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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Seminar Nasional Informatika (SEMNASIF) Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Computer Science and Informatics Engineering (J-Cosine) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Madani : Indonesian Journal of Civil Society Jurnal Teknik Informatika (JUTIF) JINAV: Journal of Information and Visualization Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika
Warih Maharani
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Nursing Public Health Social Sciences

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Logistic Regression and Naïve Bayes Comparison in Classifying Emotions on Indonesian X Social Media Rasyad, Gerald Shabran; Maharani, Warih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29120

Abstract

Emotions are integral to human interaction and decision-making, often expressed on social media platforms like X, which provides valuable data for sentiment analysis. However, analyzing texts from X poses challenges due to informal language, slang, and unique textual features. This study compares Logistic Regression and Naive Bayes in classifying emotions from Indonesian tweets, addressing gaps in prior research by exploring feature extraction methods, data split ratios, and hyperparameter tuning. Data were collected from 100 Telkom University students, resulting in 8,978 tweets labeled into four emotions: Happy, Sad, Angry, and Fear. After preprocessing, feature extraction methods TF-IDF and Bag of Words (BoW) were applied. Models were trained and tested on 10%, 20%, and 30% data splits, and performance was evaluated using accuracy, precision, recall, and F1-score. Hyperparameter tuning was conducted for Logistic Regression using GridSearch. Results showed Logistic Regression outperformed Naive Bayes, achieving 73.49% accuracy compared to 70.27%, with BoW yielding superior results over TF-IDF. The 20% data split provided the best balance for training and testing. This research demonstrates the effectiveness of Logistic Regression and highlights the importance of tailored feature extraction and parameter optimization for emotion classification in informal text datasets, particularly for Indonesian tweets.
Understanding Public Sentiments on the 2024 Presidential Election through BERT-Powered Analysis Setiawan, Abiyyu Daffa Haidar; Maharani, Warih
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29267

Abstract

Social media platforms serve as dynamic communication across boundaries, with X serving as a platform for opinion exchange. This research examines public sentiment on the 2024 Indonesian Presidential Election to understand voter sentiments based on what happened during the pre-election. Using the Twitter API, 2,146 tweets were collected based on election-related keywords and hashtags, focusing on Indonesian-language tweets with direct opinions. The method that we use is data crawling using the Twitter API. Preprocessing steps included case folding (converting text to lowercase), cleansing (removing noise like URLs and emojis), tokenization, stemming (reducing words to base forms), and stop word removal (e.g., “yang,” “dan”). Slang was standardized with a custom dictionary to ensure consistency and accurate interpretation. Leveraging BERT for sentiment analysis, the model achieved 99% accuracy; results indicate that 93.1% of analyzed tweets expressed negative sentiment, highlighting public dissatisfaction about the 2024 presidential election. Hyperparameters are also tested to optimize model performances. With the best result accuracy in 99% using an 80:20 split ratio, with a batch size of 16 and a learning rate of 0.00001. This research underlines the importance of sentiment analysis in elections, demonstrating BERT’s capability to handle linguistic complexities and providing a methodological framework for analyzing social media data in political contexts.
Analisis Berbasis Emosional pada Depresi di Media Sosial Menggunakan Pendekatan Convolutional Neural Network Aisyiyah, Syarifatul; Maharani, Warih
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak-Depresi merupakan gangguan jiwa pada seseorang. Diperkirakan sekitar 300 juta orang menderita depresi di seluruh dunia. Dikarenakan tidak adanya penanganan medis pada tahap awal. Dengan menggunakan media sosial seperti twitter menjadi tempat untuk mengemukakan perasaan atau kondisi emosional yang dialami melalui postingan. Dari postingan atau data tweet tersebut maka dapat ditemukan petunjuk bahwa pengguna mengalami depresi atau tidak. Pada penelitian ini digunakan algoritma Convolutional Neural Network (CNN) untuk membuat suatu model untuk mengklasifikasi teks yang mampu melakukan prediksi untuk mendeteksi suatu postingan pada twitter memiliki bentuk emosional yang dapat diprediksi apakah seseorang tersebut menandakan terjadinya depresi atau tidak. Data yang dikumpulkan bersumber dari hasil pengisian kuesioener oleh responden, dan data tweet didapatkan dari akun pengguna twitter yang sudah disetujui. Pengembangan sistem ini sudah dilakukan hingga tahap pengujian, model yang dihasilkan untuk memprediksi emosional mendapatkan akurasi sebesar 82% dan untuk memprediksi depresi mendapatkan akurasi 91% yang diuji dengan 4892 tweet dari 161 user dan digambarkan dengan confusion matrix sebagai alat ukur performansi.Kata kunci-depresi, emosional, postingan, Convolutional Neural Network (CNN)
Co-Authors Adhie Rachmatulloh Sugiono Adinda Putri Rosyadi Adiwijaya Agung Toto Wibowo Aisyiyah, Syarifatul Ajeung Angsaweni Aji Gunadi, Gagah Al Giffari, Muhammad Zacky Aldy Renaldi Alfian Akbar Gozali Algi Erwangga Putra Alif Rahmat Julianda Andre Agasi Simanungkalit Angelina Prima Kurniati Anisa Herdiani annisa Imadi Puti Arianti Primadhani Tirtopangarsa Arie Ardiyanti Suryani Artanto Ageng Kurniawan Asep Aprianto Aziz Alfauzi Aziz Azka Zainur Azifa Bondan Ari Bowo Daud, Hanita Dicky Wahyu Hariyanto Diska Yunita Dita Martha Pratiwi Elroi Yoshua Ersy Ervina Evizal Abdul Kadir Fadhel, Muhammad Fadhil Hadi Fairuz Ahmad Hirzani Fathin, Felicia Talitha Fika Apriliani Fikri Ilham Guntur Prabawa Kusuma Hafshah Haudli Windjatika Hilda Fahlena Holle, Alfransis Perugia Bennybeng I Kadek Bayu Arys Wisnu Kencana I Nyoman Cahyadi Wiratama Ilham Rizki Hidayat Imelda Atastina Intan Nurma Yunita Intan Ramadhani Joshua Tanuraharja Keri Nurhidayat Kurniawan Adina Kusuma Latifa, Agisni Zahra M.Syahrul Mubarok Marcello Rasel Hidayatullah Moch Arif Bijaksana Mohamad Mubarok Mohamad Syahrul Mubarok Muh. Akib A. Yani Muhammad Fadhil Mubaraq Muhammad Husein Adnan Muhammad, Noryanti Niken Dwi Wahyu Cahya Nugraha, Endri Rizki Nugroho, Bayu Seno Nungki Selviandro Nur Ghaniaviyanto Ramadhan Nyoman Rizkha Emillia Pratama, Rio Ferdinand Putra Prati Hutari Gani Prati Hutari Gani Prisla Novia Anggreyani Pursita Kania Praisar Purwanto, Zadosaadi Brahmantio Putri Ester Sumolang Putri Samapa Hutapea Rachdian Habi Yahya Raihan Nugraha Setiawan Rasyad, Gerald Shabran Ria Aniansari Rianda Khusuma Rifki Wijaya Ryan Armiditya Pratama Salsabila Anza Salasa Sendika Panji Anom Serventine Andhara Evhen Setiawan, Abiyyu Daffa Haidar Suyanto Suyanto Tiara Nabila Tri Ayu Syifa'ur Rohmah Trysha Cintantya Dewi Tsaqif, Muhammad Abiyyu Veronikha Effendy Wijaya, Yaffazka Afazillah Yantrisnandra Akbar Maulino Yanuar Ega Ariska Yanuar Firdaus AW Yusup, Axel Haikal