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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics Tematik : Jurnal Teknologi Informasi Komunikasi Walisongo Journal of Information Technology Jurnal Indonesia : Manajemen Informatika dan Komunikasi DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Jurnal Ilmiah Teknologi dan Komputer (JITTER) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
I Made Agus Dwi Suarjaya
Universitas Udayana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Journal of Information Systems and Informatics

 1 
Classification of Explicit Songs Based on Lyrics Using Random Forest Algorithm Luh Kade Devi Dwiyani; I Made Agus Dwi Suarjaya; Ni Kadek Dwi Rusjayanthi
Journal of Information System and Informatics Vol 5 No 2 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i2.491

Abstract

This study focuses on the potential negative impact of explicit songs on children and adolescents. Although an explicit song labeling program is currently in place, its coverage is limited to songs released by artists affiliated with the Recording Industry Association of America (RIAA). Consequently, songs falling outside the program's scope remain inadequately labeled. To address this issue, a machine learning model was developed to effectively classify explicit songs and mitigate mislabeling challenges. A comprehensive dataset of song lyrics was collected using web scraping techniques for the purpose of constructing the classification model. The model was trained using the TF-IDF vectorization method and the random forest algorithm. A meticulous comparison of distribution parameters was conducted between the training and testing data sets to determine the optimal model. This superior model achieved a training-testing data distribution ratio of 90:10, with an impressive accuracy of 96.3%, precision of 99.3%, recall of 93.5%, and an f1-score of 96.3%. The classification results revealed that explicit songs accounted for 39.22% of the dataset, and the visual representation highlighted the fluctuating prevalence of explicit songs over time. Additionally, the hip-hop/rap genre exhibited the highest proportion of explicit songs, reaching a staggering 92%.
Sentiment Analysis of Unemployment in Indonesia During and Post COVID-19 on X (Twitter) Using Naïve Bayes and Support Vector Machine Setiawati, Putu Ayulia; Suarjaya, I Made Agus Dwi; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.713

Abstract

The COVID-19 pandemic has impacted health, economy, and society. Social distancing measures and quarantine policies have restricted economic activities, leading to downturns in COVID-19-affected regions and a subsequent rise in unemployment rates, particularly in urban areas. Concurrently, there has been a remarkable surge in the utilization of the X (Twitter) platform, with Indonesia ranking 6th globally in X (Twitter) users. This study aims to understand the diverse perspectives of society on unemployment and the factors influencing society's views on unemployment through sentiment analysis of X (Twitter) data. By analyzing 576,764 tweets from April 2020 to October 2023, tweets are categorized into positive, neutral, and negative classes. Classification model was built to classify tweet data by implementing TF-IDF for word weighting, and a pair of machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM). Model evaluation yielded the highest accuracy of 81.5% using Naïve Bayes. The classification outcomes highlight prevalent negative perceptions of unemployment among Indonesians, totaling 50.03%. This research contributes to the literature by providing a large-scale analysis of social media data to uncover public sentiment trends and offering insights for policymakers to address unemployment and improve welfare.
Sentiment Analysis of X (Twitter) Comments on The Influence of South Korean Culture in Indonesia Savitri, Putu Rheya Ananda; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.749

Abstract

Hallyu or Korean wave refers to the phenomenon of South Korean values and culture spreading to other countries, ultimately influencing global culture. South Korean culture, such as K-pop music, dramas, films, fashion, food, and lifestyle, has gained popularity in Indonesia since 2002. Because South Korean culture influences many aspects of life in Indonesia, responses to this Korean wave are widely discussed in social media, especially through X (Twitter) ranging from positive sentiment to negative sentiment. To gain a more in-depth and detailed understanding of public opinion, a classification process was conducted on the social media platform X (Twitter) using a deep learning algorithm based on the CNN method. The results of this classification provide more accurate and informative insight into the attitudes, opinions, and reactions of the Indonesian people towards the influence of South Korean culture in this country. The research was conducted using 717,998 tweet data resulting in an accuracy of 79%.
Implementation of a Telegram-Based Child Consultation Chatbot Using IndoBERT Whurapsari, Gusti Ayu Wahyu; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1079

