Ibnu Naz'm Ar-rosyid, . (2025) RANCANG BANGUN APLIKASI ANDROID UNTUK MENDETEKSI PENYAKIT PADA DAUN TOMAT MENGGUNAKAN METODE RAD DAN RESNET-50. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Tomato (Solanum lycopersicum) is one of the most economically valuable horticultural commodities in Indonesia. However, its productivity is often disrupted by leaf diseases such as septoria leaf spot, mosaic virus, and leaf blight. Early detection of these diseases is essential to prevent further damage and reduce economic losses for farmers. This study aims to develop an Android-based application capable of automatically detecting tomato leaf diseases using the Rapid Application Development (RAD) method and the Convolutional Neural Network (CNN) architecture ResNet-50. The model was built using transfer learning and trained on a dataset of 5,250 tomato leaf images validated by agricultural experts. The application integrates the model using two approaches: local processing with TensorFlow Lite and online processing via a server-based API using TensorFlow.js. Evaluation results show that the model achieved a classification accuracy of 98.7%. In addition to disease detection, the application provides users with information on symptoms, causes, impacts, and recommended treatments. This innovation is expected to assist farmers and home growers in detecting and addressing tomato leaf diseases quickly and accurately, thereby helping maintain plant health and productivity.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110511009] [Pembimbing: Ridwan Raafi'udin] [Penguji 1: Jayanta] [Penguji 2: Radinal Setyadinsa] |
| Uncontrolled Keywords: | Disease Detection, Tomato Leaf, Android, ResNet-50, Tensorflow. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | IBNU NAZ'M AR-ROSYID |
| Date Deposited: | 06 Aug 2025 08:01 |
| Last Modified: | 06 Aug 2025 08:01 |
| URI: | http://repository.upnvj.ac.id/id/eprint/37434 |
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