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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management MEDIA KONSERVASI Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Infotech Journal Jurnal Geografi, Edukasi dan Lingkungan Journal of Economic, Bussines and Accounting (COSTING) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Krea-TIF: Jurnal Teknik Informatika Jambura Geoscience Review Jurnal Ekonomi Manajemen Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Profesi Pendidikan The Indonesian Journal of Computer Science (Journal of Environmental Sustainability Management)
Sahid Agustian Hudjimartsu
Universitas Ibn Khaldun Bogor
Agriculture, Biological Sciences & Forestry Astronomy 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 Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences

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ANALISIS AKURASI DETEKSI INDIVIDU POHON PINUS MENGGUNAKAN LOCAL MAXIMA PADA CITRA UNMANNED AERIAL VEHICLE (UAV) RESOLUSI TINGGI Siti Robiah Ritonga; Fety Fatimah; Sahid Agustian Hudjimartsu; Nurdin Sulistyono
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.60299

Abstract

Penelitian ini dilakukan di Desa Sabaganding, Kecamatan Jaya, Kabupaten Simalungun, Provinsi Sumatera Utara, yang merupakan salah satu Kawasan hutan pinus dengan cakupan wilayah yang luas. Perhitungan jumlah pohon pinus secara manual diarea tersebut tidak efisien dan memerlukan waktu yang sangat lama. Oleh karena itu penelitian ini bertujuan untuk menganalisis otomatisasi perhitungan jumlah pohon pinus menggunakan citra udara dari Unmanned Aerial Vehicle (UAV) dan Metode local Maxima pada Canopy Height Model (CHM). Proses analisis mencakup pembuatan mosaic citra, pengolahan CHM, penerapan Ratio Green, serta deteksi titik puncak pohon. Evaluasi dilakuan pada tiga kelas minimum pohon, yaitu 3 meter, 4 meter, dan 5 meter. Hasil penelitian menunjukkan bahwa kelas ketinggian minimum 4 meter memberikan hasil paling optimal, dengan nilai rata rata Commisson Error 0,07, Ommission Error 0,10, dan Overall Accuracy 0,76. Metode local maxima terbukti efektif dalam mendeteksi dan menghitung jumlah pohon pinus secara otomatis pada wilayah penelitian.
Trajektori perubahan tutupan vegetasi di Pulau Sumatra berbasis analisis spasio-temporal Setiawan, Yudi; Kustiyo, Kustiyo; Hudjimartsu, Sahid Agustian
Jurnal Pengelolaan Lingkungan Berkelanjutan (Journal of Environmental Sustainability Management) JPLB, Vol 9, No 3 (2025)
Publisher : Badan Kerjasama Pusat Studi Lingkungan (BKPSL) se-Indonesia bekerjasama dengan Pusat Penelitian Lingkungan Hidup IPB (PPLH-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36813/jplb.9.3.333-351

Abstract

Land cover change on Sumatra Island has accelerated rapidly over recent decades, marked by extensive tropical forest deforestation, widespread peatland degradation, and increasing disaster occurrences. Spatio-temporal analysis using multi-temporal satellite data provides valuable insights into the trajectories of vegetation cover change in this region. This study synthesizes findings on patterns of forest conversion to other land uses (plantations, agriculture, and infrastructure), as well as vegetation recovery patterns following fire events, particularly in peatland areas. The results reveal several dominant change trajectories: (1) deforestation followed by conversion to agricultural and plantation land, (2) cyclical changes in plantation forests, including planting, harvesting, and replanting phases, and (3) natural secondary regeneration on abandoned land after deforestation. Furthermore, in cases where deforestation is driven by land and forest fires, fire frequency plays a significant role in shaping vegetation succession pathways. Areas burned only once are able to recover toward near-original conditions after several years, whereas areas subjected to repeated fires exhibit slower recovery and tend to be dominated by shrub vegetation. These findings highlight the importance of sustainable landscape management informed by spatio-temporal data, including controlling deforestation and restoring peatlands through rewetting and vegetation rehabilitation, to prevent further degradation and support long-term ecosystem recovery.
Spatio-Temporal Detection of Vegetation Change and Recovery in Fire-Affected Peatlands of Sumatra, Indonesia Setiawan, M.Sc, Dr. Yudi; Setiawan, Yudi; Kustiyo, Kustiyo; Hudjimartsu, Sahid Agustian; Handayani, Marshela Aida; Jamil, Awaludin; Putra, Erianto Indra
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 6 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.6.1034

