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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 : Building of Informatics, Technology and Science

 1 
Depression Detection on Twitter Using Bidirectional Long Short Term Memory Putri Ester Sumolang; Warih Maharani
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1850

Abstract

The usage of social media as a platform for individual expression is one example of how the advancement of technology has an impact on society. Therefore, many Twitter users show symptoms of depressive disorders through their tweets. It is crucial to be aware of the need to consult a doctor or other specialists to prevent suicides. However, leveraging Twitter user tweet data to detect depression early on can be avoided by using the Bidirectional Long Short Term Memory (BILSTM) approach and the word2vec feature extraction method. The dataset utilized in this study was obtained from respondents who agreed to have their data used in research after completing a questionnaire based on the Depression Anxiety and Stress Scales - 42 (DASS-42). The whole data from 159 users of Twitter who have been classified as depressed or normal based on the results of the DASS-42 labeling are then preprocessed so that the data can be entered into the word2vec feature extraction and modeled by BiLSTM as a classification. The evaluation revealed an accuracy of 83.46 % and an f1-score of 87.11 %. By increasing the number of neurons, accuracy increased by 2.36 %, and f1-score climbed by 1.64 %.
Identification of Big Five Personality on Twitter Users using the AdaBoost Method Ajeung Angsaweni; Warih Maharani
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1853

Abstract

Social media is one of the lifestyles in the modern era that uses web-based technology for social interaction. As one of the most popular social media platforms, Twitter allows users to express themselves through tweets that can show their personality. The Big Five theory states that a person’s personality is divided into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several methods have been used to conduct user personality research based on activity on social media. The AdaBoost method is used in this study to identify the personality of Twitter users using sentiment, emotion, social, PCA, and POS-tag features. There are two test scenarios in this study. The first is testing the AdaBoost model with all features, and the second is testing the AdaBoost model with a combination of three features. The research indicates that the data preprocessing method can affect the model. The results showed that the AdaBoost model with all the features and without the stemming process had the highest accuracy value of 53.57%.
Personality Detection on Twitter Social Media Using IndoBERT Method Tri Ayu Syifa'ur Rohmah; Warih Maharani
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1895

Abstract

Personality is the fundamental characteristic of human beings that makes humans unique. Because of these differences in human characteristics, personality becomes a benchmark for consideration in various recruitment processes. One way to predict personality is to apply an interview system or fill out questionnaires which often experience problems due to ineffectiveness in terms of time and cost. Results become inaccurate if prospective employees do not know themselves well. The big five personality method, divided into openness, conscientiousness, extraversion, agreeableness, and neuroticism, is widely used to predict personality. This study uses a deep learning method, IndoBERT, to detect personality based on five dimensions according to the big five personalities whose data is taken from Twitter tweets with crawling data. From the results of these studies, it is known that personality research using the IndoBERT method without a stemming process has a higher accuracy rate of 0.46.
Support Vector Machine and Naïve Bayes for Personality Classification Based on Social Media Posting Patterns Nugroho, Bayu Seno; Maharani, Warih
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6411

Abstract

This research investigates the use of Support Vector Machine (SVM) and Naive Bayes models to classify the personality traits based on the social media posting patterns. This study integrates textual features obtained from the Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods, and along with the feature expansion using the Linguistic Inquiry and Word Count (LIWC) tool, to assess their influence on accuracy Classification Personality characteristics were mapped from social media posts using the Big Five Inventory (BFI-44). The research findings show that the SVM model in which uses the TF-IDF + LIWC feature set, provides the best performance, and achieve 76.60% of accuracy on the base model with a linear kernel. In comparison to the Naive Bayes model performed best with the same feature set, achieving 59.57% accuracy with a smoothing parameter of 1xE-2. Although the oversampling improved recall and precision, the undersampling was found to have a negative effect on model performance. These findings highlight the benefits of combining TF-IDF and LIWC features which improve model effectiveness, with SVM producing the best overall results in personality classification from social media data.
Comparison of Naive Bayes and SVM Methods for Identifying Anxiety Based on Social Media Nugraha, Endri Rizki; Maharani, Warih
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6506

