Article Per Year (5 Year)
p-Index From 2021 - 2026
3.611
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Community Empowerment
Yuita Arum Sari
Fakultas Ilmu Komputer , Universitas Brawijaya
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

Published : 105 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 105 Documents
Search

 7   8   9   10   11 
Optimasi Extreme Learning Machine dengan Particle Swarm Optimization untuk Klasifikasi Penyakit Jantung Koroner Rasif Nidaan Khofia Ahmadah; Bayu Rahayudi; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Heart disease is the leading cause of death globally. Several factors that can trigger heart disease include smoking, blood pressure, diabetes, lifestyle, diet, and stress levels. The minimal number of health workers in Indonesia and the different abilities of each doctor in diagnosing patients with heart disease, so that a system is needed to automatically diagnose the disease which functions to assist doctors and overcome delays in inpatient treatment. This system is a classification system using the Particle Swarm Optimization method and the Extreme Learning Machine for the diagnosis of heart disease, where the Particle Swarm Optimization method is used to optimize the parameters of the Extreme Learning Machine. In the tests carried out, the system succeeded in providing an accuracy value of 86%. This also shows that the use of PSO-ELM can increase the accuracy value than using the ELM method only in diagnosing heart disease.
Deteksi Emosi pada Tweet Berbahasa Indonesia tentang Pembelajaran Jarak Jauh Menggunakan K-Nearest Neighbor dengan Pembobotan Kata Term Frequency-Inverse Gravity Moment Fira Sukmanisa; Yuita Arum Sari; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In December 2019 in the city of Wuhan, China, a case known as coronavirus disease 2019 (Covid-19) emerged and spread rapidly throughout the world. The Indonesian government implements a distance learning policy (PJJ) to minimize the spread of Covid-19. Opinions about PJJ are conveyed by the public via tweets. Emotion detection is the process of classifying tweets into emotion classes. Term weighting is a basic problem in text classification because it can affect accuracy. TF-IDF is one of the most frequently used term weightings, but TF-IDF is not the most effective because it ignores class labels. Therefore, emotion detection in tweets is carried out in order to find out emotions about PJJ. In this study, emotion detection will go through several processes, namely preprocessing, weighting of the Term Frequency-Inverse Gravity Moment (TF-IGM), cosine similarity, classification using the K-Nearest Neighbor (KNN) method, and evaluation using confusion matrix. Based on the test results using an imbalanced dataset, the optimal TF-IGM weighting coefficient is 9 which produces the highest accuracy of 0.55 at k = 25. The use of the TF-IGM weighting coefficient provides an accuracy that is less stable when compared to the TF-IGM without the weighting coefficient. The weighted words TF-IGM and TF-IDF have the same highest accuracy value, and the distance between evaluation results is small for each k tested.
Segmentasi Citra Makanan menggunakan Clustering Improved K-Means untuk Estimasi Sisa Makanan Alip Setiawan; Yuita Arum Sari; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Every living thing needs food to live, grow and reproduce. Food is a basic need that is needed by humans as a source of energy to carry out activities in daily life. By eating nutritIoUs food can bring a person into a healthy diet. An unhealthy diet can cause a person to create diseases such as malnutrition or even death. It is important to regulate the amount of food consumption to maintain and maintain a healthy body, so we need a system that can estimate the food consumed. To estimate the weight of the food that has been consumed by someone, an image of the food is needed before and before consumption. In this study the data used is secondary data in the form of food images in the tray box which opens 31 images consisting of 124 compartment images. The improvement K-Means method was chosen for segmenting food images on the tray box. With the application of this method, it is expected to provide results with a small error rate and have good accuracy in the estimation of food waste in the tray box. the results based on the evaluation of the results of the highest average accuracy of segmentation results of 82% with the RMSE estimation of the smallest food waste weight achievement of 2.19. The test results show that the clustering method with the K-Means improvement algorithm can be used to estimate the weight of food images.
