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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Dewa Ayu Putri Wulandari
Institut Bisnis dan Teknologi Indonesia, Bali
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perbandingan Metode K-NN Dan Metode Random Forest Untuk Analisis Sentimen pada Tweet Isu Minyak Goreng di Indonesia Christina Purnama Yanti; Ni Wayan Eva Agustini; Ni Luh Wiwik Sri Rahayu Ginantra; Dewa Ayu Putri Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5900

Abstract

Along with the development of technological advances, a lot of social media is used by humans, one of which is Twitter social media. On Twitter social media, we can find a lot of text data, opinions and public opinion, as the issue of cooking oil is currently hot in Indonesia. In this study, the K-NN and Random Forest methods were used, and the purpose of this study was to compare the two methods in sentiment analysis on the issue of cooking oil. The results of the accuracy of these two methods are not too far apart. Each of the two methods used will be divided into three research scenarios, the first is scenario 1, a collection of 500 data, scenario 2, a collection of 800 data, and scenario 3, a collection of 1,000 data, where the ratio of training data and test data is 80:20. The test results for the K-NN method in scenario 2 are superior with an accuracy presentation of 74.58%, 56.75% precision and 44.57% recall and the lowest result is the K-NN method scenario 1 with an accuracy presentation of 71. 50%, 47.83% precision and 37.45% recall. The average test results for the K-NN method are 72.86% accuracy, 52.26% precision and 41.04% recall. While the average results of the random forest method are 73.37% accuracy, 52.26% precision and 34.28% recall
Penerapan Deep Learning Dalam Pengenalan Endek Bali Menggunakan Convolutional Neural Network Theresia Hendrawati; Dewa Ayu Putri Wulandari; I Gde Swiyasa Surya Dharma; Ni Luh Wiwik Sri Rahayu Ginantra, M.Kom; Christina Purnama Yanti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6721

Abstract

Endek Bali has been recognized as one of the Intellectual Property of Traditional Cultural Expressions, with registration number EBT 12.2020.0000085 on December 22, 2020. In the present era, many people find it difficult to distinguish between endek fabric and batik fabric because their patterns are quite similar. This research aims to help identify Bali's Endek fabric based on digital images. One of the approaches used is the Convolutional Neural Network method with ResNet50, which is a deep learning method used to recognize and classify objects in digital images. Evaluation result from testing the best model with new testing model using confession matrix get result of 90,69% accuracy, 90,69% recall, 90,60% precision and 90,68% f1-score. Thus, the model developed in this research demonstrates optimal performance in classifying images of Bali's Endek.
Co-Authors Christina Purnama Yanti Hendrawati, Theresia I Gde Swiyasa Surya Dharma Ni Luh Wiwik Sri Rahayu Ginantra Ni Luh Wiwik Sri Rahayu Ginantra, M.Kom Ni Wayan Eva Agustini