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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

Author-ID : 2608957

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

Published : 73 Documents Claim Missing Document

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Part of Speech Tagging untuk Bahasa Jawa dengan Hidden Markov Model Ryan Armiditya Pratama; Arie Ardiyanti Suryani; Warih Maharani
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 4 No 1 (2020): June 2020
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Indonesia has many cultures and local language, one of the most is Javanese with the Javanese language. The Javanese language is used in the region of Central Java and East Java, the word structure of the Javanese language has a similar to the Indonesian word class. Part of Speech (POS) Tagging is a process for labeling word classes for each input word that corresponding. POS Tag for Indonesian Language has been done a lot and got very good accuracy with various method application. This study aims to provide the word class label for Javanese language and the datasets used was obtained from online news with Javanese Ngoko language. The method used in this study is the Hidden Markov Model (HMM) with use of the HMM method get the highest accuracy is 96.2 %. Keywords: POS Tagging, Javanese Ngoko, Labeling, Hidden Markov Model

KLASIFIKASI DATA MENGGUNAKAN JST BACKPROPAGATION MOMENTUM DENGAN ADAPTIVE LEARNING RATE Warih Maharani
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 4 (2009): Intelligent System dan Application
Publisher : Jurusan Teknik Informatika

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

Data mining merupakan suatu proses pengekstrakan informasi penting pada data yang berukuran besar. Salah satu fungsionalitas yang sering digunakan pada data mining adalah klasifikasi, yang berfungsi menemukan sekumpulan model/fungsi sehingga dapat mambedakan kelas data untuk keperluan prediksi. Jaringan Syaraf Tiruan (JST), merupakan salah satu teknik klasifikasi yang cukup handal dikarenakan kemampuannya dalam memprediksi. JST mempunyai toleransi yang tinggi terhadap data yang mengandung noise serta bersifat adaptive, dimana jaringannya mampu belajar dari data yang dilatihkan kepadanya. Oleh karena itu penelitian ini menganalisis pengklasifikasian data dengan menggunakan JST Backpropagation Momentum dengan adaptive learning rate untuk mendapatkan hasil yang optimal. Sebelum memasuki tahap klasifikasi, proses yang dilakukan adalah feature selection. Feature selection merupakan tahap preprocessing yang bertujuan untuk mencari atribut yang relevan terhadap label kelas. Dengan kata lain, feature selection dapat dikatakan sebagai teknik mereduksi dimensi sebagai usaha untuk meningkatkan performansi dari sebuah classifier. Metode feature selection yang digunakan adalah information gain. Setelah dilakukan preprocessing data, kemudian dilakukan tahap klasifikasi menggunakan JST Backpropagation Momentum dengan adaptive learning rate.. Hasil pengujian menunjukkan bahwa dengan adanya konstanta momentum dan adaptive learning rate mempercepat kecepatan belajar jaringan. Selain itu juga berpengaruh terhadap nilai keakuratan sehingga dapat mencapai tingkat akurasi sebesar 96%.

General Depression Detection Analysis Using IndoBERT Method Ilham Rizki Hidayat; Warih Maharani
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.634

Many of the tweets we discover on Twitter are concerning feelings of depression which will be caused by varied things. The amount of tweets additionally continues to increase. To be able to decide however depressed a user is, analysing tweets from users can facilitate with that. The method of analysing the detection of depression can help to supply applicable treatment for users who are detected to own depression. During this paper, the users to be analysed are users who have more than 1000 tweets and are Indonesian tweets. Then, crawling / retrieval of user tweet data is carried out. After that, data pre-processing is done. Once that done, using the IndoBERT method to classify the data obtained. In the end, this paper provides the accuracy value of this detection analysis using the IndoBERT method with an accuracy value of 51% and F1-Score of 31%.

Depression Detection on Twitter Social Media Using Decision Tree Marcello Rasel Hidayatullah; Warih Maharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Depression is a major mood illness that causes patients to experience significant symptoms that interfere with their daily activities. As technology has developed, people now frequently express themselves through social media, especially Twitter. Twitter is a social media platform that allows users to post tweets and communicate with each other. Therefore, detecting depression based on social media can help in early treatment for sufferers before further treatment. This study created a system to detect if a person is indicating depression or not based on Depression Anxiety and Stress Scale - 42 (DASS-42) and their tweets using the Classification and Regression Tree (CART) method with TF-IDF feature extraction. The results show that the most optimal model achieved an accuracy score of 81.25% and an f1 score of 85.71%, which are higher than baseline results with an accuracy score of 62.50% and an f1 score of 66.66%. In addition, we found that there were significant effects on changing the value of the maximum features in TF-IDF and changing the maximum depth of the tree to the model performance.

Depression Detection of User in Media Social Twitter Using Random Forest Aldy Renaldi; Warih Maharani
Journal of Information System Research (JOSH) Vol 3 No 4 (2022): Juli 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

One of the disorders of mental health that often occurs in individuals is depression. Identifying depression in the first place is important for the individual. But in fact, conducting an early examination of depression still has some drawbacks. If it continues to be ignored, this can have an impact on the health of the individual. Therefore, there is a need for other methods that can represent the level of depression in individuals, through other media such as social media such as Twitter. Twitter has become one of the media to tell what users of the application experience or feel. This is encouraging to detect of depression in Twitter users. The data used is data taken from the results of the distribution of forms based on DASS-42 with a total of 159 Twitter users for each username taken 100 tweets. This study uses the Word2Vec extraction feature, to convert data from text to vector by looking at the relationship of each word and Random Forest as a classification method, to maintain the balance of data in different classes, especially very large data sets. Based on the test results, the Random Forest model produces an accuracy of 68.75%.

