Pemodelan Indeks Kemahalan Konstruksi di Pulau Papua Menggunakan Analisis Geographically Weighted Regression dengan Spatial Pattern Analysis

Yahya, Ammaar Razaan (2026) Pemodelan Indeks Kemahalan Konstruksi di Pulau Papua Menggunakan Analisis Geographically Weighted Regression dengan Spatial Pattern Analysis. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Tingginya Indeks Kemahalan Konstruksi (IKK) di wilayah Papua disebabkan oleh faktor topografi pegunungan, keterbatasan akses transportasi, distribusi material yang sulit, serta rendahnya ketersediaan infrastruktur dasar. Penelitian ini bertujuan untuk menganalisis pola spasial dan faktor-faktor yang memengaruhi Indeks Kemahalan Konstruksi (IKK) di Pulau Papua tahun 2024 menggunakan pendekatan Spatial Pattern Analysis dan Geographically Weighted Regression (GWR) pada 42 kabupaten/kota. Hasil analisis spasial menunjukkan bahwa persebaran IKK tidak bersifat acak, melainkan membentuk pola mengelompok (clustered), yang dibuktikan oleh nilai Moran’s I sebesar 0,408 dan signifikan secara statistik. Klaster High–High terkonsentrasi di wilayah pegunungan tengah. Pemodelan GWR dilakukan menunjukkan adanya heterogenitas spasial, meskipun uji dependensi spasial residual tidak signifikan. Bandwidth optimum sebesar 147.778,8 diperoleh melalui metode Cross Validation dengan nilai CV minimum 33.081,32, menggunakan kernel fixed Gaussian. Hasil GWR menunjukkan bahwa pengaruh variabel tinggi wilayah, akses air minum layak, umur harapan hidup, harapan lama sekolah, jumlah penduduk miskin, dan indeks keparahan kemiskinan bervariasi antarwilayah. Secara keseluruhan, model GWR memiliki kinerja lebih baik dibandingkan regresi linier berganda, dengan nilai AIC 355,441 dan R² 0,970, sedangkan model regresi linear berganda menghasilkan AIC 405,955 dan R² 0,878. Temuan ini menegaskan bahwa kemahalan konstruksi di Pulau Papua dipengaruhi kuat oleh faktor geografis, infrastruktur dasar, dan kondisi sosial-ekonomi yang bersifat lokal, sehingga pendekatan GWR lebih efektif dalam mendukung perumusan kebijakan pembangunan yang tepat sasaran berbasis wilayah.
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The high Construction Cost Index (IKK) in the Papua region is caused by mountainous topography, limited transportation access, difficult material distribution, and low availability of basic infrastructure. This study aims to analyze the spatial patterns and factors that influence the Construction Cost Index (CCI) in Papua Island in 2024 using Spatial Pattern Analysis and Geographically Weighted Regression (GWR) approaches in 42 districts/cities. The spatial analysis results show that the distribution of the CPI is not random but forms a clustered pattern, as evidenced by a Moran's I value of 0.408, which is statistically significant. The High–High cluster is concentrated in the central mountainous region. The GWR modeling shows spatial heterogeneity, although the spatial dependency test of the residuals is not significant. The optimum bandwidth of 147,778.8 was obtained through the Cross Validation method with a minimum CV value of 33,081.32, using a fixed Gaussian kernel. The GWR results show that the influence of the variables of regional height, access to safe drinking water, life expectancy, expected years of schooling, number of poor people, and poverty severity index varies between regions. Overall, the GWR model performed better than multiple linear regression, with an AIC value of 355.441 and R² of 0.970, while the global model produced an AIC of 405.955 and R² of 0.878. These findings confirm that the high cost of construction in Papua is strongly influenced by geographical factors, basic infrastructure, and local socio-economic conditions, making the GWR approach more effective in supporting the formulation of targeted, region-based development policies.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Geographically Weighted Regression, Heterogenitas Spasial, Indeks Kemahalan Konstruksi, Pulau Papua, Spatial Pattern Analysis, Construction Cost Index, Geographically Weighted Regression, Papua, Spatial Pattern Analysis, Spatial Heterogeneity
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Ammaar Razaan Yahya
Date Deposited:	29 Jan 2026 03:49
Last Modified:	29 Jan 2026 03:49
URI:	http://repository.its.ac.id/id/eprint/130950

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