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All Journal SITEDI Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Sofa Machabba Haeta
Universitas Pancasakti Tegal
Computer Science & IT Control & Systems Engineering Mathematics

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Decision Tree-Based Anomaly Traffic Detection for Local Area Network (LAN) Security Using Wireshark and Nmap Data Analysis Rizki Prasetyo; Sulistyaningrum; Lucky Primanda Saputra; Sofa Machabba Haeta
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

The development of information technology encourages the use of LAN networks as primary infrastructure in educational environments. The high intensity of network usage at the Faculty of Engineering and Computer Science increases security risks such as unauthorized access, open ports, and anomalous traffic, making comprehensive network security analysis necessary. This study aims to analyze LAN network security at the Faculty of Engineering and Computer Science using MikroTik (RB951Ui-2HnD) and TP-Link (TL-WR841N) devices, and to apply the Decision Tree algorithm for network traffic classification. The methods include router configuration analysis, port scanning using Nmap (Zenmap), packet sniffing analysis with Wireshark, and network traffic classification using the Decision Tree algorithm implemented in RapidMiner Studio. Port scanning results indicate that from 1000 scanned ports, only 5 ports (0.5%) were detected as open. Wireshark packet capture over 5 minutes collected 6,708 packets, revealing the presence of unencrypted HTTP packets and TCP errors. The Decision Tree model achieved an accuracy of 86.05%, precision of 99.94%, and recall of 73.85% in classifying normal and anomalous traffic. This approach effectively provides an overview of LAN security conditions and can serve as a reference for improving network security in educational institutions.
Perbandingan Algoritma C4.5, Random Forest, dan Naive Bayes untuk Memprediksi Klasifikasi Usia: Comparison of C4.5, Random Forest, and Naive Bayes Algorithms for Predicting Age Classification Sofa Machabba Haeta; Hasbi Firmansyah
SITEDI (Sistem Informasi dan Teknologi Digital) Vol. 3 No. 5 (2026): Jurnal Sistem Informasi dan Teknologi Digital (SITEDI)
Publisher : Universitas Teknologi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70888/sitedi.v3i5.105

Abstract

Penelitian ini membandingkan kinerja tiga algoritma yakni C4.5, Naive Bayes, dan Random Forest. Ketika memprediksi klasifikasi usia menggunakan data National Health and Nutrition Examination Survey (NHANES). Klasifikasi usia menjadi krusial dalam analisis kesehatan untuk mengidentifikasi pola penyakit dan faktor risiko yang spesifik pada kelompok usia berbeda. Algoritma C4.5, berbasis pohon keputusan, dan Naive Bayes, yang mengandalkan probabilitas, dibandingkan dengan Random Forest, metode ensambel yang menggabungkan banyak pohon keputusan, untuk mengevaluasi kemampuan mereka dalam menangani data kompleks. Evaluasi menggunakan Confusion Matrix, Performance Vector, dan Independent Sample T-Test menunjukkan bahwa Naive Bayes mencapai akurasi tertinggi dengan kesalahan klasifikasi rendah, sementara Random Forest unggul dalam stabilitas prediksi pada data berdimensi tinggi. Di sisi lain, C4.5 cenderung kurang akurat, terutama saat menghadapi atribut kompleks. Hasil T-Test membuktikan perbedaan signifikan secara statistik antaralgoritma, memperkuat rekomendasi penggunaan Naive Bayes untuk akurasi maksimal atau Random Forest untuk data beragam. Temuan ini diharapkan dapat menjadi acuan dalam pengembangan sistem prediksi kesehatan yang lebih adaptif dan efektif.
Co-Authors Hasbi Firmansyah Lucky Primanda Saputra Rizki Prasetyo Sulistyaningrum