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All Journal Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Ilmiah Informatika dan Komputer
Yuda Apriansyah
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Other

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Dampak Kebijakan Moneter terhadap Ketimpangan Distribusi Pendapatan Riau: Penelitian Yuda Apriansyah; Darmayuda; Supriani Sidabalok
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 1 (Juli 2025 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i1.1943

Abstract

This study aims to analyze the impact of monetary policy on income distribution inequality in Riau Province. Monetary policy is represented by two main variables: real interest rate and inflation rate. The data used are annual secondary data from 2005 to 2023, obtained from the Central Statistics Agency (BPS) and Bank Indonesia. This research employs multiple linear regression to examine the relationship between the independent variables and the Gini index as a proxy for income inequality. The results indicate that the real interest rate has a positive effect on income inequality, suggesting that an increase in the interest rate tends to widen the income gap. In contrast, inflation does not have a impact on inequality in Riau during the observation period. The coefficient of determination (R-squared) is 33.18%, indicating that the model explains a portion of the variation in income inequality, while the rest is influenced by other factors outside the model. This study recommends that monetary policy should consider its distributional effects and be integrated with fiscal policy to promote more equitable economic outcomes.
Implementation of Edge Detection Using the Sobel Operator on Papaya Leaf Images Yuda Apriansyah; Khairi, Nouval; Haikal Habibi Siregar; Supiyandi; Aidil Halim Lubis
Jurnal Ilmiah Informatika dan Komputer Vol. 2 No. 2 (2025): Desember 2025
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/ma9w7b36

Abstract

Recent advances in digital image processing and computer vision have enhanced feature extraction techniques for plant identification based on leaf morphology. Edge detection is a fundamental operation that highlights intensity discontinuities corresponding to object boundaries. This study implements the Sobel operator to perform edge detection on tropical leaf images using an experimental–computational approach. The workflow involves grayscale conversion, horizontal and vertical Sobel filtering, and gradient magnitude computation implemented in Python using the OpenCV library. Experimental evaluation demonstrates that the Sobel operator effectively delineates primary leaf contours and preserves morphological consistency, despite reduced performance under non-uniform illumination and noisy conditions. These results confirm that the Sobel operator remains a reliable preprocessing technique for leaf-based feature extraction and classification, offering a computationally efficient baseline for future integration with machine learning-based plant recognition systems.
Co-Authors Aidil Halim Lubis Darmayuda Haikal Habibi Siregar Khairi, Nouval Supiyandi Supiyandi Supriani Sidabalok