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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 Achmad Choiron Achmad Choiron Ade Susianti, Febrina Alwiyah Alwiyah Amarya, Grace Ezranda Anastasia Lidya Maukar Andhina Ika Sunardi ANGGI FIRMANSYAH Anggit Wikanningrum Anik Vega Vitianingsih Anik Vega Vitianingsih Anugrah Prasetyo, Rizky Ardi, Syaiful Ardiansyah, Alvian Dwi 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 Bungsu, Agung Pangeran Chrissanti, Agnes Maria Cilvi Chua Toh Hua Citra Dwi Ristantri Cristino Gusmao Damanik, Alaris Darasito Dananjaya, Gabriel Diah Probowulan Dian Kusuma Wardani 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 Fauzan, Rizky Fawaidul Badri Fiel Afroh, Ibna Kamelia Firda, Alverina Firdaus, M. Syahri Fitrianingsih, Evi Gilang Cahyono 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 Ismawan, Puji Jabbar, Malik Abdul Julianto Lemantara Juliardin, Muhammad Alief Kamil, Ibrahim Khairunnisa, Firstia Nurlaili Khamil, Abdul Khaulia, Ayu Tinis Khifdi, M. Yurisul Kusumaningrum Kusumaningrum Lambang Probo Sumirat, Lambang Probo Lambang Trijono Lidya Maukar, Anastasia Litafira Syahadiyanti Maharani, Astrid Maharani, Astrid Mahardika, Husein Satria Maisura Maisura Mardiansyah, Edi Maria, Lily Marmora, Auryfierou Marva Martiana, Nina Maryam Z, Amalina Ma’rifah , Ma’rifah Yuliani Mochamad Hariadi Mohamad Agus Salim Muhammad Thamrin Mursyidah4, Mursyidah Muzakir, Fohan N, Gardina Aulin Nafsiyah, Wardatun Nugraheni Sri Lestari, Veronika Nuraeni, Intan Sarifa Nurmawati Nurmawati Nursaid Nurul Qomariah Nuryadi Nuryadi Pamungkas Putra Bagyana, Caesare Paramitha, Octavia Pradana, Dwifa Yuda Pradani, Dea Yulinestria Prakoso, Dadang Pramono, Didik Eko Pratama, Andika Setia Probowulan, Diyah Putri, Indri Mariska Rachmania, Nindita Rachmaniyah, Aulia Rahmawati, Maulidia Riyadi, Anton RR. Ella Evrita Hestiandari S, Riang Agustin Sa'adah , Nur Saat Riyanto Samsul Hadi Sanorsa, Abadi Santi Wulandari, Dwi Sari , Mena Sari, Denia Ratna 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 Sulistiowati Sulistiowati Susanto, Dimas Dwi Susbiyani, Arik Susilo, Irvansyah Nuradi Suyanto Syahrani Arasid, Nova Teguh Santosa Tiara Shanty, Ratna Nur Toni, Ignasius 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 Yoyon Arie Budi Suprio Yudi Kristyawan, Yudi Zahrotul Janah Zaidan Rafli Zikrullah , Zikrullah Zubaidi, Nabila