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

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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
Analisis Karakteristik Wilayah Transmisi Covid-19 dengan Menggunakan Metode K-Means Clustering Dina Tri Utari
Jurnal Media Teknik dan Sistem Industri Vol 5, No 1 (2021)
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/jmtsi.v5i1.1220

Abstract

Since the beginning of 2020, Indonesia has become one of the countries affected by the Covid-19 pandemic. Various efforts have been made by the government to prevent wider disease transmission. Large-scale social restrictions are one of the efforts that have been made by the Government. Bali Province is one of the areas where there are quite a lot of community activities, considering that Bali is a tourist destination that is in great demand by local and foreign tourists. This study aims to see the incidence of positive cases of Covid-19 based on the type of Covid-19 transmission that has occurred in all areas of Bali, so that the mitigation design can be adjusted based on the characteristics of the source of infection in various existing areas. The results show that based on the transmission source, it can be grouped into four clusters that have their respective characteristics. The proposed mitigation strategies include restrictions on local transmission and domestic travel for areas in clusters 1, 2, and 3. Meanwhile, restrictions on local transmission and overseas travel are in the 4th cluster. Sejak awal tahun 2020, Indonesia menjadi salah satu negara terkena pandemi Covid-19. Berbagai upaya telah dilakukan oleh pemerintah untuk mencegah transmisi penyakit yang lebih luas. Pembatasan sosial berskala besar menjadi salah satu upaya yang telah dilakukan oleh Pemerintah. Provinsi Bali merupakan salah satu wilayah yang cukup banyak terjadi aktivitas masyarakat, mengingat Bali merupakan kawasan destinasi wisata yang banyak diminati wisatawan lokal maupun mancanegara. Penelitian ini bertujuan untuk melihat kejadian kasus positif Covid-19 berdasarkan jenis transmisi Covid-19 yang terjadi di seluruh wilayah Bali, sehingga rancangan mitigasi dapat disesuaikan berdasarkan karakteristik sumber infeksi di berbagai wilayah yang ada. Hasil menunjukkan, bahwa berdasarkan sumber transmisi dapat dikelompokkan menjadi empat cluster yang memiliki karakteristik masing-masing. Usulan strategi mitigasi yang diberikan antara lain pembatasan transmisi lokal dan perjalanan dalam negeri untuk wilayah yang berada pada cluster 1, 2, dan 3. Sedangkan pembatasan transmisi lokal serta perjalanan luar negeri pada cluster ke-4.
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