Article Per Year (5 Year)
p-Index From 2021 - 2026
2.559
P-Index
This Author published in this journals
All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management MEDIA KONSERVASI Forum Pasca Sarjana FORESTA BIOTROPIA - The Southeast Asian Journal of Tropical Biology TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknosains Jurnal Penelitian Hutan dan Konservasi Alam Jurnal Penelitian Hutan Tanaman Journal of Degraded and Mining Lands Management Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) BERITA BIOLOGI JURNAL BIOLOGI INDONESIA MAJALAH ILMIAH GLOBE International Journal of Remote Sensing and Earth Sciences (IJReSES) Journal of Sylva Indonesiana Indonesian Journal of Electrical Engineering and Computer Science Jurnal Risalah Kebijakan Pertanian dan Lingkungan International Journal of Electronics and Communications Systems Aceh International Journal of Science and Technology Jurnal Penelitian Kehutanan Wallacea The International Journal of Remote Sensing and Earth Sciences (IJReSES)
I Nengah Surati Jaya
Department Of Forest Management, Faculty Of Forestry, Bogor Agricultural University, Academic Ring Road, Campus IPB Dramaga, PO Box 168, Bogor, Indonesia, 16680
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Veterinary

Published : 86 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 86 Documents
Search

 5   6   7   8   9 
Developing a Decision Tree Algorithm for Detecting Agroforestry and Monoculture Coffee Plantations Using Landsat 8 Imagery: A Case Study inBandung Regency, Indonesia Adhiguna, Agasta; Surati Jaya, I Nengah; Puspaningsih, Nining
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.15.6.1009

Abstract

Kopi arabika merupakan komoditas unggulan di Kabupaten Bandung, Provinsi Jawa Barat, Indonesia, yang memiliki potensi pengembangan yang besar dengan menggunakan sistem penanaman agroforestri. Oleh karena itu, penelitian ini bertujuan untuk mendeskripsikan pengembangan algoritma pohon keputusan dengan mengkombinasikan variabel spektral yang berasal dari citra Landsat 8 dan variabel sosio-geo-biofisik. Variabel yang dikaji meliputi citra sintetis dan faktor sosio-geo-fisik, seperti elevasi, kemiringan lereng, jarak dari jalan dan sungai, jarak dari permukiman, kepadatan penduduk, jarak dari desa, dan peta tutupan lahan yang ada. Algoritma decision tree machine learning (DTML) dikembangkan untuk mendeteksi distribusi spasial penanamn kopi agroforestri dan kopi monokultur di Kabupaten Bandung. Parameter pohon keputusan yang diuji untuk mengidentifikasi bobot masing-masing variabel adalah gain ratio, information gain, dan gini indeks. Sementara itu, metode brute force diterapkan untuk memilih variabel yang paling signifikan dalam model. Hasil penelitian menunjukkan bahwa variabel yang paling signifikan untuk mengidentifikasi agroforestry dan monokultur kopi adalah kombinasi dari variabel spektral, biogeofisik, dan tutupan lahan, dengan kriteria terbaik adalah information gain. Penggunaan peta penggunaan dan tutupan lahan yang ada merupakan variabel yang paling berpengaruh dalam model. Dalam konteks ini, akurasi keseluruhan (OA) yang diperoleh adalah 84,65%, dengan akurasi kappa (KA) sebesar 82,60%.
BIOMASS ESTIMATION MODEL FOR MANGROVE FOREST USING MEDIUM-RESOLUTION IMAGERIES IN BSN CO LTD CONCESSION AREA, WEST KALIMANTAN Sendi Yusandi; I Nengah Surati Jaya; Fairus Mulia
International Journal of Remote Sensing and Earth Sciences Vol. 15 No. 1 (2018)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2018.v15.a2683

Abstract

Mangrove forest is one of the forest ecosystem types that have the highest carbon stock in the tropics. Mangrove forests have a good assimilation capability with their environmental elements as well as on carbon sequestration. However, the availability of data and information on carbon storage, especially on tree biomass content of mangrove is still limited. Conventionally, an accurate estimation of biomass could be obtained from terrestrial measurements, but those methods are very costly and time-consuming. This study offered an alternative solution to overcome these limitations by using remote sensing technology, i.e. by using Landsat 8 and SPOT 5. The objective of this study is to formulate the biomass estimation model using medium resolution satellite imagery, as well as to develop a biomass distribution map based on the selected model. The study found that the NDVI of Landsat 8 and SPOT 5 have considerably high correlation coefficients with the standing biomass with a value of higher than 0.7071. On the basis of the values of aggregation deviation, mean deviation, bias, RMSE, χ², R², and s, the best model for estimating the mangrove stand biomass for Landsat 8 is B=0.00023404 e(20 NDVI) with the R² value of 77.1% and B=0.36+25.5 NDVI² with the R² value of 49.9% for SPOT 5. In general, the concession area of Bina Silva Nusa (BSN) Group (PT Kandelia Alam and PT Bina Ovivipari Semesta) have the potential of biomass ranging from 45 to 100 ton per ha.
Unlocking the Private Sector Role in Supporting the Sustainable Multipurpose Forest Management in  Riau, Indonesia Rossita, Annuri; Nurrochmat, Dodik Ridho; Boer, Rizaldi; Santoso, Nyoto; Jaya, I Nengah Surati; Purwawangsa, Handian; Ekayani, Meti; Mutaqin, Faizal; Kautsyar, Muhammad Irsyad
Jurnal Manajemen Hutan Tropika Vol. 32 No. 1 (2026)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.32.1.73

