KLASIFIKASI PRODUKSI KEDELAI BERBASIS DEEP LEARNING MENGGUNAKAN CATBOOST

JUMARI, MUHAMMAD (2025) KLASIFIKASI PRODUKSI KEDELAI BERBASIS DEEP LEARNING MENGGUNAKAN CATBOOST. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This study analyzes and predicts soybean yield categories (low, medium, or high) using two machine learning models: CatBoost Classifier and Neural Network. The research dataset focuses on 166,349 pre-processed soybean entries extracted from a total of 1 million crop yield records. Exploratory data analysis revealed significant class imbalance within the yield distribution. Both models demonstrated high classification performance, with CatBoost achieving an accuracy of 0.9374 and the Neural Network reaching 0.9348. However, further evaluation indicated that CatBoost was superior in handling minority classes, evidenced by its better recall for the low-yield category. Based on CatBoost's feature importance analysis, fertilizer use, rainfall, and irrigation were identified as the most crucial factors significantly impacting soybean yield. These findings offer valuable insights for agricultural practices, emphasizing the optimization of these key factors to enhance soybean productivity. Keywords: Soybean, Crop Yield, Classification, CatBoost, Neural Network, Feature Importance, Machine Learning. CatBoost Classifier dan Neural Network. Dataset penelitian berfokus pada 166.349 entri data kedelai yang telah melalui proses pra-pemrosesan dari total 1 juta data panen. Hasil analisis data eksploratif menunjukkan adanya ketidakseimbangan kelas signifikan pada distribusi hasil panen. Kedua model menunjukkan performa klasifikasi yang tinggi, dengan CatBoost mencapai akurasi 0.9374 dan Neural Network 0.9348. Meskipun demikian, evaluasi lebih lanjut mengungkapkan bahwa CatBoost lebih unggul dalam menangani kelas minoritas, terbukti dari recall yang lebih baik untuk kategori panen rendah. Berdasarkan analisis feature importance dari model CatBoost, penggunaan pupuk, curah hujan, dan penggunaan irigasi merupakan faktor-faktor paling krusial yang secara signifikan memengaruhi hasil panen kedelai. Temuan ini memberikan wawasan penting bagi praktik pertanian untuk mengoptimalkan faktor-faktor kunci demi peningkatan produktivitas kedelai. Kata Kunci: Kedelai, Hasil Panen, Klasifikasi, CatBoost, Neural Network, Feature Importance, Machine Learning.

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 104
NIM/NIDN Creators:	41821010062
Uncontrolled Keywords:	Kedelai, Hasil Panen, Klasifikasi, CatBoost, Neural Network, Feature Importance, Machine Learning.
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.32 Neural Nets (Neural Network)/Jaringan Saraf Buatan 600 Technology/Teknologi > 630 Agriculture and Related Technologies/Pertanian dan Teknologi Terkait
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	02 Oct 2025 07:10
Last Modified:	02 Oct 2025 07:10
URI:	http://repository.mercubuana.ac.id/id/eprint/98698

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