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All Journal Semesta Teknika Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi)
Azizah, Laila Marifatul
Universitas Muhammadiyah Yogyakarta
Computer Science & IT Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer Azizah, Laila Marifatul; Umayah, Sitti Fadillah; Fajar, Febriyana
Semesta Teknika Vol 21, No 2 (2018): NOVEMBER 2018
Publisher : Semesta Teknika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.212229

Abstract

Mangosteen is one of Indonesian potential export fruits. Nevertheless, mangosteens quality is compulsary. A good quality fruit surface is needed in export fruit. This is the reason of this research to detect the flaw in rind surface, particularly mangosteen. Some researcher has been done many type of image processing for fruit detection. However, there aren’t any research for mangosteen rind detection especially used Deep Learning. This research used CNN (Convolutional Neural Network) as deep learning method to detect mangosteen rind surface. Our research is to find configuration which was the best accurancy value. The rind detection calcuted between epoch and layer to obtain maximum accurancy value. This method achieved maximum value by parameter 4 layer and epoch value of 30. From our experiment, the test result for rind detection was 98% accurancy.
Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer Laila Marifatul Azizah; Sitti Fadillah Umayah; Febriyana Fajar
Semesta Teknika Vol 21, No 2 (2018): NOVEMBER 2018
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.212229

Abstract

Mangosteen is one of Indonesian potential export fruits. Nevertheless, mangosteens quality is compulsary. A good quality fruit surface is needed in export fruit. This is the reason of this research to detect the flaw in rind surface, particularly mangosteen. Some researcher has been done many type of image processing for fruit detection. However, there aren’t any research for mangosteen rind detection especially used Deep Learning. This research used CNN (Convolutional Neural Network) as deep learning method to detect mangosteen rind surface. Our research is to find configuration which was the best accurancy value. The rind detection calcuted between epoch and layer to obtain maximum accurancy value. This method achieved maximum value by parameter 4 layer and epoch value of 30. From our experiment, the test result for rind detection was 98% accurancy.
Analisa Sentimen Masyarakat Terhadap Kebijakan Vaksinasi Covid-19 Di Indonesia Pada Twitter Menggunakan Algoritma LSTM La Laila Marifatul Azizah; Dimas Bagas Ajipratama; Nisrina Akbar Rizky Putri; Cahya Damarjati
IPTEK-KOM : Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi Vol 24 No 2 (2022): Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi)
Publisher : BPSDMP KOMNFO Yogyakarta, Kementerian Komunikasi dan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17933/iptekkom.24.2.2022.161-172

Abstract

Indonesia was shocked by the emergence of the first case of Covid-19 in March 2020. The Covid-19 virus can be fought with herd immunity, namely by vaccinating. On December 16, 2020, President Joko Widodo announced that he would provide the Covid-19 vaccine to the people of Indonesia. The information received various responses from the public. One of them through twitter. There are opinions that support and there are also those who reject vaccination. To find out the opinion of public sentiment regarding vaccination, a sentiment analysis process is carried out using an algorithm that aims to assist the sentiment analysis process with quite a lot of data. In this study, the sentiment analysis process uses one of the deep learning methods, namely LSTM (Long Short-Term Memory). The results of this study tend to support the vaccination program by producing 79% positive tweets, 13% neutral tweets and 8% negative tweets and getting a model accuracy of 71% using parameters of 15 epochs, 64 batch sizes and a comparison of training data and test data of 9:1 ​​(3600:400).
Co-Authors Cahya Damarjati Dimas Bagas Ajipratama Fajar, Febriyana Febriyana Fajar Nisrina Akbar Rizky Putri Sitti Fadillah Umayah Umayah, Sitti Fadillah