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All Journal Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Economic Development Analysis Journal Limits: Journal of Mathematics and Its Applications
Toha Saifudin
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Computer Science & IT Economics, Econometrics & Finance Mathematics Other

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Poverty Modeling in Indonesia: a Spatial Regression Analysis Ameliatul 'Iffah; Suliyanto Suliyanto; Sediono Sediono; Toha Saifudin; Elly Ana; Dita Amelia
Economics Development Analysis Journal Vol 12 No 4 (2023): 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.v12i4.66027

Abstract

The government has made various efforts to reduce poverty in Indonesia. However, based on the World Population Review report, Indonesia is still ranked as the 73rd poorest country in the world in 2022 based on the value of gross national income. Therefore, it is necessary to identify the factors that affect poverty. This research was conducted by comparing classical, spatial lag, and spatial error regression, and the best model will be selected. The results show that the spatial error regression model is the best, based on the highest coefficient of determination and the lowest Akaike's information criterion value. Based on the best model, it is found that the expected years of schooling, the rate of gross regional domestic product, the percentage of households that have access to proper sanitation services, and the percentage of households with electric lighting sources have a significant effect on the percentage of poor people. The percentage of poor people in a province is also influenced by the percentage of poor people in the surrounding provinces. The results of this simulation can help the government take initiatives or policies aimed at reducing poverty in Indonesia based on variables that affect poverty.
Pemodelan Kasus Tuberkulosis di Indonesia dengan Metode GWPR Guna Mendukung SDGs 2030 Toha Saifudin; Mochamad Firmansyah; Johanna Tania Victory; Mutiara Aisharezka
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 3 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Tuberculosis (TB) is the second leading cause of death after coronary heart disease. The bacterium type Humanus of Mycobacterium tuberculosis causes the infectious illnessTB. According to WHO, in 2018 Indonesia had 8% of TB cases, the third highest after India (27%) and China (9%). Therefore, efforts are needed to reduce the number of cases and deaths due to TB, in line with efforts to achieve point 3 of target 3 of the SDGs, namely ending the TB pandemic. This study uses the Geographically Weighted Poisson Regression (GWPR) model approach with the aim of analyzing the factors that influence TB, so that preventive interventions to reduce TB cases can be carried out. The data used in this study is secondary data in the form of data on the number of TB cases in 2018 obtained from the Ministry of Health (Kemenkes RI) and the Central Agency of Statistics (BPS). The observation unit is 34 provinces in Indonesia. Based on the smallest Akaike Information Criteria (AIC) value, the best GWPR model is obtained with Adaptive Bisquare weighting. Each province has a different model. The GWPR model in West Java Province which has the highest number of TB cases in Indonesia is . The results of the analysis show that the number of poor people has a very significant influence in almost all provinces in Indonesia. While this is going on, a considerable impact can be seen in the proportion of unfit homes and the percentage of unsanitary food processing facilities (TPM). Provincial governments in Indonesia can consider the results of modeling with GWPR in formulating strategies to reduce the number of TB sufferers in their regions
PERBANDINGAN PENDEKATAN DATA PANEL UNIVARIAT DAN PANEL SUR DALAM PEMODELAN STUNTING, WASTING, DAN UNDERWEIGHT DI INDONESIA Teguh Susanto; Toha Saifudin; Nur Chamidah
Seminar Nasional Hasil Riset dan Pengabdian Vol. 7 (2025): Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Ke 7 Tahun 2025
Publisher : LPPM Universitas PGRI Adi Buana

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Abstract

Indonesia berkomitmen untuk mewujudkan Sustainable Development Goals khususnya Zero Hunger 2030. Penelitian ini bertujuan untuk mengevaluasi efisiensi komparatif dan konsistensi struktural antara model regresi data panel univariat dengan model multivariat Panel Seemingly Unrelated Regression dalam memodelkan kasus stunting, wasting, dan underweight pada periode 2007–2023 di Indonesia. Pemilihan model Panel SUR didasarkan pada hasil uji diagnostik yang menunjukkan adanya korelasi signifikan antar error term pada ketiga persamaan (p < 0,001). Metode estimasi yang digunakan adalah FGLS dua arah. Hasil penelitian menunjukkan bahwa model univariat menghasilkan anomali tanda koefisien, di mana variabel berat badan lahir rendah (BBLR) berhubungan negatif dengan wasting, yang bertentangan dengan teori biologis. Sebaliknya, model Panel SUR melalui estimasi simultan berhasil memperbaiki arah hubungan tersebut menjadi positif dan meningkatkan nilai koefisien determinasi (R²) pada persamaan wasting secara signifikan. Selain itu, hasil evaluasi efisiensi berdasarkan Mean Square Error (MSE) menunjukkan bahwa model Panel SUR memberikan estimasi yang lebih efisien (MSE lebih rendah dibandingkan model univariat). Secara keseluruhan, temuan ini menunjukkan bahwa model Panel SUR lebih tepat digunakan untuk analisis sistem malnutrisi karena menawarkan konsistensi parameter yang lebih baik dan efisiensi statistik yang lebih tinggi, sehingga memberikan dasar yang lebih kuat bagi perumusan kebijakan gizi terpadu di Indonesia.
Co-Authors Ameliatul 'Iffah Dita Amelia Dita Amelia Elly Ana Johanna Tania Victory Mochamad Firmansyah Mutiara Aisharezka Nur Chamidah Sediono, Sediono Suliyanto Teguh Susanto