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All Journal Indonesian Journal of Geography Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian JURNAL ILMIAH GEOMATIKA MAJALAH ILMIAH GLOBE Sumatra Journal of Disaster, Geography and Geography Education Jurnal Geografi JURNAL BUANA JURNAL PENDIDIKAN TAMBUSAI BHUMI: Jurnal Agraria dan Pertanahan Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Civil Engineering Collaboration El-Jughrafiyah : Jurnal Geografi dan Terapannya YASIN: Jurnal Pendidikan dan Sosial Budaya Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat Jurnal Geosains West Science Asian Journal of Early Childhood and Elementary Education Jurnal Buana Journal of Innovative and Creativity Geadidaktika JURNAL BUANA
Yulfa, Arie
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Optimalisasi ANN-MLP dengan GridSearch-CV untuk Klasifikasi Tutupan Lahan Perkotaan Menggunakan Sentinel-2 Sihaloho, Mayhendra Daud; Yulfa, Arie
GEADIDAKTIKA Vol 5, No 2 (2025): Geadidaktika Agustus 2025
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/gea.v5i2.105095

Abstract

Accurate land cover classification is essential for sustainable urban planning and management. This study optimizes the Artificial Neural Network Multi-Layer Perceptron (ANN-MLP) model using GridSearchCV and Sentinel-2 imagery to classify urban land cover in Padang City. Based on 500 samples across five land cover classes and validated with high-resolution imagery, the optimized model achieved 97% accuracy and a Kappa value of 96.25%. These results highlight the effectiveness of hyperparameter optimization in improving classification performance while offering practical contributions for local governments, including mapping urban growth, identifying land-use changes, guiding development according to environmental capacity, and strengthening data-driven spatial planning policies. The proposed approach can also be replicated in other regions with similar characteristics.
Analisis Spasio Temporal Dampak Banjir Bandang Berbasis Crowdsourcing di Kecamatan Pauh Muhammad Farras Alfisar; Arie Yulfa
Journal of Innovative and Creativity (Joecy) Vol. 6 No. 2 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i2.12513

Abstract

Hydrometeorological disasters continue to increase, both in frequency and intensity, thereby necessitating rapid disaster information with high spatial resolution. Official data often requires a lengthy verification process, making it unable to fully capture on-the-ground conditions in detail at the time of the event. This study analyzes the spatiotemporal patterns of flash flood impacts based on Instagram crowdsourced data from the Pauh, Padang City. The method used is quantitative descriptive analysis, utilizing posts during the disaster period. The data was validated based on time, location, and content relevance, then analyzed using a Geographic Information System. The results show that Instagram is capable of recording the distribution of affected areas, the progression of disaster conditions, and community dynamics from the event phase through the post-disaster phase in a rapid, detailed, and contextual manner.
Visualisasi Pemetaan 3D Tsunami di Kelurahan Air Tawar Barat Kota Padang Alfabiansyah Sean Fathia Ilham; Arie Yulfa
Jurnal Geosains West Science Vol 3 No 03 (2025): Jurnal Geosains West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jgws.v3i03.2702

Abstract

Penelitian ini bertujuan untuk memvisualisasikan dampak tsunami di Kelurahan Air Tawar Barat, Kota Padang dengan menggunakan model 3D berbasis pemetaan digital. Metode yang digunakan meliputi analisis spasial dengan overlay parameter kerawanan tsunami dan simulasi numerik menggunakan perangkat lunak Tunami-N3. Parameter yang digunakan mencakup elevasi tanah, kemiringan lereng, penggunaan lahan, jarak dari pantai, dan jarak dari sungai. Hasil simulasi menunjukkan bahwa skenario gempa 8,9 Mw di zona megathrust Mentawai-Siberut dapat menghasilkan gelombang tsunami setinggi 12 meter dengan jarak inundasi hingga 11,3 km. Peta kerentanan tsunami yang dihasilkan menunjukkan bahwa wilayah penelitian terbagi dalam empat kategori: aman (13 ha), cukup rentan (427 ha), rentan (399 ha), dan sangat rentan (8 ha). Visualisasi 3D dengan QGIS dan Blender memberikan gambaran interaktif mengenai area terdampak, sehingga dapat digunakan sebagai media edukasi dan mitigasi bencana.
Pemetaan Kawasan Kebakaran Hutan di Kabupaten Muaro Jambi Menggunakan Metode Differenced Normalized Burn Ratio Berbasis Google Earth Engine Salwa Azzahra; Arie Yulfa
Jurnal Geosains West Science Vol 4 No 02 (2026): Jurnal Geosains West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jgws.v4i02.3512

