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All Journal Jurnal Aplikasi Statistika & Komputasi Statistik PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Hady Suryono
Badan Pusat Statistik
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Klasifikasi Tutupan Lahan Berdasarkan Random Forest Algorithm Menggunakan Cloud Computing Platform Hady Suryono; Arif Handoyo Marsuhandi; Setia Pramana
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i1.383

Abstract

Statistik pertanian merupakan salah satu data yang vital di dunia dan memiliki kontribusi besar terhadap pencapaian tujuan program Sustainable Development Goals (SDGs). Dalam SDGs, perhatian terhadap ketahanan pangan difokuskan pada indikator kunci kedua yaitu nol kelaparan (SDG 2). Ketersediaan data tutupan lahan yang akurat diperlukan sebagai data dasar untuk luasan baku sawah yang akan digunakan untuk mengukur tingkat ketahanan pangan. Pemetaan tanaman membutuhkan pemrosesan dan pengelolaan data citra satelit dengan volume yang sangat besar dan tidak terstruktur yang mengarah pada permasalahan Geo Big Data dan menuntut teknologi dan sumber daya baru yang mampu menangani citra satelit dalam jumlah besar. Secara khusus, munculnya sumber daya cloud computing, seperti Google Earth Engine telah mengatasi masalah Geo Big Data ini. Kami menggunakan algoritma Random Forest (RF) pada platform Google Earth Engine (GEE) di Kota Jakarta Utara pada tahun 2019 untuk mengklasifikasikan tutupan lahan. Hasil penelitian menunjukkan bahwa overall accuracy (OA)
Classification of Paddy Growth Phase with Machine Learning Algorithms to Handle Imbalanced Multi-Class Big Data Hady Suryono; Heri Kuswanto; Nur Iriawan
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.45

Abstract

The global Sustainable Development Goals (SDGs) adopted by countries in the world have significant implications for national development planning in Indonesia in the period 2015 to 2030. The Agricultural sector is one of the most important sectors in the world and has a very important contribution to achieving the goals. Availability of accurate paddy production data must be available to measure the level of food security. This can be done by monitoring the growth phase of paddy and predicting the classification of its growth phase accurately and precisely. The paddy growth phase has 6 classes with the number of class members usually not the same (imbalanced data). This study describes the results of the classification of paddy growth phases with imbalanced data in Bojonegoro Regency, East Java in 2019 using machine learning algorithms on the Google Earth Engine (GEE) platform. Classification is done by Classification and Regression Tree, Support Vector Machine, and Random Forest. Oversampling technique is used to deal the problem of imbalanced data. The Area Sampling Frame survey in 2019 conducted by BPS was used as a label for classification model training. The results showed that the overall accuracy (OA) using the Random Forest algorithm by modifying the dataset using oversampling was 82.30% and the kappa statistic was 0.76, outperforming the SVM and CART algorithms.
Co-Authors Arif Handoyo Marsuhandi Heri Kuswanto Nur Iriawan Pramana, Setia