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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Education Technology Information Social Sciences and Health
Tegas Ramadhan
Universitas Negeri Medan
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Neuroscience Nursing Public Health

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Application of K-Means Clustering for Urban Transportation Pattern Analysis Using Big Data Trip Dataset Tegas Ramadhan; Hafizh Ariiq; Muhammad Dzaki Arjun; Muhammad Ridho Ananda Aditya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2237

Abstract

The rapid growth of urban transportation systems has led to the generation of massive amounts of data, commonly referred to as big data. This study aims to analyze transportation patterns using large-scale data obtained from the NYC Taxi Trip Records. The dataset exhibits key big data characteristics, including volume, velocity, and variety. This research applies the K-Means clustering algorithm to group taxi trip data based on features such as trip distance, fare amount, and trip duration. Several preprocessing techniques are performed, including data cleaning, feature engineering, sampling, and normalization. The optimal number of clusters is determined using the Elbow Method and Silhouette Score. The results show that the dataset can be effectively grouped into three clusters representing distinct transportation patterns. These findings demonstrate the capability of clustering techniques in extracting meaningful insights from large-scale datasets and highlight their potential application in urban transportation planning.
Implementasi Sistem Deteksi Serangan Port Scanning pada Jaringan Komputer Dengan Pendekatan XGboost Felix John Pardamean Hutabarat; Dedy Kiswanto; Tegas Ramadhan; Muhammad Dzaki Arjun
Journal of Education Technology Information Social Sciences and Health Vol. 5 No. 2 (2026): September 2026
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetish.v5i2.8745

Abstract

Serangan Port Scanning merupakan salah satu tahapan awal yang umum dilakukan oleh penyerang untuk mengidentifikasi layanan dan celah keamanan pada suatu sistem jaringan. Oleh karena itu, diperlukan mekanisme deteksi yang mampu mengenali aktivitas tersebut secara cepat dan akurat. Penelitian ini bertujuan mengimplementasikan dan mengevaluasi sistem deteksi serangan Port Scanning menggunakan algoritma Extreme Gradient Boosting (XGBoost). Dataset yang digunakan adalah CIC-IDS2017, khususnya berkas Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX. Tahapan penelitian meliputi seleksi fitur bertahap, Exploratory Data Analysis (EDA), preprocessing, serta pelatihan dan evaluasi model menggunakan metode Stratified 5-Fold Cross-Validation. Proses seleksi fitur berhasil mereduksi 85 fitur awal menjadi 8 fitur prediktif utama. Setelah penghapusan data duplikat, dataset terdiri atas 110.364 observasi unik dengan rasio ketidakseimbangan kelas sekitar 66:1. Hasil pengujian menunjukkan bahwa model XGBoost mencapai akurasi sebesar 99,89%, precision 94,00%, recall 99,27%, dan F1-score 96,56% untuk kelas PortScan. Analisis learning curve menunjukkan kemampuan generalisasi yang baik tanpa indikasi overfitting yang signifikan. Selain itu, analisis eXplainable AI menggunakan SHAP mengidentifikasi bahwa fitur Total Length of Fwd Packets, PSH Flag Count, dan Fwd Packet Length Min merupakan faktor yang paling berpengaruh dalam proses klasifikasi. Hasil penelitian menunjukkan bahwa pendekatan XGBoost efektif dalam mendeteksi serangan Port Scanning dengan tingkat akurasi tinggi serta mampu memberikan interpretasi terhadap proses pengambilan keputusan model.
Co-Authors Dedy Kiswanto Felix John Pardamean Hutabarat Hafizh Ariiq Muhammad Dzaki Arjun Muhammad Ridho Ananda Aditya