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All Journal Teknika Journal of Economics, Business, & Accountancy Ventura SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Informatika dan Teknik Elektro Terapan JTT (Jurnal Teknologi Terpadu) Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Ideguru: Jurnal Karya Ilmiah Guru Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) SINTA Journal (Science, Technology, and Agricultural) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer J-Intech (Journal of Information and Technology) Jurnal Indonesia Raya Knowbase : International Journal of Knowledge in Database Jurnal Dehasen Mengabdi SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Teewan Journal Solutions Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier Dermawan, Jozu; Yusra, Yusra; Fikry, Muhammad; Agustian, Surya; Oktavia, Lola
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7475

Abstract

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Klasifikasi Sentimen Masyarakat Terhadap Prabowo Subianto Bakal Calon Presiden 2024 di Twitter Menggunakan Naïve Bayes Classifier Dwitama, Raja Zaidaan Putera; Yusra, Yusra; Fikry, Muhammad; Yanto, Febi; Budianita, Elvia
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7071

Abstract

The Indonesian President who has served for 2 consecutive terms cannot nominate again to become President. The public's attitude towards the three presidential candidates, Prabowo Subianto, Anies Baswedan, and Ganjar Pranowo, who are predicted to run for the 2024 presidential election, is also a matter for netizens' opinions from which conclusions can be drawn. Testing will be carried out in this research using information collected from tweets posted by Twitter users. Naïve Bayes Classifier is a technique that will be applied for sentiment assessment. In the upcoming presidential election, this research will be a source when determining the presidential choice. 2100 tweets with the search keywords "Presidential Candidate" and "Prabowo Subianto" are data collected by dividing 1050 positive data and 1050 negative data. Then implementation was carried out using Google Colab starting from data processing (cleaning, case folding, tokenizing, normalization, negation handling, stopword removal, stemming) followed by classification using the Naïve Bayes Classifier. According to test findings using the Confusion Matrix with three experimental test data 90:10, 80:20 and 70:30. Obtained the highest accuracy results of 89%, with a precision value of 89.7%, 88.6% recall and 88.9% f1-score in the 90:10 trial test.
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier Dermawan, Jozu; Yusra, Yusra; Fikry, Muhammad; Agustian, Surya; Oktavia, Lola
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7475

Abstract

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Co-Authors -, Yusra Adi Adi Ahadi, Ridho Alwis Nazir Ananda, Nuari Ananda, Silvia Andini, Nanda Angela, Angela Anggraeni . Anggraeni, Ni Ketut Pertiwi Anna Marina Annisa Annisa Asrianda Asrianda Ayu Fransiska Baehaqi Bahari, Bayu Dwi Prasetya Chrisnata Manihuruk Damayanti, Elok Dermawan, Jozu Detha Yurisna Dimas Pratama, Dimas Dinata, Ferdian Arya Diqti, Fadillah Fauziah Dwitama, Raja Zaidaan Putera Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eko Sumartono, Eko Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvina Afriani Fadhilah Syafria Fakhrezi, Muhammad Dzaki Faresya, Natasya Febi Yanto Febian Pratama, Mohammad Fitri Insani Fitri Insani Griz Ella, Cindi Hafizh Al Kautsar Aidilof Harahap, Nazaruddin Safaat Hasugian, Leonardo Hidayat, Rizki Hutagalung, Yorio Arwandi Wisdom Ibnu Surya Ida Wahyuni Iis Afrianty Imam Rosadi Inggih Permana Khaidar, Al kurnia, fitra Lestari Handayani Lola Oktavia Lola Oktavia Lutfi, Raihansyah Luthvy Ilhamdi Mardiansyah, M Rizki Maulana, OK Muhammad Majid Mei Lestari, Mei Muhammad Abdillah Muhammad Affandes Muhammad Dhuha, Teuku Nabil Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Ravil Munadila, Aura Naharuddin Naharuddin Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Ndruru, Arlan Joliansa Nurcholis Sunuyeko, Nurcholis Nurdin Nurdin Nurhapiza, Nurhapiza nuryana nuryana, nuryana Oktavia, Lola Pebri Setiani, Puspita Pizaini Pizaini Prananda, Alga Putra, Wahyu Eka Putri Mardatillah Rahadian, Septa Rahma Yunita, Rahma Rahmat Rizki Hidayat Rahmatillah, Siska Yuna Raihansyah, Khananda Ramadanu Putra Reski Mai Candra Rinaldi Syarfianto Ritonga, Sinta Wahyuni Sagala, Ruflica Saputra, Ikhsan Dwi Sayed Omas Tutus Arifta Sayed Sentot Imam Wahjono Siti Ramadhani Sofiah Surya Agustian Suwanto Sanjaya Tarigan, Anggun Kinanti Taufik Hidayat Tiara Dwi Arista Wirdiani, Putri Syakira Yani, Muhamamd Yani, Susmi Syahfrida Yaskur Bearly Fernandes Yenggi Putra Dinata Yolanda, Khovifah Yossie Yumiati Yuda Zafitra Fadhlan Yulinazira, Ulfa Yusra Yusra Yusra . YUSRA YUSRA Yusra, Yusra Yusriyana, Yusriyana Zukhruf, Muhammad Firmansyah