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All Journal Journal of Applied Geospatial Information Jurnal Mahasiswa Ilmu Komputer
Firmansyah, Gustian Angga
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Physics

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PENERAPAN TEKNIK ADAPTIVE DAN HISTOGRAM EQUALIZATION DALAM PENGOLAHAN CITRA Naufal, Muhammad; Al Azies, Harun; Firmansyah, Gustian Angga; Kharisma, Ni Made Kirei
Jurnal Mahasiswa Ilmu Komputer Vol. 5 No. 1 (2024): Jurnal Mahasiswa Ilmu Komputer March 2024
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ilmukomputer.v5i1.5345

Abstract

Mengantuk saat berkendara menjadi ancaman serius yang dapat meningkatkan risiko kecelakaan, yang merupakan penyebab utama kematian di seluruh dunia, termasuk di Indonesia. Deteksi dan pencegahan kondisi mengantuk pada tahap awal menjadi krusial untuk mengurangi potensi kecelakaan dan meningkatkan keselamatan berkendara. Penelitian ini fokus pada pemanfaatan citra wajah pengemudi sebagai metode efektif dalam mendeteksi mengantuk. Rendahnya kontras dalam citra dapat mempengaruhi deteksi wajah, sehingga diperlukan peningkatan kontras citra. Dalam penelitian ini, dua teknik peningkatan kontras citra, yaitu Histogram Equalization (HE) dan Adaptive Histogram Equalization (AHE), dievaluasi. Dataset yang digunakan adalah Driver Drowsiness Dataset, terdiri dari citra Drowsy sebanyak 22,348 dan Non-Drowsy sebanyak 19,445. Pra-pemrosesan melibatkan resize dan pengaburan menggunakan Gaussian Blur, diikuti oleh penerapan HE dan AHE. Evaluasi kinerja dilakukan menggunakan matriks evaluasi, menghasilkan skor Mean Squared Error, Peak Signal-to-Noise Ratio, dan Signal-to-Noise Ratio. Hasil menunjukkan bahwa HE memberikan hasil yang lebih baik dengan skor MSE 101.21, PSNR 28.11, dan SNR 0.19, dibandingkan dengan AHE yang memiliki skor MSE 103.92, PSNR 27.97, dan SNR 0.04. Oleh karena itu, dapat disimpulkan bahwa HE memberikan peningkatan kontras yang lebih baik untuk citra wajah dibandingkan dengan AHE.
Machine Learning-Enhanced Geographically Weighted Regression for Spatial Evaluation of Human Development Index across Western Indonesia Firmansyah, Gustian Angga; Zeniarja, Junta; Azies, Harun Al; winarno, Sri; Ganiswari, Syuhra Putri
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6755

Abstract

The HDI (Human Development Index) is one of the important components to measure the level of success in efforts to improve the quality of human life. The human development index is built with three dimensions, namely the longevity and health dimension, the knowledge dimension and the decent standard of living dimension. The longevity and health dimension is measured using Life expectancy at birth. The knowledge dimension is measured using expected years of schooling and average years of schooling. Meanwhile, the decent standard of living dimension is measured using Adjusted per capita expenditure. This study aims to find factors that influence HDI (Human Development Index) in Western Indonesia Region using machine learning models. The results obtained are that HDI is influenced by average years of schooling, expected years of schooling, Life expectancy at birth, and Adjusted per capita expenditure which are sorted from the most significantly influential. The model used in this study is GWR (Geographically Weighted Regression) with evaluation results including, AIC of 215.3162, AICc of 226.5107, and the accuracy level in the form of R-square of 99.38% which means this model is good to use.
Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning Ganiswari, Syuhra Putri; Azies, Harun Al; Nugraha, Adhitya; Luthfiarta, Ardytha; Firmansyah, Gustian Angga
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6757

Abstract

The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).
Co-Authors Adhitya Nugraha Ardytha Luthfiarta Ganiswari, Syuhra Putri Harun Al Azies Junta Zeniarja Kharisma, Ni Made Kirei Muhammad Naufal, Muhammad Sri Winarno