Article Per Year (5 Year)
p-Index From 2020 - 2025
1.829
P-Index
This Author published in this journals
All Journal Teknosains: Media Informasi Sains dan Teknologi JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Global Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Networks and IoT Global
Muhammad Haviz Irfani
Universitas Indo Global Mandiri
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Physics

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Informatika Global

 1 
Perbandingan Akurasi Metode Principal Component Analysis (PCA) dan Correlation-Based Feature Selection (CFS) Pada Klasifikasi Perpanjangan Kontrak Karyawan Menggunakan Metode Naïve Bayes Dewi Sartika; Imelda Saluza; Muhammad Haviz Irfani
Jurnal Informatika Global Vol 13, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i2.2292

Abstract

PT. Oasis Waters International Palembang conducts regular staff performance reviews, the findings of which are utilized to make recommendations for employee contract extension. The Human Resource Department has assigned a numerical value to 25 qualities (HRD). The process of giving a label or class to a number of examples when the value of each characteristic is known as classification. The Naïve Bayes technique is a basic classification approach that makes use of probability estimates. Based on the observations, it was discovered that one of the 25 criteria was deemed the most relevant in determining the recommendation for an employee contract renewal. As a result, in this study, a comparison of the pre-processing Principal Component Analysis (PCA) approach and the Correlation-based Feature Selection (CFS) method on the categorization of employee contract extensions at PT Oasis Waters International Palembang will be performed. According to the data, the CFS approach has a positive influence on classification performance, while PCA does not. This is demonstrated by a 30% increase in accuracy when utilizing the CFS approach. Meanwhile, both strategies have a positive influence on the model's dependability. This is demonstrated by a reduction in Root Mean Square Error (RMSE) when using the CFS approach from 0.6325 to 0.1845, whereas using the PCA method results in 0.5123.Keywords : Naïve Bayes, Principal Component Analysis, Correlation-based Feature Selection, Confusion Matrix, Root Mean Square Error
Analisis Kepuasan Learning Management System Universitas XYZ Menggunakan Metode System Usability Scale dan K-Means Muhammad Haviz Irfani; Dewi Sartika
Jurnal Ilmiah Informatika Global Vol. 14 No. 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i1.2988

Abstract

The importance of knowing the results of using the LMS (Learning Management System) learning application to determine the overall use value of users each semester. User perceptions were obtained using the SUS (system usability scale) method using a questionnaire that adopted 10 questions distributed to 118 users (students) of the XYZ University Informatics Engineering study program who had used the LMS after 1 semester. The purpose of this study is to determine user perceptions by clustering user satisfaction which has been carried out for 1 semester. Grouping perceptions using the K-Means method with variables (columns) that seem to have the greatest influence on other variables. Other tools use Google Colab in the Python programming language. The number of variables is 10 variables adopted from the questions in the System Usability Scale method. The results of this study provide a total of 3 (three) clusters which will then become the basis for scoring the criteria for the SUS method. The criteria for using the LMS system with cluster 2 have an excellent rating (SUS score of 72.04) and the number of perceptions is 49 people from 118 students. Overall, LMS users provide good value for several modules in the LMS, but the third cluster with the highest number gives the best results from the other clusters.
Co-Authors Daniel Udjulawa gasim, Gasim Imelda Saluza, Imelda Juhaini Alie Muhammad Muhardeny Nazori Suhandi Ramadhani, Kiki Sartika, Dewi Siska Anggraini Sri Rahayu