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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Economics, Business, & Accountancy Ventura Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sisfotek Global Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science
Alita, Debby
Universitas Teknokrat Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Journal : Jurnal Informatika: Jurnal Pengembangan IT

 1 
Analisis Opini Publik Tentang Boikot Produk Pro-Israel di Twitter Berbahasa Indonesia Menggunakan Metode SVM Chairunnisa fadia alifa; Debby Alita
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6559

Abstract

The century-long Israeli-Palestinian conflict has created diverse opinions in Indonesian society. The escalation of tensions in Gaza triggered calls for boycotts of products suspected of supporting Israel. In this study, a Support Vector Machine (SVM) method is used to analyze sentiment on Twitter related to pro-Israel boycotts. By understanding public opinion, this study evaluates the performance of SVM with linear kernel and RBF. Data collection was done through crawling Twitter with the keyword "Pro-Israel boycott", resulting in 2600 data. Data preprocessing involved case folding, cleaning, stopwords, stemming, and TF-IDF weighting. Manual labeling was done for 1560 support data and 1040 non-support data. Implementation of the SVM model resulted in 92.5% accuracy for the linear kernel and 91.92% for the RBF kernel. Word cloud analysis provided visualization of key words and sentiments related to the boycott. This research shows the dominance of positive sentiment with 1560 positive tweets and 1040 negative tweets. For development, it is recommended to add sentiment analysis methods, use a wider dataset, and consider supporting variables to improve accuracy and understanding of public sentiment on the issue.
Implementasi Metode SVM Pada Sentimen Analisis Terhadap Pemilihan Presiden (Pilpres) 2024 Di Twitter jenny anggraini; Debby Alita
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6560

Abstract

The focus of the research is the use of Twitter as a platform to express the political opinions of the Indonesian people regarding the 2024 Presidential Election. By utilizing sentiment analysis using the Support Vector Machine (SVM) method, this research aims to evaluate the accuracy of SVM in classifying tweets and compare the performance of four types of SVM kernels. Visualizations of positive and negative sentiments are also generated to provide a clearer picture. The stages of the research involve Twitter data collection, and pre-processing with steps such as data cleansing, case folding, tokenizing, stemming, and filtering. Labeling is done to identify sentiment, then feature extraction using TF-IDF. SVM implementation with linear, polynomial, RBF, and sigmoid kernels is performed, followed by model evaluation using precision, recall, F-measure, and accuracy metrics. The study used SVM to analyze the sentiment of the 2024 presidential election on Twitter data. As a result, out of 3938 tweets, 1575 were positive and 2363 were negative. The SVM model achieved 95.05% accuracy, superior in predicting negative sentiment. Comparison of SVM kernels shows the highest accuracy in the linear kernel 95.43%. Sentiment analysis on tweets shows a majority of positive support for Ganjar 54.9%, while Anies and Prabowo have support levels of 15.8% and 29.3% respectively.
Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024 septiana, ika; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6640

Abstract

The implementation of the 2024 elections is regulated in the General Election Commission Regulation (PKPU) Number 3 of 2022, which also stipulates the election schedule and stages.After the simultaneous general elections that took place on February 14, 2024, problems arose among the public regarding the Quick Count results, especially for the Presidential election.The Quick Count results themselves generated various opinions, both positive and negative.In the post-election Twitter page, there are many conversations in cyberspace related to the Quick Count results on Twitter. Thus, sentiment analysis can be used to classify tweets and comments about the 2024 election quick count results into three categories, namely positive, negative, and neutral.Thus, this analysis is expected to provide some significant benefits related to the quick count results in the 2024 election. Random Forest and Support Vector Machine are two machine learning techniques used to measure how accurate the resulting sentiment analysis is. From the results of the research that has been carried out, there are 2000 data collected during February 2024. After preprocessing and labeling, there are 1,116 positive class data, 730 negative class data and 154 neutral class data.From the results of the comparison of the algorithms evaluated, the accuracy value of the two algorithms was obtained.The Random Forest algorithm produces an accuracy of 78%, while the SVM algorithm produces an accuracy of 80%.This shows that in sentiment analysis on the 2024 election quick count, the SVM method obtained a greater accuracy value compared to Random Forest.
Analisis Sentimen Inses di Social Media menggunakan Algoritma Naïve Bayes Salsabilla, Tasya; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6611

