Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JAIS (Journal of Applied Intelligent System) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Heribertus Himawan
Universitas Dian Nuswantoro
Computer Science & IT

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

Found 2 Documents
Search

 1 
Different Types of Beef and Pord Using Histogram Texture and K-Means Clustering Method Heribertus Himawan; Widya Wiratama
Journal of Applied Intelligent System Vol 3, No 1 (2018): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v3i1.1892

Abstract

The largest source of protein needed by the human body comes from meat. Basically a high number of buyers and beef is much more expensive than other meats giving chances to irresponsible parties to mix beef with pork, In the Islamic religious teachings, pork is forbidden because consuming pork there is a physical, psychological and very easy to be contaminated by bacteria. Humans have the limitation of identifying beef and pork manually using the senses of human vision are still many weaknesses and less effective. In this research, meat image extraction applies histogram and clustering by applying K-Mean. By applying this method is expected to help to group the image of meat with a high degree of accuracy.
Customer Segmentation with RFM Model using Fuzzy C-Means and Genetic Programming Anas Syaifudin; Purwanto Purwanto; Heribertus Himawan; M. Arief Soeleman
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2408

Abstract

One of the strategies a company uses to retain its customers is Customer Relationship Management (CRM). CRM manages interactions and supports business strategies to build mutually beneficial relationships between companies and customers. The utilization of information technology, such as data mining used to manage the data, is critical in order to be able to find out patterns made by customers when processing transactions. Clustering techniques are possible in data mining to find out the patterns generated from customer transaction data. Fuzzy C-Means (FCM) is one of the best-known and most widely used fuzzy grouping methods. The iteration process is carried out to determine which data is in the right cluster based on the objective function. The local minimum is the condition where the resulting value is not the lowest value from the solution set. This research aims to solve the minimum local problem in the FCM algorithm using Genetic Programming (GP), which is one of the evolution-based algorithms to produce better data clusters. The result of the research is to compare the application of fuzzy c-means (FCM) and genetic programming fuzzy c-means (GP-FCM) for customer segmentation applied to the Cahaya Estetika clinic dataset. The test results of the GP-FCM yielded an objective function of 20.3091, while for the FCM algorithm, it was 32.44741. Furthermore, evaluating cluster validity using Partition Coefficient (PC), Classification Entropy (CE), and Silhouette Index proves that the results of cluster quality from gp-fcm are more optimal than fcm. The results of this study indicate that the application of genetic programming in the fuzzy c-means algorithm produces more optimal cluster quality than the fuzzy c-means algorithm.
Co-Authors Anas Syaifudin Moch Arief Soeleman Purwanto Purwanto Widya Wiratama