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All Journal International Journal of Electrical and Computer Engineering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Journal of Data Science and Its Applications eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat
Widi Astuti
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Analysis of Community Sentiment on Twitter towards COVID-19 Vaccine Booster Using Ensemble Stacking Methods Syifa Khairunnisa Salsabila; Jondri Jondri; Widi Astuti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1902

Abstract

The outbreak of the COVID-19 virus in Indonesia has not ended until the government has made various efforts to reduce this outbreak, such as the Large-Scale Social Restriction (PSBB) policy and the obligation of the entire community to vaccinate against COVID-19. The government has made a new policy for the community: booster vaccination for people who have already been vaccinated against COVID-19 1 and vaccinated against COVID-19 2. With this new policy, many people have given opinions on social media. One of them is Twitter social media. Positive and negative opinions given by Twitter users can be used as a source of information data. Because of these problems, researchers conducted a sentiment analysis of the booster vaccine using the Ensemble Stacking method. The dataset that has collected as many as 6,500 data from Twitter will be grouped into positive and negative class sentiments. The best results from this study using ensemble stacking and oversampling have an accuracy value of 80%.
Sentiment Analysis on Twitter Against IndiHome Providers Using Chi-Square and Ensemble Bagging Methods Anisa Nur Aini; Jondri Jondri; Widi Astuti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1967

Abstract

During the Covid-19 pandemic, internet usage has increased rapidly. Now the internet is used as a means in the online teaching and learning process and work from home. One of the internet service providers is IndiHome. IndiHome is an internet service provider company that has a huge number of users. A large number of IndiHome users causes frequent problems, and this is one of the factors that IndiHome users provide various kinds of opinions or responses. Sentiment analysis is used to see the opinion or opinion given by someone on a particular object or problem. This study conducted a sentiment analysis using the Chi-square and the Ensemble Bagging method with three base classifier methods, namely K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Naive Bayes (NB). Prediction results on labels obtained from each base classifier are combined using a hard majority vote. Tweet data collection was carried out in March 2022, and 6,962 tweets were collected. This study conducted two test scenarios. Scenario 1 is a scenario without oversampling with test results showing that Ensemble Bagging has the highest accuracy value of 83.32%, and in scenario 1 with hyperparameter tuning, Ensemble Bagging has the highest accuracy value of 83.93%. Scenario 2 is a scenario with oversampling, showing that Ensemble Bagging has the highest accuracy value of 84.51%, and scenario 2 with hyperparameter tuning also shows Ensemble Bagging has the highest accuracy value of 84.56%.
Sentiment Analysis Against IndiHome and First Media Internet Providers Using Ensemble Stacking Method Arya Rafif Muhammad Fikri; Jondri Jondri; Widi Astuti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1969

Abstract

Customer satisfaction is one of the factors that can be used to measure the success of service in a company. In the era of the 2000s until now, internet service providers have continued to grow throughout the world, including in Indonesia. IndiHome and First Media are companies that provide internet services that make it easy for the public to communicate and obtain information. With many uses of IndiHome and First Media internet services, there are often several obstacles that cause various responses from users. Users usually channel these responses to IndiHome or First Media customer care on Twitter. The dataset for this study was obtained from Twitter using the Twitter API and the Tweepy library. The dataset that has been collected is 6.962 tweets for the IndiHome dataset and 8,089 tweets for the First Media dataset. This study conducts sentiment analysis using the Ensemble Stacking with three base classifiers and a meta classifier. The base classifier used is Naïve Bayes, K-Nearest Neighbor, and Decision Tree, while the meta classifier used is Logistic Regression. This study uses the term frequency-inverse document frequency (TF-IDF) to determine the frequency value of a word in a document. This study uses two test scenarios: testing without oversampling and testing with oversampling on the dataset. The results show that Ensemble Stacking with term frequency-inverse document frequency feature extraction produces the highest accuracy, with an accuracy value of 88.27% on the IndiHome dataset and 92.56% on the First Media dataset by oversampling on both datasets.
Analysis of Community Sentiment on Twitter towards COVID-19 Vaccine Booster Using Ensemble Bagging Methods Artamira Rizqy Amartya Maden; Jondri Jondri; Widi Astuti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1973

Abstract

COVID-19 is an infectious disease caused by a newly discovered type of coronavirus. Based on recommendations from the Technical Advisory Group on Virus Evolution, WHO established a new variant called Omicron. Due to the rapid spread of COVID-19, a booster vaccine was created to deal with the new virus variant. However, the strategy of giving vaccines that never ends is considered controversial by the community, and this is shown by the number of people who express their opinions, both positive and negative opinions on social media, one of which is Twitter. This research was conducted by collecting data with the help of the Twitter API. The classification method uses ensemble bagging with three basic lessons, namely Naive Bayes, K-Nearest Neighbor, and Decision Tree. Meanwhile, the feature extraction used in this research is TF-IDF (Term Frequency-Inverse Document Frequency). The performance of the ensemble bagging method by applying Hyperparameter Tuning is a precision of 0.72, recall of 0.71, F1-Score of 0.72, and accuracy of 0.72.
Co-Authors Adiwijaya Afrian Hanafi Anisa Herdiani Anisa Nur Aini Aprillia Rizki Adiati Artamira Rizqy Amartya Maden Arya Rafif Muhammad Fikri Berlian Kaida Palma Bety Elysabeth Pasaribu Bintang Peryoga Dana Sulistiyo Kusumo Danang Triantoro Murdiansyah Daniel Tanta Christopher Sirait Donni Richasdy Fahriza Ichsani Rafif Ghozy Ghulamul Afif Hendro Prasetyo I Kadek Ananda Prana Widya I.G.N.P.Vasu Geramona Ichwanul Muslim Karo Karo Ilham Kurnia Syuriadi Ilham Yunirakhman Jondri Jondri Kemas Muslim Lhaksmana M. Mahfi Nurandi Karsana Mahendra Dwifebri Purbolaksono Mayya Tania Wewengkang Moch Arif Bijaksana Mochamad Agusta Naofal Hakim Mohamad Irwan Afandi Mubaroq Iqbal Muhammad Hanafiah Muhammad Naufal Mukhbit Amrullah Munirsyah Munirsyah Ragil Haditira Ramanti Dharayani Rizal Adi Putranto Rizky Dhian Syarif Salsabila Putri Adityani Sarah Suryaningsih Sean Akbar Ryanto Shidqi Aqil Naufal Suryaningsih, Sarah Syifa Khairunnisa Salsabila Talitha Kayla Amory Tiara Fitri Berlian Valentino Rossi Fierdaus Vina Mutiara Purnama Yahya Setiawan