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DWI CAHYONO
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Journal : The Indonesian Journal of Computer Science

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Sentiment Analysis on the FIFA U-20 World Cup in Argentina Using Support Vector Machine Warsito Sujatmiko, Achmad; Vitianingsih, Anik Vega; Kacung, Slamet; Cahyono, Dwi; Lidya Maukar, Anastasia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3973

Abstract

The decision made by FIFA regarding the selection of the soundtrack and the host country for the FIFA U-20 World Cup has sparked emotional reactions among the public and raised concerns about the event, especially on social media platform X. This is due to FIFA’s decision to choose a soundtrack not from the host country, Argentina, but from the previous host, Indonesia. FIFA should advocate for the creation of a soundtrack by the host country to reflect its distinctive characteristics or atmosphere. Concerns about the U-20 World Cup in Argentina have also been fueled by the country’s economic crisis, which is feared to affect the facilities and infrastructure for the young players representing their nations. This research focuses on filtering public responses to FIFA’s decisions regarding the soundtrack selection and the host country for the U-20 World Cup into positive, neutral, and negative categories using the Support Vector Machine (SVM) method. The research aims to provide policy recommendations regarding the host selection process and cultural representation in international sports events. Additionally, this study is expected to provide a deeper understanding of the preferences and values held by the public regarding international sports. The research steps include data collection, pre-processing, labeling, weighting, and classification using a Support Vector Machine. The data for this research were obtained through crawling on social media platform X, totaling 2400 data points. The performance evaluation of the SVM algorithm using a 50:50 ratio of training and testing data yielded an average accuracy of 85.71%, Precision of 85.98%, Recall of 85.71%, and F1-score of 85.58%.
Comparative Analysis of Naïve Bayes and K-NN Methods on Social Media Boycott Issue X Case Study: McDonald’s Azzahra, Morra Fatya Gisna Nourielda; Vitianingsih, Anik Vega; Cahyono, Dwi; Maukar, Anastasia Lidya; Badri, Fawaidul
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4956

