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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Ketahanan Nasional Economics Development Analysis Journal MUST: Journal of Mathematics Education, Science and Technology J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika G-Tech : Jurnal Teknologi Terapan Sains & Matematika
Suliyanto
Departemen Matematika, Fakultas Sains Dan Teknologi, Universitas Airlangga
Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Physics

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Estimasi Model Regresi Panel Komponen Error Satu Arah dengan Metode Generalized Least Square Mahfudhotin Mahfudhotin; Suliyanto Suliyanto; Eko Tjahjono
Sains dan Matematika Vol. 6 No. 1 (2017): Oktober, Sains & Matematika
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Model regresi ­panel komponen error satu arah merupakan model regresi gabungan antara data cross-section dan data time series yang memiliki spesifikasi yang tepat untuk menggambarkan  individu secara random dari populasi yang besar. Tujuan dari penulisan tugas akhir ini adalah untuk mendapatkan estimasi model regresi panel komponen error satu arah menggunakan metode Generalized Least Square dan untuk menguji kesesuaian model menggunakan uji Hausman dan uji Multiple Lagrange. Hasil estimasi parameter regresi masih bergantung pada komponen delta_g^2  dan delta_a^2  sehingga untuk mengestimasinya dilakukan proses iterasi sampai diperoleh vektor parameter yang konvergen. Model regresi ­panel komponen error satu arah dapat dituliskan dalam bentuk persamaan. Penerapan model ini dilakukan pada data Produk Domestik Regional Bruto (PDRB) perkapita pada provinsi di Indonesia periode 2007 sampai 2010 sebagai variabel dependen , sedangkan variabel prediktornya meliputi : Tingkat Pengangguran Terbuka, Investasi Penanaman Modal Asing, Investasi Penanaman Modal Dalam Negeri, Jumlah Angkatan Kerja, dan Pengeluaran Konsumsi Rumah Tangga. Model ini mempunyai nilai  R^2 = 0.9991 dan MSE = 2.7518. The one-way error panel component regression model is a combined regression model between cross-section data and time series data that has the right specifications to describe N random individuals from large populations. The purpose of writing this final project is to obtain a one-way error component panel regression model estimation using the Generalized Least Square method and to test the suitability of the model using the Hausman test and the Multiple Lagrange test. The results of the estimation of the regression parameters still depend on the components  delta_g^2  and delta_a^2so to estimate them the iteration process is performed until a converging parameter vector is obtained. The one-way error panel regression model can be written in the equation. The application of this model is carried out on the per capita Gross Regional Domestic Product (GRDP) data in provinces in Indonesia from 2007 to 2010 as the dependent variable , while the predictor variables include: Open Unemployment Rate , Foreign Investment Investment , Investment Domestic Investment , Total Labor Force , and Household Consumption Expenditures . This model has a value of  R^2 = 0.9991 and MSE = 2.7518
Penerapan Model ARIMAX-GARCH dalam Pemodelan dan Peramalan Volume Transaksi Uang Elektronik di Indonesia Christopher Andreas; Sediono Sediono; Elly Ana; Suliyanto Suliyanto; M. Fariz Fadillah Mardianto
MUST: Journal of Mathematics Education, Science and Technology Vol 6, No 2 (2021): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v6i2.11214

Abstract

Di era ekonomi digital, berbagai aktivitas ekonomi telah banyak memanfaatkan penggunaan uang elektronik. Penggunaan uang elektronik memberi berbagai dampak positif terhadap perekonomian dan pertumbuhan ekonomi. Untuk itu, perkembangan ekonomi digital terus didorong dalam upaya untuk meningkatkan pertumbuhan ekonomi seperti salah satu pilar tujuan dari Sustainable Development Goals (SDGs). Hal ini menunjukkan bahwa pemodelan dan peramalan volume transaksi uang elektronik sangat penting untuk dilakukan karena volume transaksi uang elektronik tersebut merupakan salah satu indikator perkembangan ekonomi digital di Indonesia. Penelitian ini bertujuan untuk menciptakan model statistika yang memiliki akurasi tinggi guna meramalkan volume transaksi uang elektronik di Indonesia. Dalam hal ini, pemodelan dilakukan dengan mempertimbangkan dua variabel eksogen yaitu infrastruktur uang elektronik dan kondisi pandemi Covid-19. Penelitian ini dilakukan dengan melakukan analisis data berdasarkan data yang bersumber dari Bank Indonesia. Dengan menerapkan model ARIMAX-GARCH, diperoleh model statistika yang memiliki akurasi tinggi dalam meramalkan volume transaksi uang elektronik di Indonesia. Hal ini ditandai melalui nilai Mean Absolute Percentage Error (MAPE) sebesar 11,33%. Selain itu, kedua variabel eksogen yaitu infrastruktur uang elektronik dan kondisi pandemi Covid-19 berpengaruh signifikan terhadap volume transaksi uang elektronik di Indonesia. Penelitian ini bermanfaat sebagai landasan dalam melakukan evaluasi kebijakan terkait perkembangan ekonomi digital khususnya penggunaan uang elektronik di Indonesia.
Pemodelan Persentase Kepesertaan BPJS Non Penerima Bantuan Iuran Dengan Pendekatan Regresi Data Panel Dhyana Venosia; Suliyanto; Sediono; Nur Chamidah
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 15 No 1 (2022): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.897 KB) | DOI: 10.36456/jstat.vol15.no1.a4863

