PEMODELAN INDEKS WILLIAMSON DI PULAU JAWA DAN KALIMANTAN DENGAN PENDEKATAN MODEL DURBIN SPASIAL DINAMIS

Wardoyo, Christina Theora Putri (2026) PEMODELAN INDEKS WILLIAMSON DI PULAU JAWA DAN KALIMANTAN DENGAN PENDEKATAN MODEL DURBIN SPASIAL DINAMIS. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Ketimpangan pembangunan antarwilayah masih menjadi salah satu permasalahan utama dalam pembangunan ekonomi Indonesia, khususnya di Pulau Jawa dan Kalimantan. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi disparitas regional antarprovinsi selama periode 2016–2023 dengan mempertimbangkan pengaruh spasial dan dinamika waktu. Disparitas regional diukur menggunakan Indeks Williamson, sedangkan analisis dilakukan menggunakan Spatial Durbin Model (SDM) dinamis berbasis data panel dengan matriks pembobot spasial K-Nearest Neighbor (KNN) dan direct flight. Pemilihan model terbaik dilakukan menggunakan kriteria Akaike Information Criterion (AIC) dan Bayesian Information Criterion (BIC). Hasil penelitian menunjukkan bahwa SDM dinamis dengan pembobot K-Nearest Neighbor (KNN) merupakan model terbaik berdasarkan nilai AIC dan BIC. Hasil estimasi mengindikasikan adanya ketergantungan spasial dan dinamika temporal yang signifikan dalam pembentukan disparitas regional. Variabel Indeks Pembangunan Manusia (IPM) berpengaruh positif dan signifikan, sedangkan proporsi tenaga kerja sektor industri (SIND) berpengaruh negatif dan signifikan terhadap disparitas regional. Selain itu, efek spillover PMDN, IPM, IKF, dan SIND terbukti signifikan, yang menunjukkan bahwa karakteristik suatu provinsi turut memengaruhi disparitas di provinsi lain yang memiliki keterkaitan spasial.
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Regional development disparity remains one of the major challenges in Indonesia's economic development, particularly across the provinces of Java and Kalimantan. This study aims to analyze the factors influencing regional disparity among provinces during the 2016– 2023 period by considering spatial dependence and temporal dynamics. Regional disparity is measured using the Williamson Index, while the analysis employs a dynamic Spatial Durbin Model (SDM) for panel data with two spatial weighting matrices: K-Nearest Neighbor (KNN) and direct flight. The best model is selected based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results indicate that the dynamic SDM with the K-Nearest Neighbor (KNN) spatial weighting matrix is the best-performing model according to the AIC and BIC values. The estimation results reveal significant spatial dependence and temporal dynamics in explaining regional disparity. The Human Development Index (HDI) has a positive and significant effect, while the proportion of employment in the industrial sector has a negative and significant effect on regional disparity. Furthermore, the spatial spillover effects of Domestic Investment (PMDN), HDI, Fiscal Capacity Index (IKF), and the industrial employment share are statistically significant, indicating that the characteristics of one province influence regional disparity in neighboring provinces through spatial interactions.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Disparitas Regional, Indeks Williamson, Model Durbin Spasial Dinamis Regional Disparities, Williamson Index, Spatial Durbin Model
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Christina Theora Putri Wardoyo
Date Deposited:	30 Sep 2026 06:22
Last Modified:	30 Sep 2026 06:22
URI:	http://repository.its.ac.id/id/eprint/144952

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