Abstract

This research aims to detect anxiety patterns from social media posts using Naive Bayes (NB) and Support Vector Machine (SVM) algorithms. Tweets are extracted using Data Crawling techniques, then continued their way into labeling using Depression Anxiety Stress Scale (DASS-42) questionnaire along with Random Oversampler to balance out the unbalanced dataset and NB and SVM were chosen for their effectiveness in text sentiment classification. This study integrates textual features obtained from the Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) methods. The study compares the performance of these algorithms in detecting anxiety using datasets from the X platform. The comparison aims to identify the advantages and limitations of each method in handling textual sentiment data. This research aims to analyze sentiment data by calculating accuracy, recall, and F1-score to determine the most optimal performance outcome. The results indicate that the SVM with TF-IDF feature extraction achieved the highest accuracy of 72% and an average F1-Score of 61%, while the NB with BoW achieved 56% accuracy and an average F1-Score of 49%. These findings highlight the effectiveness of combining SVM and TF-IDF features which improve model effectiveness with SVM producing the best overall result in identifying anxiety from social media data.
Comparison of Random Forest and Decision Tree for Depression Detection Using Interaction Patterns Fathin, Felicia Talitha; Maharani, Warih
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6660

Abstract

This research focuses on evaluating the efficacy of Random Forest and Decision Tree, in detecting depression on tweets and interaction patterns on X social media. Depression as a global health problem often happens because of individuals' online behavior. This study uses data from X social media users in Indonesia who have filled out the DASS-42 questionnaire with an analysis approach that includes crawling data that includes tweets and interactions on X. The purpose of this research is to more accurately and comprehensively identify signs of depression by analyzing the interaction patterns of users on social media platforms through the integration of of several many methods for feature extraction and preprocessing situations.The methods used include data preprocessing, feature combination using TF-IDF, Bag of Words, and Word2Vec and model evaluation utilizing metrics such as Precision, Recall, Accuracy, and F1-score. The findings of this research show that Random Forest performs better than Decision Tree, with a combination of TF-IDF, BoW, Word2Vec and TF-IDF, Word2Vec features obtained an accuracy of 0.60. Although Random Forest is superior, both models are difficult to identify the positive class of depression which can be seen from the relatively low F1-score and recall values. Other factors affecting model performance include lack of data relevance, low interaction rate, and limited feature extraction.
Comparison of Random Forest and Decision Tree Methods for Emotion Classification based on Social Media Posts Tsaqif, Muhammad Abiyyu; Maharani, Warih
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6677

Abstract

Social media platforms like X (formerly Twitter) have become essential for expressing emotions and opinions, making emotion classification a critical task with applications in mental health, public sentiment monitoring, and customer feedback analysis. This study compares Random Forest and Decision Tree algorithms for classifying emotions such as joy, sadness, anger, and fear which are from social media posts. Data collection involved crawling tweets and manual labeling. Preprocessing included tokenization, stemming, and stopword removal, with feature extraction using TF-IDF and Bag of Words. Experimental scenarios tested data split ratios, resampling for class balance, and parameter tuning. Decision Tree parameters included criterion (gini, entropy), max depth (none, fixed values), min samples split (2, 5), and min samples leaf (1, 2). Random Forest parameters tuned were n_estimators (100–400), max depth (none, fixed values), min samples split (2, 5, 10), and min samples leaf (1, 2). Results showed Random Forest achieving a maximum accuracy of 76.17%, outperforming Decision Tree’s 72.62%. The combination of TF-IDF and Bag of Words delivered the highest accuracy for both models. This study underscores the importance of preprocessing, balanced datasets, and parameter optimization for effective emotion classification. The findings offer insights into advancing sentiment analysis and natural language processing, enabling practical applications in public sentiment tracking, customer experience enhancement, and crisis management.
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