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All Journal Media Statistika Jurnal Pendidikan Matematika (Kudus)
Irfandi, Yoga Prastya
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Decision Sciences, Operations Research & Management Mathematics

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PER CAPITA CONSUMPTION ESTIMATION IN SURABAYA USING ENSEMBLE MODEL APPROACH Sutikno, Sutikno; Purnomo, Jerry Dwi Trijoyo; Harfianto, Unggul; Irfandi, Yoga Prastya; Anisa, Kartika Nur; Cahyoko, Fajar Dwi
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.170-181

Abstract

The categorization of the Low-Income Community category is based on the poverty indicators in the Multidimensional Poverty Index, including the dimensions of health, education, and living standards. The Proxy Means Test (PMT) can estimate household income or consumption by taking into account household conditions that are readily observable and cannot be manipulated. This method offers the advantage of being capable of determining both the poverty level of a household and the household's characteristics based on asset ownership and socio-demographic conditions. This study aims to estimate per capita consumption using OLS, Robust, Quantile, LASSO, and Ensemble methods. The application of these methods is intended to address various issues, including the presence of outlier data, multicollinearity, and uncertainties. The results indicate that none of the four methods used achieved the highest accuracy based on the MSE, MAE, and sMAPE criteria. Consequently, employing an ensemble model becomes essential to accommodate the element of uncertainty present in these four models. The application of the ensemble method is not only as a comparison between the models, but also as a means to capture the uncertainty contained in each model
Classification of Hepatitis Patients Using Logistic Regression and Support Vector Machines Methods Nurlaily, Diana; Irfandi, Yoga Prastya; Santoso, Noviyanti; Qomariyah, Siti; Wibowo, Dandy
Jurnal Pendidikan Matematika Vol 5, No 2 (2022): Jurnal Pendidikan Matematika (Kudus)
Publisher : Universitas Islam Negeri Sunan Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/jpmk.v5i2.17052

Abstract

Hepatitis is an inflammatory disease of the liver. The virus often causes hepatitis and it becomes the number one world health problem. From 2019 to 2020, there were 1.5 million new cases of hepatitis B and C infection per year. WHO (World Health Organization) aims to eliminate hepatitis by 2030. Based on this problem, it is necessary to classify which health indicators may be vulnerable to the survival of hepatitis patients. This research aims to obtain the best method for classifying hepatitis patients by comparing the logistic regression method and SVM (Support Vector Machines). The classification using logistic regression and SVM is the suitable alternative for this case because the response category is binary data. This research is quantitative research and the researcher uses the hepatitis data set obtained from the UCI repository learning machine. The hepatitis data set contains 19 predictive variables (6 continuous and 13 discrete variables). The patients are divided into two groups, living, and dead patients’ groups. The results show that the best accuracy value produced by using the logistic regression method is 79.3%, and by using the SVM method is 81.94%. Thus, the best classification result for the hepatitis data set is the holdout stratified SVM method using Kernel radians with an accuracy value of 81.94%. This result indicates that the holdout stratified SVM method using Kernel radians can classify hepatitis patients’ data. Hepatitis adalah penyakit peradangan pada hati. Hepatitis sering disebabkan oleh virus. Hepatitis termasuk masalah kesehatan dunia. Tahun 2019 sampai dengan 2020, terdapat 1,5 juta kasus baru infeksi hepatitis B dan C per tahun. WHO (World Health Organization) bertujuan untuk menghilangkan penyakit hepatitis pada tahun 2030. Berpondasikan masalah tersebut, perlu adanya pengklasifikasian untuk mengetahui indikator kesehatan mana yang mungkin rentan terhadap kelangsungan hidup pasien hepatitis. Tujuan penelitian ini untuk mendapatkan metode terbaik dalam mengklasifikasikan pasien hepatitis dengan cara membandingkan metode regresi logistik dan SVM (Support Vector Machines). Klasifikasi menggunakan regresi logistik dan SVM merupakan alternatif yang tepat untuk kasus ini, karena kategori respon adalah data biner. Penelitian ini merupakan penelitian kuantitatif. Penelitian ini menggunakan dataset hepatitis yang diperoleh dari UCI machine learning repository. Kumpulan data hepatitis berisi 19 variabel prediksi (6 variabel kontinu dan 13 variabel diskrit). Pasien dibagi menjadi dua kelas yaitu hidup dan mati. Hasil penelitian menunjukkan bahwa nilai akurasi terbaik yang dihasilkan metode regresi logistik adalah 79.3% sementara menggunakan metode SVM adalah 81.94%. Jadi hasil klasifikasi terbaik untuk dataset hepatitis adalah metode SVM holdout stratified menggunakan kernel radian dengan akurasi sebesar 81,94%. Hasil ini mengindikasikan bahwa metode SVM holdout stratified menggunakan kernel radian dapat digunakan untuk mengklasifikasikan data pasien hepatitis.
Co-Authors Cahyoko, Fajar Dwi Harfianto, Unggul Jerry Dwi Trijoyo Purnomo Kartika Nur Anisa Nurlaily, Diana Santoso, Noviyanti Siti Qomariyah Sutikno Sutikno Wibowo, Dandy