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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 Jurnal Sains & Teknologi Lingkungan 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 PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND 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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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.
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.
Nowcasting Pergerakan Indeks Saham Lingkungan Berdasarkan Minat Publik terhadap Isu Lingkungan Zareka, Andi Muh. Zulfadhil; Ayuningrum, Adinda Safira Santoso; Adnyana, I Kadek Surya Wisesa; Kurniawan, Robert
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2585

Abstract

The growing awareness among investors regarding environmental, social, and governance (ESG) aspects has increased attention toward the performance of environmentally-based stock indices. This condition has created a need for a nowcasting approach that is responsive to real-time public interest dynamics and market sentiment. This study aims to analyze public interest in environmental issues measured using Google Trends web search volume as a proxy for collective sentiment in predicting the movement of environmental stock indices. ARIMAX, SARIMAX, Random Forest, SVR, and XGBoost models are implemented and evaluated for their performance in predicting index movements. The results show that SVR, with an RMSE of 20.3646, is the best-performing model. These findings indicate that public interest in environmental issues has significant potential as an effective indicator for real-time prediction of environmental stock index movements, offering valuable insights for investors and market analysts in developing investment strategies that are more responsive to market dynamics influenced by sustainability factors.
Pengaruh Mobilitas Penduduk dan Indikator Sosial Ekonomi terhadap Emisi Karbon di Indonesia Tahun 2020–2022: indonesia Kurniasari, Agustin; Sakina, Dara; Zaldi, Muhammad Afif Wirdiyan; Kurniawan, Robert
Jurnal Sains & Teknologi Lingkungan Vol. 18 No. 1 (2026): SAINS & TEKNOLOGI LINGKUNGAN
Publisher : Teknik Lingkungan Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jstl.vol18.iss1.art2

Abstract

Indonesia is one of the world's largest contributors to carbon emissions, primarily from the fossil-fueled land transportation sector. The COVID-19 pandemic demonstrated that a reduction in mobility could significantly decrease emissions, especially in densely populated areas with suboptimal public transportation systems. In addition, inequality in population distribution and differences in socioeconomic characteristics between regions lead to environmental pressures that vary across Indonesia. This study aims to analyze the impact of population mobility and socio-economic indicators on carbon monoxide (CO) emissions in Indonesia during the unique pandemic period of 2020-2022. The method used is the Random Effect Model for panel data regression. The results show that mobility to workplaces and stores, economic growth, and poverty levels have a significant negative effect on emissions. Conversely, mobility in residential areas and population density have a significant positive effect. The variables of mobility to transit stations and Foreign Direct Investment (FDI) were found to be not significant. These findings point to the need for low-emission transportation and household energy efficiency policies that are responsive to mobility dynamics and socio-economic characteristics of the community.
Evaluating the Impact of Ibu Kota Nusantara (IKN) Development on Land Cover Using Machine Learning-Based Sentinel-2A Satellite Image Classification Aimariyadi, Wisnu; Batrisybazla, Adinda; Tobing, Vanessa Ruth Evelyn; Kurniawan, Robert
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 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.v2025i1.431

Abstract

The development of Ibu Kota Nusantara (IKN) in East Kalimantan as Indonesia's new capital city has the potential to cause significant changes to land cover patterns, especially in tropical rainforest areas. This study aims to evaluate the impact of IKN development on land cover using Sentinel-2A satellite image data and a machine learning approach. The study area is focused on the IKN Core Urban Area by comparing land cover conditions in 2022 before development and 2024 after development. Three classification methods were used including Random Forest, Support Vector Machines, and Classification and Regression Trees. The results showed that the RF model had the best accuracy with an overall accuracy value above 93% in both time periods. Spatial analysis showed a decrease in vegetation area and an increase in open land as an indication of intensive land clearing activities. These findings emphasize the importance of continuous land cover monitoring to support IKN's vision as a green city and achieve sustainable development targets (SDGs 11 and 15). This research is expected to serve as a reference for the formulation of adaptive and environmentally friendly spatial policies.
Detecting Marine Debris Using Sentinel-2 Satellite Images: (Case Study: Kuta Beach, Bali) Faradinah Nasir, Fadiah; Kurniawan, Robert
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 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.v2025i1.552

Abstract

Plastic waste pollution in the oceans remains a global problem. Kuta Beach is one of Bali's tourist destinations that has been affected by plastic waste pollution. This is not in line with the 14th SDGs, which is to prevent and reduce marine debris pollution. However, the marine debris monitoring process carried out by the Ministry of Environment and Forestry requires officers to conduct direct monitoring in the field, which incurs higher costs. Therefore, satellite imagery can be an alternative option for more effective and efficient marine debris detection. This study aims to detect marine debris on Kuta Beach using machine learning algorithms, namely Random Forest (RF), XGBoost, and LightGBM. This study uses the Marine Debris Archive (MARIDA) dataset, which has marine debris labels, and Sentinel-2 images of Kuta Beach from 2019–2023. The LightGBM algorithm provided the best performance in detecting marine debris with an F1-score of 95.16%. The area detected as marine debris on Kuta Beach in 2019–2023 was 500 m2, 0 m2, 100 m2, 300 m2, and 400 m2, respectively. Based on these results, marine debris is generally detected around the coastline, particularly in the southern area of Kuta Beach, which is located near a shopping center.
Water Quality Measurement in Illegal Gold Mining Areas Using Sentinel-2A MSI Satellite Images of the Batanghari River, Tebo Tengah District Sinaga, Baginda; Kurniawan, Robert
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 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.v2025i1.570

