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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 Proceeding of Community Service and Engagement
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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Hoax Detection Tweets of the COVID-19 on Twitter Using LSTM-CNN with Word2Vec Prisla Novia Anggreyani; Warih Maharani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4564

Abstract

The growth of Twitter users is increasing every year, impacting activities in social media such as hoaxes that are increasingly widespread on various platforms. During this pandemic, the rate of hoaxes is growing because nowadays, it is very easy for humans to interact with each other, have opinions, and exchange information. One of the hoaxes that often appears is the hoax about the Covid-19 virus. Therefore, a method for detecting hoaxes is needed, especially for the topic of the Covid-19 virus in Indonesia. The method used in hoax detection is LSTM-CNN with Word2Vec. More than 1000 tweets data are used in this study, divided into hoax and non-hoax categories. Detection is carried out to analyze the hoax results obtained by using Word2Vec as a method to convert data as a classification vector and LSTM-CNN to classify the data. This work's result showed that the LSTM-CNN model with Word2Vec achieves 79.71% accuracy, surpassing the LSTM model and CNN model.
Depression Detection on Social Media Twitter Using Long Short-Term Memory Hafshah Haudli Windjatika; Warih Maharani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4457

Abstract

Mental health problems in the world, especially in Indonesia are still significant. According to the Ministry of Health of the Republic of Indonesia stated that depression is experienced by adolescents from the age of 15 to 24 years. The depression experienced by a person is sometimes not realized by the sufferer, so social media becomes an intermediary to express feelings in text form. From the available data, this case pushes the research to detect depression disorder. Detecting depression performs to know the Twitter user who experiences depression. Data used from 159 Twitter users for every username is taken from 100 tweets. In this research, we use Word2Vec and LSTM (Long Short-Term Memory) features as the classification method. The Word2Vec works in converting data as vector and seeing the relation for every word. LSTM is chosen since the dataset is used to collect tweet from the past tense and this method be able to save the data from the past doing prediction. The classification is performed by processing the data trained such as tweeting which becomes a model for processing the data trained test. Based on the test result produce the accuracy data is 77.95% and the F1-Score is 57.14%.
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.
Personality Detection of Twitter Social Media Users using the Support Vector Machine Method Salsabila Anza Salasa; Warih Maharani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5345

Abstract

 Personality is a person's psychological tendency to carry out certain social behaviors, whether in the form of feelings, thoughts, attitudes and will or actions. Big Five is the most popular and widely used personality model, therefore this proposal uses the Big Five Personality model. In this technological era, humans interact using social media. One of the fastest growing social media is Twitter. Twitter is a social media used by all groups. Every human being has a different personality. Personality detectors are needed for employee recruitment to dig up information about the personality of prospective employees. So personality detection or BigFive Personality can be done through tweets that are shared on Twitter. With this, it is necessary to detect personality using the Support Vector Machine (SVM) method. From the results of the study, it was found that the maximum performance parameter combination in detecting personality on Twitter users was a combination of Linear parameters and C = 10 which obtained an accuracy of 0.979. The data used is the result of crawling on the Twitter site with 146 user usernames and 38853 tweets.
Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method Putri Samapa Hutapea; Warih Maharani
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1504

Abstract

Shopee is e-commerce widely accessed and used in this era. Many people use Shopee because the products offered are cheaper and more affordable. Despite the fact that Shopee is a well-known e-commerce, it still requires responses and suggestions from the public to maintain or improve the features required. In this study, public sentiment analysis was carried out on Twitter social media related to the Shopee marketplace. This study collected data that contained tweets from predetermined keywords and used Word2Vec and Support Vector Machine classification methods. The use of Word2Vec influenced the level of accuracy so that it increased for each SVM kernel. Meanwhile, the best hyperparameter tuning was found in the polynomial kernel, with an accuracy rate of 93.20%.
Disaster Management Sentiment Analysis Using the BiLSTM Method Rachdian Habi Yahya; Warih Maharani; Rifki Wijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5573

