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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Media Teknik dan Sistem Industri JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Dina Tri Utari
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Islam Indonesia
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Journal : EKSAKTA: Journal of Sciences and Data Analysis

 1 
Forecasting The Exchange Rate (IDR) of US Dollar (USD) Using Locally Stationary Wavelet Dina Tri Utari
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 18, ISSUE 2, August 2018
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/eksakta.vol18.iss2.art6

Abstract

Currency exchange rate of a country to the other countries is fluctuative. The movement of the exchange rate affects the country’s economy. The exchange rate can change any time according to the market mechanism, therefore currency exchange predictions is required to determine future economic policy. Based on the impact of exchange rate in economy fluctuations, an accurate model is needed to determine the exchange rate movements.In this case, the model is Locally Stationary Wavelet (LSW). This model combines stocastic process class based on wavelet non decimated. LSW model can catch most of the information in time series data. Based on the application of LSW mtehod on the data of the rupiah against the US dollar for the period April 2016 - March 2017, it can be concluded that model provides forecasting results approaching actual data therefore it can be used for forecasting exchange rates. The value of the mean absolute percentage error (MAPE) is 0,1201293%. 
Hierarchical Clustering Approach for Region Analysis of Contraceptive Users Dina Tri Utari; Denesa Salma Hanun
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 2, August 2021
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol2.iss2.art3

Abstract

Through increasing the use of contraceptives to limit births, the Family Planning (KB) Program is one of the government's efforts to control the rate of population growth. Klaten Districts is one of the regencies in Central Java Province with a relatively high number of births and relatively low coverage of active family planning. This study aimed to determine the grouping of sub-districts and these characteristics in the Klaten Districts in 2020. The method used in this study was a hierarchical cluster analysis method, with the best method being the centroid method. In this study obtained 3 clusters with cluster 1 consisting of 23 sub-districts, cluster 2 consists of 2 sub-districts and cluster 3 with 1 sub-district. The cluster characteristics based on the highest number of users of contraceptive methods are cluster 1-contraceptives injection, cluster 2- contraception implant, and IUDs in cluster 3
Comparison of the Naïve Bayes Classifier and Decision Tree J48 for Credit Classification of Bank Customers Alifia Tanza; Dina Tri Utari
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art2

Abstract

The bank conducts an analysis or survey in the credit system to determine whether the customer is eligible to receive credit. With a case study of Bank BJB debtor data in December 2021, credit classification analysis was carried out by forming a model using the Naïve Bayes Classifier and Decision Tree J48. Thus it is expected to minimize the occurrence of bad loans. The data are divided into several categories: debtors with good, substandard, doubtful, and bad credit. The analysis was carried out using a 10-fold cross-validation model, where the results obtained from both tests, the highest accuracy value was the Decision Tree J48 of 78.26%. While the Naïve Bayes Classifier has a lower level of accuracy, the prediction results tend to be better than the Decision Tree J48. The prediction results with the Naïve Bayes Classifier can predict all classes and the most influential variable in classifying credit is the loan term.
Co-Authors Alifia Tanza Denesa Salma Hanun Istina Alya Rosyada