Abstract

Water quality in Indonesian rivers has declined due to pollution from solid and liquid waste from industrial and domestic sources. The Batanghari River, the longest river on the island of Sumatra, faces various environmental problems, including pollution from illegal mining activities. Artisanal and small-scale gold mining (ASGM) contributes to mercury release, contaminating water and soil and posing health risks to communities. Conventional monitoring methods have limitations in coverage and efficiency. Therefore, this study utilizes Sentinel-2A MSI satellite imagery to assess and map water quality conditions around illegal gold mining areas along the Batanghari River in Tebo Tengah District. The developed model uses K- Means, Fuzzy C-Means (FCM), Principal Component Analysis (PCA), and Weighted Arithmetic Water Quality Index (WAWQI) to extract water quality features. The findings indicate that WAWQI provides a more representative quantitative assessment, revealing that areas near illegal gold mining sites in Batanghari river exhibit moderately to heavily polluted water quality. This approach is expected to support water quality monitoring and assist policymakers in managing water resources and the environment.
Pengembangan Sistem Aplikasi Web Scraper Harga Komoditas Menggunakan Metode Design Oriented Research Arief, Muhammad Irsad; Kurniawan, Robert
Jambura Journal of Informatics VOL 2, NO 1: APRIL 2020
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (889.191 KB) | DOI: 10.37905/jji.v2i1.4474

Abstract

Teknologi informasi turut berkembang sejalan dengan perkembangan peradaban manusia Salah satu jenis implementasi teknologi informasi dalam hal bisnis adalah perdagangan elektronik (e-commerce) yang mengakibatkan tersedianya data harga produk secara online. Hal ini menimbulkan peluang baru untuk proses pengumpulan data harga yang lebih efisien dan efektif. Salah satu metode pungumpulan data secara online yang lebih efisien, murah dan mudah adalah web scraping. Penelitian ini bertujuan mengembangkan sistem aplikasi yang mampu melakukan web scraping harga dengan mudah, membuat sistem basis data untuk menampung hasil web scraping serta membangun sistem yang dapat memberikan informasi harga dengan akurat dan real time. Selain informasi tentang harga, sistem aplikasi yang dihasilkan juga menyediakan informasi peramalan harga di masa yang akan datang. Penelitian ini menggunakan metode Design Oriented Research dengan hasil yang diharapkan berupa artefak sistem aplikasi web scraper data harga komoditas, dengan mengidentifikasi masalah yang terjadi serta usaha dalam penyelesaiannya. Selain itu, juga dilakukan pengujian dengan menggunakan pengujian blackbox testing, whitebox testing, dan system usability scale (SUS). Hasil dari pengujian aplikasi menunjukkan bahwa aplikasi berjalan sesuai dengan hasil yang diharapkan dan dapat diterima dengan baik dengan skor SUS 75,5. Information technology develops in line with human civilization development. One type of information technology implementation in business is electronic commerce (e-commerce), which resulted in the availability of online price data. It raises new opportunities for more efficient and effective price data collection. One method of online data collection that is more efficient, cheaper, and more accessible is web scraping. This research develops an application system which can do web scraping of price easily, database system to accommodate web scraping result and a dashboard system that can give price information accurately and real-time. In addition to pricing information, the resulting application system also provides price forecasting. This research uses the Design Oriented Research method with the expected results in the form of a web scraper application system for commodity price data, by identifying problems that occur as well as efforts in their resolution. Also, testing is done by black-box testing, white-box testing, and system usability scale (SUS). The results of the application show that the use is acceptable with 75.5 scores and goes as expected result.
Co-Authors Adnyana, I Kadek Surya Wisesa Agung Purwanto Agustini, Peni Aimariyadi, Wisnu Akbar, Vicha Amalia Alif Andika Putra Amalia Noviani Arie Wahyu Wijayanto Arief, Muhammad Irsad Asikin, Munawar Asshidiq, Isna Aissatussiri Ayuningrum, Adinda Safira Santoso Azhar, Daris Bagus Sumargo, Bagus Baiq Nurul Haqiqi Baiq Nurul Haqiqi, Baiq Nurul Batrisybazla, Adinda Betik Endaryati, Betik Dini Arifatin Dora, Rika Fadhlullah Fadhlullah Faradinah Nasir, Fadiah 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 Kurniasari, Agustin Marsisno, Waris Muhammad Iqbal Muhammad Yusuf Aristyanto Murti, Sartika Andari 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. Safariza, Dena Sakina, Dara Sartika Andari Murti Sepnita Wulandari Sinaga, Baginda Singrapati, Lalu Riza Siregar, Dania Sitorus, Agnes Vera Yanti Sugiarto S Sukim, Sukim Syaifudin Syaifudin, Syaifudin Tobing, Vanessa Ruth Evelyn Umbara, Danu Victor Trismanjaya Hulu Wahyu Hassapni Wahyuni, Krismanti Tri Wardani, Martha Budi Wilantika, Nori Yuniarto, Budi Zaldi, Muhammad Afif Wirdiyan Zalukhu, Bill Van Ricardo Zareka, Andi Muh. Zulfadhil