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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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Analisis Sentimen Pada Media Sosial Twitter Berbahasa Indonesia Dengan Metode Glove Dicky Wahyu Hariyanto; Warih Maharani
eProceedings of Engineering Vol 7, No 3 (2020): Desember 2020
Publisher : eProceedings of Engineering

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Abstract

Abstrak Seiring dengan perkembangan media sosial, warganet Indonesia ramai menggunakan media sosial untuk berbagi informasi. Salah satunya menggunakan fitur cuitan pada media sosial Twitter, untuk membahas suatu topik tertentu. Bencana merupakan salah satu topik yang ramai dibahas, mulai dari kenapa terjadinya bencana dan bagaimana penanganannya oleh pihak berwenang. Analisa sentimen dapat dilakukan untuk menganalisa cuitan dengan topik bencana ini, agar dapat digunakan sebagai tolak ukur bagaimana penangan bencana dan kenapa bencana itu terjadi menurut pendapat warganet. Pada penelitian ini dibuat analisa sentimen menggunakan word embedding Global Vector (GloVe) yang bertujuan untuk meningkat performa analisa sentimen. Model Global Vector dibentuk dari korpus Wikipedia Indonesia, dengan dataset cuitan dengan topik bencana yang berjumlah 1500 data cuitan. Klasifikasi sentimen yang digunakan adalah metode deep learning model Long Short-Term Memory (LSTM). Yang mana model Global Vector diembedd ke dalam layernya. Dalam penelitian ini akan dilakukan dua scenario pengujian dengan data cuitan dengan label data sentimen seimbang dan pengujian dengan label data sentimen tidak seimbang. Dari hasil pengujian dengan data seimbang didapatkan akurasi sebesar 73% dan pada data dengan label tidak seimbang didapatkan presisi 74,5% dan recall 74,5% dengan akurasi 75%. Kata kunci : analisa sentimen, GloVe, LSTM, Twitter, word embedding Abstract Along with the development of social media, Indonesian citizens are often using social media to share information. One of them uses twitter's social media tweeting feature, to discuss a particular topic. Disasters are one of the topics that are discussed, ranging from why disasters occur and how they are handled by the authorities. Sentiment analysis can be done to analyze tweets on the topic of this disaster, so that it can be used as a benchmark for how disaster management and why disasters occur in the opinion of citizens. In this study, sentiment analysis was made using the word embedding Global Vector (GloVe) which aims to improve the performance of sentiment analysis. The Global Vector model was formed from the Corpus Wikipedia Indonesia, with a dataset of tweets with disaster topics totaling 1500 tweet data. The sentiment classification used is the deep learning method of the Long Short-Term Memory (LSTM) model. Which is where the Global Vector model is dimbedded into its layers. In this study, two test scenarios were conducted with tweet data with balanced sentiment data labels and tests with disproportionate sentiment data labels. From the test results with balanced data obtained 73% accuracy and in data with unbalanced labels obtained 74.5% precision and 74.5% recall with 75% accuracy. Keywords: GloVe, LSTM, sentiment analysis, word embedding
Analisis Ulasan Produk Pada Media Sosial (twitter) Untuk Meningkatkan Kualitas Produk Handphone Menggunakan Metode Aspect-based Dengan Pendekatan Lexicon Serventine Andhara Evhen; Warih Maharani
eProceedings of Engineering Vol 8, No 2 (2021): April 2021
Publisher : eProceedings of Engineering

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Abstract

Abstrak Media sosial juga sudah marak digunakan sebagai media untuk menyampaikan aspirasi atau ulasan tentang suatu produk. Kegiatan ini membuat banyak data ulasan tersebar luas di jejaring media sosial. Twitter adalah salah satu platform yang sering digunakan untuk menulis ulasan karena bersifat terbuka dan bebas untuk mengekspresikan pendapat. Data yang tersebar dapat menjadi acuan peningkatan kualitas produk dengan dibuat sebuah analisis sentimen berbasis aspek. Analisis sentimen berbasis aspek mengacu pada aspek atau fitur pada produk tersebut. Penilitian dilakukan dengan menggunakan tiga ulasan produk handphone Iphone 11 pada platform twitter dengan menggunakan analisis sentimen menggunakan klasifikasi lexicon. Hasil evaluasi terbaik didapatkan dengan skenario pertama yang menggunakan parameter full preprocessing dan kamus lexicon Liu. Kamus yang digunakan sudah di terjemahkan kedalam bahasa Indonesia. Skenario ini memiliki hasil exact match error ratio sebesar 42.11%. Kata kunci : aspect based sentimen analysis, ekstraksi aspek, lexicon Indonesia Abstract Social media has also been widely used as a medium to convey aspirations or reviews about a product. This activity makes a lot of data reviews widely spread on social media networks. Twitter is one of the platforms that is often used to write reviews because it is open and free to express opinions. The scattered data can be used as a reference for improving product quality by making an aspect-based sentiment analysis. Aspect-based sentiment analysis refers to an aspect or feature of the product. The research was conducted using data on iPhone 11 mobile product reviews on the Twitter platform using sentiment analysis using the Lexicon classification. The best evaluation results are obtained with the first scenario using full preprocessing parameters and the Liu’s Lexicon dictionary. The dictionary used has been translated into Indonesian. This scenario has an exact match error ratio of 42.11%. Keywords: aspect based sentiment analysis, aspect extraction, Indonesian lexicon
Sentiment Analysis of Forest Fires on Social Media Networks Twitter Using the Long Short Term Memory (LSTM) Method Aziz Alfauzi Aziz; Warih Maharani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.866