Abstract

Sexual violence, especially against women and children, is a serious problem in Indonesia. Cases are increasing every year, including incest, which involves sexual relations between close family members. Girls, who are often considered weak and vulnerable, are the main victims. The latest data from the National Commission on Violence Against Women records a decrease in incest cases from 1,210 in 2017 to 215 in 2020. However, attention is still needed, especially because biological fathers are the largest perpetrators. This research uses the Naïve Bayes algorithm for sentiment analysis. This algorithm is an effective classification method based on Bayes' theorem with simple assumptions but is quite effective. Assuming that each feature in the data is independent, Naïve Bayes can work well in text analysis. The research results showed an accuracy rate of 94%. Continued attention to sexual violence, especially incest, is needed to protect vulnerable girls. Protection efforts must continue to be improved, including the application of sentiment analysis methods such as Naïve Bayes for monitoring and early detection. Public awareness and cross-sector cooperation are also key in overcoming this phenomenon.
Analisis Sentimen Twitter Terhadap Pemindahan Ibu Kota Negara Menggunakan Support Vector Machine Saputri, Gita Aprinda; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6612

Abstract

The Indonesian government announced plans to move the capital from Jakarta to East Kalimantan due to the high population burden and economic contribution on the island of Java. Statistical data shows that the island of Java has a large population, reaching 151.59 million people or around 56.10% of the total population of Indonesia, and will provide a large participation in national GDP in 2021. Moving the capital city is seen as a step. . for the sake of equal distribution of population and economy throughout Indonesia. Rapid urbanization on the island of Java, especially in the buffer areas of the capital city of Jakarta, is one of the main reasons behind this decision. This research uses data from the social media platform Twitter to analyze sentiment using 2 categories, namely positive and negative sentiment regarding the relocation of the National Capital, analyzed using the Support Vector Machine method. In this study, the SVM kernel type was used, namely a linear kernel with an accuracy of 92.70%, then improved with Stratified k-Fold Cross Validation, getting 100% accuracy in iterations 1 and 5. The classification results using the Support Vector Machine method are statedthat the linear kernel has better accuracy. This sentiment analysis provides insight into the public's views on the proposed measure. This research can be used as material for consideration of future government policy regarding relocating the capital city.
Co-Authors ., Rusliyawati Adam Indra Sakti Ade Dwi Putra Ade Setiawan Andi Kurniawan Andi Nurkholis Aris Munandar As Shidiq, M Febrian Asmawati Asmawati Asmawati Asmawati Bambang Satrio Gandhi Chairunnisa fadia alifa Cholid Fadilah Hasri Danuarta, Ervin Dedi Darwis Dinda Titian Lestari Dyah Aminatun Dyah Ayu Megawaty Dyah Ayu Megawaty Emi Suwarni Erliyan Redy Susanto Fadila Shely Amalia Feby Amanda Fenty ariany Handika, H Hasrina, Cut Delsi Hayatunnufus Hayatunnufus Heni Sulistiani Ika Septiana Imam Ahmad Imam Ahmad Indah Sari Irwan Tubagus Isnain, Auliya Rahman jenny anggraini Jepi Supriyanto Krisna, Anan Lathifah L Lathifah Lathifah Lisa Ariyanti Mandasari, Berlinda Miswanto Miswanto Muhammad Najib Dwi Satria Muhammad Pajar Kharisma Putra Muhammad Saefudin Muhtad Fadly Munti Sarida Nur Rokhman Nurhasan Nugroho Nurman Satya Marga Nurman Satya Marga Nuroji Nuroji Permata Permata Prita Dellia Puspaningtyas, Nicky Dwi Pustika, Reza Putra, Ade Dwi Putri Sukma Dewi Rahmawati, Ira Tri Rani, Ni Made Suka RB Ali Shodiqin Rillya Arundaa Riska Amalia Praptiwi S. Samsugi Salsabilla, Tasya SANTIKA, YULI Saputri, Gita Aprinda Sari, Raras Kartika Sena, Fikih Yuhada Setiawansyah Setiawansyah Sigit Priyanta Styawati Styawati suaidah suaidah Suhadi, Miki Surahman, Ade Suwarni, Emi Syahwaluddin, Risal Taufiq Martiwansyah Tenda, Edwin Wang, Junhai YOHANA TRI UTAMI, YOHANA TRI Yuri Rahmanto Yusra Fernando