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All Journal Jurnal Informatika Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Animation & Games Studies Fountain of Informatics Journal Jurnal Ilmiah KOMPUTASI Jurnal Pengabdian UntukMu NegeRI JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) PrimaryEdu - Journal of Primary Education Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi Academia Open Indonesian Journal of Cultural and Community Development Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Innovation Studies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PELS (Procedia of Engineering and Life Science) Proceedings of The ICECRS Procedia of Social Sciences and Humanities Publikasi Pengabdian Masyarakat Komputer dan Teknologi (PUNDIMASKOT) JOINCS (Journal of Informatics, Network, and Computer Science) Jurnal Sistem Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Indonesian Journal of Applied Technology Journal of Technology and System Information Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Electrical Engineering Smatika Jurnal : STIKI Informatika Jurnal
Yulian Findawati
Universitas Muhammadiyah Sidoarjo
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
SENTIMENT ANALYSIS OF POST-COVID-19 INFLATION BASED ON TWITTER USING THE K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS Ratih Puspitasari; Findawati, Yulian; Rosid, Mochamad Alfan
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.801

Abstract

The COVID-19 pandemic caused a crisis in global economic growth. The impact of injuries due to the COVID-19 pandemic has also caused price increases and an increase in the inflation rate. Inflation is a price increase caused by a certain factor so that it has an impact on the prices of nearby goods which increase the circulation of money in society to increase. Many people expressed their various opinions or criticisms of the post-COVID-19 price increase policy on social media, one of which was via Twitter. Sentiment analysis was carried out to see how public sentiment is towards the price increase policy after the COVID-19 pandemic, and these sentiments are combined into multiclasses, namely positive, negative and neutral sentiments. So that this sentiment can later be used as material for evaluation regarding the post-COVID-19 price increase policy. This study aims to see and compare the accuracy of the two classification methods, namely K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) in the sentiment classification process. The data used was 5989 tweets with the keywords ""Stuffets Go Up Post-Pandemic", "Fuel Goes Up", "Inflation 2022", "Covid19 Inflation", "Inflation Post-Pandemic" with a data collection period from August to October 2022. The data obtained then enter the text preprocessing stage before later entering the classification stage. The results obtained after carrying out the classification using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) methods show that the Support Vector Machine (SVM) method has a higher accuracy of 79%, while the K-Nearest Neighbor (K -NN) has an accuracy of 54%.
CLASSIFICATION OF VOCATIONAL HIGH SCHOOL GRADUATES' ABILITY IN INDUSTRY USING EXTREME GRADIENT BOOSTING (XGBOOST), RANDOM FOREST, AND LOGISTIC REGRESSION Agustiningsih, Afikah; Findawati, Yulian; Alnarus Kautsar, Irwan
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.945

Abstract

The education world is one of the main sources in producing Human Resources. Vocational High School (SMK) is one level of school that presents various majors that are ready to compete in the industrial world. Therefore, a school institution needs to have a system to determine the quality of education provided to students so that they can compete in the industrial world. This study designs a system that is capable of classifying SMK student graduates as an evaluation for the school institution. The goal is to enable the school to devise strategies for producing better student quality in the following year. There are four classes in this study, namely those who work, those who are not working yet, those who are in college, and those who are entrepreneurs. There are several stages in building the classification system, including pre-processing, processing, and evaluation. This research uses three machine learning algorithms, namely XGBoost, Random Forest, and Logistic Regression. The results of the three methods obtained a training score of 91.70%, a test score of 66.88%, and an accuracy score of 67% generated by the XGBoost algorithm. The Random Forest algorithm produced a training score of 97.36%, a test score of 68.71%, and an accuracy score of 67%. Meanwhile, Logistic Regression produced a training score of 51.14%, a test score of 50.43%, and an accuracy score of 50%.
TOPIC MODELING IN COVID-19 VACCINATION REFUSAL CASES USING LATENT DIRICHLET ALLOCATION AND LATENT SEMANTIC ANALYSIS Malihatin S, Ulfah; Findawati, Yulian; Indahyanti, Uce
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.951