Analisis Sentimen Twitter menggunakan Metode Naive Bayes dengan Relevance Frequency Feature Selection (Studi Kasus: Opini Masyarakat mengenai Kebijakan New Normal) Kresentia Verena Septiana Toy; Yuita Arum Sari; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

COVID-19 first appeared in wuhan, China and spread around the world. The virus spread very rapidly, including in Indonesia. The Indonesian government seeks to implement policies to suppress the COVID-19 case increase. Implemented policies have a new impact on communities, such as job downsizing, layoffs of work relationships and other effects on the country's economy. Consequently, the government adopted a new policy called new normal. New normal has become a topic of debate among the public on twitter's social media. Public opinion can be classified into positive, negative, and neutral opinions and require analytic sentiments. The sentiment analysis process is based on pre-processing for opinion processing, Relevance Frequency Feature Selection to reduce the number of features, and the classification using Naive Bayes methods. The dataset is 300 public opinion data, with the distribution of data using k-fold validation in k=5. The results of 5 tests using Naive Bayes classification, obtained an average accuracy of 62,6%, while the results of classification accuracy tests with the addition of Relevance Frequency Feature Selection obtained an average accuracy of 65,3%.
Perbandingan Pembobotan Term Frequency-Inverse Document Frequency dan Term Frequency-Relevance Frequency terhadap Fitur N-Gram pada Analisis Sentimen Randy Ramadhan; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sentiment analysis is a method used to extract sentiments in sentences based on their content. Sentiment analysis is a method in text mining that uses a text preprocessing process after which there is a process, namely word weighting. Term Frequency-Inverse Document Frequency (TF-IDF) is the most popular word-weighting method from the unsupervised term weighting category reported which is not suitable for grouping texts. Term Frequency-Relevance Frequency (TF-RF) is a method of combining TF and RF with the aim of getting better performance, this method focuses on all documents that contain terms or do not contain terms. Twitter is a place for people to express their thoughts about the pandemic they are experiencing. Reviews about employees being sent home on Twitter need to be classified into positive, negative, and neutral reviews, which are useful for companies and government consideration to make decisions in PSBB policies. There are several stages of this research, namely preprocessing for document processing, and using unigram and bigram features as well as word weighting using the TF-IDF and TF-RF methods in classification using the K-Nearest Neighbor classification method. The data used were 246 training data and 90 test data. The best results from the evaluation comparisons obtained are using TF.RF word weighting with the unigram feature in the KNN classification with a value of K = 3, namely accuracy of 0.677, precision of 0.526, recall of 0.654, and f-measure of 0.583. Bigram value does not have a big effect in this study because the best f-measure value is obtained Bigram with a value of 0.591, and the best unigram value is with a value of 0.583.
Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM) Muhammad Zaini Rahman; Yuita Arum Sari; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Government policies related to quarantine have generated various responses from the community, some people feel that The quarantine must be done so that the spread of the COVID-19 disease can be suppressed, but others also feel that this is detrimental to the community because their activities are being limited, this response can be found in their Twitter post. By analyzing the sentiments on people's Twitter posts, we can conclude whether a policy tends to get more positive or negative responses to the affected community. To carry out this analysis, deep learning method is used, namely Long-Short Term Memoryf (LSTM) with the addition of Word Embedding to 1364 independently crawled Indonesian people's Twitter data. Performance using the LSTM method produces 81% accuracy, 80% precision, 80% recall, and 81% f-measure. This LSTM method produces better performance than the other 2 methods, namely Naive Bayes and Recurrent Neural Network (RNN) with a difference of + 8%, with details of 74% accuracy, 72% precision, 74% recall, and 69% f-measure for the Naive Bayes method and 71% accuracy, 71% precision, 72% recall, and 72% f-measure for the RNN method.
Analisis Sentimen terhadap Opini Masyarakat mengenai Kebijakan PSBB menggunakan Metode Naive Bayes dengan Seleksi Fitur Improved Gini Index Kenza Dwi Anggita; Yuita Arum Sari; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesian governments have launched PSBB policy to emphasize the growth rate of COVID-19 cases in Indonesia. This caused a variety of responses from the public, one of them on social media Twitter. public opinion contained on social media Twitter, can help the government to know how the public opinion about psbb policy in Indonesia. This study tried to analyze the public's response about PSBB policy on social media Twitter, through sentiment analysts and classified into three classes, namely positive, negative, and neutral. By using Naive Bayes classification method and Improved Gini Index (IGI) feature selection to reduce the number of features in the classification process. The process on sentiment analysis consists of preprocessing, feature selection using the Improved Gini Index (IGI) method, and classification with Naive Bayes. The results of Naive Bayes classification test without feature selection obtained accuracy of 64%, while the results of classification accuracy test with feature selection using six different threshold values obtained the highest accuracy results at the threshold value of 30%, where there are 70% of the total terms removed and obtained accuracy of 68%.