Abstract

This study aims to discover the private sector’s perspective on the sustainable transition of degraded forestlands, including the transformation of oil palm plantations in forest areas into multipurpose forests, identify current barriers in adopting sustainable multipurpose forest management practices on financing and policy aspects, and explore how the private sector can step up its role in forestland restoration. This study was based on field observations, key informant interviews, focus group discussions (FGDs), and literature reviews. This study aims to navigate a path for policy implementation toward decarbonization, as tenurial conflicts, particularly between oil palm plantations and forest areas, are critical for sustainable forest management in Riau. The private sector's interest in sustainable multipurpose forest management is higher when additional benefits from non-timber forest products (NTFPs) are high. This study also found that the private sector’s desire to support sustainable multipurpose forest management stems from the potential benefits of carbon trading. Regarding the carbon market, most respondents are willing to join when  carbon prices are USD4–6 ton-1 of CO2e. It indicates that the private sector is willing to support the domestic carbon market as regulated under the Minister of Environment and Forestry Regulation 21/2022. While the private sector has complied with most transformative policies and mechanisms, respondents expect further incentives and support, particularly to resolve the forestland conflict.
Evaluating logistic regression and decision tree for landslide susceptibility mapping: A comparison with heuristic methods in Indonesia Rama, Khairut Tamam Dwi; Hendrayanto, Hendrayanto; Jaya, I Nengah Surati
Journal of Degraded and Mining Lands Management Vol. 13 No. 3 (2026)
Publisher : Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15243/jdmlm.2026.133.10307

Abstract

Landslides are one of the most frequent and destructive natural disasters in Indonesia, yet national susceptibility assessments still rely heavily on heuristic frameworks based on expert judgments. This study evaluated the performance and interpretability of two Machine Learning (ML) models, Logistic Regression (LR) and Decision Tree (DT), compared with the heuristic baseline for landslide susceptibility mapping in the Ciujung sub-watershed, West Java-Banten. The models were developed using the NASA Global Landslide Catalog and 13 conditioning factors at 30 m spatial resolution. The LR model passed the multicollinearity test and identified annual rainfall, soil texture, and land use as dominant factors, while DT identified the same factors as the main controller, but emphasized the role of local threshold and interactions between factors. Slope does not appear as a dominant factor due to the mudslide character that not only occurs on steep slopes, but also spreads to flatter areas as deposition zones, thereby reducing the discriminating power of slope in pixel-based modeling. The models of LR and DT performed quite well (AUC>0.9) and generated spatially limited susceptibility zones. In contrast to the heuristic baseline, which sometimes overestimates the hazard areas. The findings indicated that LR and DT frameworks are able to improve the methodological transparency, spatial accuracy, and policy relevance, offering a viable approach for evidence-based catastrophe governance.
Decision-tree-based machine learning for detecting coffee agroforestry using SPOT-7 I Made Khrisna Yoga Devandra; I Nengah Surati Jaya; Tatang Tiryana
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27747

Abstract

This study develops a decision-tree-based machine-learning (ML) approach to identify coffee agroforestry plants using SPOT-7 satellite imagery. The algorithm was developed by examining the combination of image indices derived from SPOT-7 and biophysical variables. Detection using spectral variables is often hampered by spectral similarity between vegetation cover classes. This study found that a ML method that combines spectral and biophysical variables can significantly improve overall accuracy, from 60.4% (using conventional spectral variables alone) to 94% (using integrated spectral-biophysical variables). For detecting and identifying agroforestry coffee classes typically found under tree canopies, the addition of the “land cover” variable published by the Ministry of Environment and Forestry contributes significantly to the classification of agroforestry coffee. Important variables identified in this model are normalized difference vegetation index (NDVI), visible difference vegetation index (VDVI), normalized red-green vegetation index (NRGI), elevation, and land cover.
RESTORATION PRIORITY INDEX DEVELOPMENT OF DEGRADED TROPICAL FOREST LANDSCAPE IN BATANG TORU WATERSHED, NORTH SUMATERA INDONESIA Samsuri Samsuri; I Nengah Surati Jaya; Cecep Kusmana; Kukuh Murtilaksono
BIOTROPIA Vol. 21 No. 2 (2014): BIOTROPIA Vol. 21 No. 2 December 2014
Publisher : SEAMEO BIOTROP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.235 KB) | DOI: 10.11598/btb.2014.21.2.394

