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All Journal Jurnal Bisnis dan Manajemen JURNAL PENDIDIKAN EKONOMI: Jurnal Ilmiah Ilmu Pendidikan, Ilmu Ekonomi dan Ilmu Sosial JOIV : International Journal on Informatics Visualization Jurnal Manajemen Indonesia COMPTON: Jurnal Ilmiah Pendidikan Fisika Al-Hikmah: Jurnal Agama dan Ilmu Pengetahuan Jurnal Ilmiah Edunomika (JIE) Journal of Applied Data Sciences International Journal of Education, Social Studies, And Management (IJESSM) TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Jurnal Didactical Mathematics Annals of Human Resource Management Research Polinomial : Jurnal Pendidikan Matematika Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pengabdian Masyarakat Bangsa Journal International Dakwah and Communication Jurnal Teologi Pambelum Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Information Systems Management and Digital Business Journal of Management and Innovation Entrepreneurship (JMIE) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Kesehatan Bakti Tunas Husada SANISKALA: Jurnal Pengabdian Masyarakat Jurnal Mediasas : Media Ilmu Syari'ah dan Ahwal Al-Syakhsiyyah Jurnal Pengabdian Masyarakat Ilmu Komputer Jurnal Pengabdian Masyarakat Ekonomi dan Bisnis Digital Jurnal Ekonomi, Bisnis dan Manajemen Ijarme
Sudianto, Sudianto
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Journal : JOIV : International Journal on Informatics Visualization

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Classification of Sugarcane Area Using Landsat 8 and Random Forest based on Phenology Knowledge Sudianto, Sudianto; Herdiyeni, Yeni; Prasetyo, Lilik Budi
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.1401

Abstract

Indonesia is one of the largest countries globally with an area for planting sugarcane. The current problem is that determining the planting area of sugarcane is still done conventionally; this is very limited and wastes time. Thus, knowing the sugarcane planting area becomes essential for policymaking through Remote Sensing technology. However, the challenge of Remote Sensing is the limited data due to weather and the spectral variability of other plants. So, it is necessary to classify based on phenological knowledge. The study aims to classify sugarcane areas based on phenological knowledge using Remote Sensing and Machine Learning. This application finished on the cloud platform Google Earth Engine (GEE) through Landsat 8 satellite imagery data. The knowledge of sugarcane phenology was built based on the Normalized Difference Vegetation Index (NDVI) spectral value and built with the harmonic model. In addition, classification is accomplished by object-oriented (OO) methods for segmentation classification. Object-oriented is solved by the Simple Non-Iterative Clustering (SNIC) algorithm for spatial cluster identification, the Gray-Level Co-occurrence Matrix (GLCM) for texture extraction, and the Random Forest algorithm for Land Use-Land Cover (LULC) classification. The results of the accuracy analysis using the confusion matrix and the classification of sugar cane areas based on phenological knowledge obtained the best results with an overall accuracy of 95.9% with a Kappa coefficient of 0.92. It can be concluded that a classification approach with knowledge of plant phenology can help better classify the availability of land for plantations in the future.
Co-Authors ABDUL HAMID Abdul Hamid Adriansyah, Hikari Afandi, Widi Agus Rofi’i Akhmad, Fajar Kamaludin Alfani, Mufti Hasan Anang Martoyo Andi Amang Andika Prasetya Nugraha Angela, Maria Apriana Audina, Silfi Baharudin, Muhammad Yusuf Saaih Budhi, Widodo Dede Salim Nahdi Dedy Agung Prabowo Dewi Permata Sari Dewi, Susantriana Djunaedi Djunaedi Dyah Kurniawati, Ajeng Erlangga, Muhammad Fadlililah, Andi Hidayatul Fauziah, Inayah Firdaus Firdaus Hamzah, Zulfadli Hendayana, Alya Fatihah Humaira, Novida Iik Nurhikmayati, Iik Insiyah, Cici' Irwan Irwan Ismayanti , Syifa Jamahsyari, Yolan Faiz Jatisunda, Moh Gilar Julia, Rida Kafabi, Muhammad Hilmi Kartiwa, Cece Enjang Kusumah, Firdan Gusmara Kusumaningrum, Andini Mulia Laelasari Laelasari, Laelasari LILIK BUDIPRASETYO Listyani , Elfa Maesaroh , Nita Mahmudin, Dede Maleh, D.Th, Kinurung Maleh, Kinurung Maria, Hana Diana Marsally, Silvia Van Martiyaningsih, Dwi Puspa Martoyo, Anang Muhamad Azrino Gustalika Muhammad Arif Mulyadi Mulyadi Muna, Bunga Laelatul Ni’amah, Khoirun Nugraha, Jeffy Nuraini, Putri Nurfaeda , Sofia Muawalina Pamungkas, Adie Pebrianti, Yesi Utami Rachman, Ari Rakhma, Nazwa Aulia Ramdiani , Rani Ripaldi Rohaeti, Titi Rohimatunisa, Dela Sabri Sabri Santoso, Erik Saputro, Satria Nur Soemedhy, Chandra Ayunda Apta Sri Mulyani Nurfazriyah Sudi, Mohamad Suhada, Karya Sulastri, Desi sumardin, sumardin Supriyadi Supriyadi Suriadi Suriadi Sutopo Sutopo Trivetisia, Nora Usman, Muhammad Lulu Latif Utami, Tri Wulandari Vici Suciawati Wibowo, Rohim Isnain Septian Wicaksono, Apri Pandu Winanti, Nawang Anggita Wulandari, Dewi Eka Yaqin, Silpi Syamrotul Zulkifli Zulkifli