Analisis Sentimen menggunakan Metode Naive Bayes Classifier terhadap Review Produk Perawatan Kulit Wajah menggunakan Seleksi Fitur N-gram dan Document Frequency Thresholding Sinta Kusuma Wardani; Yuita Arum Sari; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The influence of this growing culture and lifestyle makes people pay more attention to their appearance. One of the factors that affect appearance is the condition of a person's facial skin. Each product used by consumers has different reactions from one consumer to another, thus making many consumers review the products they use. Reviews given by consumers can be used to measure the quality of a beauty product. However, the large number of reviews given makes the review grouping unable to be done manually and sentiment analysis must be done to group the reviews into several categories. One of the algorithms for classifying sentiment analysis is using the Naive Bayes Classifier method which is a simple method that has faster performance in training data, is easy to implement, and has high effectiveness. In the classification process, feature selection will be used using the N-gram algorithm and DF-Thresholding to reduce the dimensions of the features in the data. The purpose of this study is to determine the effect of DF-Thresholding algorithm on the accuracy of the Naive Bayes Classifier algorithm using the N-gram. The result showed a reduction of 16.312 features to 43 features and the highest accuracy value for bigram and unigram combination, which is 49%, precision is 0,23, recall is 0,26 and f-measure is 0,24.
Segmentasi Citra pada Kue Tradisional berbasis Clustering dengan menggunakan Algoritme DBSCAN Fatwa Reza Rizqika; Yuita Arum Sari; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Traditional cakes or generally referred to as market snacks are one of the many traditional specialties originating from Indonesia and are usually traded in traditional markets. The cakes that are traded are of various types and have a distinctive taste and are not inferior to modern food. The traditional cake is usually packaged in an attractive and unique form, the wrapper can come from leaves that have fallen or are still alive to be wrapped in plastic in order to attract consumers or buyers. Even though in today's era, there are not a few instant and practical food products, even some imported products from abroad whose packaging is more attractive. And this traditional cake is one of many cultural heritages that should be more commensurate with other Indonesian cultural assets. Therefore, as the color of Indonesia, we should maintain and preserve and further introduce to all levels of society that traditional cakes are no less delicious than modern food, especially children today who are foreign to these traditional cakes. Therefore, a system is needed to identify traditional foods, especially traditional cakes, by utilizing the sophistication of technology that exists in the current digital era. This study proposes the application of image segmentation on traditional cakes using the DBSCAN algorithm to obtain cake image segmentation results with an average Intersection over Union (IoU) accuracy of 91.3% and a maximum value of 99.8%. This shows that the proposed method is able to provide the best results.
Analisis Sentimen pada Twitter Bencana Alam di Kalimantan Selatan menggunakan Metode Naive Bayes Adi Mashabbi Maksun; Yuita Arum Sari; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The great flood disaster that hit the South Kalimantan region caused conversation and debate among the community and government, especially on Twitter which was trending, and thousands of tweets appeared on Twitter with the hashtags #PrayForKalsel, and #KalselJugaIndonesia. The tweets of the public and the government clashed for their own defense of the truth and gave rise to many positive and negative opinions. Twitter is now a place to chat and complain about various groups. For this research, it is hoped that it can help and make it easier to conduct research using public opinion on Twitter that contains positive or negative opinions. The method used in this study is using Naive Bayes, the process of this system starts from the data preprocessing process which includes case folding, tokenization, filtering, normalization, and finally stemming then word weighting using Raw TF and the classification process used is Naive Bayes. The data used comes from twitter which is taken by crawling and scrapping using the hashtags #PrayForKalsel, and #KalselJugaIndonesia with a total of 520 data. The data was taken using the Twitter API. using the confusion matrix test from the 5 experiments, the average value reaches an accuracy of 0.81, a precision of 0.81, a recall of 0.81, and an f-measure of 0.81, and the highest test value is an accuracy of 0.88, a precision of 0.89, recall 0.87, and f-measure 0.87.
