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Wahyu Nurkholis Hadi Syahputra
Chiang Mai University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Engineering Medicine & Pharmacology

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Assessing Technique For Mapping Public Response To DKI Jakarta Governor Policy In Handling COVID-19 Pandemic Using SVM BASED Sentiment Analysis Bagus Setya Rintyarna; Wahyu Nurkholis Hadi Syahputra; Triawan Adi Cahyanto; Riska Nur Maulida
International Applied Science Vol. 1 No. 1 (2022): Proceedings of International Conference on Rural Development (ICRD) 2020
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/ias.v1i1.50

Abstract

Since the coronavirus outbreak or known as COVID-19 spread throughout the world, especially in Indonesia. The Governor of DKI Jakarta issued several policies to deal with the spread of COVID-19. However, this policy has become a conversation on social media such as Youtube. Through audience interaction in the comments column, giving lots of positive and negative sentiment comments, the audience response is classified using the sentiment analysis technique of comments to find out which sentiments are positive, negative, and neutral for each comment. In this study, the data were taken from news video comments. The method used is the Support Vector Machine and the selection feature uses the Term Frequency-Inverse Document Frequency (TF-IDF). The data used amounted to 945 Indonesian language comments. Accurate results obtained by using the addition of a stoplist at the preprocessing stage a.
Identification of Long Bean Seed Varieties Using Digital Image Processing Coupled With Neural Network Analysis Wahyu Nurkholis Hadi Syahputra; Dandi Citra Nugraha; Abdul Jalil; Chatchawan Chaichana
International Applied Science Vol. 1 No. 2 (2022)
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/ias.v1i2.164

Abstract

Identification of long bean seed varieties can be used to save plant variety and intellectual property rights. Using digital image processing combined with artificial neural networks (ANN) has a possibility to recognize the seed morphology. The purpose of this research is to identify the image variables that can be used to identify long bean seed varieties so that the best algorithm of artificial neural networks can be arranged and the level of accuracy in expecting the long bean varieties. The samples used in this study were long bean seeds of parade tavi, kanton tavi, branjangan, and petiwi varieties. For each variety, 400 samples were taken for training data and 200 samples for testing data, so the total sample was 2400 long bean seeds. The research stages include image acquisition, image retrieval, image variable estimation, image processing program development, data analysis, ANN training, long bean variety identification program preparation, and program validation. The results showed that ANN with 10 hidden layers is the best model to develop a long bean seed identification. The identification program of long bean seed varieties resulting from the integration of image processing with artificial neural networks has an accuracy of 99.75%.
Co-Authors Abdul Jalil Bagus Setya Rintyarna Chatchawan Chaichana Dandi Citra Nugraha Riska Nur Maulida Triawan Adi Cahyanto