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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 5 Documents
 1 
Search results for , issue "Vol. 25 No. 1 (2024)" : 5 Documents clear
The Application of Dicrete Wavelet Transform for Digital Image Compression Ahmad Khairul Umam; Pukky Tetralian Bantining Ngastiti; Aris Alfan; Zaqiyatus Shahadah; Amanda Fatma Muamalah
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.v25i1.3955.2024

Abstract

This article explains Discrete Wavelet Transform (DWT) in image compression. Wavelet transform is a generalization of Fourier transform, consisting of discrete and continuous wavelet transform. DWT has many uses, including image compression, fingerprint recognition, and image denoising. This research aims to know the steps of digital image compression using DWT and compare the original and resulting images. Efforts of DWT in digital image compression go by DWT's process, determining the threshold, sorting the absolute value of the image whether it is minor or more significant (equal to) threshold value, then is processed, Inverse Discrete Wavelet Transform (IDWT). This research explains the Peak Signal-to-Noise Ratio (PSNR), computing time, and compression ratio for three examples: the image of the cameraman, Lena, and a cat. The results determine that the highest PSNR values are wavelet of coiflets 3 for the cameraman, biorthogonal 3.5 for Lena, and coiflets 3 for the cat. The fastest computation times are wavelet of symlets 4 for the cameraman, symlets 4, coiflets 3 for Lena, and Daubechies 4 for the cat. Then, the highest compression ratios are wavelet of symlets 4, biorthogonal 3.5, coiflets 3 for the cameraman, Haar for Lena, and symlets 4, biorthogonal 3.5 for the cat. The results of this research are we get steps of the discrete wavelet transform for digital image compression. Also, we obtain types of wavelets with the highest PSNR values, the fastest computation times, and the highest compression ratios.
Implementation of Random Oversampling Technique in the K-Nearest Neighbor Method for Creditworthiness Analysis Ayu Dhita Putri Wulandari; Shantika Martha; Wirda Andani
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.v25i1.4909.2024

Abstract

Banks are financial institutions, one of whose main activities is providing credit to their customers. The existence of credit granting activities requires the bank to know the feasibility of prospective debtors in receiving credit. Because in practice, credit granting activities still often have bad credit problems. The problem of bad credit can be overcome by analyzing the feasibility of granting credit to prospective debtors. The data used in this study consists of 10 independent variables and 1 dependent variable is collectibility (kol). The collectibility (col) data consists of 500 data for the current debtor class and 26 data for the non-current debtor class, this indicates an imbalance class. So in this study, the application of the random oversampling (ROS) technique is used to overcome the imbalance class with the K-Nearest Neighbor (KNN) method in classifying current and non-current debtor data. ROS was chosen because it can generally provide better results and does not eliminate information from existing data. The analysis results obtained show that the use of the KNN method with the application of ROS is better than the KNN model without ROS, with an accuracy of 84.91% at data testing. The KNN model with ROS can improve the model's ability to classify noncurrent debtor data or the specificity value of the model increases by 25%. In the KNN model without ROS the model cannot classify non-current debtor data correctly at all, this can endanger the bank in making decisions.
Forecasting Number of Train Passengers Using Time Series Regression Integrated Calendar Variation and Covid 19 Intervention Mega Silfiani; Farida Nur Hayati
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.v25i1.4941.2024

Abstract

The purpose of this study is to obtain a forecasting model for the number of train passengers using time series regression integrated with variations in the Islamic calendar and the effects of COVID 19. This study uses the number of train passengers in Jabodetabek, Java (Non-Jabodetabek), and Sumatra from January 2006 to December 2022 as the data source. Time series regression with variations of the Islamic calendar and the effects of COVID 19 for Jabodetabek, Java (non-Jabodetabek), and Sumatra has an RMSE value for each category of 7657,821; 2453.827 and 275.901. In general, the number of train passengers for all categories (Jabodetabek, Java, Sumatra) has a seasonality. In Jabodetabek and Sumatra, Eid al-Fitr has a big impact on the number of train passengers. Meanwhile, one month before Eid al-Fitr has a big impact on the number of train passengers in Java (Non Jabodetabek). In addition, the impact of COVID 19 significantly affected the number of train passengers for all categories.
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.

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