Abstract

Tropical peatlands are among the most fire-prone ecosystems in Southeast Asia, where recurrent burning causes long-term degradation, carbon loss, and biodiversity decline. Assessing spatio-temporal patterns of recovery is therefore essential for guiding effective peatland restoration and fire prevention strategies. This study investigated vegetation recovery dynamics in a fire-affected peatland in Sumatra, Indonesia. Multi-temporal satellite imagery was processed to extract the Enhanced Vegetation Index (EVI) and Normalized Burn Ratio (NBR). Fire frequency and severity were further analyzed through hotspot distributions and fire history. The results revealed that NBR and dNBR were highly effective in detecting burned areas and assessing severity, while EVI provided complementary perspectives on recovery trajectories. Vegetation in once-burned areas showed relatively steady regrowth, with EVI values approaching pre-fire levels after several years. In contrast, repeatedly burned areas exhibited slower and more heterogeneous recovery, with fluctuating EVI pattern reflecting vegetation growth succession. Field vegetation surveys confirmed that repeated fires drastically simplified forest structure, reducing tree and pole density and favoring shrubs and ferns such as Stenochlaena palustris and Melastoma malabathricum. Overall, the study demonstrates that fire frequency and severity are critical determinants of peatland recovery. The EVI offers valuable insights into vegetation dynamics, while NBR provides reliable fire history mapping. These findings underscore the importance of combining spectral indicators with ground-based vegetation data for long-term monitoring and highlight the need for targeted restoration strategies, including hydrological rewetting and assisted natural regeneration, in repeatedly burned peatlands.
Pengembangan Sistem Informasi Kesesuaian Lahan Tanaman Pangan Berdasarkan Faktor Cuaca Berbasis Website Utami, Putri; Abdullah, Asrul; Hudjimartsu, Sahid Agustian; Wicaksono, Aditya; Viona, Tiara Aurilia
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3758

Abstract

Evaluasi lahan dapat dilakukan untuk meningkatkan kualitas dan kuantitas komoditas pertanian. Salah satunya dengan persyaratan penggunaan lahan dengan mempertimbangkan karakteristik lahan. Namun, Dinas Pertanian selaku koordinator sulit mendapatkan informasi terkait karakteristik lahan yang sesuai dengan jenis tanaman berdasarkan faktor cuaca. Anomali cuaca menyebabkan turunnya produktitivitas tanaman. Tujuan penelitian ini adalah mengembangkan sistem informasi kesesuaian lahan untuk menentukan jenis tanaman pangan beradasarkan karakteristik lahan serta evaluasi kesesuaian lahan tanaman. Metode dalam penelitian ini adalah Framework for the Application of System Thinking (FAST). Tahapan FAST yaitu scope definition, problem analysis, requirement analysis, decision analysis, design, contruction and testing, dan instalation and delivery. Berdasarkan hasil uji kelayakan aplikasi menghasilkan nilai 87% dengan kriteria baik. Hasil ini menunjukkan bahwa sistem informasi kesesuaian lahan tanaman pangan dapat digunakan dengan baik.
The Effect of Deep Learning Through Canva on English Achievement of SMA Class X Students Marhamah, Marhamah; Ismail, Syofianis; Zetriuslita, Zetriuslita; Hudjimartsu, Sahid Agustian; Kurniawan, Kurniawan; Dewanto, Dewanto; Abshor, Febri
Jurnal Ilmiah Profesi Pendidikan Vol. 11 No. 1 (2026): Februari
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jipp.v11i1.4428

