IMPLEMENTASI METODE CNN DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KEMATANGAN TANAMAN CABAI RAWIT

Ulum, Muhammad Rifki Bahrul (2024) IMPLEMENTASI METODE CNN DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI TINGKAT KEMATANGAN TANAMAN CABAI RAWIT. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Identifying the maturity level of cayenne pepper plants is an important step for farmers in dealing with market fluctuations as well as in post-harvest cultivation and handling. However, the current harvest process is still done manually which is very dependent on farmer factors which can cause subjective harvest results and inconsistent results. So to minimize these problems, a touch of technology is needed that can classify the maturity level of cayenne pepper plants mechanically. This research aims to develop a classification model for the maturity level of cayenne pepper plants. The method proposed in this research is to utilize the CNN method as feature extraction and the KNN method is used to classify data based on the features extracted by the CNN method. This research compares the CNN model classification with the classification performed by KNN based on CNN feature extraction. From the test scenarios conducted, the classification performed by KNN based on CNN feature extraction gets the best accuracy of 99.33%, while the CNN classification model gets the best accuracy of 87.33%.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Rahmat, Basuki	NIDN0023076907	basukirahmat.if@upnjatim.ac.id
Thesis advisor	Swari, Made Hanindia Prami	NIDN0805028901	madehanindia.fik@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Muhammad Rifki Bahrul Ulum

Date Deposited:

18 Jul 2024 05:09

Last Modified:

18 Jul 2024 05:09

URI:

https://repository.upnjatim.ac.id/id/eprint/26385