PEMODELAN PERTUMBUHAN EKONOMI MENGGUNAKAN GEOGRAPHICALLY WEIGHTED PANEL REGRESSION DENGAN PEMBOBOT ADAPTIVE GAUSSIAN KERNEL DAN ADAPTIVE EXPONENTIAL KERNEL

ALFIANI, SIVA, B2A018018 (2022) PEMODELAN PERTUMBUHAN EKONOMI MENGGUNAKAN GEOGRAPHICALLY WEIGHTED PANEL REGRESSION DENGAN PEMBOBOT ADAPTIVE GAUSSIAN KERNEL DAN ADAPTIVE EXPONENTIAL KERNEL. Sarjana / Sarjana Terapan (S1/D4) thesis, Universitas Muhammadiyah Semarang.

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Abstract

ABSTRAK Alfiani,Siva, 2022, Pemodelan Pertumbuhan Ekonomi Menggunakan Geographically Weighted Panel Regression Dengan Pembobot Adaptive Gaussian Kernel Dan Adaptive Exponential Kernel. Skripsi, Program Studi Sarjana Statistika, Universitas Muhammadiyah Semarang.Pembimbing : Prizka Rismawati Arum, M.Stat, Indah Manfaati Nur, M.Si. Meningkatkan pertumbuhan ekonomi adalah salah satu tujuan negara. Untuk mewujudkan tujuan tersebut diperlukan pembangunan ekonomi untuk mencapai masyarakat yang sejahtera. Produk Domestik Regional Bruto (PDRB) merupakan salah satu indikator pertumbuhan ekonomi. Data yang digunakan yaitu data sekunder tentang produk domestik regional bruto, jumlah penduduk miskin, pengeluaran pemerintah, rata - rata lama sekolah, tingkat partisipasi angkatan kerja, fasilitas kesehatan, tingkat pengangguran terbuka, pada tahun 2018 - 2020 di Provinsi Jawa Barat yang diperoleh dari website resmi Badan Pusat Statistik. Metode yang digunakan yaitu Geographically Weighted Panel Regression dengan model fixed effect dan pembobot adaptive kernel gaussian serta adaptive kernel exponential. Penelitian ini bertujuan mengetahui gambaran umum, mendapatkan model, dan memperoleh model terbaik pertumbuhan ekonomi di Jawa Barat. Hasil dari penelitian ini, model dengan pembobot Adaptive Exponential Kernel lebih baik daripada Adaptive Gaussian Kernel karena memiliki nilai AIC terkecil dan R2 terbesar. Nilai AICnya sebesar 230,574 dan nilai R2 sebesar 0,8303014. Kata kunci : PDRB, Geographically Weighted Panel Regression, Adaptive Gaussian Kernel, Adaptive Exponential Kernel. ABSTRACT Alfiani, Siva, 2022, Economic Growth Modeling Using Geographically Weighted Panel Regression With Weighted Adaptive Gaussian Kernel and Adaptive Exponential Kernel. Thesis, Undergraduate Statistics Study Program, University of Muhammadiyah Semarang. Supervisor : Prizka Rismawati Arum, M.Stat, Indah Manfaati Nur, M.Si. Increasing economic growth is one of the country's goals. To realize this goal, economic development is needed to achieve a prosperous society. Gross Regional Domestic Product (GDP) is one of the indicators of economic growth. The data used are secondary data on gross regional domestic product, number of poor people, government spending, average length of schooling, labor force participation rate, health facilities, open unemployment rate, in 2018 - 2020 in West Java Province obtained from the website official Central Bureau of Statistics. The method used is Geographically Weighted Panel Regression with a fixed effect model and weighting adaptive kernel gaussian and adaptive kernel exponential. This study aims to determine the general description, obtain a model, and obtain the best model of economic growth in West Java. The results of this study, the model with the weighted Adaptive Exponential Kernel is better than the Adaptive Gaussian Kernel because it has the smallest AIC value and the largest R2. The AIC value is 230.574 and the R2 value is 0.8303014. Keyword : GRDP, Geographically Weighted Panel Regression, Adaptive Gaussian Kernel, Adaptive Exponential Kernel.

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