Abstract

The topic of forest fires is of significant interest on social media platforms. In this case, Twitter has been used by 11.8 million users as a means to spread information about forest fires. Twitter, a microblogging service launched on July 13, 2006, allows users to share information for free to themselves and others. Public sentiment related to forest fires can be analyzed through opinions and discussions on Twitter social media. This research aims to analyze the Sentiment of Forest Fires on Twitter Social Networks using the Long Short Term Memory (LSTM) Method. The research data was obtained by crawling the Twitter API using the keyword "forest fire." After crawling, 7,000 tweet texts were collected and labeled as "Negative" and "Positive." Through the preprocessing stage, using a 7,000 dataset, the TF-IDF accuracy of the developed LSTM model reached 68.14%. In addition, the GloVe expansion feature was performed with the Tweet corpus, which resulted in an increase in accuracy of 11.77% to 80.13% in the LSTM model. Meanwhile, the FastText expansion feature with the Common Crawl corpus also increased the accuracy by 11.99% to 80.59% on the LSTM model.
Big Five Personality Detection on Twitter Users Using Gradient Boosted Decision Tree Method Adhie Rachmatulloh Sugiono; Warih Maharani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.933

Abstract

In 2020, the Covid-19 virus caused a pandemic that made most people more active on social media, such as Twitter. Twitter has a tweet feature allows its users to send short messages about how they feel and think at that moment. Based on someone's tweet, we know their mindset, and it allows us to know the personality of that person. One model of personality is the Big Five personality. Big Five divides personality into five classes: openness, conscientiousness, extraversion, agreeableness, and neuroticism. Several ways can be done to determine personality, such as taking a psychological test. However, it can take a long time and total concentration. Therefore, this study conducted a Big Five personality detection on Twitter users using the Gradient Boosted Decision Tree (GBDT) method. This study aims to obtain a high accuracy value by weighting it through the TF-IDF method and using sentiment and emotion features. This study utilized an Indonesian dataset that was collected through Twitter API. This study consists of two scenario tests, with the first scenario test being carried out with an imbalanced dataset and the second scenario test being carried out by applying the oversampling technique with SMOTE method to handle the imbalanced dataset. By applying SMOTE method, this study obtained a high accuracy with a value of 60.36%.
Personality Detection on Reddit Using DistilBERT Alif Rahmat Julianda; Warih Maharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5236

Abstract

Personality is a unique set of motivations, feelings, and behaviors humans possess. Personality detection on social media is a research topic commonly conducted in computer science. Personality models often used for personality detection research are the Big Five Indicator (BFI) and Myers-Briggs Type Indicator (MBTI) models. Unlike the BFI, which classifies personalities based on an individual’s traits, the MBTI model classifies personalities based on the type of the individual. So, MBTI performs better in several scenarios than the Big Five model. Many studies use machine learning to detect personality on social media, such as Logistic Regression, Naïve Bayes, and Support Vector Machine. With the recent popularity of Deep Learning, we can use language models such as DistilBERT to classify personality on social media. Because of DistilBERT’s ability to process large sentences and the ability for parallelization thanks to the transformer architecture. Therefore, the proposed research will detect MBTI personality on Reddit using DistilBERT. The evaluation shows that removing stopwords on the data preprocessing stage can reduce the model’s performance, and with class imbalance handling, DistilBERT performs worse than without class imbalance handling. Also, as a comparison, DistilBERT outperforms other machine learning classifiers such as Naïve Bayes, SVM, and Logistic Regression in accuracy, precision, recall, and f1-score.
Depression Detection of Users in Social Media X using IndoBERTweet Fadhel, Muhammad; Maharani, Warih
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.13354

