Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Scientific Journal of Informatics Journal of Soft Computing Exploration
Pratama, Rizka Nur
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Customer Segmentation Using the Integration of the Recency Frequency Monetary Model and the K-Means Cluster Algorithm Alamsyah, Alamsyah; Prasetyo, P. Eko; Sunyoto, Sunyoto; Bintari, Siti Harnina; Saputro, Danang Dwi; Rohman, Shohihatur; Pratama, Rizka Nur
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.39437

Abstract

Purpose: This research aims to do customer segmentation in retail companies by implementing the Recency Frequency Monetary (RFM) K-Means cluster model and algorithm optimized by the Elbow method.Methods: This study uses several methods. The RFM model method was chosen to segment customers because it is one of the optimal methods for segmenting customers. The K-Means cluster algorithm method was chosen because it is easy to interpret, implement, fast in convergence, and adapt, but lacks sensitivity to the initial partitioning of the number of clusters. To help classify each category of customers and know the level of loyalty, they use a combination of the RFM model and the K-Means method. The Elbow method is used to improve the performance of the K-Means algorithm by correcting the weakness of the K-Means algorithm, which helps to choose the optimal k value to be used when clustering.Result: This research produces customer segmentation 3 clusters with a Sum of Square Error (SSE) value of 25,829.39 and a Callinski-Harabaz Index (CHI) value of 36,625.89. The SSE and CHI values are the largest ones, so they are the optimal cluster values.Novelty: The application of the integrated RFM model and the K-Means cluster algorithm optimized by the Elbow method can be used as a method for customer segmentation.
Optimization of breast cancer classification using feature selection on neural network Jumanto, Jumanto; Mardiansyah, M Fadil; Pratama, Rizka Nur; Hakim, M. Faris Al; Rawat, Bibek
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.78

Abstract

Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
Co-Authors Alamsyah - Hakim, M. Faris Al Jumanto Jumanto, Jumanto Mardiansyah, M Fadil P. Eko Prasetyo Rawat, Bibek Rohman, Shohihatur Siti Harnina Bintari Sunyoto Sunyoto