Abstract

Children’s health and development are crucial aspects that require proper attention from parents. However, many parents lack easy access to immediate consultation regarding their child's health and well-being. To address this issue, this study develops a child consultation chatbot on Telegram using the IndoBERT model. The chatbot utilizes data from Halodoc and Alodokter, structured into an intent-based format with 227 tags, 5,428 patterns, and 278 responses. The dataset undergoes preprocessing, including lowercasing, text cleaning, normalization, stopword removal, and stemming. Four preprocessing scenarios are tested, including the use of term frequency-based stopwords without applying stemming, the use of NLTK stopwords without stemming, the use of term frequency-based stopwords combined with stemming, and the use of NLTK stopwords combined with stemming. The best model, trained with an 80:20 training-validation split using term frequency-based stopwords without stemming, achieves 98% accuracy, 98.5% F1-score, 98.9% precision, and 98.5% recall. The chatbot successfully classifies user intent and ensures structured interactions through a confidence-based response mechanism. This research demonstrates that an IndoBERT-based chatbot can effectively assist parents in obtaining quick and relevant information regarding their children's health and development.
Co-Authors A.A. Ketut Agung Cahyawan W Aditama, I Putu Dede Raditya Adyatma, Putu Nanda Arya Agus Kerta Nugraha, I Wayan Anak Agung Ketut Agung Cahyawan Wiranatha Anak Agung Ketut Agung Cahyawan Wiranatha Apriana, Krisna Astuti, Ni Nyoman Indri Wika Ayu Krisnasari Ni Komang Ayu Wirdiani Ayu Wirdiani Bakkara, Kevin Christopher Bhagaskara, I Made Bagita Bouchra, Ferhati Cahyawan Wiranatha, Anak Agung Ketut Agung Candra, I Putu Wijaya Adi Danito, Philip Datar, Fandy Kusumaraditya Dewa Gede Kesuma Yoga Dextiro, Kadek Deksy Dharmawan, I Putu Yogi Prasetya Diatmika, Nyoman Gede Rayka Sedana Dwi Putra Githa Dwi Rusjayanthi, Dwi Efraim William Solang Eva Martina Sitorus G M Arya Sasmita Gede Widya Dharma Geovaldo, I Putu Hendra Gusti Agung Ayu Putri Gusti Agung Mayun Kukuh Jaluwana I Gusti Ngurah Bagus Picessa Kresna Mandala I Ketut Adi Purnawan I ketut Gede Darma Putra I Made Adhiarta Wikantyasa I Made Sukarsa I Made Sunia Raharja I Made Sunia Raharja, I Made Sunia I Nyoman Piarsa I Putu Agung Bayupati I Putu Agus Eka Pratama I Putu Arya Dharmaadi I Putu Wira Cahaya Pratama Yudha Ida Bagus Gde Dwipermana Sidhi Ida Bagus Kade Taruna Ida Bagus Nyoman Yoga Ligia Prapta Johan Tamin Kadek Suar Wibawa Ketut Mediana Ayu Candrayani Komang Arta Wibawa Krisnadinatha, I Gede Arya Kristina Kristina Luh Kade Devi Dwiyani Made Andika Verdiana Mahadiputra, Putu Gede Krisna Mahaputra, Putu Andre Mahayana, I Putu Gede Panji Badra Nalista, Ni Made Naila Narayana, I Putu Kevin Ari Ngeo Goa, Mario Valentino Ngurah Indra Purnayasa Ni Luh Ketut Inggitarahayu Anggasemara Ni Made Ika Marini Mandenni Ni Putu Ayu Widiari Ni Putu Viona Viandari Novenrodumetasa, Nathania nugraha, gemara adiyasa parahita Nugraha, Made Adhi Satrya Pande Nengah Purnawan Permana, Kadek Arya Putra Prabhaswara, Ilham Yoga Pratama , I Putu Agus Eka Pratama, I Putu Yoga pramesia Purwanthi, Luh Putu Ary Putu Adhika Dharmesta Putu Ratih Wulandari Putu Wira Buana Putu Yudha Yarcana Rahaditya Kusuma, Nyoman Tri Reyhan Todo Noer Yamin Ridho Hisbi Sulaiman Rusjayanthi, Ni Kadek Dwi Sadhaka, Anak Agung Istri Prabhaisvari Salsabila, Archels Ramadhany Saputra, Putu Alta Sari, Ni Kadek Ratna Sasmita, Gusti Made Arya Satriya, Rizki Dwi Savitri, Putu Rheya Ananda Setiawati, Putu Ayulia Shevira, Sheila Solang, Efraim William Susila, A.A Ngurah Hary Trisna , I Nyoman Prayana Trisna, I Nyoman Prayana Vidya Chandradev Wayan Oger Vihikan Wayan Oger Vihikan, Wayan Oger Whurapsari, Gusti Ayu Wahyu Wiartha, I Gusti Made Diva Widia Widhiasih, Ni Putu Nirmala Dewi Widiantari, Ni Putu Triska Wiranatha, A.A. Ketut Agung Cahyawan Wiranatha, Anak Agung Ketut Agung Cahyawan Wiranatha, Anak Agung Ketut Cahyawan Wiratama, Bayu Adhya Yanisa Putri, Komang Sri Zebedeus Cheyso