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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika dan Teknik Elektro Terapan The IJICS (International Journal of Informatics and Computer Science) Jurnal Manajemen Informatika dan Sistem Informasi HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Jurnal Manajemen Informatika & Teknologi Jurnal Ilmiah Teknik Informatika dan Komunikasi KETIK : Jurnal Informatika
Franki Yusuf Bisilisin
STIKOM Uyelindo Kupang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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SISTEM PENDUKUNG KEPUTUSAN UNTUK MENGEVALUASI KINERJA DOSEN PRODI DI STIKOM UYELINDO KUPANG MENGGUNAKAN METODE TOPSIS Bisilisin, Franki Yusuf; Naatonis, Remerta R.
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 10 No. 2 (2019): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol10no2.p59-65

Abstract

STIKOM Uyelindo Kupang was established in the year 2000 as an information technology-based tertiary institution which has three study programs, namely under graduate of informatics engineering, diploma three informatics engineering and under graduate of information systems. The three study programs always strive to improve the status of accreditation by continuously improving internal quality and making accreditation a strategy to compete with other universities. To maintain quality, STIKOM Uyelindo Kupang, especially the undergraduate informatics engineering study program routinely monitors and evaluates the performance of lecturers. The problem that is often faced in routine monitoring and evaluation of lecturer performance is the performance evaluation process that is still objective so that to overcome these problems, a decision support system is needed that can assist in evaluating the performance of lecturers at STIKOM Uyelindo Kupang. The purpose of this study is to make a decision support system for the assessment of performance of lecturers of the first-degree informatics engineering study program at STIKOM Uyelindo Kupang using TOPSIS method. The results of this study are in the form of a desktop-based application that can facilitate the monitoring and performance evaluation teams of lecturers in evaluating the performance of lecturers of study programs
Klasifikasi Pola Konsumsi Energi Listrik Rumah Tangga Menggunakan Metode K-Nearest Neighbor Febiola Hutni Mosa; Franki Yusuf Bisilisin
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1676

Abstract

The increasing demand for electrical energy in Kupang City, particularly in the Kayu Putih Subdistrict, necessitates a system capable of efficiently and accurately identifying electricity consumption patterns. The continuously rising demand for electrical energy may lead to various problems if not properly managed, such as supply disruptions or energy wastage. Therefore, this study aims to classify household electricity consumption patterns using a data-driven approach based on the K-Nearest Neighbor (KNN) method. The KNN method was chosen for its effectiveness in classifying data with a high level of accuracy, especially for datasets with complex characteristics. The designed system categorizes household electricity consumption into three main classes: low, medium, and high. This classification considers several important factors, including the number of family members, the types of electrical appliances used, and their daily usage habits. The results of the study indicate that the KNN method successfully classified household electricity consumption patterns with good performance. Testing using a confusion matrix achieved the highest accuracy of 97% at K = 4. This model was selected for implementation in the household electricity consumption classification system using the K-Nearest Neighbor (KNN) method.
Co-Authors Bib Paruhum Silalahi Claudia Krista Minggu Dabbo, Paulina Edwin Ariesto Umbu Malahina Febiola Hutni Mosa Islahul Fikri mado, silvester Michael Lopez Naatonis, Remerta R. Remerta Noni Naatonis Ryan Peterzon Hadjon Theresia Felisitas Dena