Abstract

Indonesia is a country prone to natural disasters. Natural disasters occur due to the process of adjustment to changes in natural conditions due to human behavior or biological processes. Community responses through tweets on Twitter are crucial for decision-making and action in disaster management and recovery processes. From the many public reactions via Twitter, sentiment analysis can be carried out. Classification using the BiLSTM method can be carried out to determine the categories of positive and negative responses after previously being compared using the SVM, which resulted in an accuracy of 82.73% and a BERT of 81.78%. After the classification process, the testing process is carried out with Word2Vec. From a total of 2,686 Twitter data, it was concluded that there were around 2,081 positive sentiments and 605 negative sentiments related to disaster management in Indonesia. At the same time, the test results obtained accuracy reached 84%, precision 88%, recall 92%, and f1-score reached 90%.
Personality Classification on Twitter Social Media using BERT Yantrisnandra Akbar Maulino; Warih Maharani; Prati Hutari Gani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5597

Abstract

In the modern era, social media is a platform often used to interact with people. Twitter is a popular social media, especially for human interaction. Using tweets on Twitter can describe how a person's personality and can also describe characteristics of a person. Humans themselves based on the Big Five Model Nursing Theory (Big Five Personality), have five general personalities, namely openness, conscientiousness, extraversion, agreeableness, and neuroticism. Personality itself influences a person's judgment of many things, knowing the personality of a person can make it easier to know the characteristics, habits, and ways of that person in their daily activities. In addition, understanding someone's personality can be a reference in seeing how someone can interact with others. It can also be used when looking for a job according to their personality. Thus, this research builds a system to classify personality using the BERT model with the dataset used in the form of tweets from Twitter users by making several changes such as parameters and using tests with several ratios in determining test data and also training data. The results acquired in this study are 50%.
Analyzing Cyberbullying Negative Content on Twitter Social Media with the RoBERTa Method Muh. Akib A. Yani; Warih Maharani
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1543

Abstract

Social media is an online-based communication tool that can make it easier for users to interact among fellow users without any area and time restrictions. Indonesia has the highest number of social media users in the world. The Twitter social media platform is a place where users can pour out their whole hearts in the form of tweets. Free and diverse interactions on Twitter have a considerable influence on the psychological condition of its users. Cyberbullying or online bullying is an act of humiliating or hurting other people's feelings intentionally and repeatedly on social media, messages, or in other ways. In this study, the RoBERTa classification method was used to detect cyberbullying tweets with a best accuracy score of 86.9% and an f1-score of 77.5%.
Co-Authors Adhie Rachmatulloh Sugiono Adinda Putri Rosyadi Adiwijaya Agin Permana Yogaswara Agung Toto Wibowo Aisyiyah, Syarifatul Ajeung Angsaweni Aji Gunadi, Gagah Al Giffari, Muhammad Zacky Aldy Renaldi Alfathur Rizki Hermawan Alfian Akbar Gozali Algi Erwangga Putra Alif Rahmat Julianda Andre Agasi Simanungkalit Angelina Prima Kurniati Anisa Herdiani annisa Imadi Puti Antonius Simon Arianti Primadhani Tirtopangarsa Arie Ardiyanti Suryani Artanto Ageng Kurniawan Asep Aprianto Aziz Alfauzi Aziz Azka Zainur Azifa Bondan Ari Bowo Chacha Alisha Dewintasari 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 Hafizh Putra 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 Jason Kusuma 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 Arif Faisal Muhammad Fadhil Mubaraq Muhammad Husein Adnan Muhammad Rafan Muhammad, Noryanti Nadia Nurhalija Zuaeni Nida Anggraeni 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 Rashad Bima Rasyad, Gerald Shabran Ria Aniansari Rianda Khusuma Rifki Wijaya Rizky Adityana Ryan Armiditya Pratama Salsabila Anza Salasa Salvin Nafisah Husnatuzzahwa Sendika Panji Anom Serventine Andhara Evhen Setiawan, Abiyyu Daffa Haidar Suyanto Suyanto Tia Daniati 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 Yusry Anandita Yusup, Axel Haikal Zhafirah Salsabila