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All Journal Mechatronics, Electrical Power, and Vehicular Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi dan Sistem Komputer
Yuliani, Asri Rizki
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Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
Peningkatan Performa Pengelompokan Pola Berpikir Siswa dalam Belajar pada Media Pembelajaran Menggunakan Direct Batch Growing Self Organizing Map Izzuddin, Mochammad; Supianto, Ahmad Afif; Tibyani, Tibyani; Pardede, Hilman Ferdinandus; Yuliani, Asri Rizki; Ramdan, Ade
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 1: Februari 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022915573

Abstract

Tidak sedikit siswa mengalami kendala untuk keluar dari kebuntuan berpikir saat belajar. Setiap siswa memiliki caranya masing-masing untuk menyelesaikan masalah kebuntuan tersebut, yang disebabkan oleh pola berpikir yang berbeda-beda. Kendati berbeda, pola berpikir tersebut memiliki kemiripan yang dapat dikelompokkan agar pemberian umpan balik dapat dilakukan dengan tepat secara berkelompok. Salah satu cara yang dapat dilakukan untuk mengelompokkan siswa berdasarkan pola berpikirnya adalah clustering. Penelitian untuk pengelompokkan berdasarkan kecerdasan sudah pernah dilakukan menggunakan salah satu teknik clustering yaitu Self Organizing Map (SOM). Namun SOM memiliki keterbatasan dalam menentukan ukuran jaringan karena bersifat statis. Keterbatasan yang ada pada SOM dapat diatasi, penelitian ini mengusulkan Direct Batch Growing Self Organizing Map (DBGSOM) yang bersifat dinamis dalam ukuran jaringan dan lebih cepat dalam proses pelatihannya. Penelitian ini dimulai dengan mengidentifikasi masalah untuk mengetahui kemungkinan penyelesaian permasalahan. Tahap selanjutnya adalah pengumpulan data dan pemilihan data yang digunakan dalam penelitian. Tahap akhir, evaluasi dilakukan terhadap data yang terdiri dari 12 assignment untuk mengetahui performa terbaik dari DBGSOM. Hasil evaluasi yang telah dilakukan menunjukkan bahwa clustering DBGSOM memperoleh performa lebih baik daripada SOM pada 11 assignment dari 12 assignment. Pengukuran signifikansi perbandingan dilakukan dengan metode Wilcoxon yang menghasilkan nilai test stat 8 dan critical value 13. Hal ini membuktikan bahwa penerapan DBGSOM mampu memberikan peningkatkan performa clustering yang signifikan dari SOM. AbstractA few times, students have difficulty getting out of the deadlock in thinking while studying. Each student has their own way of solving the deadlock problem, which is caused by different thinking patterns. Although different, these thinking patterns have similarities that can be grouped so that giving feedback can be done appropriately in groups. One way that can be done to group students based on their thinking patterns is clustering. Research for grouping based on intelligence has been done using one of the clustering techniques, namely Self Organizing Map (SOM). However, SOM has limitations in determining network size because it is static. The limitations that exist in SOM can be overcome, this study proposes a Direct Batch Growing Self Organizing Map (DBGSOM) which is dynamic in network size and faster in the training process. This research begins by identifying the problem to determine the possibility of solving the problem. The next stage is data collection and data selection used in research. The final stage, evaluation is carried out on the data consisting of 12 assignments to find out the best performance of DBGSOM. The results of the evaluation that have been carried out show that DBGSOM clustering has better performance than SOM on 11 assignments out of 12 assignments. The comparison significance measurement was carried out using the Wilcoxon method which resulted in a test stat value of 8 and a critical value of 13. This proves that the application of DBGSOM is able to provide a significant increase in clustering performance from SOM.
Co-Authors Ade Ramdan Ahmad Afif Supianto Amri, M Faizal Andria Arisal, Andria Heryana, Ana Izzuddin, Mochammad Kadar, Jimmy Abdel Krisnandi, Dikdik Kusumo, R. Budiarianto Suryo Pardede, Hilman F. Pardede, Hilman Ferdinandus Pramanik, Subrata Suryawati, Endang Suryo Kusumo, Raden Budiarianto Tibyani Tibyani, Tibyani Vicky Zilvan, Vicky Yuawana, Raden Sandra Yuwana, Raden Sandra