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Fajar Sidik
Universitas Negeri Yogyakarta
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Journal : Bandung Conference Series: Statistics

 1 
Penerapan SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Sentimen Genosida Palestina Menggunakan Multinomial Naive Bayes Fajar Sidik; Ilham Faishal Mahdy
Bandung Conference Series: Statistics Vol. 4 No. 2 (2024): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v4i2.12693

Abstract

Abstract. Social media, including the X platform (formerly known as Twitter), has become an important source of information in understanding public sentiment towards various issues, including the Palestinian genocide. Text data obtained from social media is often unbalanced, with more negative sentiment than positive and neutral sentiment because people do not agree with the genocide. This imbalance can interfere with the accuracy of the sentiment classification model. Therefore, this research aims to overcome the problem of data imbalance by applying the SMOTE (Synthetic Minority Over-sampling Technique) method in the sentiment classification of the Palestinian genocide using the Multinomial Naive Bayes algorithm. The data used in this study was collected from X social media platform, consisting of 1000 posts related to the Palestinian genocide taken backward on February 29, 2024. The classification method used is Multinomial Naive Bayes, which was chosen for its ability to handle text data by considering the frequency of occurrence of words. The results showed that the sentiment analysis mostly showed negative sentiments towards the Palestinian genocide issue. The application of SMOTE successfully balanced the data distribution, increasing the representation of positive and neutral sentiments. Model performance evaluation shows that the resulting model is able to achieve an accuracy rate of 80%, with a precision of 43%, recall of 54%, and F1-score of 46%. In conclusion, the application of SMOTE can optimize text classification performance in unbalanced data conditions, providing more insight into public sentiment towards the Palestinian genocide on X social media. Abstrak. Media sosial, termasuk platform X (sebelumnya dikenal sebagai Twitter), telah menjadi sumber informasi yang penting dalam memahami sentimen masyarakat terhadap berbagai isu, termasuk genosida Palestina. Data teks yang diperoleh dari media sosial seringkali tidak seimbang, adanya kecenderungan lebih banyak sentimen negatif dibandingkan sentimen positif dan netral karena masyarakat tidak setuju dengan adanya genosida. Ketidakseimbangan ini dapat mengganggu keakuratan model klasifikasi sentimen. Oleh karena itu, penelitian ini bertujuan untuk mengatasi masalah ketidakseimbangan data dengan menerapkan metode SMOTE (Synthetic Minority Over-sampling Technique) dalam klasifikasi sentimen genosida Palestina menggunakan algoritma Multinomial Naive Bayes. Data yang digunakan dalam penelitian ini dikumpulkan dari platform media sosial X, terdiri dari 1000 post terkait genosida Palestina yang diambil mundur pada tanggal 29 Februari 2024. Metode klasifikasi yang digunakan adalah Multinomial Naive Bayes, yang dipilih karena kemampuannya dalam menangani data teks dengan mempertimbangkan frekuensi kemunculan kata-kata. Hasil penelitian menunjukkan bahwa analisis sentimen mayoritas menunjukkan sentimen negatif terhadap isu genosida Palestina. Penerapan SMOTE berhasil menyeimbangkan distribusi data, meningkatkan representasi sentimen positif dan netral. Evaluasi kinerja model menunjukkan bahwa model yang dihasilkan mampu mencapai tingkat akurasi sebesar 80%, dengan precision sebesar 43%, recall sebesar 54%, dan F1-score sebesar 46%. Kesimpulannya, penerapan SMOTE dapat mengoptimalkan performa klasifikasi teks dalam kondisi data yang tidak seimbang, memberikan wawasan lebih mengenai sentimen masyarakat terhadap genosida Palestina di media sosial X.
Co-Authors Aan Triwijayanto Abdul Rahman Afikah, Prista Afrizal Tanjung Agus Rofi’i Ahmad, Nurwadjah Amrih Setyo Raharjo Andewi Suhartini, Andewi ANDIKA SAPUTRA Angga Damayanto Angriyani, Silma ANI OKTARINI SARI, ANI OKTARINI Anisa Padilla Annisaa' Siti Shalihah Ariza, Afifa Nur Arman Maiwa Asri, Rahma Risa Azhar Haikal Anwar Azim, Muhamad Bau Toknok Bibit Sudarsono Charolina Charolina D. Elysa Putri Mambang Deni Nugraha Dhiaul, Asyraf Diana Diana Diansyah, Nur Dzilla Khoerunnisa Ebni Sholikhah Eka Raysha, Najwa Meidinar Eka Raysha, Njawa Meidinar Elizal, Elizal Erga Yuhandra Evi Rovikoh Indah Saputri Fachry Abda El Rahman Fadilah, Rahmi Fatih Gama Abisono Nasution Fatimah Az Zahra Fatoni, Tamrin Ferdian, Feri Fikri, M Khoirul Fikri, M. khairul Firman Noor Hasan Firmansyah, Firmansyah Gina Nuzul Ulwan Grance Cristin Tektonik Zai Hamka Hamka, Hamka Haqibul Mujib Harmawan, Violintikha Hartini, Dewi Sri Hendra Pribadi Herawati Herawati Herawati Herawati Herawati Herawati Hidayatullah, Ma’ruf Hulu, Amati Eltriman Ibnu Suhada Ilham Faishal Mahdy Imas Masyitoh Istiqamah, Nurul Kartika Megawati Kayla Salsabilla L. Hendrowibowo Ladiva, Ladiva Lajai Madam, Sri Yulandasari Lareke, Amir A Marlina Marlina Meidinar Eka, Najwa Misrah Misrah Mubarrok, Dede Husni Muhamad Yaqutatul Hadro Mustabsyiroh Mustabsyiroh Mustaghfirin, Ahmad Nasution, Fatih Gama Abisono Nur Aziza, Syalma Alisa Nurul Malikah Pardomuan Tanjung Perwito, Perwito Putri Marshanda Rina Mediaswati, Rina Rini Rini Riski Gusri Utami Rozanah Katrina Herda Safha, Libbya Safitri Endah Winarti Sandi Lesmana Serly, Vanica Setia Budi, Firmansah Siti Mauliya Kusumah Sofyan Sofyan Suyuthie, Hijriyantomi Syifa Utami Asrianty Teguh Mulya Sandi Teja Endra Eng Tju Tengku Riza Zarzani N Thalia, Dinda Whydiantoro Widad Ristia Zulkha Winda Mey Lestari Munthe Wirdawati, Ayu Wulanda Aditiya Wulansari, Nidia Yeni Elviza Febrianti Youmil Abrian Yudi Daryadi