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All Journal JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem)
Afghani, Azhar Al
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Computer Science & IT

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Implementasi Data Mining Metode C.45 untuk Memprediksi Peminat Kuota Internet pada Masa Covid 19 Susana, Heliyanti; Khofidoh, Shanti; Afghani, Azhar Al
MEANS (Media Informasi Analisa dan Sistem) Volume 6 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (703.023 KB) | DOI: 10.54367/means.v6i2.1513

Abstract

During the Covid 19 pandemic, Telering experienced an increase in sales and there were several internet SIM cards that consumers were less interested in. Various kinds of internet cards are sold and offered to attract consumers' attention, but it cannot be predicted which internet cards the consumers will be interested in. The C4.5 algorithm is a data classification algorithm with a decision tree technique that has advantages. These advantages, for example, can process numeric (continuous) and discrete data, can handle missing attribute values, produce easy rules. The results of the study explain that all providers including Telkomsel, Indosat, and axis are in demand by consumers. In analyzing using the C.45 algorithm with the Rapidminer Tools, the first is that the decision tree results in determining consumer interest are seen from the price, while the accuracy using the C.45 algorithm or decision tree produces an accuracy of 94.67%.
Analisis Log Server dengan Data Mining untuk Deteksi Aktifitas Malicious Hilmi, Muhammad Anis Al; Cahyanto, Kurnia Adi; Afghani, Azhar Al; Hadibrata, Badrudin
JUKI : Jurnal Komputer dan Informatika Vol. 8 No. 1 (2026): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v8i1.2318

Abstract

Web server security is a primary concern amid the rising wave of cyber threats. Every user interaction with a web application is recorded in server logs, which contain valuable information including IP addresses, request methods, response status codes, and data sizes. This study leverages server log data from January to July 2019 collected from an educational institution to detect malicious activities using a data mining approach. After preprocessing and rule-based labeling into three classes Safe, Suspicious, and Dangerous  dimensionality reduction was applied via Linear Discriminant Analysis (LDA) before classification using five algorithms: SVM-RBF, SVM-Linear, SVM-Polynomial, K-NN via GridSearch, and Decision Tree. Results show that SVM-RBF delivers the most stable performance, achieving a training accuracy of 88% and testing accuracy of 86%. However, class imbalance affects recall scores for certain categories. This study confirms the effectiveness of combining LDA and SVM-RBF as a basis for log-based intrusion detection systems, while also highlighting the need for further development through data balancing techniques and additional feature engineering.
Co-Authors Badrudin Hadibrata Khofidoh, Shanti Kurnia Adi Cahyanto Muhammad Anis Al Hilmi Susana, Heliyanti