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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Geography Geo Media: Majalah Ilmiah dan Informasi Kegeografian Ecotrophic, Journal of Environmental Science Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Ilmu Lingkungan Tunas Geografi TELKOMNIKA (Telecommunication Computing Electronics and Control) Majalah Geografi Indonesia JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Geografi Jurnal Geografi International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal IMPACT: Implementation and Action GEOGRAPHY : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan Indonesian Journal of Earth Sciences Priviet Social Sciences Journal Jurnal Geografi LIngkungan Tropik (Journal of Geography of Tropical Environments) The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Muhammad Dimyati
Universitas Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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DIFFERENCES OF COASTALLINE CHANGES IN THE AREA AFFECTED BY LAND COVER CHANGES AND COASTAL GEOMORPHOLOGICAL SOUTH BALI 1995 - 2021 Muhammad Dimyati; Muhamad Rafli; Astrid Damayanti
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 2 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3781

Abstract

The South Bali coast is prone to abrasion due to its geographical position facing the Indian Ocean. High sea waves and currents in the south of Bali will erode beaches whose lithology and morphology are prone to abrasion. Land cover conditions that do not support coastal protection will also affect the high abrasion of the southern coast of Bali. This study aims to analyze the shoreline changes in South Bali from 1995-2021. The analytical method used is the Digital shoreline analysis system (DSAS), with data from Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI/TIRS, and Sentinel 2A. The analysis results show that the area directly facing the waves is relatively high, with volcanic rock formations, and there is no mangrove as coastal protection. The lack of good coastal management shows the area with the highest abrasion. It was found in the western part of Tabanan Regency, eastern Gianyar, and southern Badung. Meanwhile, the average coastal accretion was relatively high in the neck of South Bali, in areas where the land cover was mangrove and adjacent to river mouths, which experienced much sedimentation.
Digital Twins and Urban Heat Island Modeling: A Systematic Review of Conceptual, Technical, and Geospatial Gaps in Next-Generation Urban Climate Systems Saipiatuddin Saipiatuddin; Rokhmatuloh Rokhmatuloh; Hayuning Anggrahita; Muhammad Dimyati
JURNAL GEOGRAFI Vol. 18 No. 1 (2026): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v18i1.71340

Abstract

This systematic review examines the emerging integration of Digital Twin technologies with Urban Heat Island modeling to advance next-generation urban climate systems. Increasing urbanization and rising thermal stress have intensified the need for dynamic, data-driven tools capable of representing and predicting microclimate conditions in real time. Through a structured PRISMA-based screening of major scientific databases (Scopus, IEEE Xplore, ScienceDirect), 268 initial records were identified, from which 19 studies were ultimately included after systematic deduplication (25 duplicates removed) and eligibility screening (16 studies excluded: 4 lacking technical information, 10 non-urban settings, 2 non-English). These 19 studies collectively illustrate three major knowledge domains: conceptual frameworks, technical architectures, and geospatial modeling characteristics. The findings indicate that Digital Twin is progressively regarded as a real-time, adaptive digital representation of the urban environment; however, it lacks standardized definitions for climate applications (identified in 68% of reviewed studies). Technically, the integration of heterogeneous data—ranging from Internet of Things (IoT) sensors, UAV thermal imagery, and satellite-derived land surface temperatures—remains limited by challenges in latency, model calibration, data interoperability, and computational scalability (reported in 74% of studies). Geospatial analysis further highlights inconsistencies in spatial-temporal resolution and inadequate representation of suburban areas (noted in 63% of studies), constraining robust Urban Heat Island simulations across scales. Overall, this review identifies critical gaps and emerging opportunities for developing intelligent, multi-scale, and hybrid modeling approaches that combine physics-based simulations with machine learning. The findings call for harmonized Digital Twin frameworks, improved geospatial data infrastructures, and stronger interdisciplinary collaboration to support climate-resilient urban planning and adaptive heat mitigation strategies.
Harnessing artificial intelligence for census in Nigeria: Advancing accuracy, efficiency, and governance outcomes Inuwa Sani Sani; Muhammad Dimyati; Aliyu Aminu Umar
Priviet Social Sciences Journal Vol. 5 No. 11 (2025): November 2025
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v5i11.662

