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All Journal Jurnal Inovasi Teknologi Pendidikan Jurnal Manajemen Pendidikan CogITo Smart Journal Frontiers Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Rianto, Indra
Jurusan Pendidikan Teknologi Informasi Dan Komunikasi, Fakultas Teknik, Universitas Negeri Manado
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Other

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Deteksi Anomali Trafik Jaringan Menggunakan K-Means Clustering Berdasarkan Data Log Firewall Christian Vieri Tololiu; Indra Rianto; Rudy Harijadi Wibowo Pardanus; Arje Cerullo Djamen
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.462

Abstract

ABSTRAK Penelitian ini mengembangkan sistem deteksi anomali trafik jaringan internet di lingkungan UPA TIK Universitas Negeri Manado menggunakan algoritma K-Means Clustering dengan pendekatan Action Research. Sistem monitoring jaringan yang ada masih bersifat reaktif, sehingga anomali baru terdeteksi setelah laporan pengguna masuk. Data log firewall berupa Report of Connection Events periode 2-9 Februari 2026 dengan sekitar 10.000 baris koneksi valid digunakan sebagai dataset penelitian. Tahapan preprocessing mencakup pembersihan data, rekayasa fitur berbasis perilaku jaringan meliputi Source Connection Count (fsrc), Destination Port Entropy (Hport), dan Byte-to-Connection Ratio (Rbyte), serta normalisasi menggunakan Z-Score Standardization. Penentuan jumlah klaster optimal dilakukan dengan Elbow Method menghasilkan K=3, yang dikonfirmasi dengan Silhouette Score sebesar 0,6493 dan Davies-Bouldin Index sebesar 0,5379. Hasil clustering membagi trafik menjadi tiga kelompok: trafik normal (82,5%), semi-normal (16,5%), dan anomali potensial (1,0%). Visualisasi dengan Principal Component Analysis (PCA) mengkonfirmasi pemisahan klaster secara geometris. Deteksi anomali berbasis threshold persentil ke-99 (P99) berhasil mengidentifikasi empat kategori anomali utama: aktivitas BitTorrent, akses massal ke banyak negara, probing ke zona DMZ, dan penggunaan port tidak umum. Penelitian membuktikan bahwa K-Means Clustering efektif sebagai metode deteksi anomali berbasis data pada jaringan kampus tanpa memerlukan pelabelan data. ABSTRACT              This study develops an internet network traffic anomaly detection system at the UPA TIK environment of Universitas Negeri Manado using the K-Means Clustering algorithm with an Action Research approach. The existing network monitoring system is still reactive, meaning anomalies are only detected after user complaints are received. Firewall log data in the form of Report of Connection Events for the period of February 2-9, 2026, consisting of approximately 10,000 valid connection records, was used as the research dataset. Preprocessing stages include data cleaning, network behavior-based feature engineering comprising Source Connection Count (fsrc), Destination Port Entropy (Hport), and Byte-to-Connection Ratio (Rbyte), as well as normalization using Z-Score Standardization. The optimal number of clusters was determined using the Elbow Method, resulting in K=3, confirmed by a Silhouette Score of 0.6493 and a Davies-Bouldin Index of 0.5379. Clustering results divided traffic into three groups: normal traffic (82.5%), semi-normal (16.5%), and potential anomalies (1.0%). Visualization using Principal Component Analysis (PCA) geometrically confirmed cluster separation. Anomaly detection based on the 99th percentile threshold (P99) successfully identified four main anomaly categories: BitTorrent activity, mass access to multiple countries, DMZ zone probing, and use of non-standard ports. The study proves that K-Means Clustering is effective as a data-driven anomaly detection method for campus networks without requiring labeled data.
Analisis Routing Dinamis (Routing Information Protocol dan Open Shortest Path First) dengan Menggunakan Simulator Cisco Packet Tracer Marshel R Kapahang; Indra Rianto; Arje Cerullo Djamen
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.463

