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All Journal ELKHA : Jurnal Teknik Elektro Semantik Jurnas Nasional Teknologi dan Sistem Informasi Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Pemanfaatan Algoritma K-Means untuk Membuktikan Implementasi Undang-Undang Pelanggaran Hukum Korupsi di Pengadilan Negeri Banjarmasin Paramita, Cinantya; Rafrastara, Fauzi Adi; Supriyanto, catur
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5216

Abstract

This research aims to demonstrate the implementation of the Anti-Corruption Law in the Banjarmasin District Court by utilizing the K-Means algorithm. Corruption, which persists in Indonesia over a prolonged period, has reached a critical level, making it crucial to enforce the law fairly and firmly. In this study, the panel of judges in the Banjarmasin District Court was analyzed using the K-Means Clustering method and silhouette coefficient to decide corruption cases that result in state losses. The research findings indicate that the optimal number of clusters is 3, with a value of 0.686. However, there is also a lowest value among the 4 clusters, which is 0.454. These clusters are then divided into three categories of enforcement, namely cases that have been executed (108 cases), cases that will be executed (26 cases), and cases that have not been executed (2 cases). All clusters have a silhouette score of 0.742, indicating successful enforcement. This research provides concrete evidence that the panel of judges in the Banjarmasin District Court has implemented the Anti-Corruption Law while considering state losses. By utilizing the K-Means algorithm, this study also contributes to a better understanding of enforcement practices in the court. It is expected that the results of this research will support efforts to enhance the implementation of the Anti-Corruption Law in Indonesia, particularly in the Banjarmasin District Court
Deteksi Malware menggunakan Metode Stacking berbasis Ensemble Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4606

Abstract

Serangan malware kian hari kian memprihatinkan. Evolusi malware yang cepat dan semakin destruktif menimbulkan kekhawatiran bagi banyak pihak. Oleh karena itu, deteksi malware yang efektif sangat dibutuhkan. Data mining memainkan peran yang krusial dalam bidang ini, mengingat algoritma-algoritma yang ada pada data mining bisa dilatih hingga menghasilkan akurasi yang paling tinggi. Untuk mengklasifikasi suatu file, apakah tergolong malware atau tidak, dalam penelitian ini metode stacking digunakan karena dapat meningkatkan akurasi jika dibandingkan dengan algoritma-algoritma klasifikasi konvensional. Empat Algoritma dilibatkan dalam eksperimen yang dilakukan, yaitu: Neural Network, Random Forest, kNN, dan Logistic Regression. Tiga algoritma pertama digunakan sebagai classifier pada level 0, sementara itu Logistic Regression digunakan classifier pada level 1 (meta classifier).  Dengan kombinasi 4 algoritma tersebut, akurasi yang diperoleh adalah sebesar 98.7%, dan akurasi tersebut merupakan yang paling tinggi jika dibandingkan dengan masing-masing algoritma jika dieksekusi secara individual.
Aplikasi Prediksi IHSG Berbasis Web Dengan Integrasi Multi-Algoritma Waluyo, Dwi Eko; Paramita, Cinantya; Kinasih, Hayu Wikan; Pergiwati, Dewi; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6193

Abstract

The four regression algorithms used in predicting the Composite Stock Price Index (IHSG) have contributed significantly, as the test results show that the Decision Tree algorithm outperforms k-Nearest Neighbor, Linear Regression, and Random Forest, especially in terms of Mean Squared Error (MSE) and R2 score. The stages of data collection, pre-processing, and modeling, followed by model performance measurement, have provided valuable insights into the effectiveness of each algorithm. The success of the Decision Tree in this testing has further propelled its development into a web-based application. This conversion process, following the outlined flowchart, integrates various essential aspects of the model, including user interface and back-end integration, ensuring that the application can be accessed and used efficiently and effectively. Furthermore, the black box testing and User Acceptance Testing (UAT) results, using the Mean Opinion Score (MOS), enhance the validity and reliability of the application. Black box testing involving 2 features with 37 steps demonstrates the system's effectiveness in producing valid outputs, from the initial menu display to the prediction results. Additionally, UAT involving students and entrepreneurs as respondents provides in-depth insights into user acceptance. With a focus on functionality at 97.08%, reliability at 96.09%, and usability at 98.09%, UAT yields high scores in all three aspects, with usability achieving the highest score. These results not only confirm the efficiency of the system in performing its functions but also indicate a high level of user satisfaction, strongly suggesting the potential for widespread adoption of this application in the future.
Pengembangan Sistem Klasifikasi Karakteristik Siswa Berbasis Website dengan menggunakan Algoritma C4.5 Paramita, Cinantya; Rafrastara, Fauzi Adi; Kencana, Lisdi Inu
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4678

