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Hendrik, Billy
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PREDIKSI PRODUKTIVITAS LAHAN KELAPA SAWIT PASCA PEREMAJAAN MENGGUNAKAN ALGORITMA MACHINE LEARNING Sukardi, Sukardi; Hendrik, Billy
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3279

Abstract

Abstract: Oil palm land rejuvenation is an essential step in maintaining long-term productivity. However, uncertainty about post-rejuvenation productivity poses a significant challenge for farmers and plantation managers. This study aims to develop a predictive model for oil palm land productivity after rejuvenation using machine learning algorithms. The methods used include historical production data collection, environmental factors, and plant characteristics. The tested algorithms include Linear Regression, Random Forest, and Artificial Neural Network (ANN). The results indicate that the Random Forest model has the highest accuracy in predicting productivity with an R-squared value of 0.87. Implementing this model is expected to support more accurate decision-making in oil palm plantation management. Keyword: Oil Palm; Rejuvenation; Productivity Prediction; Machine Learning Abstrak: Peremajaan lahan kelapa sawit merupakan langkah penting dalam mempertahankan produktivitas jangka panjang. Namun, ketidakpastian mengenai produktivitas pasca peremajaan menjadi tantangan utama bagi para petani dan pengelola perkebunan. Studi ini bertujuan untuk mengembangkan model prediksi produktivitas lahan kelapa sawit pasca peremajaan dengan menggunakan algoritma machine learning. Metode yang digunakan mencakup pengumpulan data historis produksi, faktor lingkungan, serta karakteristik tanaman. Algoritma yang diuji meliputi Regresi Linear, Random Forest, dan Artificial Neural Network (ANN). Hasil penelitian menunjukkan bahwa model Random Forest memiliki akurasi tertinggi dalam memprediksi produktivitas dengan nilai R-squared sebesar 0,87. Implementasi model ini diharapkan dapat membantu pengambilan keputusan yang lebih akurat dalam manajemen perkebunan kelapa sawit. Kata kunci: Kelapa Sawit; Peremajaan; Prediksi Produktivitas; Machine Learning
Co-Authors A'yuni, Qurrata Abdi Juliantho, Dwana Abdi Julianto, Dwana Ade saputra Akbar, Syifa Chairunnissa Deliva Amin Amirul Mukminin, Andi Amri, Yassirli Andi, Muhammad Yusril Haffandi Arby, Willya Aswandi, Nopan Awal, Hasri Budiantoro, Hendro Deti Karmanita Dian Maharani, Dian Febby Olivia, Ladyka Firdaus Firdaus Gevindo, Aprilian Gunadi Widi Nurcahyo Haffandi, Muhammad Yusril Hasibuan, Elpina Sari Dewi Ikhlas, Ariza Indhira, Sonia Karseno, Doni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Mardhiah Masril Muhammad, Imam Fakhri Nasution, Ayu Lestiani Nugraha, Fajri Olivia, Ladyka Febby Raharjo, Tio Doli Resnawita Ridwan, Ridwan Ritna Wahyuni Ruri Hartika Zain Rusydi, Rezki Sahara Lubis, Siti Sani, Fadhila Putri Sari, Ade Puspita Sarjon Defit Sonia Indhira Sovia, Rini Subarja, Roy Efendi Sukardi Sukardi Sumijan Supperianto, Bambang Suri, Melati Rahma Sutri, Ridwan Syafri Arlis Tamin, Zulfiqar Vidyanti, Angela Waruwu, Kalfinus Wira, M Wira Sanjaya Wirdawati, Wira Yuhandri Yuhandri, Y