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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) Signifikan : Jurnal Ilmu Ekonomi Media Statistika Jurnal Statistika Universitas Muhammadiyah Semarang Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Matematika Sains dan Teknologi Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal Information System Development Jambura Journal of Informatics Jurnal Aplikasi Statistika & Komputasi Statistik Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems International Journal of Business, Law, and Education JUSTIN (Jurnal Sistem dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Prosiding Seminar Nasional Official Statistics
Robert Kurniawan, Robert
Jurusan Komputasi Statistik Sekolah Tinggi Ilmu Statistik (STIS) Jakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Deep Learning and Remote Sensing for Agricultural Land Use Monitoring: A Spatio-Multitemporal Analysis of Rice Field Conversion using Optical Satellite Images Wijayanto, Arie Wahyu; Zalukhu, Bill Van Ricardo; Putri, Salwa Rizqina; Wilantika, Nori; Yuniarto, Budi; Kurniawan, Robert; Pratama, Ahmad R.
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1385

Abstract

Rice is a staple food for over half of the global population, making its production crucial for food security, especially in Indonesia, the world's third-largest rice consumer. Population growth and urban expansion have led to agricultural land conversion, necessitating efficient monitoring methods. Traditional approaches, such as area sample frameworks and tile surveys, are costly and time-consuming, prompting the need for remote sensing and deep learning solutions. This study utilizes medium-resolution Sentinel-1, Sentinel-2, and Landsat-8 optical satellite imagery from 2013 and 2021 to analyze land cover changes in West Bandung and Purwakarta Regencies, key agricultural regions in Indonesia. A deep learning model is developed to classify land cover, validated through ground-truth evaluation, and applied to assess spatio-multitemporal land use conversion, paddy field estimation, and conversion rates. Results show that deep learning models effectively classify land cover with high accuracy, revealing significant agricultural land loss due to urban expansion. This research contributes to artificial intelligence (AI)-driven land monitoring, particularly in tropical regions, and supports policymakers in sustainable food agriculture land management. The findings highlight the potential of integrating remote sensing and deep learning for cost-effective agricultural monitoring, ensuring food security and sustainable land use. Future research should explore higher-resolution imagery and advanced AI techniques to enhance predictive accuracy and decision-making.
IMPLEMENTATION OF THE DBSCAN ALGORITHM FOR CLUSTERING STUNTING PREVALENCE TYPOLOGY IN WEST JAVA, CENTRAL JAVA, AND EAST JAVA REGIONS Sumargo, Bagus; Kadir, Kadir; Safariza, Dena; Asikin, Munawar; Siregar, Dania; Sari, Nilam Novita; Umbara, Danu; Hilmianto, Rizky; Kurniawan, Robert; Firmansyah, Irman
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1779-1790

Abstract

Stunting, a condition where children are malnourished for a long period, causes growth failure in children. West Java, Central Java, and East Java are the 3 provinces with the highest prevalence of stunting in 2021. This study aims to group districts/cities in these provinces based on factors that influence stunting using the DBSCAN method (there has been no previous research using this method for this case), so the typology of stunting prevalence is implied. The group results can be valuable input for policy priorities in overcoming stunting. The study used the DBSCAN (Density-Based Spatial Clustering of Application with Noise) method, which can also detect noises (outliers). The determination of eps and MinPts is based on the average value of the distance from each data to its closest neighbor. The distance obtained then was used in the KNN algorithm to determine eps and MinPts parameters. Clustering is done using standardized data and DBSCAN parameters obtained from the k-dist plot, eps is 1.92, and MinPts is 2. The validation test used is the silhouette coefficient to determine the goodness of the cluster results. The clustering results show that there are 2 clusters and 1 noise that have special characteristics related to factors that influence the prevalence of stunting. Cluster 1 consisted of 97 districts/cities and was characterized by a high percentage of infants under 6 months receiving exclusive breastfeeding and the lowest average per capita household expenditure. Cluster 2 (Bekasi City and Depok City) was characterized by the lowest percentage of households with proper health facilities and infants aged 0-59 months receiving complete immunization. The noise (high stunting prevalence) in Bandung City is characterized by the lowest percentage of households having proper sanitation.
FUZZY TIME SERIES IN FORECASTING EXPORT PERFORMANCE OF INDONESIAN SEAWEED PRODUCTS Agustina, Neli; Asshidiq, Isna Aissatussiri; Kurniawan, Robert
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2907-2920

Abstract

This study applies the Fuzzy Time Series method to forecast the export performance of Indonesian processed seaweed, one of the country's main export commodities, contributing significantly to foreign exchange earnings. The Fuzzy Time Series method is employed for its simplicity and effectiveness in handling time series data with high variability and uncertainty—characteristics often found in export data. Unlike traditional statistical methods, Fuzzy Time Series does not require strict assumptions such as stationarity or normality, making it suitable for real-world applications. Although more appropriate for short-term forecasting, the method still provides meaningful insights for planning and policy. The analysis uses monthly export data from January 2013 to December 2021 to generate forecasts for January to December 2022. The results indicate a positive trend in export performance, with projections showing an increase from 1,707,070 kg in December 2021 to approximately 1,759,763 kg in January 2022. Despite Indonesia's processed seaweed still lagging behind some competitors in terms of competitiveness, its steady growth and rising demand abroad highlight its strong development potential. The forecasting results can be a strategic reference to optimize the commodity's development, increase its added value, and ultimately enhance the country's foreign exchange income.
Analisis dan Prediksi Indeks Kualitas Udara Jakarta: Penerapan Algoritma XGBoost Mustika Sari, Evandha; Sabila, Cahya; Fakhrizal Adam, Rifqi; Kurniawan, Robert
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.161-169