Co-Authors Achmad Arwan Achmad Dinda Basofi Sudirman Ade Kurniawan Adella Ayu Paramitha Adi Mashabbi Maksun Adinugroho, Sigit Agus Wahyu Widodo Ahmad Efriza Irsad Ahmad Fauzi Ahsani Akbar Imani Yudhaputra Akhmad Muzanni Safi'i Akhmad Rohim Akmilatul Maghfiroh Alip Setiawan Amalia Safitri Hidayati Amelia Kosasih Andina Dyanti Putri Anggita Mahardika Ani Enggarwati Arrizal Amin Barbara Sonya Hutagaol Bayu Rahayudi Berlian Bidari Ratna Sari B Binti Najibah Agus Ratri Budi Darma Setiawan Cahya Chaqiqi Candra Dewi Chindy Putri Beauty Dea Valentina Delischa Novia Sabilla Destin Eva Dila Purnama Sari Devinta Setyaningtyas Atmaja Dhimas Anjar Prabowo Dian Eka Ratnawati Dika Perdana Sinaga Dyva Agna Fauzan Edy Santoso Eka Dewi Lukmana Sari Eka Novita Shandra Fachrul Rozy Saputra Rangkuti Fadhil Yusuf Rahadika Fajar Pradana Fakhruddin Farid Irfani Faraz Dhia Alkadri Farid Rahmat Hartono Fatwa Reza Rizqika Febriana Ranta Lidya Fida Dwi Febriani Fira Sukmanisa Fitra Abdurrachman Bachtiar Fitria Indriani Frisma Yessy Nabella Gabriel Mulyawan Gagas Budi Waluyo Galuh Fadillah Grandis Gregorius Ivan Sebastian Hafid Satrio Priambodo Hamim Fathul Aziz Haris Bahtiar Asidik Ian Lord Perdana Ibnu Rasyid Wijayanto Imam Cholissodin Imam Cholissodin Inas Istiqlaliyyah Indriati Indriati Irma Pujadayanti Ivan Ivan Juniman Arief Karunia Ayuningsih Kenza Dwi Anggita Kresentia Verena Septiana Toy Kukuh Wiliam Mahardika Lita Handayani Tampubolon M. Ali Fauzi M. Ali Fauzi Mala Nurhidayati Marji Marji Moch Alyur Ridho Moch. Ali Fauzi Mohammad Rizky Hidayatullah Muh. Arif Rahman Muhammad Abdan Mulia Muhammad Bima Zehansyah Muhammad Faiz Al-Hadiid Muhammad Rizky Setiawan Muhammad Sanzabi Libianto Muhammad Tanzil Furqon Muhammad Zaini Rahman Nadhif Sanggara Fathullah Noerhayati Djumaah Manis Nova Amynarto Novan Dimas Pratama Novanto Yudistira Nugroho Dwi Saksono Nur Aisyah Asriani Ofi Eka Novyanti Panji Gemilang Panji Prasuci Saputra Pretty Natalia Hutapea Putra Pandu Adika Putra Pandu Adikara Putri Harnis Raditya Rinandyaswara Randy Cahya Wihandika Randy Ramadhan Rasif Nidaan Khofia Ahmadah Ratih Kartika Dewi Ratna Tri Utami Refi Fadholi Renaza Afidianti Nandini Rendi Cahya Wihandika Restu Amara Rezza Pratama Rhevitta Widyaning Palupi Rifki Akbar Siregar Rizky Ardiawan Rizky Maulana Iqbal Rosintan Fatwa Safira Dyah Karina San Sayidul Akdam Augusta Sarah Najla Adha Sarah Yuli Evangelista Simarmata Sigit Adi Nugroho Sigit Adinugroho Sinta Kusuma Wardani Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Rahayuni Tuahta Ramadhani Utaminingrum, Fitri Vriza Wahyu Saputra Wahyuni Lubis Willy Karunia Sandy Yosua Dwi Amerta