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All Journal Mechatronics, Electrical Power, and Vehicular Technology Jurnal INKOM CommIT (Communication & Information Technology) Jurnal Teknologi dan Sistem Komputer
Vicky Zilvan, Vicky
LIPI
Computer Science & IT Electrical & Electronics Engineering

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Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network Krisnandi, Dikdik; Pardede, Hilman F.; Yuwana, R. Sandra; Zilvan, Vicky; Heryana, Ana; Fauziah, Fani; Rahadi, Vitria Puspitasari
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5886

Abstract

Plant diseases can cause a significant decrease in tea crop production. Early disease detection can help to minimize the loss. For tea plants, experts can identify the diseases by visual inspection on the leaves. However, providing experts to deal with disease identification may be very costly. The machine learning technology can be implemented to provide automatic plant disease detection. Currently, deep learning is state-of-the-art for object identification in computer vision. In this study, the researchers propose the Convolutional Neural Network (CNN) for tea disease detections. The researchers focus on the implementation of concatenated CNN, namely GoogleNet, Xception, and Inception-ResNet-v2, for this task. About 4727 images of tea leaves are collected, comprising of three types of diseases that commonly occur in Indonesia and a healthy class. The experimental results confirm the effectiveness of concatenated CNN for tea disease detections. The accuracy of 89.64% is achieved.
Identifikasi Pembicara Menggunakan Algoritme VFI5 dengan MFCC sebagai Pengekstraksi Ciri Zilvan, Vicky; Muttaqien, Furqon Hensan
INKOM Journal Vol 5, No 1 (2011)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.592 KB) | DOI: 10.14203/j.inkom.97

Abstract

Voting Feature Instervals (VFI) 5 memiliki akurasi yang cukup tinggi dalam mengklasifikasikan data berbasis teks dan citra. Berdasarkan hal tersebut dikembangkanlah metode identifikasi pembicara menggunakan algoritme VFI5 dengan Mel Frequency Ceptrum Coefficients (MFCC) sebagai pengekstraksi ciri suara untuk melihat keakuratan algoritme VFI5 dalam mengklasifikasikan data berbasis suara. Jenis identifikasi pembicara pada penelitian ini bersifat tertutup dan bergantung pada text. Pada penelitian ini juga dilakukan percobaan menggunakan suara ber-noise untuk melihat kehandalan VFI5 dalam mengklasifikasikan suara ber-noise. Dari hasil pengujian didapatkan bahwa metode yang telah dikembangkan ini memiliki akurasi cukup tinggi dengan akurasi tertinggi sebesar 97% untuk data suara tanpa noise.  Selain itu juga diketahui bahwa jumlah data latih yang optimal untuk menghasilkan akurasi yang tinggi adalah 11. Sedangkan untuk suara bernoise dengan SNR sebesar 30 dB, akurasi tertinggi mencapai 81,5 % dan untuk suara bernoise dengan SNR sebesar 20 dB tingkat akurasi tertinggi mencapai 59 %.
Ekstraksi Objek pada Citra Radar FM-CW dengan Metode DBSCAN Zilvan, Vicky
INKOM Journal Vol 9, No 1 (2015)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.726 KB) | DOI: 10.14203/j.inkom.419

Abstract

Makalah ini membahas rancang bangun dan implementasi ekstraksi objek pada radar FM-CW untuk mengatasi permasalahan kualitas citra yang ditangkap oleh radar. Teknik clustering density based spatial clustering of applications with noise (DBSCAN) digunakan untuk mengekstraksi objek dari data input. Hasil dari penelitian ini adalah rancang bangun ekstrasi objek dengan nilai minPts sebesar 4 dan nilai eps sebesar 4 sebagai parameter input untuk DBSCAN. Hasil dari rancang bangun ekstraksi objek adalah titik-titik data hasil ekstraksi objek yang lebih sederhana yang mampu mengatasi permasalahan kualitas citra yang ditangkap oleh radar. Selain itu, titik-titik data yang dihasilkan juga memiliki kualitas data yang lebih baik karena teknik clustering DBSCAN memiliki kemampuan untuk memisahkan noise dari data input.
Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder Zilvan, Vicky; Ramdan, Ade; Supianto, Ahmad Afif; Heryana, Ana; Arisal, Andria; Yuliani, Asri Rizki; Krisnandi, Dikdik; Suryawati, Endang; Suryo Kusumo, Raden Budiarianto; Yuawana, Raden Sandra; Kadar, Jimmy Abdel; Pardede, Hilman F.
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14250

Abstract

Precision agriculture is a management strategy for sustaining and increasing the production of agricultural commodities. One of its implementations is for crop disease detection. Currently, deep learning methods have become widespread methods for the automatic detection of crop diseases. Most deep learning methods showed better performance when using an original image in raw form as inputs. However, the original image of crop diseases may appear similar between one disease to another.  Therefore, the deep learning methods may misclassify the data. To deal with these, we propose the gamma transformation with a deep convolutional autoencoder to extract good features from the original image data. We use the output of the gamma transformation with a deep convolutional autoencoder as inputs to a classifier for the automatic detection of crop diseases. Our experiments show that the average accuracies of our method improve the performance of crop disease detection compared to only using raw data as inputs.
Robust remaining useful life prediction of lithium-ion battery with convolutional denoising autoencoder Yuliani, Asri Rizki; Pardede, Hilman Ferdinandus; Ramdan, Ade; Zilvan, Vicky; Yuwana, Raden Sandra; Amri, M Faizal; Kusumo, R. Budiarianto Suryo; Pramanik, Subrata
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 15, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2024.905

Abstract

Using lithium-ion (Li-ion) batteries exceeding their useful lifetime may be dangerous for users, and hence, developing an accurate prediction system for batteries that remain useful for life is necessary. Many deep learning models, such as gated recurrent units and long short-term memory (LSTM), have been proposed for that purpose and have shown good results. However, their performance when dealing with noisy data degrades significantly. This may hamper their implementations for the real world since battery data are prone to noise. In this paper, we develop a robust prediction model in a noisy environment for predicting the remaining useful life (RUL) of Li-ion batteries. We propose a denoising autoencoder (DAE) utilized to remove noise from the data. The DAE is built with convolutional layers instead of traditional feed-forward networks here. We combine DAE with LSTM as the predictor. The proposed framework is evaluated using artificially corrupted battery data provided by National Aeronautics and Space Administration (NASA). The results reveal that our proposed method improves robustness when data contain various types of noise. A comparative study using the traditional approach has also been conducted. Our evaluation shows that convolutional layers are more effective than the traditional approach and that the original composition of the DAE was built using traditional feed-forward networks. DAE with convolutional layers has the best average performance with MSE of 0.61 and is the most consistent model.
Co-Authors Ade Ramdan Ahmad Afif Supianto Amri, M Faizal Andria Arisal, Andria Fani Fauziah, Fani Furqon Hensan Muttaqien Heryana, Ana Kadar, Jimmy Abdel Krisnandi, Dikdik Kusumo, R. Budiarianto Suryo Pardede, Hilman F. Pardede, Hilman Ferdinandus Pramanik, Subrata Suryawati, Endang Suryo Kusumo, Raden Budiarianto Vitria Puspitasari Rahadi Yuawana, Raden Sandra Yuliani, Asri Rizki Yuwana, R. Sandra Yuwana, Raden Sandra