Abstract

Successful administration of national censuses in Nigeria has been a protracted agony plagued by inherent problems, including logistic, political, and methodological issues, which cumulatively have caused delays in enumeration, undercounting, and inconsistency of data. These defects diminish the credibility of demographic data needed for evidence-based governance, economic planning, and equitable resource allocation._. In this study, we explored opportunities for harnessing Artificial Intelligence (AI) to transform census activities in Nigeria through the injection of state-of-the-art computational approaches into the national enumeration exercise. We showcased a multimodal AI pipeline comprising Convolutional Neural Networks (CNNs) for population density estimation from satellite images, Natural Language Processing (NLP) pipelines for address standardization and matching in various languages, and unsupervised anomaly detection algorithms for real-time data quality verification. AI-based enumeration methods were simulated at both national and sub-national levels. CNN-generated heatmaps revealed population concentration trends in Lagos and other states and enabled the precise delineation of high-density urban agglomerations and underserved rural enclaves. The NLP tool generalized well to the linguistically diverse environments in Nigeria, with F1-scores greater than 0.90 for all but a few states for broken address reconciliation. Anomaly detection models built using Isolation Forest algorithms detected anomalous enumeration patterns as flags for potential undercounts or data manipulation. Population pyramid analysis for Lagos revealed an extremely young population structure, consistent with country-wide age trends. These findings provide empirical evidence that AI integration can promote census accuracy, operational efficiency and government effectiveness in Nigeria.
Cloud removal on satellite imagery using blended model: case study using quick look of high-resolution image of Indonesia Muhammad Dimyati; Adlyani Husna; Puji Tri Handayani; Devy Nur Annisa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.21085

Abstract

The problem with the acquisition of satellite imagery in the tropics, especially in the area around the equator is that it is almost covered by clouds throughout the year. Users need cloud cover information and the possibility of obtaining cloudless satellite images before they get the data. An overview of the availability of cloud coverage distribution, especially those presented in a spatial format, was very beneficial and increased efficiency for users to select image data in the area of interest (AoI). This study aimed to develop a cloud removal, so-called blended cloud removal (BCR) model, which was applied in a part of West Java Province. The data used for this study were 33 images of quick looks at high-resolution satellite images of the 2013-2015 period that could be obtained free of charge on the website. The results showed that the distribution of efficiency was that AoI-1 obtained 99.67% efficiency of cloud removal image, AoI-2 was 76.51%, and AoI-3 obtained 98.34%. These three AoI locations have an average efficiency of 91.50%. As a result, there was substantial evidence that fewer than 10% of cloud cover remains after cloud removal. This suggests that by using the BCR model, a considerable change in cloud cover for the AoI location might be obtained, meeting the Geospatial Information Agency’s standards.
ILLEGAL OIL MINING DETECTION THROUGH REMOTE SENSING IN MUSI BANYUASIN REGENCY, SOUTH SUMATRA, INDONESIA Restu Setiadi; Supriatna; Muhammad Dimyati; Ibrahim Arsyad
International Journal of Remote Sensing and Earth Sciences Vol. 21 No. 2 (2024)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/ijreses.v21i2.13244

Abstract

Illegal oil mining activities present significant environmental, economic, and regulatory challenges, particularly in resource-abundant regions that are difficult to monitor such as Musi Banyuasin Regency in South Sumatra. This study applied an integrated method that combines drone-based remote sensing, visual interpretation, and spatial statistical analysis to detect, map, and evaluate the spatial distribution of illegal shallow oil wells. High-resolution aerial imagery was acquired using DJI Phantom 4 Pro drones, processed into orthomosaic images, and interpreted visually to identify suspected well locations. A total of 2664 illegal oil wells were identified and georeferenced. The results of spatial autocorrelation analysis using Moran’s I indicated a clustered distribution pattern, with significant concentrations found in subdistricts such as Lawang Wetan, Batang Hari Leko, and Tungkal Jaya. The Moran’s I index value of 0.652075 confirmed a statistically significant spatial clustering. Ground validation was conducted through direct field surveys, which verified the presence of the wells and provided supporting photographic documentation and GPS coordinates. The dataset was also compared with official records of legal oil wells to ensure accuracy and distinction between legal and illegal infrastructure. The findings demonstrate that unmanned aerial vehicle-based spatial analysis offers a reliable and scalable solution for monitoring unregulated extraction activities. This approach supports data-driven enforcement, enhances environmental oversight, and informs the development of more effective regulatory policies in regions impacted by informal oil production.
SYSTEMATIC LITERATURE REVIEW: POLA SPASIAL, TREN DAN DINAMIKA DEFORESTASI HUTAN DALAM PRESPEKTIF PENGINDERAAN JAUH Marwah Noer; Muhammad Dimyati
GEOGRAPHY : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan Vol 12, No 1 (2024): APRIL
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/geography.v12i1.17122