Abstract

The boycott movement against McDonald’s, triggered by its alleged support for Israel during the conflict in Gaza, has generated significant public discourse, particularly on the social media platform X (formerly Twitter). This study investigates public sentiment regarding the boycott campaign by analyzing comments and reactions to related content. A total of 1,585 tweets were collected using techniques for web scraping and underwent a comprehensive pre-processing phase, encompassing cleaning, tokenization, filtering, and stemming. Sentiment categories, namely positive, neutral, and negative, are automatically assigned using a lexicon-based technique customized for the Indonesian language. Text data was transformed into numerical form through the Term Frequency-Inverse Document Frequency (TF-IDF) technique, followed by sentiment classification using two supervised machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Evaluation of both models was conducted using a confusion matrix and classification metrics. The results show that the dataset is highly imbalanced, with 93.5% of the tweets labelled as negative, 6.1% as neutral, and only 0.3% as positive. The K-NN model achieved better performance than Naïve Bayes (NB), with an accuracy of 93%, a precision of 31%, a recall of 33%, and an F1-score of 32%. On the other hand, the Naïve Bayes algorithm reached 39% accuracy, 33% precision, 29% recall, and an F1-score of 22%. These findings highlight the dominance of negative sentiment toward McDonald’s and demonstrate the efficacy of the K-NN algorithm in sentiment classification in unbalanced datasets. The insights from this study can inform public relations strategies and corporate reputation management in the face of socio-political controversies.
Co-Authors A.A. Ketut Agung Cahyawan W Abadi Sanosra Abdullah, Anton Achmad Choiron Achmad Choiron Ade Susianti, Febrina Afriyani, Suci Ryski Nur Ahmad, Haidar Alif Putra Gunawan, Maulana Alwiyah Alwiyah Amalia, Direstu Amarya, Grace Ezranda Anastasia Lidya Maukar Anda Lusia, Anda Andhina Ika Sunardi ANGGI FIRMANSYAH Anggit Wikanningrum Anik Vega Vitianingsih Anik Vega Vitianingsih Apriani Sijabat Ari Wulanjati, Herlina Arief Rahman Hakim Arif Budianto Arthur Simanjuntak Aspirandi, Rendy Mirwan Astutik, Rika Aswadi Jaya, Aswadi Ayun Maduwinarti Ayuningtyas Palupi, Risqi Ekanti Azzahra, Morra Fatya Gisna Nourielda Bambang Bagus Harianto Brown, Christina Bungsu, Agung Pangeran Chrissanti, Agnes Maria Cilvi Chua Toh Hua Citra Dwi Ristantri Cristino Gusmao Damanik, Alaris Darasito Dananjaya, Gabriel Deliana, Dian Diah Probowulan Dian Kusuma Wardani Dicha Kusuma Priambudi, Prisca Dinarso, Rahmat Cahyo Dwi Budi Santoso Dwi Prasetyo, Septian Dwi Ristantri, Citra Dwi Riyono, Ady Fajar Eko Budi Santoso Eko Budi Satoto Evi Lestari Evi, Evi Lestari Fahrudin, Rifqi Fatmawati, Indra Fatmawati, Suci Nur Fawaidul Badri Fiel Afroh, Ibna Kamelia Firda, Alverina Firdaus, M. Syahri Fitrianingsih, Evi Guntoro, Widy Guntur, Yohanes Sri Gusmao, Cristino Haris Hermawan Haris Hermawan, Haris Herlambang, Toni I.G.N. Andhika Mahendra Ibnu Hanafi Ida Ayu Putu Sri Widnyani Indrawati, Linda Intan Sari, Rosi Avinda Jabbar, Malik Abdul Julianingsih, Dwi Julianto Lemantara Juliardin, Muhammad Alief Kamil, Ibrahim Khairunnisa, Firstia Nurlaili Khamil, Abdul Khaulia, Ayu Tinis Khifdi, M. Yurisul Kusdarwanto, Heru Kusumaningrum Kusumaningrum Lailatul Qodriyah, Emilyati Lambang Trijono Lidya Maukar, Anastasia Litafira Syahadiyanti Lorenzo, Agung Maharani, Astrid Maharani, Astrid Mahardika, Husein Satria Maisura Maisura Mardiansyah, Edi Maria, Lily Martiana, Nina Maryam Z, Amalina Mas, Ayu Masito, Fitri Ma’rifah , Ma’rifah Yuliani Minulya Eska Nugraha Mochamad Hariadi Mochammad Irfan Efendi Mohamad Agus Salim Muhammad Thamrin Mursyidah4, Mursyidah Muzakir, Fohan N, Gardina Aulin Nafsiyah, Wardatun Nirmala Kartikasari, Yudita Nur Annisa, Nenden Nur Said Nuraeni, Intan Sarifa Nursaid Nurul Qomariah Nuryadi Nuryadi Pamungkas Putra Bagyana, Caesare PANJAITAN, MUKTAR B Paramitha, Octavia parjan, parjan Pradana, Dwifa Yuda Pradani, Dea Yulinestria Prakoso, Dadang Pramono, Didik Eko Probowulan, Diyah Purbosaputro, Erwin Putra Widhi Wardhana, Erwindhu Putri, Indri Mariska Rachmania, Nindita Rachmaniyah, Aulia Rafly Eka Saputra, Muhammad Rahmawati, Maulidia Ratna Nur Tiara Shanty Ristumanda, Siti Salbiah Riyadi, Anton Rizaldi, Choireza RR. Ella Evrita Hestiandari Rusgianto S, Riang Agustin Sa'adah , Nur Saat Riyanto Samsul Hadi Sanorsa, Abadi Santi Wulandari, Dwi Sari , Mena Sari Indrayati, Fatma Sari, Denia Ratna Sari, Risa Riana Puspita Savitri, Nikke Indah Sinclair, Amelia Siti Juariyah Situmorang, Marolop Bungaran Tua Slamet Kacung, Slamet Slamet Riyadi, Slamet Riyadi Soetjahyo, Soetjahyo Sofianto, Rendra Hadi Suhada Suhada, Suhada Sujatmiko Sujatmiko Sukahir, Sukahir Sulistiowati Sulistiowati Susbiyani, Arik Suyamto Suyamto Suyanto Syahrani Arasid, Nova Syahrul, Maulia Syamilah, Aliyatus Teguh Santosa Tiara Shanty, Ratna Nur Tua S, Marolop Bungaran Uliyah, Dian Alfiyatul Umam, Azizul Warsito Sujatmiko, Achmad Wasi’ah, Ainin Lufaful Wati, Seftin Fitri Ana Wati, Wiwin Sri Niscahya Widiantoro, Edwin Wikaningrum, Anggit Wulandari, Erike Wulandari, Retno S. Yeti Komalasari Zahrotul Janah Zarkasi , Imam Zikrullah , Zikrullah Zubaidi, Nabila