Abstract

According to the Ministry of Home Affairs, the population of Indonesia stands at 273 million, Indonesia has approximately 167 million active subscribers to virtual entertainment platforms, including YouTube, Facebook, Instagram, and Twitter. The use of online entertainment is huge, particularly on Twitter, and has been associated with mental health implications, such as depression. This research objective is to do a comprehensive study about the IndoBertweet deep learning framework to investigate the prevalence of depression in social media, focusing on Twitter. Utilizing the DASS-42, the research estimates depression levels based on user interactions and reactions to tweets. The results of this research showed that the IndoBERTweet method achieved an accuracy rate of 82% in detecting depression using Twitter data. This research highlights the importance of intervention strategies to support the mental health of social media users, emphasizing the importance of proactive measures in addressing mental well-being issues in the digital space.
Big five personality prediction based in Indonesian tweets using machine learning methods Maharani, Warih; Effendy, Veronikha
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1973-1981

Abstract

The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.
Implementasi Website sebagai Media Promosi Desa Wisata Kemawi Kusuma, Guntur Prabawa; Prima Kurniati, Angelina; Atastina, Imelda; Maharani, Warih; Ervina, Ersy; Aji Gunadi, Gagah; Wijaya, Yaffazka Afazillah; Purwanto, Zadosaadi Brahmantio; Al Giffari, Muhammad Zacky
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.2060

Abstract

Desa wisata merupakan salah satu jenis destinasi wisata yang berpotensi meningkatkan kegiatan ekonomi suatu desa. Namun sering kali informasi tentang potensi desa hanya tersebar terbatas dengan metode promosi lisan. Hasil identifikasi awal diKelompok Sadar Wisata Desa Kemawi di Kecamatan Sumowono, Jawa Tengah, telah menunjukkan bahwa desa mereka memiliki potensi wisata alam, pertanian, perkebunan, dan budaya. Salah satu yang dibutuhkan adalah sarana promosi desa wisata menggunakan website. Website dapat menyebarkan informasi secara cepat dan menjadi “landing page” sebagai muara jalur informasi yang disebarkan melalui media sosial seperti Instagram. Website sebagai sarana promosi desa wisata perlu dipelihara agar dapat berjalan dengan baik secara terus-menerus. Pengelolaan website desa wisata oleh tenaga-tenaga lokal desa wisata sangat diperlukan untuk menjamin keberlanjutan konten website. Selain itu, diperlukan pula pelatihan tentang penyusunan paket wisata secara komprehensif untuk mempromosikan seluruh potensi wisata dan meningkatkan kualitas layanan. Kegiatan pengabdian masyarakat ini memberikan kontribusi berupa pembangunan website sebagai sarana promosi Desa Wisata Kemawi, pembentukan tim pengelola website, pelatihan pengelola website, dan peningkatan kualitas layanan desa wisata. Aparat desa dan tim pengelola website telah merasakan manfaat dari kegiatan pengabdian masyarakat ini dan menyatakan siap untuk: 1) melanjutkan pengelolaan website sebagai media promosi desa wisata, serta 2) memasarkan potensi desa wisata.
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.
Leveraging Social Media Data for Forest Fires Sentiment Classification: A Data-Driven Method Maharani, Warih; Daud, Hanita; Muhammad, Noryanti; Kadir, Evizal Abdul
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 3 (2024): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.3.392-407

Abstract

Background: The rise in forest fires over the last two years, which is due to rise in dry weather conditions and human activities, have greatly impacted an area of 1.6 million hectares, leading to significant ecological, economic, and health issues, hence the need to improve disaster response strategies. Previous research determined the lack of coverage regarding public response during forest fires with conventional methods such as satellite images and sensor data. However, social media platforms provide real-time information generated by users, along with location information of disaster events. Sentiment analysis helps in understanding the public reactions and responses to natural disasters, thereby increasing awareness about forest fires. Objective: The purpose of this research is to assess the efficiency of Long Short-Term Memory (LSTM) method in classifying sentiment for social networks in regard to forest fires. This research aims to examine the effect of TF-IDF, unigram, and the FastText features on the effectiveness of the classification of sentiment. Methods: The precision, recall, and F1 score of 2, 3, and 4 determined in the LSTM models with commonly available sentiment analysis tools, such as the Vader Sentiment Analysis and SentiWordNet was used to evaluate the performance of the model. Results: With an improvement of roughly 10%, the four layers of the LSTM model generated the best performance for the evaluation of sentiments about forest fires. The LSTM model with FastText achieved F1, recall and precision scores of 0.649, 0.641, and 0.659, which exceeds lexicon-based method including SentiWordNet and Vader. Conclusion: The experimental results showed that the LSTM model outperformed lexicon-based methods when used to analyse the tweets related to forest fire. Additional research is required to integrate rule-based models and LSTM models to develop a more robust model for dynamic data.   Keywords: Forest Fire, Disaster, Long Short-Term Memory, LSTM, Vader, SentiWordnet
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