Abstract

Forest degradation has been important in the context of global issues since it contributes to the global climate change significanly.  Forest degradation of tropical rain forest area reduces global biological resources and has impact on occurence of poverty in community.  It also increases forest fragmentation and decreases forest connectivity as wildlife habitat.  To improve the connectivity, landscape restoration approach is used.  This paper describes the forest landscape restoration priority index to decide where the site gets restoration priority.  Restoration index is constructed by four factors indices that are index of forest degradation, forest connectivity, forest fragmentation, and socio economic of forest communities. Batang Toru forest landscape was getting pressure during the period 1989-2013.  Forest landscape fragmentation degree increased in the period 1989-2013, while the degree of connectivity tends to increase.  Forest connectivity index effects the restoration index more than other.  It implies that connectivity of Batang Toru forest landscape needs to be maintained and enhanced. It will provide proper habitat and reduce loss of biodiversity. Restoration index classifies Batang Toru forest landscape into low, medium and high priority for restoration. Sub-watershed of Sarula has high restoration index value so that it must be restored firstly.
Co-Authors Abdul Rosyid Adelia Juli Kardika Adhiguna, Agasta Agung Budi Cahyono Agung Budi Cahyono Agus P. Kartono Ahyar Gunawan Andry Indrawan Anita Zaitunah Anita Zaitunah Antonius B Wijanarto Antonius B Wijanarto Antonius B Wijanarto Arianti, Iin Bambang Hero Saharjo Bambang Sapto Pratomosunu Bejo Slamet Beni Iskandar Boedi Tjahjono Bramasto Nugroho Budi Kuncahyo Cecep Kusmana Dahlan Dahlan Dahlan Dahlan Darwo Darwo Darwo Darwo Dede Dirgahayu Dewayany Sutrisno Dewayany Sutrisno Diana Septriana Dito Cahya Renaldi Dito Cahya Renaldi Dodik Ridho Nurrochmat Dwi Noventasari Dwi Putra Apriyanto Dwi Shanty Apriliani Gunadi Elias Elias Ema Kurnia ENDANG SUHENDANG Endang Suhendang Endang Suhendang Suhendang Endes Nurfilmarasa Dahlan Eva Achmad F Gunarwan Suratmo Fahmi Amhar Faid Abdul Manan Fairus Mulia Fairus Mulia Farida Herry Susanty Farida Herry Susanty Florentina Sri Hardiyanti Purwadhi Handian Purwawangsa Hanifah Ikhsani Hardian, Dwika Hardjanto Hardjanto Hardjanto Hariaji Setiawan Haryo Tabah Wibisono Hasriani Muis Hendrayanto . Hendri Nurwanto Hermanu Triwidodo Herry Purnomo Herry Purnomo Hidayat Pawitan I Gusti Bagus Wiksuana I Made Khrisna Yoga Devandra Imas Sukaesih Sitanggang Irdika Mansur Ismail HJ Hashim Israr Albar Ita Carolita Iwan Gunawan Jarunton Boonyanuphap Kartodihardjo, Hariadi Kautsyar, Muhammad Irsyad Kukuh Murtilakono Kukuh Murtilaksono Kukuh Murtilaksono Kusnadi Lailan Syaufina LILIK BUDIPRASETYO Liu Qian Liu Qian Lukman Hakim Lukman Mulyanto M. Bismark Makin Basuki Marlina, Etty Meti Ekayani Moch. Anwar Muhammad Ardiansyah Muhammad Buce Saleh Muhammad Ikhwan Mulyaningrum Mulyaningrum Mutaqin, Faizal Muzailin Affan Muzailin Affan N Nurhendra Naik Sinukaban Naik Sinukaban Nanin Anggraini Nining Puspaningsih Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nitya Ade Santi Nobuyuki Abe Nurdin Sulistiyono Nyoto Santoso Omo Rusdiana Oteng Haridjaja Oteng Haridjaja Patrich Phill Edrich Papilaya Pratiwi Pratiwi Pratiwi Pratiwi Purnama, Edwin Setia R Assyfa El Lestari Rahimahyuni Fatmi Noor'an Rama, Khairut Tamam Dwi Rizaldi Boer Robert Parulian Silalahi Rossita, Annuri Rudi Ichsan Ismail Samsuri Samsuri Samsuri Samsuri Samsuri Samsuri Samsuri, Samsuri Sendi Yusandi Sigit Nugroho Soedari Hardjoprajitno Sri Wahyuni Suria Darma Tarigan Suyadi Suyadi Suyadi Suyadi Syamsu Rijal Tatang Tiryana Teddy Rusolono Tien Lastini Tirta Negara Tirta Negara Tomi Yuwono Tomi Yuwono, Tomi Unik, Mitra Uus Saepul Mukarom Wang Xuenjun Wang Xuenjun Wibisono, Haryo Tabah Wibisono, Haryo Tabah Wibisono, Haryo Tabah Widi Atmaka Widyananto Basuki Aryono Wijanarto, Antonius B. Wijanarto, Antonius B. Yadi Setiadi YANTO SANTOSA Zhang Yuxing