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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Dasar SAINSMAT IPTEK Journal of Proceedings Series KONSELI: Jurnal Bimbingan dan Konseling (E-Journal) Journal of Regional and City Planning Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi Journal of Applied Geospatial Information Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Unisda Journal of Mathematics and Computer Science (UJMC) Jurnal Penelitian dan Pengembangan Pelayanan Kesehatan International Journal of Pedagogy and Teacher Education J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika METIK JURNAL Building of Informatics, Technology and Science Jurnal Perencanaan Pembangunan Journal of Education and Learning Mathematics Research (JELMaR) International Journal of Advances in Data and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) TheJournalish: Social and Government The Journal of Indonesia Sustainable Development Planning (JISDeP) JoMMiT : Jurnal Multi Media dan IT Jurnal Riset Ilmu Ekonomi Jurnal Algoritma East Java Economic Journal SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Mahasiswa Ilmu Komputer PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Journal of Multiscale Materials Informatics Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Harun Al Azies
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Transportation Other

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Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach HARUN AL AZIES
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.348

Abstract

The air quality in the Jakarta area is examined in this study using artificial intelligence (AI) to assist a semi-supervised learning technique. The clustering approach is used in this article to separate air pollution into three main categories moderate, low, and high levels. This clustering helps identify shared characteristics among measures like particulates (PM10 and PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), even when air quality labels are not always accessible. Using the Random Forest method, the air quality will be categorized in this experiment with an accuracy rate of 93%. Additionally, the results of variable significance analysis are examined on this article to identify the variables with the biggest effects on air quality, notably PM10, SO2, and NO2. This study demonstrates the enormous potential of applying machine learning techniques, particularly semi-supervised learning approaches, to assist sustainable environmental regulations while also monitoring and enhancing Jakarta's air quality. We describe the experimental procedures, the findings, and the implications of our research for comprehending and addressing urban air pollution in this article.
Unravelling Income Inequality in Indonesia: A Machine Learning Approach to Understanding The Impact Of Information and Communication Technology Harun Al Azies; Wise Herowati
Jurnal Riset Ilmu Ekonomi Vol. 3 No. 2 (2023): Jurnal Riset Ilmu Ekonomi (JRIE) Edisi Agustus 2023
Publisher : Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jrie.v3i2.63

Abstract

This study aims to analyze the effect of Information and Communication Technology (ICT) on income inequality in Indonesia. The analytical method used includes linear regression to evaluate the causal relationship between the dependent variable (Gini Ratio) and ICT indicators. Furthermore, the k-means clustering algorithm is used to group provinces based on ICT characteristics. The results of the regression analysis show that the ICT variables have a significant influence on the Gini Ratio, illustrating the close relationship between ICT and income inequality. In addition, clustering produces two regional groups: Cluster 1, with better access and use of ICT, and Cluster 2, with lower ICT characteristics but advantages in telecommunication infrastructure. This research shows the importance of inclusive and sustainable ICT development to reduce income inequality in Indonesia. Appropriate policies for increasing the accessibility and use of ICTs can have a positive impact on social and economic development throughout Indonesia.
THE RELATIONSHIP BETWEEN PUBLIC INFORMATION OPENNESS AND ICT DEVELOPMENT Harun Al Azies; Ishak Bintang Dikaputra
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 2 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i2.4238

Abstract

The relationship between Information and Communication Technology (ICT) development and the level of Public Information Openness (KIP) holds significant implications for inclusive and sustainable societal development. This study employs statistical analysis, including Pearson correlation, to examine this relationship across Indonesian provinces in 2022. Findings indicate a positive correlation between ICT development and KIP. Access to ICT infrastructure and ICT usage show significant correlations with IKIP levels across various provinces. Provinces with better ICT development generally exhibit higher KIP levels. However, the relationship with ICT skills is comparatively weaker, indicating other influencing factors on ICT literacy within the community. The conclusion drawn from this research is that ICT development positively contributes to enhancing Public Information Transparency in Indonesia. Therefore, further efforts are needed to support equitable ICT development, enhance digital literacy, and strengthen public information transparency, enabling the population to effectively harness information and communication technology
TOWARDS OPTIMIZATION: A DATA-DRIVEN APPROACH USING K-MEDOIDS CLUSTERING ALGORITHM FOR REGIONAL EDUCATION QUALITY ASSESSMENT Harun Al Azies; Fawwaz Atha Rohmatullah; Hani Brilianti Rochmanto; Devi Putri Isnarwaty
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4862

