Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Journal of Research and Publication Innovation Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA)
Azis Suhendra
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perancangan Aplikasi Absensi Karyawan Berbasis Web dengan QR Code sebagai Alat Identifikasi pada PT Konsultan Pajak Barli dan Rekan Bintang Hartawan Nugraha; Azis Suhendra; Saeful Hasim; Iwan Giri Waluyo
Buletin Ilmiah Ilmu Komputer dan Multimedia Vol 1 No 1 (2023): Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) INPRESS
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Web-based employee attendance application with QR Code as an identification tool is an efficient and practical solution for PT Konsultan Pajak Barli and Rekan in managing employee attendance. In this journal, we describe the design and implementation of the application, as well as discuss the expected benefits from using this application. The methods used in developing this application are requirement analysis, system design, implementation, and testing. The result is an employee attendance application that utilizes QR Code technology to accurately identify employees and efficiently manage attendance data.
PENGELOMPOKAN DATA PENJUALAN ALAT PERABOTAN MENGGUNAKAN K MEANS DI TOKO EKA PERABOT KOTA TANGERANG Azis Suhendra
Journal of Research and Publication Innovation Vol 3 No 4 (2025): OCTOBER
Publisher : Journal of Research and Publication Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study uses the K-Means clustering algorithm to examine furniture sales patterns at Eka Perabot Store in Tangerang City. Understanding sales patterns can help the store develop better inventory management and marketing strategies. This study analyzed sales data to identify clustering patterns based on product price and quantity. Sales data collection, preprocessing, and the application of the K-Means algorithm using RapidMiner software were all part of the research process. The cleaned data was then grouped into clusters based on similar characteristics, resulting in product groups with specific characteristics, such as low-priced and high-selling products. The results showed that organizing with K-Means successfully divided products into categories appropriate to the store. The K-Means clustering method proved effective in helping Eka Perabot Store understand customer preferences and develop better sales strategies because each cluster has unique characteristics that can be used as a basis for business decision-making.
Co-Authors Bintang Hartawan Nugraha Iwan Giri Waluyo Saeful Hasim