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All Journal Journal of Science and Technology UNP Journal of Statistics and Data Science
Nisa Ulhusna
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Analysis Analysis of The Influence of Job Resources and Leadership Quality on Job Satisfaction Using Structural Equation Modeling Azizah Apriyerni; Nisa Ulhusna; Rahmadani; Mira Meilisa
UNP Journal of Statistics and Data Science Vol. 4 No. 1 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss1/469

Abstract

Job Satisfaction is a essential factor influencing employee performance, commitment, and organizational sustainability. Low levels of Job Resources and suboptimal Leadership Quality are common causes of decreased job satisfaction across various institutions. This study aims to analyze the effect of job resources and leadership quality on Jjob Satisfaction using the Structural Equation Modeling (SEM) method. The research data were obtained from a Likert-scale survey (1-8) consisting of three latent variabless and their respective indicators, and wer analyzed through Confirmatory Factor Analysis (CFA) and Structural Model assesment. The result of the CFA indicate that all indicators meet the criteria for validity and reliability, with factor loadings above 0.50, a Composite Reliability (CR) value of 0.9667, and an Average Variance Extracted (AVE) value of 0.6769. the Goodness of Fit evaluation shows that the final model is highly acceptable, as reflected by a low Chi-square/df value, RMSEA = 0.005, and CFI, TLI, GFI, and NFI value of 1.000. the Structural analysis further demonstrates that Job Resources have a positive and significant impact on Job Satisfaction. Simultaneously, both variables contribute significantly to explaining variations in Job Satisfaction. This study highlights that enhancing Job Resources and improving Leadership Quality are crucial strategies to strengthen employee Job Satisfaction. The findings provide empirical insight that can assist organizations in developing more effective and sustainable human resource management policies
Analisis Regresi Spasial Tingkat Pengangguran Terbuka di Sumatera Barat Menggunakan Model Spatial Autoregressive dengan Pembobot K-Nearest Neighbor Nisa Ulhusna; Tessy Octavia Mukhti; Fadhilah Fitri
JOSTECH Journal of Science and Technology Vol 6, No 1: Maret 2026
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v6i1.12872

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang berpengaruh terhadap Tingkat Pengangguran Terbuka (TPT) di Provinsi Sumatera Barat dengan memperhitungkan pengaruh spasial antarwilayah. Regresi spasial digunakan sebagai metode utama melalui model Spatial Autoregressive (SAR) dan matriks pembobot K-Nearest Neighbor (K-NN), berdasarkan data sekunder yang disediakan Badan Pusat Statistik (BPS) tahun 2024 yang mencakup 19 kabupaten/kota. Variabel yang diteliti meliputi TPAK, PDRB, Rata-rata Lama Sekolah (RLS), Umur Harapan Hidup (UHH), dan Kepadatan Penduduk. Hasil analisis menunjukkan bahwa model SAR lebih sesuai daripada model OLS memiliki nilai  dan . Secara parsial, variabel TPAK signifikan memiliki pengaruh negatif terhadap TPT, sedangkan PDRB signifikan memiliki pengaruh positif. Nilai koefisien spasial  sebesar  menunjukkan adanya pengaruh spasial negatif antarwilayah, yang berarti wilayah dengan tingkat pengangguran tinggi dapat menekan pengangguran di wilayah sekitarnya melalui redistribusi tenaga kerja. Temuan ini menegaskan bahwa TPT di Sumatera Barat tidak hanya dipengaruhi oleh karakteristik internal wilayah, tetapi juga oleh interaksi spasial antarwilayah. Hasil yang diperoleh diharapkan dapat berperan sebagai pertimbangan pemerintah daerah dalam pengambilan kebijakan ketenagakerjaan yang lebih efektif dan berbasis wilayah.
Co-Authors Azizah Apriyerni Fadhilah Fitri Mira Meilisa Rahmadani Tessy Octavia Mukhti