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All Journal TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan
jafar, nurul aisyah
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Computer Science & IT Control & Systems Engineering

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Analisis Performa Quality of Service (QoS) pada Perancangan Surveillance Camera untuk Tujuan Kemanan jafar, Nurul Aisyah
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 13 No. 1 (2025): TELEKONTRAN vol 13 no 1 April 2025
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v13i1.15444

Abstract

The rapid advancement of technology, such conditions can stimulate thinking and generate ideas to develop cutting-edge technology that benefits human life. One such application is in the field of security, supporting surveillance systems for the surrounding environment, such as parental monitoring of children in crowded places where loss of supervision may occur, as well as monitoring demonstrations. This study focuses on analyzing network quality in the design of an integrated surveillance camera system. In this research, the Wireshark software is used to assess network performance in the developed surveillance camera system. The results of the integration with the surveillance camera yielded Quality of Service (QoS) test results in both indoor and outdoor locations, where the average delay recorded in indoor conditions was 161.671 ms and 118.895 ms, and the packet loss value in both locations was 0%. Although certain locations exhibited signal loss, overall, the delay values in the outdoor tests were better compared to the indoor tests due to fewer obstacles in the outdoor environment.
Smart Classification: Perbandingan Algoritma Gaussian Naive Bayes dan K-Nearest Neighbor untuk Prediksi Kinerja Akademik Mahasiswa jafar, Nurul Aisyah
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol. 14 No. 1 (2026): TELEKONTRAN vol 14 no 1 April 2026
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v14i1.18682

Abstract

Sistem pendukung keputusan akademik berbasis data membutuhkan algoritma machine learning yang akurat, stabil, dan efisien secara komputasi. Namun, pemilihan parameter algoritma yang kurang optimal serta terbatasnya evaluasi komputasi pada dataset akademik berukuran kecil hingga menengah masih menjadi permasalahan utama dalam penelitian sebelumnya. Penelitian ini mengusulkan evaluasi dan optimasi algoritma Gaussian Naive Bayes (GNB) dan K-Nearest Neighbor (KNN) melalui penerapan feature engineering teknis dan analisis parameter K, serta perancangan arsitektur sistem yang mendukung integrasi ke Decision-Support System (DSS) akademik. Evaluasi kinerja dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil pengujian menunjukkan bahwa KNN dengan K=15 memberikan performa terbaik dengan akurasi sebesar 87%, presisi 0,85, recall 0,86, dan F1-score 0,86, sedangkan GNB menghasilkan akurasi 81% dengan keunggulan pada efisiensi komputasi. Penelitian ini menegaskan pentingnya optimasi parameter algoritma serta menyediakan arsitektur sistem klasifikasi yang berpotensi diimplementasikan secara near real-time dalam sistem pendukung keputusan akademik  
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