Abstract

Indonesia merupakan salah satu negara yang mengembangkan konsep Universal Health Coverage (UHC) pada sektor kesehatan yang diterapkan pada Sistem Jaminan Sosial Nasional (SJSN) melalui program Jaminan Kesehatan Nasional (JKN) yang dikelola Badan Penyelenggara Jaminan Sosial (BPJS) Kesehatan dengan tujuan sebagaimana tertuang pada Undang-Undang Republik Indonesia Nomor 40 Tahun 2004. Peserta JKN terbagi menjadi Penerima Bantuan Iuran (PBI) dan Non Penerima Bantuan Iuran (Non PBI). Penelitian ini, untuk menganalisis faktor yang mempengaruhi persentase kepesertaan BPJS Non PBI yang diharapkan dapat memberikan prediksi pengoptimalan. Pengoptimalan diperlukan karena, realitanya persentase kepesertaan BPJS Non PBI masih jauh dari target pemerintah, khususnya Provinsi Jawa Timur pada tahun 2017 hingga 2020. Walaupun mengalami peningkatan, di setiap Kabupaten/Kota Provinsi Jawa Timur terindikasi mengalami fluktuasi. Maka, dalam mengestimasi fenomena tersebut digunakan metode regresi data panel melalui pendekatan Fixed Eeffect Model (FEM) dengan alpha sebesar 5 persen. Maka, secara statistik diperoleh kesimpulan bahwa yang berpengaruh signifikan adalah persentase penduduk miskin dan Tingkat Pengangguran Terbuka (TPT).
Pemodelan Penderita Tuberkulosis di Jawa Timur Berdasarkan Pendekatan Geographically Weighted Regression (GWR) Diah Puspita Ningrum; Toha Saifudin; Suliyanto Suliyanto; Nur Chamidah
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 1 (2022): SEPTEMBER, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i1.21262

Abstract

Tuberculosis is the 13th trigger of death causes around the world. Even after Covid-19, tuberculosis ranks 2nd as a contagious killer disease. In 2020, Indonesia ranks 2nd out of 8 countries with the highest contributor to tuberculosis sufferers after India. East Java Province is the region with the largest number of tuberculosis cases in order of 8. Tuberculosis cases in East Java in 2020 have decreased, but when viewed from the success rate of treatment of tuberculosis cases per district/city in East Java, it was found that 53% still did not meet the target of 90%. According to (World Health Organization), gender affects the occurrence of tuberculosis disease, where men are more susceptible than women. In finding treatment for all tuberculosis incidents in East Java, the highest patient was male. This study was conducted to model tuberculosis in men in the East Java area. The results of the study prove that the modeling of male tuberculosis in East Java used linear regression and GWR  (Geographically Weighted Regression) obtained the best model was GWR with Fixed Gaussian Kernel weighting, CV value of 5.68, and R2 86.47%. Variables that have a significant effect on male tuberculosis in East Java are BCG immunization for male infants, public places meeting health requirements, youth who smoke tobacco every day, sex ratio, and households with access to proper sanitation facilities.      
Pemodelan Indeks Ketahanan Pangan di Indonesia Berdasarkan Pendekatan Regresi Logistik Ordinal Data Panel Efek Acak Anisa Laila Azhar; Suliyanto Suliyanto; Nur Chamidah; Elly Ana; Dita Amelia
Jurnal Ketahanan Nasional Vol 29, No 2 (2023)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkn.86511

Abstract

ABSTRACTIndonesia is an agricultural country with the agricultural sector being an important sector in supporting food needs. Food availability that is less than necessary can lead to an unstable economy, as well as disrupt national food security. This study was conducted to model The Food Security Index (Indeks Ketahanan Pangan, IKP) and to find out what factors affect the status of food security in Indonesia.The analysis method used in this study is the logistic regression analysis of panel data with random effects. The data used in this study is secondary data related to IKP sourced from the Ministry of Agriculture and factors that are suspected to affect IKP in each province sourced from the Central Statistics Agency (Badan Pusat Statistik, BPS) from 2019 to 2021. The results of the analysis showed that statistically, the variable percentage of stunted toddlers and the variable percentage of households with access to electricity had a significant effect on the IKP. In addition, the results of the model conformity test showed that the random effect panel data logistic regression model was more in line with the classification accuracy of 50.98% when compared to the standard logistic regression with a classification accuracy of 40.80%.
Pemodelan Indeks Kebahagiaan di Indonesia Berdasarkan Pendekatan Mixed Geographically Weighted Regression Alfredi Yoani; Fina Insyiroh; Leni Sartika Panjaitan; Toha Saifudin; Suliyanto
G-Tech: Jurnal Teknologi Terapan Vol 8 No 1 (2024): G-Tech, Vol. 8 No. 1 Januari 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i1.3639

Abstract

The well-being of society, involving the fulfilment of basic needs and opportunities for education and employment, can be measured through the happiness index. This research aims to assist the Indonesian government in achieving Sustainable Development Goal 3 related to Health and Well-being. It is hoped that by comprehending these factors, the government can improve the health and well-being of the Indonesian population. The happiness index varies across different geographical regions due to factors such as culture, social dynamics, and the environment, which can have different impacts from one region to another. Given the randomness in data patterns stemming from the diverse provinces in Indonesia, this study employs the Mixed Geographically Weighted Regression (MGWR) method. Results reveal that the MGWR model, utilizing a fixed Gaussian kernel weight, yields the lowest Akaike’s Information Criterion Corrected and the highest at 87.2%, underscoring its precision in modeling the happiness index in Indonesia.
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.
Co-Authors Alfredi Yoani Ameliatul 'Iffah Ana, Elly Anisa Laila Azhar Christopher Andreas Dhyana Venosia Diah Puspita Ningrum Dita Amelia Eko Tjahjono Elly Ana Elly Ana Fina Insyiroh Leni Sartika Panjaitan M. Fariz Fadillah Mardianto Mahfudhotin Mahfudhotin Nur Chamidah Sediono Sediono Toha Saifudin Toha Saifudin Toha Saifudin