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All Journal Jurnal Gaussian Indonesian Journal of Applied Statistics
Saputra, Bagus Arya
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Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Other

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PREDIKSI HARGA DAGING SAPI DI KABUPATEN BREBES MENGGUNAKAN PEMODELAN ARFIMA DENGAN EFEK GARCH Imani, Nanda Diva Lingkar; Tarno, Tarno; Saputra, Bagus Arya
Jurnal Gaussian Vol 12, No 4 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.4.570-580

Abstract

Beef is a source of animal protein which is rich in nutrients and much-loved by the people of Indonesia. Brebes Regency is an area in Indonesia that has local livestock assets, namely Java Brebes cattle or also known as Jabres cattle. The existence of this jabres cattle is one of the guardians of beef price stability in Brebes in particular and in Central Java in general. The price of beef often fluctuates, to minimize losses, it is necessary to predict the market price. The model for predicting research data is the ARFIMA-GARCH model which is a model that can explain long memory patterns in time series data and experience heteroscedasticity problems. This study aims to obtain the best model with time series analysis and predict the selling price of beef in Brebes Regency for the next 52 weeks using ARFIMA modeling which is enhanced using the addition of the GARCH model. The results of the analysis that has been carried out on beef price data in Brebes Regency can be concluded that the best model obtained is the ARFIMA model ([9], 0.5461747, 0) – GARCH (1, 1). Based on the predictions that have been made using the best model, the resulting MAPE value is 1.56375%, so the model is very good for predicting beef prices in Brebes Regency in the next several periods.
Estimator Cramer Von Mises bagi Parameter Distribusi Kumaraswamy-Lindley Saputra, Bagus Arya; Rafsanjani, Zani Anjani
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.79911

Abstract

The Kumaraswamy-Lindley (KL) distribution is a combination of the Lindley distribution and the Kumaraswamy distribution. The KL distribution is widely used to examine lifetime data. The importance of the application of the KL distribution in explaining lifetime data makes it necessary to estimate distribution parameters well. Therefore, this research will discuss the Cramer Von Mises Estimator (ECM) for the Kumaraswamy-Lindley distribution parameters. The formula for the ECM is obtained and the simulation is carried out using the same initial parameters with different generation sample sizes. The simulation results show that for the same initial parameters, estimation with a larger sample size has better results.
Klasifikasi Menggunakan Algoritma K-Nearest Neighbor pada Imbalance Class Data dengan SMOTE. (Studi Kasus: Nasabah Bank Perkreditan Rakyat “X”) Ardhana, Salsabilla Rizka; Widiharih, Tatik; Saputra, Bagus Arya
Indonesian Journal of Applied Statistics Vol 6, No 2 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i2.79389

Abstract

Rural Banks (Bank Perkreditan Rakyat/BPR) provide financial services to micro-businesses and low repayment communities, especially in rural areas. The main activity of the bank is lending. Customer credit classification is expected to assist BPR in anticipating potential bad loans. K-Nearest Neighbor classify current and potential bad credit status based on customer data from BPR “X” in Central Java in October 2022. K-Nearest Neighbor is effective against a large amount of training data and works based on the nearest neighbor. There is an imbalance class data which causes the classification process to focus more on the majority class. Imbalance class data is handled using Synthetic Minority Oversampling Technique (SMOTE) as an oversampling approach. Classification with the addition of SMOTE can improve the evaluation of classification accuracy, especially G-mean. G-mean is the most comprehensive measurement in term of  accuracy, sensitivity and specificity in evaluating classification performance on imbalance class data. The results of this research were able to increase g-mean to 58.55% and sensitivity to 45.46% by implementing SMOTE. Based on the classification results, it is concluded that K-Nearest Neighbor with SMOTE at k = 19 and a proportion of training data to test data of 70:30 is a more appropriate classification model to use for customer credit status. Keywords: Credit Status; K-Nearest Neighbor; Imbalance Class Data; SMOTE
Co-Authors Ardhana, Salsabilla Rizka Imani, Nanda Diva Lingkar Rafsanjani, Zani Anjani Tarno Tarno Tatik Widiharih