Abstract

Student characteristics are one of the attributes of knowing a student's thinking skills and academic abilities. In the process of teaching and learning, appropriate learning strategies must be applied to students. The Hippocrates-Galenus typology categorizes personality types into four different categories, namely sanguine, choleric, melancholic and phlegmatic. Classification of characteristics that use an approach to students based only on experience or intuition can produce inaccurate results and take a lot of time to process. A system with the ability to predict student characteristics is needed in order to be able to assess students more quickly. In this study, the C4.5 algorithm was implemented into a system that aims to carry out the process of classifying the characteristics of students. From the results of the tests carried out, the C4.5 algorithm obtains an accuracy of 90.08%. This shows it is able to classify student characteristics well by using the C4.5 algorithm
Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan Waluyo, Dwi Eko; Kinasih, Hayu Wikan; Paramita, Cinantya; Pergiwati, Dewi; Nohan, Rajendra; Rafrastara, Fauzi Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 1 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i1.6105

Abstract

Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.
Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti; Ahmed, Foez
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5207

Abstract

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.
Optimizing URL-Based Phishing Detection Using XGBoost and Relief Feature Selection Tyas, Wahyu Suryaning; Rafrastara, Fauzi Adi; Ghozi, Wildanil
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15651

Abstract

Phishing is a significant cybersecurity threat in which attackers exploit manipulated URLs to deceive users and obtain confidential information. As phishing attacks continue to grow in complexity, automated machine learning based detection methods have become essential to strengthen digital security. This study proposes a URL based phishing detection model using boosting algorithms while analyzing the role of feature selection in improving classification performance and computational efficiency. The experiments were conducted on a dataset consisting of 10000 instances with 50 features and balanced class labels. After data preparation, 48 features were retained as input variables, and min max normalization was applied to ensure uniform feature scaling. Three boosting algorithms namely Gradient Boosting, XGBoost, and AdaBoost were evaluated using accuracy, precision, recall, and F1 score. Among these methods, XGBoost achieved the highest accuracy of 98.8 percent, demonstrating its effectiveness in learning complex URL patterns. Subsequently, three feature selection techniques namely Information Gain, Chi Square, and ReliefF were applied and evaluated using 10 fold cross validation. The results indicate that ReliefF provides the most effective feature reduction by selecting 37 features while maintaining the same classification accuracy. Unlike previous studies that mainly focus on classifier comparison, this study demonstrates that integrating XGBoost with ReliefF enables significant feature dimensionality reduction without compromising predictive accuracy. This finding highlights an efficient trade off between detection performance and computational complexity. Overall, the proposed framework offers a robust, efficient, and scalable solution for fast and adaptive phishing detection in modern cybersecurity environments.
Enhancing Fraud Detection Performance in E-Commerce Platforms Using Gradient Boosting Algorithms Ardi Saputra; Fauzi Adi Rafrastara; Wildanil Ghozi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s8q6t594

