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All Journal Jurnal Telematika Jurnal Informatika: Jurnal Pengembangan IT
Nur Nafiiyah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Klasifikasi Jenis Kelengkeng Berdasarkan Daun Menggunakan Convolutional Neural Network Multilayer Perceptron Nur Nafiiyah; Puguh Rouf Prasetyo
Jurnal Telematika Vol 17, No 2 (2022)
Publisher : Institut Teknologi Harapan Bangsa

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Abstract

Research related to the introduction of longan types based on leaves requires increased feature extraction of longan leaves, whether based on features, shape, or texture. Existing research uses more feature extraction processes using color, texture, and shape, so this study proposes the introduction of longan types based on leaf images of all image pixels using the convolution neural network method. This research aims to identify longan species using feature extraction of all leaf image intensities. The image used to identify the type of longan is a color image measuring 128x128. The types of longan studied were itoh, diamond river, and local with a total of 150 training data images and 30 image test data. Longan-type classification results using multilayer perceptron are good. The resulting multilayer perceptron accuracy value is 96.7%. Penelitian terkait pengenalan jenis kelengkeng berdasarkan daun membutuhkan peningkatan ekstraksi fitur daun kelengkeng, baik berdasarkan fitur, bentuk, atau tekstur. Penelitian yang sudah ada lebih banyak proses ekstraksi fitur menggunakan warna, tekstur, dan bentuk sehingga penelitian ini mengusulkan pengenalan jenis kelengkeng berdasarkan citra daun dari seluruh piksel citra dengan metode convolution neural network. Tujuan penelitian ini mengenali jenis kelengkeng menggunakan ekstraksi fitur seluruh intensitas citra daun. Citra yang digunakan untuk mengenali jenis kelengkeng adalah citra warna berukuran 128x128. Jenis kelengkeng yang diteliti adalah itoh, diamond river, dan lokal dengan total seluruh data latih 150 citra dan data tes 30 citra. Hasil klasifikasi jenis kelengkeng dengan menggunakan multilayer perceptron adalah baik. Nilai akurasi multilayer perceptron yang dihasilkan adalah sebesar 96,7%.
Algoritma Deep Learning dalam Memprediksi Hasil Panen Padi di Kabupaten Lamongan Retno Wardhani; Nur Nafiiyah; Muhammad Ali Haydar
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 1 (2022): JPIT, Januari 2022
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i1.2581

Abstract

Based on data, bps.go.id harvest from the 2nd year 018 to 2019 decreased about an 7.76%. The government must constantly analyze the rice yields of farmers in Indonesia to determine whether these crops can meet the Indonesian people's primary food needs. Research this will predict rice yields in Lamongan. This study aims to assist the government in overcoming the occurrence of significant food shortages in Lamongan. A system that can be used as a reference tool to assist in policy or rule in the district Lamongan. This research proposes deep learning algorithms to predict the harvest based on the land area (m2), spacing (cm), the type of rice, the number of times to fertilize, fertilizer, and crop yields (quintals). The dataset used in the study was collected through questionnaires. Questionnaires were distributed via a google form and contained as many as 390 rows of data. Some of the data produced were incorrect, so the processing was carried out. The results of data processing, the data that can be used are 380 rows. The proposed architectural model's test results show that the loss values of MSE, MAE, or MAPE are the same. The MSE, MAE, and MAPE values are 2939977.418, 301,788, and 83,798, respectively.
Co-Authors Muhammad Ali Haydar Puguh Rouf Prasetyo Retno Wardhani