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All Journal Jurnal Teknoinfo Jurnal Informatika dan Rekayasa Perangkat Lunak
Pratomo Prawirodirjo, Raden Ronggo Bintang
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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PERBANDINGAN PERFORMA ARSITEKTUR CONVULUTIONAL NEURAL NETWORK UNTUK DETEKSI HAMA DAUN SAWI HIJAU Pratomo Prawirodirjo, Raden Ronggo Bintang; Meiwasandi, Putu Niar; Marcelindo, Fitto; Kusuma, Anndya Dyah; Palupiningsih, Pritasari
Jurnal Teknoinfo Vol 19, No 1 (2025): January 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v19i1.4453

Abstract

Sawi hijau merupakan komoditas pertanian penting di Indonesia, namun rentan terhadap serangan hama yang dapat menurunkan kualitas dan hasil panen. Penelitian ini mengeksplorasi penggunaan teknologi kecerdasan buatan, khususnya Convolutional Neural Networks (CNN), untuk mendeteksi hama pada tanaman sawi hijau secara akurat dan efisien. Tiga arsitektur CNN, yaitu VGG19, InceptionV3, dan Xception, diterapkan dan dibandingkan performanya dalam mengklasifikasikan citra daun sawi yang terserang hama. Metodologi meliputi pengumpulan dan preprocessing data citra, pemodelan dengan ketiga arsitektur, serta evaluasi menggunakan berbagai metrik kinerja. Hasil menunjukkan bahwa arsitektur VGG19 unggul dalam hal akurasi yaitu mencapai 96%, efisiensi penggunaan sumber daya, dan nilai MAPE terendah yaitu 4,61, menjadikannya pilihan optimal untuk implementasi sistem deteksi hama pada tanaman sawi hijau.
Implementation of VGG19 Model for Pest Detection on Mustard Leaves Pratomo Prawirodirjo, Raden Ronggo Bintang; Sahira, Putri; Aulia, Zahra Windi; Syakirin, Hirzan Fakhrusy; Febriansyah; Prayitno, Budi; Siregar, Riki Ruli Affandi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 1 (2025): Volume 6 Number 1 March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i1.33

Abstract

Green mustard is a leading agricultural commodity in Indonesia but often faces pest attacks such as Spodoptera litura, which can reduce productivity by up to 85%. This study aims to develop an automated pesticide sprayer prototype using Convolutional Neural Network (CNN) technology with the VGG19 architecture. The system utilizes Raspberry Pi, Arduino, ESP8266, and a camera to detect pests in real-time and accurately spray pesticides. The dataset used consists of 1,380 images, divided into 10% for testing, 25% for validation, and 75% for training. The model evaluation values for the ‘mustard with pests’ class achieved precision, recall, and F1-Score of 96% each, while for the ‘mustard without pests’ class they were 95% each. In addition, the MAPE (Mean Absolute Percentage Error) value of 4.61% shows that the percentage error of the model prediction is very small. The developed VGG19 model achieved an accuracy of 95% and high efficiency after conversion to the TFLite format, reducing model size by 75.57%. This tool is highly recommended to enhance farmers' work efficiency, reduce excessive pesticide use, and support sustainable agriculture. Its ability to operate autonomously and precisely makes it an ideal solution to assist farmers regarding pest problems.
Co-Authors Aulia, Zahra Windi Budi Prayitno Febriansyah Kusuma, Anndya Dyah Marcelindo, Fitto Meiwasandi, Putu Niar Palupiningsih, Pritasari Sahira, Putri Siregar, Riki Ruli Affandi Syakirin, Hirzan Fakhrusy