Machine (SVM). The dataset consists of ciphertext generated using the AES, DES, and Vigenère Cipher cryptographic algorithms. The feature extraction process utilizes ten statistical features to capture the distinctive patterns of each type of ciphertext. The SVM model is developed using a data split of 90% for training and 10% for testing. Performance evaluation is conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The result demonstrate an average accuracy 0f 92,33%, with the vigenere cipher being perfectly classified (100% accuracy). Howefer, slight misclassifications occured beetween AES and DES duet o their similiar entropy chraracteristic. Experimental results demonstrate that the SVM model is capable of identifying encryption algorithms with high accuracy and balanced classification performance across the three algorithm classes. These findings highlight the potential of machine learning approaches for detecting encryption algorithms in cyber-attacks, thereby making a meaningful contribution to the improvement of proactive data security mitigation and response strategies.
Perancangan Aplikasi Mobile Pengelolaan Data Mahasiswa Berbasis Kodular Dengan Integrasi Airtable Alda, Muhammad; dewo Pangestu, Aji; Pasaribu, M Arif Rahmat; Mahayudi, Indra Putra; Muzammil, Umar
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.v15i3.10845

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan aplikasi mobile pengelolaan data mahasiswa menggunakan platform no-code Kodular yang terintegrasi dengan Airtable sebagai cloud database. Aplikasi ini dikembangkan untuk mempermudah proses administrasi data mahasiswa seperti input, edit, hapus, tampilan (view), dan pembaruan (refresh) data secara daring. Pengembangan dilakukan dengan menggunakan metode Waterfall yang terdiri dari lima tahapan, yaitu analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Implementasi dilakukan dengan membangun satu layar utama (single screen application) yang dilengkapi form input NIM, nama, jurusan, dan angkatan serta lima tombol utama yang terhubung ke Airtable melalui API. Pengujian menggunakan metode black-box menunjukkan bahwa seluruh fungsi berjalan sesuai spesifikasi dan aplikasi dapat melakukan sinkronisasi data secara real-time. Hasil penelitian menunjukkan bahwa aplikasi ini layak digunakan sebagai solusi digital untuk pengelolaan data akademik sederhana dan dapat menjadi referensi pengembangan aplikasi serupa di bidang pendidikan.
Analisis Perbandingan Tools Forensic Pada Aplikasi Facebook Messenger Menggunakan Metode National Institute of Standards Technology (NIST) Mualfah, Desti; Israndi, Febri; Ramadhan, Rizdqi Akbar
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.v15i3.10862

Abstract

Therapid development of digital technology has driven a significant increase in internet and social media use across all levels of society. This situation not only facilitates communication and information exchange but also opens up opportunities for various forms of cybercrime. One social media platform frequently exploited in cybercrime activities is Facebook, particularly through its instant messaging feature. Therefore, a systematic and standardized digital forensic investigation process is needed to accurately obtain and analyze digital evidence. This study aims to analyze the application of digital forensic stages using the National Institute of Standards and Technology (NIST) method on the Facebook social media platform. The research method used is acase study with a digital forensic approach based on the NIST framework, which includes the stages of collection, examination, analysis, and reporting. The process of acquiring and analyzing digital evidence was carried out on an Android-based smartphone device using two forensic tools, namely Magnet AXIOM and MOBILedit Forensic. The results of the study indicate that MOBILedit Forensic has better data acquisition capabilities, especially in extracting application artifacts and image data relevant to the case. Meanwhile, Magnet AXIOM demonstrates superiority in aspects of data analysis, result visualization, and integration with other forensic platforms. Based on the results of the comparison, MOBILedit Forensic is recommended as a more effective digital forensic tool for the investigation process on Android devices, especially in handling cybercrime cases involving social media applications.
Perbandingan Algoritma Regresi dalam Memprediksi Penjualan Berdasarkan Indikator Sosial Ekonomi Kabupaten Cirebon (2010-2023) Rahmah, Muthia; Ramadanti, Kanaya; Aulia, Imelda Fransiska
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.v16i1.9729

Abstract

A comparative study of four regression algorithms, namely Support Vector Regression (SVR), Gradient Boosting Regressor (GBR), Random Forest Regressor (RFR), and Extreme Gradient Boosting (XGBoost), was conducted to predict annual aggregate sales based on socioeconomic indicators in Cirebon Regency from 2010 to 2023. The study utilized secondary data obtained from the Central Bureau of Statistics (Badan Pusat Statistik) of Cirebon Regency. Five predictor variables were employed, including life expectancy, expected years of schooling, mean years of schooling, per capita expenditure, and the Human Development Index (HDI). Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R-squared). The experimental results indicate that the GBR model achieved the best predictive performance, with the lowest error values (MAE = 127.98 and RMSE = 185.63) and the highest R² value (0.94), outperforming RFR, XGBoost, and SVR after parameter tuning. Feature importance analysis consistently identified life expectancy as the most influential variable across models. These findings demonstrate that ensemble-based regression methods, particularly boosting algorithms, are effective for modeling complex socioeconomic patterns and can support data-driven economic forecasting and regional policy planning
Analisis Sentimen Program Makan Bergizi Gratis Menggunakan Lexicon-Based dan Support Vector Machine Akbar, Zulfikri; Riadi, Imam; Umar, Rusydi
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.v16i1.10948

Abstract

Public policy initiatives often trigger massive shifts in digital public opinion, such as the Free Nutritious Meal Program (MBG), which has garnered extensive attention from the Indonesian public on social media. Sentiment analysis serves as a vital instrument to map public opinion trends, particularly when dealing with large-scale, unstructured, and heterogeneous textual data. This study aims to analyze the distribution of public sentiment toward the MBG Program and evaluate the effectiveness of the lexicon-based method and Support Vector Machine (SVM) algorithm in classifying opinion texts. The dataset was collected from Twitter (X) via the Kaggle platform, comprising 10,524 public comments. The methodology begins with text preprocessing, including cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Sentiment labeling was performed automatically using a lexicon-based approach referring to the InSet Lexicon to categorize data into three classes: positive, negative, and neutral. Subsequently, text representation was conducted using the Term Frequency–Inverse Document Frequency (TF–IDF) method and classified using an SVM model with a nested cross-validation scheme to maintain performance stability. The results indicate that public opinion is dominated by neutral sentiment at 48.1% (5,066 data points), followed by positive sentiment at 30.8%, and negative sentiment at 21.0%. This dominance of neutral sentiment reflects an informative, descriptive, and cautious public stance toward a policy still in its early implementation stages. Evaluation of the SVM model demonstrates highly stable and reliable performance, achieving an accuracy of 89.26%, with precision, recall, and F1-score each at 89%. This study concludes that the combination of lexicon-based automatic labeling and SVM is effective for public policy sentiment analysis, providing insights into public expectations and concerns regarding government programs.

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