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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) INOVTEK Polbeng - Seri Informatika Journal of Community Based Environmental Engineering and Management Indonesian Journal of Computing, Engineering, and Design Journal of Applied Data Sciences Journal of Educational Technology and Learning Creativity Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Luhur Bayuaji, Luhur
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Other

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Journal : Indonesian Journal of Computing, Engineering, and Design

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Optimizing Malware Detection and Prevention on Proxy Servers Through Random Forest and Lexical Feature Analysis Andalas Saputra, Meitro Hartanto; Pebrianti, Dwi; Bayuaji, Luhur; Rusdah
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 7 No. 1 (2025): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v7i1.485

Abstract

Malware has become a significant concern due to the increase in malicious websites hosting spam, phishing, malware, and other threats. This research aims to predict malware URLs using lexical features for feature extraction and random forest for classification. The dataset, sourced from kaggle.com, includes benign, phishing, spam, malware, and defacement URLs. To address data imbalance, random oversampling was applied for balanced training. Recursive feature elimination was used to optimize lexical features, testing various sets of features (10, 15, 19, 23, 29, 35) for classification accuracy, achieving 98% accuracy using 23 features. Validation tests with actual university network data confirmed this model’s effectiveness, classifying malicious URLs in 9 minutes using 11,566 samples. URL filtering involved log analyzer tools capturing internet traffic during working hours over one month. Results suggest that this approach can efficiently classify malicious URLs and could be implemented for real-time detection in proxy server logs, aiding IT departments in preventing malware spread via web traffic.
Using Content-Based Filtering and Apriori for Recommendation Systems in a Smart Shopping System Pebrianti, Dwi; Ahmad, Denis; Bayuaji, Luhur; Wijayanti, Linda; Mulyadi, Melisa
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 6 No. 1 (2024): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v6i1.393

Abstract

This research is motivated by the increasing significance of online shopping platforms and the challenges faced by users in locating products that align with their preferences and requirements, which can significantly influence the sales performance of online retailers. Consequently, the primary objective of this study is to design and implement a recommendation system capable of identifying suitable products and forecasting the purchase frequency for various product combinations, while also integrating this recommendation system with a smart shopping platform. To achieve this objective, the research employs machine learning techniques, specifically content-based filtering and the Apriori algorithm. Content-based filtering is utilized to analyze user preferences and behavioral patterns related to visited products, while the Apriori algorithm is employed to evaluate support and confidence values for item set combinations, thereby generating frequency values for future transactions involving product combinations. Additionally, a smart shopping system is developed and integrated, enhancing the shopping experience through smartphone applications and streamlining the payment process to facilitate seamless product purchases. The research methodology involves data collection pertaining to products and user preferences, followed by several testing involving a sample group of user respondents. The results demonstrate that the developed recommendation system effectively delivers relevant product recommendations based on user preferences, achieving a confidence value up to 98%. Furthermore, the smart shopping system proves capable of independently assisting users throughout the transaction process, thereby enhancing overall user experience and convenience.
Co-Authors Adi Purnama Adrianus I Wayan Ilia Yuda Sukmana Ahmad, Denis Andalas Saputra, Meitro Hartanto Bagus Alit Prasetyo Dadan Dasari Dadang Juandi Dr. I Nyoman Tika,M.Si . Haris Kurniawan, Haris Hendriyanto, Agus Herlistiono, Iwa Ovyawan Husni Teja Sukmana Huynh, Phuoc-Hai I Gede Astawan I Gusti Ayu Tri Agustiana Melisa Mulyadi Pahmi, Samsul Pebrianti, Dwi Raden Danang Aryo Putro Satriyono Rahman, Abdul Wahab Abdul Rusdah Rusdah Rusdah Siswanto, Rizki Dwi Siti Ummi Masruroh Sriyani Violina Tengku Riza Zarzani N Viddi Mardiansyah Wijayanti, Linda Yamin, Mochamad Amzah Yaya S. Kusumah Zainal Arifin Hasibuan