Abstract

COVID -19 vaccination is a program provided by the Indonesian government to minimize the spread of the virus. The COVID-19 vaccination program in Indonesia goes hand in hand with issues that are circulating, causing controversy and rejection of vaccination on social media, especially Twitter. There are many factors that influence vaccine rejection on Twitter, to summarize frequently discussed topics and find out hidden topics, this study uses the Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) methods from 1797 Twitter scrapping data. Both models require a set of words that have been converted into a matrix, so before conducting LDA topic modeling, the dataset will undergo a bag of word (BOW) calculation. Meanwhile, in LSA topic modeling, the existing dataset will undergo word weighting of frequently occurring words using Term Frequency - Inverse Document Frequency (TF-IDF). This study was conducted to find and summarize hidden information in the form of frequently discussed topics, thus understanding public opinions related to the COVID -19 vaccination refusal case. LDA and LSA methods will display topics based on the probability and mathematical calculations of word occurrences in each topic in the document. The topics that appear will be further analyzed through coherence score by applying a limit of 20 topics to display the best value. Further modeling experiments are carried out to display topics through LDA and LSA models, this study takes 6 topics with the highest coherence values including the right of individuals to choose whether to be vaccinated or not (0.484607), the Ribka Tjiptaning controversy (0.473368), rejection of the COVID-19 vaccine by groups represented by public figures (0.463631), punishment for non-compliance in the form of fines (0.324924), and halal certification (0.312521).
Co-Authors A.A. Ketut Agung Cahyawan W Ade Eviyanti Ade Eviyanti Aditya Kurniawan Adni Navastara, Dini Agustiningsih, Afikah Ahmad Rizqi Efendi Aji, Bagas Prakoso Aldio Nur Samsi Alim, Kholqi Aljunza, Marshal Sheva Alnarus Kautsar, Irwan ardhi pradana Ari Setiawan Arif Senja Fitrani Arif Senja Fitroni Astutik, Ika Ratna Indra Budi Raharjo, Agus Cindy Cahyaning Astuti Diana Purwitasari Dwi Cahyono, Qitfirul Eni Fariyatul Fahyuni Erfina, Idha Maharani Ericka Sukma Putri Wilujeng Ericka Sukma Putri Wilujeng, Ericka Sukma Putri Firdausi Usqi Salsabilah Fitroni, Arif Senja Ganang Ganindra Aulia Akbar Hanafi, Rizal Hidayah, Firmansyah Nur Hindarto Ida Rindaningsih Ida Rindaningsih, Ida Idha Maharani Erfina Ika Ratna Indra Astutik Imron Hidayat Intan Mauliana, Metatia Irwan A. Kautsar Irwan Alnarus Kautsar Irwan Alnarus Kautsar Islam Al-Hazmi, Auliansyah Jihaan Anisa Mukti Kautsar, Irwan Alnanrus Khubro, Jamaluddin Jumadil Maghfiroh, Alfiah Malihatin S, Ulfah Malna, Intan Afriza Mardhatillah, Radhita Fitra Maulana, Mahardika Rafi maulana, Metatia intan Mauliana, Metatia Intan Mochamad Alfan Rosid Moh. Attar Jibran Mohammad Fadli Zaka Mohammad Suryawinata Muhamad Alfin Firdiansyah Muhammad Alfin Firdiansyah Muhammad Choir Ridho Azizi Muhammad Fedy Rifki Muhammad Hilal Hamdi Muhammad Sayyi Syeh Putradifa Muhammad Syafri Romadhon Nahariqi, Muhammad Iqbal Ni'matu Zahroh Nuril Lutvi Azizah Nurwijayanti Pangestu, Krisna Aji Pratama, Chandra Hary Puspitasari, Anastasya Nadia Putri, Dewi Melisa Ramadhan, Aldo Reghan Ratih Puspitasari Ratih Puspitasari Rizaldy, Moch Dimas Fahmi Rizky Fajar Ryandi Rohman Dijaya Rosid, Muhammad Alfan Saputra, Abhirama Senja Fitrani, Arif Septian Dwi Pratama Septian Dwi Pratama Setiawan Bagus Rustianto Setyaningsih, Yuni Siska Dyah Pertiwi Siti Nur Haliza Steven Owen Purnawan Suhendro Busono Sumarno , Sumarno Sumarno . Suprianto Supriyanto - Suryani, Siti Dwi Sutarman Taurusta, Cindy Uce Indahyanti Wiwik Dwi Hastuti Wiwik Sumarmi Yasinta, Aulia Nur Yatestha, Anak Agung Yonathan, Vincent Yunianita Rahmawati Yunianita Rahmawati, Yunianita Zaka, Mohammad Fadli