PERBANDINGAN AKURASI MACHINE LEARNING DAN DEEP LEARNING DALAM DETEKSI VIRUS GEMINI PADA DAUN TANAMAN CABAI

ISMAIL, MUHAMMAD HILMI (2023) PERBANDINGAN AKURASI MACHINE LEARNING DAN DEEP LEARNING DALAM DETEKSI VIRUS GEMINI PADA DAUN TANAMAN CABAI. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Agriculture is a significant contributor to Indonesia's economic growth. One of the main crops in Indonesian agriculture is chili plants due to their high economic value. According to the BPS - Statistics Indonesia, the national chili production was 2.77 million tons in 2020. However, behind the increasing national chili production, there is an attack from the geminivirus which causes yellowing of the leaves and stunting of the plant, thereby reducing the quality of the chili. Therefore, identifying diseases in the agricultural industry plays an important role. But to confirm a plant is infected with a virus, the polymerase chain reaction (PCR) method is needed which requires time and laboratory work. To address this challenge, the development of machine learning (ML) and deep learning (DL) methods is imperative. This study will evaluate and compare the performance of ML models, namely Support Vector Machine (SVM) and Random Forest (RF), and DL models, VGG-16 and Inception-v3, in detecting the geminivirus in chili plant leaves. The accuracy achieved by each model is as follows: SVM 87%, RF 88%, VGG-16 92%, and Inception-v3 86%. From these results, it can be seen that Inception-v3 has the lowest accuracy, while VGG-16 has the best accuracy. Keywords: chili plant disease, geminivirus, machine learning, deep learning Pertanian merupakan salah satu sumber pertumbuhan ekonomi Indonesia. Salah satu tanaman utama pertanian Indonesia yaitu tanaman cabai dikarenakan secara ekonomis memiliki nilai yang tinggi. Menurut Badan Pusat Statistik (BPS), cabai nasional menghasilkan produksi sebesar 2,77 juta ton pada 2020. Namun, dibalik produksi cabai nasional yang terus meningkat terdapat serangan virus gemini yang mengakibatkan daun menguning hingga tanaman menjadi kerdil sehingga kualitas cabai menurun. Oleh karena itu, identifikasi penyakit pada industri pertanian memegang peranan penting. Tetapi untuk memastikan suatu tanaman terinfeksi virus perlu menggunakan metode polymerase chain reaction (PCR) yang memerlukan waktu dan pengerjaan di laboratorium. Maka metode machine learning (ML) dan deep learning (DL) perlu dikembangkan. Berdasarkan penelitian terdahulu, penelitian ini akan membandingkan performa ML yaitu Support Vector Machine (SVM) dan Random Forest (RF) serta DL yaitu VGG-16 dan Inception-v3 dalam deteksi virus gemini pada daun tanaman cabai. Akurasi yang diraih dari tiap model adalah sebagai berikut: SVM 87%, RF 88%, VGG-16 92%, dan Inception-v3 86%. Dari hasil tersebut dapat diketahui bahwa Inceptionv3 meraih akurasi terendah, sedangkan VGG-16 meraih akurasi terbaik. Kata Kunci: penyakit tanaman cabai, virus gemini, pembelajaran mesin, pembelajaran mendalam

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 015
Call Number:	SIK/15/23/019
NIM/NIDN Creators:	41519010053
Uncontrolled Keywords:	penyakit tanaman cabai, virus gemini, pembelajaran mesin, pembelajaran mendalam
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 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 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 600 Technology/Teknologi > 600. Technology/Teknologi
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	ADELINA HASNA SETIAWATI
Date Deposited:	24 Mar 2023 04:45
Last Modified:	24 Mar 2023 04:45
URI:	http://repository.mercubuana.ac.id/id/eprint/75416

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