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INDONESIA
Jurnal Matematika Sains dan Teknologi
Published by Universitas Terbuka
ISSN : 14111934     EISSN : 24429147     DOI : -
Core Subject : Education, Agriculture,
Agriculture, Biological Sciences & Forestry Mathematics Other
Merupakan media informasi dan komunikasi para praktisi, peneliti, dan akademisi yang berkecimpung dan menaruh minat serta perhatian pada pengembangan Matematika, ilmu pengetahuan dan teknologi. Diterbitkan oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Terbuka.
Arjuna Subject : -
Articles 403 Documents
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Application of Synthetic Minority Over-Sampling Technique (SMOTE) to Outlier Data for Probabilistic Neural Network (PNN) Ramdan Hayati; Isran Hasan; Novianita Achmad
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.7209.2024

Abstract

One common model of Artificial Neural Network (ANN) used in classification tasks is the Probabilistic Neural Network (PNN). PNN is an algorithm that utilizes probability functions, eliminating the necessity for a large dataset during its development process. In this research, the best model parameters were initially determined using the sigma parameter and Kernel Density Estimation (KDE) function on a randomly sampled dataset employing the Stratified Random Sampling (SRS) method. The optimal sigma parameter obtained from this process is 0.075, with a Gaussian KDE function. The data used in this study is related to direct marketing campaigns (phone calls) from Portuguese banking institutions collected by S ́ergio. Subsequently, PNN is applied to this dataset to determine its Accuracy and F1-Score values. The results indicate an accuracy rate of 87.117% and an F1-Score of 92.755%. Following this, Synthetic Minority Over-Sampling Technique (SMOTE) is applied to the dataset to balance the data. PNN is then implemented on the oversampled data, and in this phase, an evaluation of the Accuracy and F1-Score values is conducted, resulting in respective figures of 93.437% and 93.511%.
Identifying Factors Influencing the Number of Diarrhea Cases in Children Under Five in West Java Using Negative Binomial Regression Akbar Rizki; Utami Dyah Syafitri; Christin Halim
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 1 (2024)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i1.7582.2024

Abstract

The WHO states that diarrhea is the leading killer of children under five worldwide, and Indonesia is no exception, where 10.3% of under-five deaths are caused by diarrhea. West Java Province, with the largest population in Indonesia, has the highest diarrhea cases under five. The potential for diarrhea to become an extraordinary event, which is often accompanied by death, is very likely to occur because diarrhea is an endemic disease in West Java. Therefore, analyzing the factors influencing the children under five diarrhea cases in West Java is essential. Negative binomial regression was used in this study because the response was to count data on the incidence of diarrhea in children under five in West Java. The analysis results show that an increase in the percentage of public premises (PPP) meeting health requirements and population density per km2 will increase the number of diarrhea cases under five in West Java. However, an increase in the percentage of Community-Based Total Sanitation (CBTS), percentage of the population living in poverty, and percentage of households practicing Clean and Healthy Behavior (CHB) will decrease the number of diarrhea cases in West Java.
Application of teh Hybrid Singular Spectrum Analysis – ARIMA Model for Indonesia's Inflation Rate (2018-2023) Sri Rahayu; Aswi Aswi; Muhammad Fahmuddin Sudding
Jurnal Matematika Sains dan Teknologi Vol. 25 No. 2 (2024): September (in Progress)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v25i2.7982.2024

Abstract

This research aims to determine the results and accuracy of forecasting inflation rates in Indonesia using Hybrid Singular Spectrum Analysis (SSA) – Autoregressive Integrated Moving Average (ARIMA). Hybrid SSA-ARIMA combines two time series methods to increase forecasting accuracy, especially for economic data that contains trend and seasonal components. The data used is data on the national consumer price inflation rate (Y-on-Y) for the period January 2018 to December 2023. The forecast accuracy obtained by the MAPE value for Singular Spectrum Analysis was 56.26797%, and Hybrid SSA-ARIMA was 18.88851%. This shows that Hybrid SSA-ARIMA has better forecasting capabilities than Singular Spectrum Analysis in predicting the inflation rate in Indonesia.

Page 41 of 41 | Total Record : 403

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