Abstract

Kebakaran hutan dan lahan di Kabupaten Muaro Jambi memicu kerusakan lingkungan yang sangat besar, terkhusus pada jenis lahan gambut. Penelitian ini bertujuan untuk menganalisis luas area terdampak dan tingkat keparahan kebakaran dari tahun 2015 sampai 2025 dan mengevaluasi batasan teknis akurasi skala pada citra sentinel 2 di Google Earth Engine. Penelitian menggunakan metode analisis penginderaan jauh melalui indeks selisih ratio pembakaran ternormalisasi dari citra sentinel 2 pada Google Earth Engine. Pengujian akurasi menggunakan confusion matrix dengan perhitungan overall accuracy dan koefisien kappa. Hasil luasan menunjukan bahwa kebakaran tinggi terjadi di tahun 2015 sebesar 3.100 Ha dan Kembali tinggi pada tahun 2024, 2025 sebesar 1.500 Ha. Kelas yang mendominasi kebakaran berada di kategori rendah hingga sedang. Hasil akurasi memperoleh 87,2% pada 2015 dan 92,8% pada tahun 2025. Pembaruan pada penelitian ini terletak pada pemanfaatan Google Earth Engine untuk analisis berbasis dNBR di Kabupaten Muaro Jambi. Hasil penelitian diharapkan dapat mendukung pemantauan kebakaran hutan secara efektif.
Precision and Paradigm Shift: Transforming Land and Building Tax Assessment Through Uav-Lidar Derived High-Resolution 3d Spatial Data Arie Yulfa; Muhammad Giatman; Syarief, Azhari; Ramadhan, Risky; Zikra, Afdhal
Sumatra Journal of Disaster, Geography and Geography Education Vol. 10 No. 1 (2026): Sumatra Journal of Disaster, Geography and Geography Education (June Edition)
Publisher : Sumatra Journal of Disaster, Geography and Geography Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/sjdgge.v10i1.730

Abstract

The conventional Land and Building Tax (PBB) evaluation methods in Indonesia, namely in Baso District, Agam Regency, depend on two-dimensional measures with accuracy rates ranging from 85% to 95%, resulting in possible revenue deficits and assessment inconsistencies. This work seeks to create and execute a more precise tax assessment system utilizing high-resolution 3D spatial data obtained by UAV-LiDAR technology. The study utilizes a quantitative applied methodology, employing a DJI Matrice 350 RTK outfitted with LiDAR sensors for data acquisition. Data processing with DJI Terra and TerraSolid software produces Digital Terrain Models (DTM) and Digital Surface Models (DSM), facilitating accurate land boundary delineation and 3D building digitization. The findings indicate a substantial enhancement in spatial measuring accuracy, attaining 97-99% precision relative to traditional approaches. The implementation facilitates thorough property evaluation via precise measurements of building heights, volumes, and land areas, thereby offering a more dependable foundation for tax computations. The study suggests that, despite constraints including elevated initial costs and training demands, the inclusion of UAV-LiDAR technology significantly improves the accuracy, efficiency, and transparency of PBB assessments, representing a notable improvement in contemporary property tax administration systems.
Co-Authors Abel Tasman Adek Andreas Adinda Putri Aditya, Trias Akram, Naufal Muhammad Alfabiansyah Sean Fathia Ilham Alfin Oktary Alvia, Rosalina Amor, Giant andre, bhareta Andri Ferriansyah ARDIANSYAH, ANDRE Aulia Rahmaini Azhari Syarief Bayu Wijayanto Beben Graha Putra Bella Aprillia Bigharta Bekti Susetyo Dedi Hermon Dian Adhetya Arif Edrinaldi Edrinaldi Ernawati Ernawati Erniwati Erniwati Fadila, Rahayu Fahen Dayanda Farhan Mursyid Feri Ferdian Ferriansyah, Andri Firma Maulidna Fungky Novendri Giant Amor Havez Al Asad, M. Helfia Edial Hendra Naldi Henzulkifli Rahman Ibniul Husna Ibrahim Sayfuddin ikhlas, fahmi yasir ilham yuhanda Illahi, Manisa Rahmi Imaarah, Ainiyah Indah Purwati Khairul Nizam Khairul Nizam KHAIRUL ZIKRI Kusmiarto, Kusmiarto M. Mursyid Al Fahri M. Mursyid Alfahri MARIA BINTANG Marta Poli Zulva Mega Nurhidayanti Mendra Saputra Miqdam Kharisma Okli Yuanda Muhammad Farras Alfisar Muhammad Giatman Mursyid, Farhan Mustofa, Fahmi Charish Natasya Febriani Nofrelia, Kessy Prabowo, Rizky Pramudito, Bevan Eka Putra, Randu Prayoga Putri, Ramona Zeka Rahayu Lestari Rahmaini, Aulia Rahmat Alfayat rahmat ilham 125 Rahmat Rafif Rajwa Febalismanriva Ramadhan, Risky Randa Rozian Ratna Wilis Rizky Oktaviandra Salwa Azzahra Sanny, Fica Fadhilia Saumia, Zulfa Sepiati, Dian Sihaloho, Mayhendra Daud Sihite, Hana Pebrina Silvi Widya Rahmi Siregar, Dina Khoriah Sri Ayu Novriawati Surtani Surtani Sutanta, Heri Syafri Anwar Syafril, Rizki Syafrina, Yelda Syahar, fitriana Taufikri Taufikri Thuba Imam Fauzi tio buana putra Titin Shartinah Triyatno Try yani Haryani Ulya Putra Kazurna Wahyu Hidayat Yaseen, Egita Yudi Antomi yuhanda, ilham Zikra, Afdhal