Abstract

This study applies the k-medoids clustering machine learning approach to assess regional clustering in Indonesia based on educational quality. Data on the quality of education, including indicators of school enrollment rate (APS), gross enrollment rate (APK), and pure participation rate (APM), is gathered and processed from all provinces in Indonesia. The k-medoids clustering technique is used to carry out the clustering process, while metrics like Dunn's index, connection coefficient, and silhouette score are used to evaluate the results. The study's findings indicate that three clusters are the ideal amount, with a silhouette score of 0.2388, a connectivity coefficient of 7.1405, and a Dunn's index value of 0.1651. Cluster homogeneity is likewise moderate, despite the regions' moderate distances from one another. This assessment offers a thorough understanding of Indonesia's educational quality clustering pattern, which can serve as a foundation for developing education strategies in different areas
GWRPCA ALGORITHMIC FRAMEWORK: ANALYZING SPATIAL DYNAMICS OF POVERTY IN EAST JAVA PROVINCE Harun Al Azies; Noval Ariyanto
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3945

Abstract

This study employs Regression Principal Component Analysis (RPCA) and Geographically Weighted Regression Principal Component Analysis (GWRPCA) algorithms to analyze poverty in East Java Province, using data from Statistics Indonesia (BPS). The research investigates regency/city-level poverty percentages and identifies influential factors such as education, literacy rates, housing conditions, and economic indicators. The results reveal that GWRPCA, with an 85.10% R2 value, outperforms RPCA, highlighting its effectiveness in capturing spatial diversity and providing a nuanced portrayal of poverty characteristics across regencies/cities in East Java. In conclusion, GWRPCA emerges as a powerful algorithmic tool for informing targeted poverty alleviation policies, offering insights into spatial variations. The study suggests future research directions to explore evolving spatial patterns and consider additional variables for a more comprehensive analysis. The findings emphasize the significance of spatial considerations in devising effective, context-specific strategies for each regency/city in East Java
Predicting Methanol Space-Time Yield from CO? Hydrogenation Using Machine Learning: Statistical Evaluation of Penalized Regression Techniques Harun Al Azies; Muhamad Akrom; Setyo Budi; Gustina Alfa Trisnapradika; Aprilyani Nur Safitri
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1341

Abstract

This study investigates the effectiveness of machine learning techniques, specifically penalized regression models Ridge Regression, Lasso Regression, and Elastic Net Regression in predicting methanol space-time yield (STY) from CO? hydrogenation data. Using a dataset derived from Cu-based catalyst research, the study implemented a comprehensive preprocessing approach, including data cleaning, imputation, outlier removal, and normalization. The models were rigorously evaluated through 10-fold cross-validation and tested on unseen data. Ridge Regression outperformed the other models, achieving the lowest Root Mean Squared Error (RMSE) of 0.7706, Mean Absolute Error (MAE) of 0.5627, and Mean Squared Error (MSE) of 0.5938. In comparison, Lasso and Elastic Net Regression models exhibited higher error metrics. Feature importance analysis revealed that Gas Hourly Space Velocity (GHSV) and Molar Masses of Support significantly influence catalytic activity. These findings suggest that Ridge Regression is a promising tool for accurately predicting methanol production, providing valuable insights for optimizing catalytic processes and advancing sustainable practices in chemical engineering.
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).
Integrating Water Indicators In A Data-Driven Artificial Intelligence Model For Food Security Classification Azies, Harun Al
TheJournalish: Social and Government Vol. 4 No. 5 (2023): Special Issue
Publisher : CV The Journal Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55314/tsg.v4i5.607