Abstract

Air pollution is a serious problem that has an impact on the health and quality of life of people in metropolitan cities like Jakarta. To overcome these challenges, an accurate and reliable air quality prediction method is needed. Extreme Gradient Boosting (XGBoost) is a machine learning algorithm that excels at handling non-linear and complex data, making it ideal for modeling air quality. This study aims to develop an air quality prediction model in Jakarta using XGBoost, utilizing pollutant data that builds an Air Quality Index (AQI) obtained through a data mining process using the Earth Engine Code Editor.Model evaluation was carried out using RMSE, MAE, R2, and RSE metrics, which showed that XGBoost provided excellent prediction performance. The feature importance analysis identified SO2, PM2.5, and PM10 as the main factors affecting air quality in Jakarta. The results of this study are expected to support the government in making air pollution mitigation policies and developing an effective early warning system to improve the quality of life of the community.
Perbandingan Algoritma Deep Learning untuk Analisis Sentimen Ekowisata di Bogor: Comparison of Deep Learning Algorithm in Sentiment Analysis Ecotourism in Bogor Agustini, Peni; Iqbal, Muhammad; Akbar, Vicha Amalia; Kurniawan, Robert
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.2191

Abstract

Bogor memiliki destinasi ekowisata unggulan di Indonesia yang menawarkan keasrian alam dan kemudahan akses dari Jakarta. Namun, peningkatan jumlah wisatawan menimbulkan hambatan terhadap pengelolaan lingkungan, seperti pengelolaan sampah dan tekanan terhadap sumber daya alam. Media sosial, khususnya Google Maps, berperan penting dalam promosi dan memahami perilaku wisatawan melalui fitur ulasan. Studi ini bertujuan melakukan analisis sentimen mengenai ulasan ekowisata di Bogor yang diambil dari Google Maps, menggunakan metode Deep Learning berbasis neural network, yaitu Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM), dan membandingkan performa ketiga model tersebut untuk menentukan metode terbaik dalam mengklasifikasikan sentimen pengunjung. Hasil studi ini menunjukkan, model CNN memiliki akurasi tertinggi yaitu sebesar 72 persen dan lebih unggul dibanding model RNN dan LSTM. Model CNN dapat digunakan sebagai acuan utama dalam menerapkan analisis sentimen pada topik yang sejenis.
The Use of Satellite Imagery Data for Poverty Clustering at the District Level Administration in Indonesia Khamila, Azzahra Dhisa; Wardani, Martha Budi; Kurniawan, Robert
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25278

Abstract

Poverty is a problem that will never be separated from every country, including Indonesia. One of the efforts that can be taken to reduce poverty is to carry out comprehensive monitoring of data related to poverty. The use of satellite imagery strongly supports this effort. Data taken to describe poverty in a region are CO, SO2, NO2, Night Time Light (NTL), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), also per capita expenditure data that can be accessed through the BPS website. Based on the theory, all of these variables negatively affect the poverty of a region except for the NDVI variable. The use of clustering with K-Means method can be implemented in this situation in order to cluster poverty in every district in Indonesia. Then it is supported by a descriptive analysis of each variable in order to describe the distribution of variables in each district in Indonesia. Based on the clustering results, it can be seen that there are 2 clusters, namely cluster 1 which shows a cluster with low poverty and cluster 2 with high poverty. There are a total of 46 districts included in cluster 1, which constitute the majority of economic centers in it's region, and 468 other districts included in cluster 2. The results of this clustering are expected to be used by stakeholders in making decisions according to the characteristics of the district.
Co-Authors Agung Purwanto Agustini, Peni Akbar, Vicha Amalia Alif Andika Putra Amalia Noviani Arie Wahyu Wijayanto Asikin, Munawar Asshidiq, Isna Aissatussiri Azhar, Daris Bagus Sumargo, Bagus Baiq Nurul Haqiqi Baiq Nurul Haqiqi, Baiq Nurul Betik Endaryati, Betik Dini Arifatin Dora, Rika Fadhlullah Fadhlullah Fakhrizal Adam, Rifqi Fella Ulandari Frans Judea Samosir Hasabi, Rafif Hidayat, Arief Ramadhan Rifky Hilmianto, Rizky Hutabarat, Rizky Theofilus Ignatius Sandyawan Ilmi Aulia Akbar Irman Firmansyah Joshua Ariel Perkasa Kadek Angga Wicaksana Kadir Kadir Kamilia Wafa Pakuani Khamila, Azzahra Dhisa Marsisno, Waris Muhammad Iqbal Muhammad Irsad Arief Muhammad Yusuf Aristyanto Murti, Sartika Andari Mustika Sari, Evandha Nashir Wahyudi Neli Agustina Nilam Novita Sari Nugroho, Yoga Dwi Nurmawati, Erna Nurmawiya - Parina, Okta Prabowo, Edhi Pratama, Ahmad R. Putri, Salwa Rizqina Ratu Kintan Karina Ribut Nurul Tri Wahyuni Rivan Destyanugraha Riza F. Ramadhan, Riza F. Sabila, Cahya Safariza, Dena Sartika Andari Murti Sepnita Wulandari Singrapati, Lalu Riza Siregar, Dania Sitorus, Agnes Vera Yanti Sugiarto S Sukim, Sukim Syaifudin Syaifudin, Syaifudin Umbara, Danu Victor Trismanjaya Hulu Wahyu Hassapni Wahyuni, Krismanti Tri Wardani, Martha Budi Wilantika, Nori Yuniarto, Budi Zalukhu, Bill Van Ricardo