Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB
Aswan Sunge
STT Pelita Bangsa
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
OPTIMASI ALGORITMA C4.5 DENGAN MENGGUNAKAN ALGORITMA GENETIKA UNTUK MEMPREDIKSI MEREK METERAN AIR PAM Aswan Sunge; Risnawati Risnawati
Jurnal SIGMA Vol 10 No 2 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.801 KB)

Abstract

The Drinking Water Company has endeavored to provide services to meet the need for clean water, but in its journey often received complaints from the community or pam customers. Customer complaints can be caused from damage to the meter Water causes water to die into the house, a leakage in the meter count, leakage in the meter body or excess usage when checking takes place in each house, the number of meters damaged causes the stock in the warehouse to be incompatible with the usage needs new installation and use due to meter damage. Due to the lack of clean water in the area, there is a demand from the community to optimize their needs, but the fact is that it cannot be optimized yet because the water pipeline has not yet been installed. Extracting large amounts of data is usually called data mining. Because there is no research on meter brands using the C4.5 Algorithm method and is optimized using the Genetic Algorithm. In conducting this test the tools used are Rapidminer. The results obtained using the C4.5 algorithm without optimization are 63.33% and the results obtained by the C4.5 algorithm and genetic algorithm optimization are 90.00% or an increase of 26.67% from the C4.5 algorithm without optimization. Keywords: Data Mining, Meter Brand, C4.5 Algorithm, Genetic Algorithm.
Prediksi Produk Laris Mobil Honda Dengan Metode Klasifikasi Menggunakan Algoritma C4.5 (STUDI KASUS : DATA PENJUALAN SALES PT PROSPECT MOTOR, CIKARANG) Aswan Sunge; Heri Fidiawan2
Jurnal SIGMA Vol 9 No 4 (2019): Juni 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.379 KB)

Abstract

The amount of competition in the business world, especially in the car sales industry requires developers to find a strategy that can increase sales and marketing of products sold. Companies must pay attention to the type of product sales transactions both new products and old products which are marketed in various ways so as to improve the effectiveness of the company's performance in processing sales transaction data. Knowing the prediction results by looking at the accuracy of the C4.5 algorithm so that Honda car sales can obtain targets according to the planning that has been determined by the company. Secondary data used in this study are sales data of PT Prospect Motor Cikarang's sales executive. Forming a prediction model using the C4.5 method. In C4.5 algorithm, entropy and information gain calculations are performed where the best-selling attribute is the destination attribute, while the class, model, transmission, income, leasing, tenor and discount as source attributes to obtain root nodes and other nodes. Based on the results of the classification using the C4.5 algorithm shows that the accuracy reached 67.5%, which shows that the C4.5 algorithm is suitable for measuring sales predictions for sales of Honda cars. Keywords : Data Mining, Product Sale, Decision Tree, Algoritma C4.5.
PENGELOMPOKAN INDUSTRI MIKRO DI INDONESIA DENGAN METODE K-MEANS CLUSTERING Aswan Sunge; Heri Hermawanto2
Jurnal SIGMA Vol 10 No 1 (2019): Maret 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (256.99 KB) | DOI: 10.37366/sigma.v10i1.483

Abstract

Industry is part of the production sector that does not take these materials directly from nature for consumption but the materials are processed and finally become valuable and valuable to society. Micro industry is an industrial company whose workforce consists of 5-19 people. The purpose of this research is to classify the micro industry in Indonesia based on 3 clusters, namely the low cluster, medium cluster and high cluster using the K-Means Clustering method. The data used in this study is data on the number of micro industrial companies in Indonesia by province in 2013 - 2015. Based on this data, it was obtained that the regions with the number of micro industrial companies with low clusters (C0) were 31 provinces, medium cluster (C1) 2 provinces and clusters. height (C2) 1 province. Keywords : Industry, Clastering, Algoritma K-means
Co-Authors Heri Fidiawan2 Heri Hermawanto2 Risnawati Risnawati