Abstract

Penelitian ini menjelaskan integrasi indikator air dalam pengklasifikasian ketahanan pangan provinsi di Indonesia. Pentingnya topik ini terkait dengan hubungan yang signifikan antara air, pangan, dan energi dalam mencapai ketahanan pangan berkelanjutan. Penggunaan kecerdasan buatan, khususnya dalam bentuk algoritma XGBoost, merupakan langkah cerdas dalam mengolah data dan melakukan pengklasifikasian ketahanan pangan. Data indikator air dan cut-off point Indeks Ketahanan Pangan digunakan dalam mengembangkan model XGBoost yang bertujuan mengklasifikasikan provinsi-provinsi sebagai high food vulnerability atau "high food security. Metode penelitian melibatkan pengumpulan data, preprocessing data, serta penerapan algoritma XGBoost dengan tuning parameter. Hasil penelitian menunjukkan bahwa model yang dikembangkan memiliki akurasi sebesar 91%, dengan variabel proporsi rumah tangga yang memiliki akses terhadap sumber air minum yang aman (X1) sebagai faktor paling berpengaruh dalam pengklasifikasian ketahanan pangan. Penelitian ini bukan hanya memberikan wawasan penting terkait ketahanan pangan provinsi di Indonesia, tetapi juga menunjukkan potensi besar kecerdasan buatan dalam mengatasi permasalahan kompleks seperti ketahanan pangan. Dengan hasil yang diperoleh, dapat diperkuat argumen pentingnya penerapan teknologi kecerdasan buatan dalam mendukung kebijakan dan tindakan nyata dalam upaya mencapai ketahanan pangan yang lebih baik dan berkelanjutan.
Co-Authors Achmad Wahid Kurniawan Achmad Wahid Kurniawan Adhitya Nugraha Agus Suharsono Akrom, Muhamad Alfa Trisnapradika, Gustina Alzami, Farrikh Ananda, Imanuel Khrisna Andrean, Muhammad Niko Anwar Efendi Nasution Aprilyani Nur Safitri Ardytha Luthfiarta Ariyanto, Noval Ayu Febriana Dwi Rositawati Ayu Pertiwi Ayu Pertiwi Bambang Widjanarko Otok Brilianti Rochmanto, Hani Brilianto, Rivaldo Mersis Budi, Setyo Dea Trishnanti Dea Trishnanti Devi Putri Isnarwaty Dikaputra, Ishak Bintang Elvira Mustikawati P.H Fahmi Amiq Fawwaz Atha Rohmatullah Firmansyah, Gustian Angga Fitriani, Fenny Gangga Anuraga Ganiswari, Syuhra Putri Guruh Fajar Shidik Gustina Alfa Trisnapradika Hani Brilianti Rochmanto Herawati, Wise Herowati, Wise Hidayat, Novianto Hidayat, Novianto Nur Irnanda, Muhammad Diva Ishak Bintang Dikaputra Isnarwaty, Devi Putri ISWAHYUDI ISWAHYUDI Junta Zeniarja Kharisma, Ni Made Kirei Megantara, Rama Aria Moch Anjas Aprihartha Muhamad Akrom Muhammad Naufal Muhammad Naufal, Muhammad Muljono Muljono Noor Ageng Setiyanto, Noor Ageng Noval Ariyanto Novianto Hidayat Nugroho, Dandy Prasetyo Nur Safitri, Aprilyani Prabowo, Wahyu Aji Eko Pratama, Ananta Surya Pravesti, Cindy Asli Pulung Nurtantio Andono Purhadi Purhadi Putra, Permana Langgeng Wicaksono Ellwid Rahman, Irfan Fauzia Rahmawati Erma Standsyah Ramadhan Rakhmat Sani Rohmatullah, Fawwaz Atha Safitri, Aprilyani Nur Sari Ayu Wulandari Setyo Budi Sri Winarno Sri Winarno Sudibyo, Usman Supriadi Rustad Trishnanti, Dea Trisnapradika, Gustina Alfa Umam, Taufiqul Usman Sudibyo Vivi Mentari Dewi Wahyu Wisnu Wardana Wise Herawati Wise Herowati Zahro, Azzula Cerliana Zain, Affa Fahmi Zami, Farrikh Al