Depression Detection on Social Media Twitter Using Hierarchical Attention Network Method Raihan Nugraha Setiawan; Warih Maharani
Journal of Information System Research (JOSH) Vol 3 No 4 (2022): Juli 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Mental illness, including depression, is not a mild condition that only some mentally weak people experience. Technology is developing so rapidly, especially communication technology through social media. Twitter is a very popular social media today. Users can easily quickly and simply communicate all the feelings they are experiencing through tweets, which allows us to find information about emotional feelings to the level of user depression. Auto-mated analysis of social media has the potential to provide a method for early detection. This study aims to predict early signs of depression using data from social media Twitter. The method used in this research is classification by analyzing social media sentiment using the Hierarchical Attention Network. Classification using the Hierarchical Attention Network method was chosen because the method showed outstanding results for classifying texts in previous studies. The classification model in this study that represents the best accuracy, 74%, was performed by applying the Hierarchical Attention Network.

Depression Levels Detection Through Twitter Tweets Using RoBERTa Method Algi Erwangga Putra; Warih Maharani
Journal of Information System Research (JOSH) Vol 3 No 4 (2022): Juli 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Mental health conditions are one thing that needs to be considered as important as physical health. Depression is a mental disorder that can affect a person's social life. The Twitter social media platform is where users can pour their hearts out in the form of tweets. This is often the background of a person's level of depression. Accessible and diverse interactions on Twitter considerably influence the psychological condition of its users. This study aims to detect the level of depression through data obtained from Twitter social media tweets. The method used is RoBERTa which is an optimized BERT retraining. Several scenarios are used to get the best accuracy results in text classification. To get the evaluation results, it is necessary to measure using the Confusion Matrix method. The accuracy value is 72% based on numerous tests that have been done.

Analyze Detection Depression In Social Media Twitter Using Bidirectional Encoder Representations from Transformers Fikri Ilham; Warih Maharani
Journal of Information System Research (JOSH) Vol 3 No 4 (2022): Juli 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Human health is an essential part of the welfare of a country. Early detection of a disease is necessary to prevent it from spreading in an area. Social media is now a rapid and widespread development of information to provide convenience for the public to communicate. Depressed people have a variety of depressive symptoms from every human behaviour. Psychological doctors often conduct face-to-face interviews on commonly used diagnoses and statistical manual criteria for mental disorders. Depression is a mental disorder that typically appears in humans with the characteristics of depressed mood, loss of interest and pleasure, unstable body energy, and poor concentration. In conducting this research, the aim is to detect people who are depressed by using the Machine Learning-based BERT (Bidirectional Encoder Representations from Transformers) method. BERT can binarily classify text on social media, namely Twitter, which contains Depression detection. Based on the tests that have been carried out, the best accuracy value is 0.7176 or 71%.

Predicting Depressive Disorder Based on DASS-42 on Twitter Using XLNet's Pretrained Model Classification Text Intan Ramadhani; Warih Maharani
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2157

Twitter is a free social media site that is not only a place to share posts and multimedia content but also offers its users to express their feelings, emotions, and sentiments about an issue. So with this, it is often found that Twitter users make posts that show how the user's behavior includes mental problems experienced users such as symptoms of depression, anxiety, and stress disorders. Only about half of depression cases can be detected by doctors or other experts, this is because until now, the diagnosis of depression starts from reports of patients, family, or close friends of patients, or also starts from the results of certain tests such as questionnaires. So this research builds a model to predict depression by building a model that predicts whether someone is depressed through tweets on Twitter using the XLNet pre-trained text classification model. Testing is done by removing stemming from the preprocessing stage. Testing is also done by adding hyperparameters for fine-tuning the XLNet model. Testing is also carried out using a dataset that filters out foreign words where foreign data is translated into Indonesian. The data stored is data that uses words based on the KBBI dictionary. Based on the results of model testing that has been carried out using confusion matrix, the model can predict tweets that indicate depression and get an accuracy value of 78.57%.

Sentiment Analysis on Twitter Social Media towards Climate Change on Indonesia Using IndoBERT Model Muhammad Fadhil Mubaraq; 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.v6i4.4570

The phenomenon of climate change is a change in temperature and weather patterns in the long term. This incident became a frightening specter for everyone because consciously or unconsciously the bad effects of climate change are already in sight. This has become an urgency for all levels of society so that this topic has become quite hot on Social Media, especially on Twitter. The topic of climate change in Indonesia on Twitter Social Media can be analyzed so that it can be seen how people's sentiments towards this phenomenon. This research utilizes the Transformer architecture, namely IndoBERT, IndoBERT itself is the development of the BERT architecture by the IndoNLU team which has 74 million words from various Bahasa Indonesia sources. Therefore, this method was chosen in the hope of helping sentiment analysis on the topic of climate change so that public sentiment can be mapped. The test results obtained an F1-Score values of 95.6% with a tuning parameter of 0.00002 learning rate and 16 of batch size. Hopefully the results of this research can be used in future research.

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

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