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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Gunadi Widi Nurcahyo
Universitas Putra Indonesia “YPTK”, Padang, Indonesia
Computer Science & IT Control & Systems Engineering

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Perbandingan Tingkat Optimalisasi Metode K-Nearest Neighbor Dan Naïve Bayes Dalam Klasifikasi Kelayakan Alat Laboratorium Kimia Sri Mulya; Gunadi Widi Nurcahyo; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.357

Abstract

Classification of the appropriateness of equipment in the laboratory is needed by university management to determine future laboratory development steps. The suitability of laboratory equipment can be influenced by various factors, so it is necessary to know which variables are crucial in influencing the condition of the laboratory equipment's suitability. Data mining techniques can be used to explore new knowledge so that it can produce appropriate laboratory equipment. Some algorithms that can be used are K-Nearest Neighbord and Naive Bayes. The aim of this research is to compare the level of optimization of two methods in classifying the suitability of Chemistry laboratory equipment at FMIPA Unand using the K-Nearest Neighbor and Naive Bayes methods. The attributes used are year of procurement, level of use, level of damage, length of use of the tool, and condition of tool accessories. The data used is Materials Chemistry laboratory equipment, FMIPA, Andalas University from 2010-2023 with a total of 105 data. The research results show that the accuracy level of the Naive Bayes Method is better than the K-Nearest Neighbor Method. This is proven by the results of the Rapidminer test, which obtained the highest accuracy of 94.74% at a total testing data of 30% of the total data, while for the K-Nearest Neighbor method, the highest accuracy was obtained at 79.03% at a total testing data of 50% of the total data. It is hoped that the results of the tool classification can serve as guidance and evaluation to support the development of the FMIPA Chemistry laboratory at Andalas University
enerapan Metode Weighted Product Untuk Penerima Insentif Karyawan Romzi Rahman; Gunadi Widi Nurcahyo; Y Yuhandri
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.358

Abstract

The development of science from time to time has succeeded in bringing humans into an era of information technology. Organizations need to change the way they manage and develop human resources in the face of technological change. Stakeholders need to evaluate employee performance periodically so that it can become a reference for determining employee incentives. Partial incentives will have a positive effect on psychological empowerment which will then also have a positive effect on employee performance. This research aims to build a Decision Support System in determining employee incentive recipients. The method used in this research is the weighted product method. This method has six stages, namely the alternative value of each criterion, the alternative value of each criterion after weighting, determining the preference weight of the criteria, calculating the preference value of Vector S, calculating the value of Vector V, and ranking results. The processed dataset comes from Institut Teknologi dan Bisnis Haji Agus Salim Bukittinggi. The dataset consists of 14 employee data with their respective criteria values. The results of this research can determine employee incentive recipients with an accuracy rate of 86%. Therefore, this research can be a reference for stakeholders to determine recipients of employee incentives in a certain period. 
Co-Authors Billy Hendrik Romzi Rahman Sri Mulya Y Yuhandri