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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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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
PERSONALITY DETECTION ON TWITTER USER USING XGBOOST ALGORITHM Adinda Putri Rosyadi; Warih Maharani; Prati Hutari Gani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1166

Abstract

Personality is a person's identity that is addressed to the public. The Big Five personality is the most commonly used personality model. Detecting a person's personality is still a difficult task today. Because personality detection still often requires humans to fill out lengthy questionnaires to evaluate various personality traits. Therefore, a system that is able to identify personality easily and specifically is needed. By using social media, individuals often express their feelings. Twitter is the most popular social networking platform today. In this research, we use the XGBoost Algorithm, a powerful machine learning method, to create a personality detection system that improves upon existing approaches. Our research aims to determine how well the XGBoost algorithm can recognize Big Five personality features in Twitter users. We achieved encouraging results through in-depth investigation and experimentation. The XGBoost algorithm successfully developed a model that can recognize all Big Five personality trait labels but with different precision, recall and f1-score values. The highest value was obtained for the Extroversion label with a precision of 0.92, recall of 1.00 and f1-score of 0.96. Meanwhile, the lowest value is owned by the Agreeableness label with a precision value of 0.29, recall 0.29, and f1-score of 0.29. This research demonstrates the potential of the XGBoost Algorithm for personality discovery on social media platforms, providing a fast and accurate method to identify distinctive characteristics. Overall, the results of this study demonstrate the efficiency of the XGBoost Algorithm in the context of personality recognition, opening the door for further development in understanding and evaluating human behavior through social media platforms such as Twitter.
DEPRESSION DETECTION ON TWITTER USING GATED RECURRENT UNIT Holle, Alfransis Perugia Bennybeng; Warih Maharani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1187

Abstract

In the present era, technological advancements have significantly impacted society, particularly in the use of social media. One popular social media platform is Twitter, where people could share moments, thoughts, and statuses. However, since the COVID-19 pandemic, the usage of Twitter increased, and some users began exhibiting symptoms of depression. The condition of depression required a means to channel emotions that could assist users in coping. By employing the GRU method and Word2Vec feature extraction, we developed a depression detection system capable of analyzing users' Twitter posts and identifying potential signs of depression. The dataset used in this research was obtained from 165 participants who agreed to utilize their personal Twitter data and completed a questionnaire based on the Depression Anxiety and Stress Scales-42 (DASS-42). The questionnaire results served as labels that were processed for Word2Vec feature extraction and subsequently fed into the GRU model. The evaluation revealed an accuracy rate of 57.58% and an f1-score of 56.25. By using the bidirectional layer in the model, there is an improvement in precision, recall, and f1-score values.
Analyzing Public Sentiment on the Relocation of Indonesia's Capital to Kalimantan as the Ibu Kota Nusantara Using Logistic Regression Maharani, Warih; Latifa, Agisni Zahra
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4230

Abstract

The Ibu Kota Nusantara (IKN) relocation project aims to equalize economic development and reduce the burden on Jakarta, but has elicited mixed reactions from the public, including both support and opposition. Therefore, this study applies machine learning-based sentiment analysis, using Logistic Regression to explore public opinion on the relocation, and leveraging social media data from platform X to gain insights into information, opinions, and public reactions. The Textblob, VADER, and SentiWordNet labeling methods employ a majority vote of the three labels to determine the final label. In order to achieve data balance, SMOTE is employed in this study. Moreover, this study applies a combination of preprocessing, N-gram, and TF-IDF to illuminate the impact of this combination on model performance. The results indicate that the combination of preprocessing Scenario 3 with unigram, bigram, trigram, and TF-IDF feature extraction yields the best performance, achieving a precision of 0.7641, recall of 0.7767, F1-score of 0.7634, and accuracy of 0.7641. This research demonstrates the efficacy of proper preprocessing and feature extraction in enhancing the performance of the Logistic Regression model for sentiment classification, thereby contributing to the analysis of public opinion on IKN policy regarding other issues in the future.
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