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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Silvikultur Tropika (JST) MEDIA KONSERVASI FORESTA BIOTROPIA - The Southeast Asian Journal of Tropical Biology Perfecting a Video Game with Game Metrics Jurnal Penelitian Hutan Tanaman Indonesian Journal of Agricultural Science Konversi Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Tataloka JURNAL KONVERSI MAJALAH ILMIAH GLOBE SEJ (Science Education Journal) LIMNOTEK - Perairan Darat Tropis di Indonesia International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL METEOROLOGI DAN GEOFISIKA Journal of Sylva Indonesiana Indonesian Journal of Electrical Engineering and Computer Science Jurnal Tanah dan Iklim Jurnal Risalah Kebijakan Pertanian dan Lingkungan Jurnal Informatika dan Teknologi Informasi
Lailan Syaufina
Departemen Silvikultur Tropika, Fakultas Kehutanan IPB, Bogor 16680
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Physics Social Sciences Veterinary

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Journal : Perfecting a Video Game with Game Metrics

 1 
Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau Putri Thariqa; Imas Sukaesih Sitanggang; Lailan Syaufina
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.3540

Abstract

 Over one-year period (March 2013 – March 2014), 58 percent of all detected hotspots in Indonesia are found in Riau Province. According to the data, Rokan Hilir shared the greatest number of hotspots, about 75% hotspots alert occur in peatland areas. This study applied spatial decision tree algorithms to classify classes before burned, burned, and after burned from remote sensed data of peatland area in Kubu and Pasir Limau Kapas subdistrict, Rokan Hilir, Riau. The decision tree algorithm based on spatial autocorrelation is applied by involving Neigborhood Split Autocorrelation Ratio (NSAR) to the information gain of CART algorithm. This spatial decision tree classification method is compared to the conventional decision tree algorithms, namely, Classification and Regression Trees (CART),  C5.0, and C4.5 algorithm. The experimental results showed that the C5.0 algorithm generate the most accurate classifier with the accuracy of  99.79%. The implementation of spatial decision tree algorithm succesfuly improve the accuracy of CART algorithm.
Detection and Prediction of Peatland Cover Changes Using Support Vector Machine and Markov Chain Model Ulfa Khaira; Imas Sukaesih Sitanggang; Lailan Syaufina
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i1.2400

Abstract

Detection and prediction of peatland cover changes needs to be done in the rapid rate of deforestation in Indonesia. This work applied Support Vector Machine (SVM) and Markov Chain Model on multitemporal satellite data. The study area is located in the Rokan Hilir district, Riau Province. SVM classification technique used to extract information from satellite data for the years 2000, 2004, 2006, 2009 and 2013. The Markov Chain Model was used to predict future peatland cover. The SVM classification result showed that the Kappa accuracy of peatland cover classification is more than 0.92. The non vegetation areas increased to 307% and the sparse vegetation areas increased to 22% between 2000 and 2013, while dense vegetation areas decreased to 61%. Prediction of future land cover by the Markov Chain Model showed that the use of multitemporal satellite data with 3 years interval provides accurate result for predicting peatland cover changes.
Poisson Clustering Process on Hotspot in Peatland Area using Kulldorff’s Scan Statistics Method Annisa Puspa Kirana; Imas Sukaesih Sitanggang; Lailan Syaufina
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.2272

Abstract

The increase in peatland fire’s intensity has encouraged people to develop methods of preventing wildfire. One of the prevention methods is recognizing the distribution pattern of hotspot as one of forest and land fire indicators. We could determine the area that has high fires density based on distribution patterns so any early prevention steps could be performed in that area. This research proposed to recognize the distribution pattern of hotspot clusters in the peatland areas in Sumatera in the year 2014 using Kulldorff’s Scan Statistics (KSS) method with Poisson model. This approach was specifically designed to detect clusters and assess their significance via Monte Carlo replication. Results showed that the method is reliable to detect the clusters of hotspots which have the accuracy of 95%. Riau and South Sumatera province have the highest density of cluster distributions of the hotspot. Based on the maturity level of peat, cluster distributions of hotspot were mostly found in ‘hemic’ maturity level. Based on peatland thickness, cluster distribution of hotspot was mostly found in ‘very deep’ thickness.
Co-Authors Agus Buono Agus Siswono Agus Siswono Ahmad Ainuddin Nuruddin Aisyah Anggraini Andini Tribuana Tunggadewi Anggie Yohanna Mandalahi Anissa Rezainy Anita Zaitunah Annisa Puspa Kirana Arzyana Sunkar Asri Buliyansih Ati Dwi Nurhayati Ati Dwi Nurhayati Ati Dwi Nurhayati Bahruni Bahruni Bambang Hero Saharjo Boedi Tjahjono Chandrasa E Sjamsudin Denni Prasetia Diah Zuhriana Didik Suharjito Dinda Aisyah Fadhillah Hafni Drucella Benala Dyahati Eduardo Fernando Martins de Carvalho Eka Intan Kumala Putri Eko Heriyanto Elsa Elvira Awal Entin Kartini Erfan Noor Yulian Erianto Indra Putra Fakhri Sukma Afina Fransisxo GS Tambunan Gatot Setiawan Gatot Setiawana Gunawan, Andi Gusti Zainal Anshari Hariyadi I Nengah Surati Jaya Ichwandi, Iin Imam Suyodono Imas Sukaesih Sitanggang Indah Prasasti Indah Prasasti Irdika Mansur Istomo . Jamaluddin Basharuddin Jumani Jumani Khaira, Ulfa Khairia Nafia Khulfi M Khalwani Komarsa Gandasasmita Kurniawati Purwaka Putri Lai Food See Lilik Budi Prasetyo M. Syamsul Maarif M. Taufan Tirkaamiana M. Taufan Tirkaamiana Mirzha Hanifah Mochamad Asep Maksum Mohid Rashid Mohd Yusof Muhammad Ardiansyah Muhammad Hawari Azka Muhammad Hudzaifah Rihuljihad Muhammad Ikhsan Muhammad Imam Nugraha Muhammad Nur Aidi Nalar Istiqomah Nining Puspaningsih Noor Farikhah Haneda Nova Puspitasari Nuniek Sutanti Nurheni Wijayanto Prima Trie Wijaya Putri Thariqa Rinenggo Siwi Rizaldi Boer Samsuri Samsuri Samsuri Sandhi Imam Maulana Sigit Purwanto Sitanggang, Imas S. Siti Badriyah Rushayati Sobri Effendy Sofia Fitriana Sri Mulatsih Sri Mulatsih SRI UTAMI Sugiarto, Dwi Putro Supriyadi, Andi Supriyanto Supriyanto Suryawan Ramadhan Syaiful Anwar Titik Ernawati Tri Tiana Ahmadi Putri Vera Linda Purba Wahida Annisa Wardana Wardana Widiatmaka . Wiwin Ambarwulan Yenni Vetrita Yuli Sunarti