Abstract

The rapid growth of e-commerce has attracted many users. However, as transaction volumes increase, so do cases of fraud. This not only causes financial losses for sellers but also threatens the trust that is so important in the e-commerce industry. Previous studies have used the Naïve Bayes and Multilayer Perceptron algorithms to detect fraud in e-commerce with accuracy percentages of 95.00% and 94.00%, respectively, without other assessment measures, including precision, recall, and F1-score. This research seeks to create a predictive model for the likelihood of online sales fraud by comparing Gradient Boosting, Neural Network, Random Forest, and Naïve Bayes models through feature extraction and feature scaling pre-processing, with 10-fold cross-validation. The dataset used was taken from the Kaggle platform. The features included in the dataset include buyer characteristics, products sold, transaction volume, devices used, and other fraud indicators. The study's findings demonstrate that the Gradient Boosting algorithm excels in detecting fraud risk with an accuracy rate of 95.30%, precision of 94.10%, recall of 95.30%, and an F1-score of 93.80%.  These findings are anticipated to enhance the development of more efficient e-commerce security solutions.
Improving Internet of Things Cyber Attack Detection with Information Gain and Decision Tree Alvin Mufidha Ahmad; Fauzi Adi Rafrastara
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/p2c33t87

Abstract

The rapid proliferation of Internet of Things (IoT) devices within digital ecosystems has enhanced efficiency and availability but has also expanded the attack surface for cyber threats. This study aims to improve intrusion detection accuracy in IoT environments by addressing two key challenges: class imbalance and high feature dimensionality. Random Undersampling (RUS) is employed to mitigate data imbalance in the CIC IoT 2023 dataset, while feature selection is performed using the filter-based Information Gain method. A decision tree classifier is implemented and validated using k-fold cross-validation to ensure result reliability. Experimental results demonstrate that the proposed approach achieves an accuracy of 88.7%, outperforming a wrapper-based method, which attained 87.3%. These findings confirm that an appropriately designed filter-based feature selection strategy can effectively enhance the performance of intrusion detection systems for IoT security.
A Comparative Analysis of P-Value and Mutual Information Feature Selection Methods for Random Forest-Based Phishing Detection Fahmi Bahtiar Adi Nugroho; Wildanil Ghozi; Fauzi Adi Rafrastara
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 3 (2025): Desember 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i3.2025.377-386

Abstract

The application of ANOVA's P-Value-based feature selection method, namely the F-test, in phishing detection with the Random Forest algorithm indicates that a configuration of 25 features yields the quickest inference time, rendering it appropriate for scenarios demanding great computational efficiency and responsiveness. However, if the user's primary priority is to achieve the highest level of detection accuracy, the 29-feature configuration is more feasible because it exhibits higher accuracy performance and better prediction stability. Consequently, there is no definitive trade-off between 25 or 29 features, there exists a selection of solutions that can be tailored to the application's requirements. This methodology enables users to achieve an optimal equilibrium between superior performance and minimal inference time in a phishing detection system, contingent upon the implementation context and operational priorities. This study successfully shows that a simple statistical approach such as P-Value is not only competitive but also provides superior results compared to more complex methods, offering a practical and efficient solution for real-world implementation.
Co-Authors Abdollah, Mohd. Faizal Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Adhitya Nugraha Adhitya Nugraha Agus Winarno Ahmed, Foez Ajib Susanto Al Fahreza, Muhammad Daffa Alomoush, Ashraf Alvin Mufidha Ahmad Amalia, Syafira Rosa Amiral, Afinzaki Anargya, Muhammad Alden Nayef Ardi Saputra Astuti, Yani Parti Bagus Nurseto, Muhammad Narendra Bintoro, Jevan Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Cinantya Paramita Cinantya Paramita Dewi Pergiwati Dwi Eko Waluyo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erlin Dolphina Etika Kartikadarma Fahmi Bahtiar Adi Nugroho Firmansyah, Muhammad David Foez Ahmed Ghozi, Wildani Hamadeh, Emad Hayu Wikan Kinasih Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ilma, Zidni Kencana, Lisdi Inu L. Budi Handoko Latifah, Ines Aulia Lisdi Inu Kencana Mahendra, Syafrie Naufal Megantara, Rama Aria Mohamed Yassin, Warusia Nohan, Rajendra Osman, Waleed Mahgoub Paramita, Cinantya Pergiwati, Dewi Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Rahmawati, Firnanda Ramadhan Rakhmat Sani Ramadhani, Arsabilla Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Solichul Huda Sudibyo, Usman T. Sutojo Tyas, Wahyu Suryaning Usman Sudibyo Widyatama, Thierry Wildanil Ghozi Yani Parti Astuti Yani Parti Astuti Yani Parti Astuti Yassin, Warusia