Abstract

The lack of student interest in learning English significantly influences their learning outcomes. During the learning process, students greatly require media and learning approaches that can support their engagement and activeness in the classroom. The deep learning approach and Canva Magic AI media can serve as a potential solution to the problems faced by these students. This research aims to investigate the effect of deep learning aided by the Canva on the English achievement of Class X students at SMA IT Imam Syafi’i 2 Pekanbaru. The research method used is a quasi-experiment with a non-equivalent control group design. A total of classes was randomly selected, consisting of 40 students. One class served as receiving English learning integrated with deep learning and Canva, and the other as receiving conventional learning. The data collection instrument was an English achievement test administered before and after the treatment. The collected data were analyzed using inferential statistical tests to compare the improvement in learning achievement between the two groups. The results of this study are expected to provide the effectiveness of combining deep learning and Canva in improving the English learning outcomes of SMA students, as well as its implications for innovation in language teaching practices.
Health Classification of Rice Plants Based on UAV Remote Sensing Using Random Forest Algorithm Harvevi Oktarin Andra Perwira Sari; Erwin Hermawan; Sahid Agustian Hudjimartsu
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 7 No. 5 (2026): Jurnal Ekonomi Manajemen Sistem Informasi (Mei - Juni 2026)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v7i5.8180

Abstract

Bogor Regency acts as a central hub for rice production in West Java, yet frequent disease outbreaks often jeopardize the consistency of agricultural yields. Farmers struggle with these plant diseases because the infections frequently result in significant crop losses or total harvest failure. The immense size of paddy fields makes manual monitoring methods inefficient, driving a requirement for automated systems to monitor crop health across large areas. The current research focuses on building a classification model that identifies whether rice plants are healthy or diseased using aerial photographs. The process utilizes drone-based remote sensing technology where the data is analyzed using the Random Forest algorithm. Final model evaluations show solid performance with an accuracy of 85% and a precision of 100%. The system also achieved a recall of 70% and an F1-Score of 0.82. Evidence suggests that the Random Forest algorithm works effectively to separate healthy rice from diseased crops using drone imagery. Farmers can use such technological approaches as practical tools to detect diseases early and manage their fields better.
Co-Authors Abshor, Febri Aditya Wicaksono Agung Siswoyo AHMAD JUNAEDI Arien Heryansyah Arif K Wijayanto Asrul Abdullah Aswin Rahadian Budi Susetyo Dewanto Dewanto Dewi Primasari Eka Triyani Erianto Indra Putra Erwin Hermawan Fadhil Sulaiman, Muhammad Fajri T, Hersanto Fitrah Satrya F.K Gibtha Fitri Laxmi Hakim, Ashif Ahmad Handayani, Marshela Aida Hanif Hidayaturrahman Harvevi Oktarin Andra Perwira Sari Hendriawan Nur, Wawan Hidayat Al-Ikhsan, Safaruddin Hulwana Sharfina Iksal Yanuarsyah Ismail, Syofianis Jamil, Awaludin Kamali, Alviyan Badro Kurniawan Kurniawan Kustiyo Kustiyo, Kustiyo Lestari, Nur Annisa Indah LILIK BUDIPRASETYO Lulu Lutfi Latifah Marhamah Marhamah Miftahul B.R. Khamid Minati Minati Muflihatul Maghfiroh Islami Muhammad Nanang Prayudyanto Muhammad, Rizky Nurandi Rachim, Yusril Nurdin Sulistyono Putri Utami, Putri Putri Yuli Utami Rafli Farhan, Muhamad Ramadhan, Alfarisi Risza Purnomo, Muhammad Rizky Haerudin, Asep Setiawan, M.Sc, Dr. Yudi Setya Permana Sutisna Sinta Lestari Sinta Lestari, Sinta Siti Robiah Ritonga Teddy Rusolono Tiara Aurilia Viona Trisna Mukti Viona, Tiara Aurilia Wulandari, Berlina Yana Armanto, Devi Yudi Setiawan Yudi Setiawan Zetriuslita