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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Economic Development Analysis Journal
Taly Purwa
Badan Pusat Statistik (BPS) Provinsi Bali
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Perbandingan Metode Regresi Logistik dan Random Forest untuk Klasifikasi Data Imbalanced (Studi Kasus: Klasifikasi Rumah Tangga Miskin di Kabupaten Karangasem, Bali Tahun 2017) Taly Purwa
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 1 (2019): JMSK, July, 2019
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (465.921 KB) | DOI: 10.20956/jmsk.v16i1.6494

Abstract

Penelitian ini bertujuan untuk mendapatkan model terbaik untuk klasifikasi data imbalanced, yaitu  rumah tangga sampel Susenas Maret 2017 di Kabupaten Karangasem, ke dalam kategori miskin atau tidak. Metode yang digunakan adalah Regresi Logistik dan Random Forest dimana masing-masing diterapkan skema cross validation (CV), yaitu stratified 5-fold CV, skema under sampling, oversampling dan combine sampling untuk mengatasi masalah data imbalanced serta proses feature selection. Hasil penelitian menunjukkan bahwa penerapan skema under sampling, oversampling dan combine sampling pada model Regresi Logistik memberikan efek meningkatnya rata-rata nilai sensitivity dan turunnya rata-rata nilai akurasi dan specificity. Sedangkan pada model Random Forest, efek tersebut hanya terlihat dari hasil skema under sampling saja. Proses feature selection dapat menurunkan varian nilai akurasi, specificity, sensitivity dan AUC pada model Regresi Logistik dan Random Forest hanya pada skema tertentu. Model terbaik secara keseluruhan adalah model model Regresi Logistik dengan skema combine sampling dan tanpa proses feature selection dengan rata-rata nilai akurasi, specificity, sensitivity dan AUC masing-masing sebesar 78,13%, 79,16%, 64,44% dan 77,77%.
COMPARISON OF ARIMA, TRANSFER FUNCTION AND VAR MODELS FOR FORECASTING CPI, STOCK PRICES, AND INDONESIAN EXCHANGE RATE: ACCURACY VS. EXPLAINABILITY Taly Purwa; Ulin Nafngiyana; Suhartono Suhartono
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (735.856 KB) | DOI: 10.14710/medstat.13.1.1-12

Abstract

The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.
Performance of Manufacturing MSEs in Bali Amidst the Covid-19 Pandemic Taly Purwa
Economics Development Analysis Journal Vol 11 No 2 (2022): Economics Development Analysis Journal
Publisher : Economics Development Department, Universitas Negeri Semarang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edaj.v11i2.49905

Abstract

This study measured the impact of Covid-19 on the total revenue of manufacturing micro and small enterprises (MSEs) in Bali, in aggregate and specifically by each type of 2-digit ISIC. Using the Cobb-Douglas model, the impact of fixed capital, the number of labors, and factors related to technical inefficiency were also evaluated. According to the findings, total revenue decreased by about one-fifth during the pandemic. Specifically, the significant impact was experienced by MSEs with 2-digit ISIC: 11 and 13 in the form of a positive impact and 14 in the form of a negative impact. As expected, the two input factors had positive elasticity on the total revenue. Education is the only non-statistically significant factor associated with technical inefficiency. MSEs with male entrepreneurs, productive age groups, capital that is not dominated by own capital, applying for a loan from people's business loans (KUR), and using the internet in the business process tend to perform better. Overall, technical efficiency before and during the pandemic is comparable, ranging between 40 and 80, while the decreasing distributional pattern of technical efficiency shows for some 2-digit ISIC
Co-Authors Suhartono Suhartono Ulin Nafngiyana