Abstract

ABSTRAK Berkembangnya jaringan komputer berdasarkan protokol routing juga memainkan peran penting dalam jaringan komputer dimana pun. Saat ini, internet telah digunakan oleh lebih dari 5 miliar pengguna di seluruh dunia dan menangani lebih dari 300 exabyte data setiap bulan. Berdasarkan laporan Cisco Annual Internet Report, trafik IP global diproyeksikan melampaui 400 exabyte per bulan pada tahun 2023. Volume lalu lintas data yang sangat besar ini menuntut sistem pengelolaan jaringan yang efisien. Dalam kondisi tersebut, efisiensi protokol routing menjadi faktor utama dalam menjaga stabilitas jaringan, analisis terhadap kinerja protokol routing dinamis seperti Routing Information Protocol (RIP) dan Open Shortest Path First (OSPF) menjadi penting untuk mengetahui protokol yang lebih efektif dalam menghadapi beban trafik yang besar. Penelitian ini bertujuan untuk menganalisis bagaimana waktu konvergensi routing RIP dan OSPF pada topologi jaringan di Universitas Negeri Manado baik dalam menggunakan jaringan kabel maupun nirkabel (wireless). Metode penelitian menggunakan metode eksperimental berbasis simulasi dengan menggunakan pendekatan kuantitatif dan dilakukan analisis deskriptif. Setelah dilakukan pengukuran waktu konvergensi dan data yang ditemukan telah dianalisis, maka di Universitas Negeri Manado yang memiliki jaringan berskala sedang sampai besar direkomendasikan untuk menggunakan routing OSPF sebagai routing internal dengan melihat waktu konvergensi. ABSTRACT The development of computer networks based on routing protocols also plays a crucial role in computer networks everywhere. Currently, the internet is used by over 5 billion users worldwide and handles over 300 exabytes of data each month. According to the Cisco Annual Internet Report, global IP traffic is projected to exceed 400 exabytes per month by 2023. This enormous volume of data traffic demands an efficient network management system. Under these conditions, routing protocol efficiency is a key factor in maintaining network stability. Analyzing the performance of dynamic routing protocols such as Routing Information Protocol (RIP) and Open Shortest Path First (OSPF) is crucial to determine which protocols are more effective in handling large traffic loads. This study aims to analyze the convergence times of RIP and OSPF routing in network topologies at Manado State University, both using wired and wireless networks. The research method used is an experimental simulation-based method with a quantitative approach and descriptive analysis. After measuring convergence times and analyzing the data, Manado State University, which has a medium-to-large network, recommends using OSPF routing for internal routing, based on convergence times.
User Acceptance Analysis of the ChatGPT Application in Schools in South Tondano District Indra Rianto; Tesalonika Inryanti Tampi
Jurnal Inovasi Teknologi Pendidikan Vol. 13 No. 2 (2026): June (On Progress)
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v13i2.87595

Abstract

This research aims to analyze the relationship among Perceived Ease of Use, Perceived Usefulness, and Behavioral Intention to Use within the context of technology acceptance. The research model is tested using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to evaluate the validity, reliability, and the interrelationship among the variables. The analysis is conducted by examining the Measurement Model to ensure the validity and reliability of the indicators, and the Structural Model to test the research hypotheses. Validity is assessed using factor loadings, the Fornell-Larcker Criterion, and cross-loadings, while reliability is examined through Cronbach’s Alpha, composite reliability, and Average Variance Extracted (AVE). Hypotheses are tested using PLS Bootstrapping via the SmartPLS software. The results indicate that Perceived Ease of Use has a significant influence on both Behavioral Intention to Use and Perceived Usefulness. However, Perceived Usefulness does not have a significant effect on Behavioral Intention to Use. These findings underscore that ease of use is the primary factor influencing an individual’s intention to use a technology, while the perceived benefits may depend more on contextual factors. The outcomes of this study can serve as a guideline for technology developers to enhance user acceptance by focusing more on ease-of-use aspects.
Co-Authors Aipassa, Rana Harvie Alfrina Mewengkang Alfrits Victory Supit Arje Cerullo Djamen Banunaek, Rofinus Billy M.H. Kilis Christaria Edwina Priscilia Wowor Christian Vieri Tololiu Christian, Jessicha Christine Takarina Meitty Manoppo Daniel Riano Kaparang Darma Fijai Poluan Djami Olii Dopong, Jesika Jelsi Elisabeth Sinaga Fransosco Z. Frans Gabriela Sambur Gideon Christ Raphael Limbat Glendy Rongko Hendrawan Igirisa Herry Sumual Hiskia Kamang Manggopa Igirisa, Hendrawan Ihcwan Malah Jeniffer Hawkins Pamela Elias Jessicha Christian Joulanda A. M. Rawis Kalalo, Marvell Lambert Keith Francis Ratumbuisang Langitan, Jentelino Silvester Liando, Olivia Eunike Selvie Limbat, Gideon Christ Raphael Malah, Ihcwan Mario Tulenan Parinsi Marshel R Kapahang Marvell Lambert Kalalo Meifiana Cikatasia Tukan Merriam Listiany Modeong Misgter Gidion Maru Modeong, Merriam L. Olivia Eunike Selvie Liando Palilingan, Verry Ronny Pardanus, Rudy Harijadi Wibowo Poluan, Darma Fijai Pratasik, Stralen Rana Harvie Aipassa Ratu, Renaldo B. Ridel Wiliam Samuel Sampouw Rongko, Glendy Rudy H. W. Pardanus Rudy Harijadi Wibowo Pardanus Rudy Harijadi Wibowo Pardanus Runtuwene, Sevenlee Oscar Sambur, Gabriela Sampouw, Ridel Wiliam Samuel Sanjaya, I Wayan Gian Sendiang, Dhea Jessica Shelty D. M. Sumual Supit, Alfrits Victory Tampi, Tesalonika Inryanti Teofilus Tumundo Tesalonika Inryanti Tampi Tesalonika Inryanti Tampi Trudi Komansilan Tukan, Meifiana Cikatasia Tumundo, Teofilus Wensi Ronald Lesli Paat Wowor, Christaria Edwina Priscilia