Abstract

Abstrak: Deforestasi merupakan hal yang menjadi perhatian dunia, laju deforestasi yang kian meningkat menjadi hal yang penting dikaji pola dan penyebabnya. Memahami pola, tren, dan dinamika deforestasi sangat penting untuk mewujudkan pembangunan berkelanjutan. Penginderaan jauh merupakan teknik yang paling sering digunakan dalam memetakan perubahan penggunaan lahan atau tutupan lahan termasuk deforestasi. Review ini dipandu oleh model PRISMA (Preferred Reporting Items for Systemic Review and Meta-Analyses). Lima artikel terkait deforestasi dan penginderaan jauh ditinjau dan dibandingkan menggunakan variabel judul, kata kunci, tujuan, sumber data, variabel, lokasi, metode, dan temuan utama. Hasil systematic literature review ini adalah metode penginderaan jauh yang dipadukan dengan GIS merupaka metode yang sangat baik dan cocok untuk melihat pola spasial, tren dan dinamika deforestasi hutan. Metode ini dianggap sangat efektif karena data penginderaan jauh saat ini sudah banyak tersedia dan dapat diakses dengan mudah. Landsat merupakan citra satelit yang paling banyak digunakan dalam kajian deforestasi. Variabel umum yang digunakan dalam penelitian deforestasi adalah luas hutan, lahan terbangun, lahan pertanian/ perkebunan dan tanah kosong. Dengan mengkaji tren dan dinamika deforestasi di berbagai negara, diharapkan dapat menghambat laju deforestasi di negara tersebut dan juga diharapkan adanya kebijakan yang sesuai untuk masing-masing negara dalam memperbaiki pengelolaan hutan.Abstract:  Deforestation is a matter of global concern. The increasing rate of deforestation is an important matter to study its pattern and causes. Understanding deforestation patterns, trends, and dynamics is essential to realizing sustainable development. Remote sensing is the most frequently used technique in mapping land use or cover changes, including deforestation. This review was guided by the PRISMA model (Preferred Reporting Items for Systemic Review and Meta-Analyses). Five articles related to deforestation and remote sensing were reviewed and compared using the variables title, keywords, objectives, data sources, variables, location, methods, and main findings. This systematic literature review shows that remote sensing combined with GIS is an excellent and suitable method for viewing spatial patterns, trends, and dynamics of forest deforestation. This method is considered very effective because currently remote sensing data is widely available and can be accessed easily. Landsat is the most widely used satellite imagery in deforestation studies. Common variables used in deforestation research are forest area, built-up land, agricultural/ plantation land, and vacant land. By studying the trends and dynamics of deforestation in various countries, it is hoped that this will inhibit the rate of deforestation in these countries and it is also hoped that appropriate policies will be developed for each country in improving forest management.
Co-Authors Adlyani Husna Ahmad Fakhruddin Ahmad Zubair Akhmad Fauzy Aliyu Aminu Umar Anang Muchlis Andriyana Lailissaum Andry Rustanto Anggara Setyabawana Putra Arief Wicaksono Ash Shidiq, Iqbal Putut Astrid Damayanti Astridia Putri Nurhaliza Azis Musthofa Babag Purbantoro Bawiling, Hendry Budianto Budianto Devy Nur Annisa Dewi Susiloningtyas Dimas Bayu Ichsandya Dimas Novandias Damar Pratama Dimyati, Ratih Dewanti Efriana, Anisya Feby Enshito, Grizzly Pradipta Singhasana Evi Anggraheni, Evi Fadhilah, Raina Arfa Fani Setyawan Farhan Makarim Zein Faris Zulkarnain Faris Zulkarnain Gracia, Enrico Hartono Hartono Hartono Hartono Hayuning Anggrahita Heinrich Rakuasa I Wayan Gede Krisna Arimjaya Ibrahim Arsyad Inuwa Sani Sani Irma Susanti Isnaini, Eva Nur Khairunnisa, N Kintan Maulidina Kustiyo Kustiyo Kustiyo Kustiyo Legowo, Dewanti Aisyah Logan, Axel Gilbert M. Martono Mamat Suhermat Maranti, Pinta Mardalena, Ayu Marfai, Muh Aris Marwah Noer Masita Dwi Mandini Manesa MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI Muhamad Rafli Muhamad Rafli Muhammad Adnan Shafry Untoro Muhammad Haidar Nurrokhmah Rizqihandari Nurul Khakhim Nurul Sri Rahatiningtyas Onki Alexander Panji Nurul Achmadi Pranita Giardini Prasetya, Ferdian Adhy Projo Danoedoro Pryanto, Muhammad Bagus Puji Tri Handayani Purbantoro, Babag Putri, Ratih Fitria Raden Ramadhani Yudha Adiwijaya Rahatiningtyas, Nurul Sri Raisya Afifah Ratih Dewanti Ratih Dewanti Dimyati Ratih Dewanti Dimyati Ratih Dewanti Dimyati Ratri Candra Restuti Restu Setiadi Riza Putera Syamsuddin Rokhmatuloh Rokhmatuloh Rustanto, Andry S. Supriatna Saipiatuddin Sakina, N C Sani, Inuwa Sani Satria Indratmoko Setiadi, Hafid Siddiq, Ayyasy Siswanto Siswanto Siti Aisyah Supriatna Supriatna Supriatna Taufik Walinono Tito Latif Indra Triarko Nurlambang Triarko Nurlambang Tuty Handayani Umar, Aliyu Aminu Verdyansyah, Aprizal Wahyu Lazuardi Zubair, Ahmad Zulfikri Isnaen Zulkarnain, Faris