Article Per Year (5 Year)
p-Index From 2019 - 2024
0
P-Index
This Author published in this journals
All Journal Jurnal Matematika: MANTIK
Rini Silvi
Universitas Padjadjaran
Mathematics

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

Found 2 Documents
Search

 1 
Analisis Cluster dengan Data Outlier Menggunakan Centroid Linkage dan K-Means Clustering untuk Pengelompokkan Indikator HIV/AIDS di Indonesia Rini Silvi
Jurnal Matematika MANTIK Vol. 4 No. 1 (2018): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.198 KB) | DOI: 10.15642/mantik.2018.4.1.22-31

Abstract

Cluster analysis is a method to group data (objects) or observations based on their similarities. Objects that become members of a group have similarities among them. Cluster analyses used in this research are K-means clustering and Centroid Linkage clustering. K-means clustering, which falls under non-hierarchical cluster analysis, is a simple and easy to implement method. On the other hand, Centroid Linkage clustering, which belongs to hierarchical cluster analysis, is useful in handling outliers by preventing them skewing the cluster analysis. To keep it simple, outliers are often removed even though outliers often contain important information. HIV/AIDS is a serious challenge for global public health since HIV/AIDS is an infectious disease attacking body’s immune system that in turn lowering the ability to fight infections which in the end causing death. HIV/AIDS indicators data in Indonesia contain outliers. This research uses gap statistic to define the number of clusters based on HIV/AIDS indicators that groups Indonesia provinces into 7 clusters. By comparing S­w­/S­b ratio, Centroid Linkage clustering is more homogenous than K-means clustering. Using clustering, the government shall be able to create a better policy for fighting HIV/AIDS based on the dominant indicators in each cluster.
Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network Mertha Endah Ervina; Rini Silvi; Intaniah Ratna Nur Wisisono
Jurnal Matematika MANTIK Vol. 4 No. 2 (2018): Mathematics and Applied Mathematics
Publisher : Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.818 KB) | DOI: 10.15642/mantik.2018.4.2.90-99

Abstract

Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.
Co-Authors Intaniah Ratna Nur Wisisono Mertha Endah Ervina