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All Journal Cakrawala Pendidikan Harmonia: Journal of Research and Education Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Science in Information Technology Letters International journal of education and learning Bulletin of Social Informatics Theory and Application
Dwiyanto, Felix Andika
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Social Sciences

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Analisis Performa Metode Support Vector Regression (SVR) dalam Memprediksi Harga Bahan Sembako Nasional Azis, Huzain; Purnawansyah, Purnawansyah; Nirwana, Nirwana; Dwiyanto, Felix Andika
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1686.390-397

Abstract

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.
Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN Jayadianti, Herlina; Kaswidjanti, Wilis; Utomo, Agung Tri; Saifullah, Shoffan; Dwiyanto, Felix Andika; Drezewski, Rafal
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1505.348-354

Abstract

Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
Co-Authors Aji Prasetya Wibawa Atmaja, Nimas Hadi Citra, Hana Rachma Drezewski, Rafal Eddy Sutadji Hakkun Elmunsyah Hariyono Hariyono Herlina Jayadianti Hidhayat, Ahmad Rizkyka Agung Humairoh, Nanda Lailatul I Nyoman Gede Arya Astawa Irawan, Novta Danyel Lauretta, Giovanny Cyntia Muhammad Aris Ichwanto Nabila, Khurin Nafalski, Andrew Ningtyas, Yana Nirwana, Nirwana Noviani, Erina Fika Purnama, Ida Bagus Irawan Purnawansyah Purnawansyah Purnomo Purnomo Rabbimov, Ilyos Ramadhan, Sany Putra Saifullah, Shoffan Sutoko, Sutoko Tri Kuncoro Tuti Purwaningsih, Tuti Utama , Agung Bella Putra Utama, Agung Bella Putra Utomo, Agung Tri Wilis Kaswidjanti Yoto Yoto, Yoto Yudistira, Setya Yusriel Ardian Zaeni, Ilham Ari Elbaith