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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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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%.
SMALL ROAD MONITORING SYSTEM FOR FOUR WHEEL VEHICLES Solang, Efraim William; Suarjaya, I Made Agus Dwi; Pratama , I Putu Agus Eka
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.139

Abstract

The application of the vehicle regulation system at intersections in rural areas does not yet have a good regulatory system compared to the vehicle control system in urban areas. The lack of equitable distribution of road widening in rural areas makes people often have problems accessing roads when using four-wheeled vehicles. People often experience problems when accessing small roads, especially when more than one vehicle passes at the same time. This problem was also experienced in DesaTanjung Sari, Kecamatan Cijeruk, Kabupaten Bogor. This study aims to design and build a small road monitoring system so that car drivers get a warning if there are four-wheeled vehicles that have passed the small road. The object recognition method used in the small road monitoring system for four-wheeled vehicles is the HC-SR04 ultrasonic sensor which is placed on the left, right, and top sides. Arduino Uno R3 microcontroller will be used as the main system device. The communication between points used is LoRa Ra-02 as the sender and receiver. The results of this study show the results of the system prototype running well in accordance with the planned functions.
Analisis Sentimen Masyarakat terhadap Tayangan Televisi Nasional menggunakan Metode Deep Learning Bouchra, Ferhati; Suarjaya, I Made Agus Dwi; Rusjayanthi, Ni Kadek Dwi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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

Abstract

Indonesia’s television industry faces fierce competition, particularly in chasing ratings and ad revenue. This has ultimately led to declining broadcast quality on some national TV stations. This research aims to understand perceptions towards content quality by focusing on public opinion through sentiment analysis of social media (Twitter) using Bi-LSTM and Word2Vec methods. The research involved data collection, preprocessing, vectorization, data splitting, model training and testing, evaluation to find the best model, sentiment data classification, and finally, sentiment data analysis. Using a dataset of 515,492 sentiment points, the model achieved an accuracy of 96.4%, precision of 72.1%, recall of 72.0%, and f1-score of 72.8%. Analysis of Twitter user sentiment leans towards neutral and positive perceptions. The results of the sentiment analysis of Twitter users tend to be neutral and positive. The results of the public satisfaction trend show a change in the pattern of public satisfaction with the quality of television station content.
Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison Dharmawan, I Putu Yogi Prasetya; Suarjaya, I Made Agus Dwi; Vihikan, Wayan Oger
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9167

Abstract

Baby cry classification is an important topic in Machine Learning, especially in the healthcare field, as crying is the primary form of communication for infants to convey their needs or conditions. Many inexperienced parents tend to interpret baby cries in a limited way, even though each cry has unique characteristics that represent specific needs such as hunger, discomfort, sleepiness, flatulence, and abdominal pain. With the advancement of technology, identification of baby cries can now be done automatically through AI-based applications, but the implementation is still limited. This study compares the performance of ensemble learning methods, namely Random Forest and XGBoost, with the Whisper model in classifying baby cries. The results show that the Whisper-small model has the best performance with precision 0.9115 and recall 0.9007, followed by XGBoost with slightly degraded performance after hyperparameter optimization. Random Forest showed the lowest performance among the three models. Transformer-based models such as Whisper-small proved to be superior in capturing the complex patterns of infant cries, compared to tree-based models. These findings indicate the great potential of accurate and reliable models to help parents understand the needs of infants more effectively, thereby improving the quality of infant care.
Sistem Pengenalan Huruf Braille Menggunakan Metode Deep Learning Berbasis Website I Made Agus Dwi Suarjaya; Wiratama, Bayu Adhya; Ayu Wirdiani
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 3 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.3.244

Abstract

Braille letters are used as a written language for people with visual impairments. To this day, Braille letters are used in in inclusive schools where they are taught to disabled students. However, there are physical capability barriers faced by teachers when correcting Braille answer sheets written by visually impaired students. The ability to read Braille letters is also important for family members to support the students' learning process. This research’s purpose was to create a system that can transliterate Braille letters into the Latin alphabet using deep learning methods. The proposed deep learning methods include Base Convolutional Neural Network (CNN), ResNet50, VGG-16, and Inception-v3. The Braille Character image dataset used consists of 12,641 data divided into 37 classes from the AEyeAlliance repository. The Base CNN model used achieved 98% training accuracy, 99% validation accuracy, and 99.1% testing accuracy.
Automatic Pet Feeder Rotational Model Using MQTT and Mobile Application Mahadiputra, Putu Gede Krisna; Suarjaya, I Made Agus Dwi; Wibawa, Kadek Suar
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 12 No 2 (2024): Vol. 12, No. 2, August 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2024.v12.i02.p04

Abstract

Automatic pet feeding systems with dispenser models have limitations on the type of feed that is only accomodate dry food, while pets (cats) with a tendency consuming dry food are prone to disease in the urinary system. This research focuses on designing a rotational model automatic pet feeding system that can accommodate both dry food and wet food, also set the feeding time through mobile applications via MQTT by utilizing an ESP32 microcontroller connected to a 5V stepper motor, RTC and load cell sensor. The system has success rate about 90% on pet feeding automation, but the container upper fold has difference angel about 5.4o with the expected position (error rate 6%). Every weight sensor has the successive error rates are 1.41% (1st feed block), 1.91% (2nd feed block), and 0.68% (3rd feed block). The Android application takes only 1-1.5 seconds to displaye the latest feed weight data.
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.
Analysis of Indonesian Public Satisfaction Towards Goods Delivery Services using the CNN-LSTM Model Sadhaka, Anak Agung Istri Prabhaisvari; Agus Dwi Suarjaya, I Made; Buana, Putu Wira
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101507

Abstract

The fast-paced development of e-commerce in Indonesia has driven the expansion and rising demand for goods delivery services. Every goods delivery service offers consultations or Q&A sessions about their services through Twitter. The number of tweets from users of goods delivery services can be used to determine the level of public satisfaction with these delivery services. The sentiment analysis process is conducted using a CNN-LSTM deep learning model. Evaluation of the CNN-LSTM model resulted in an accuracy of 0.838, an F1-score of 0.838 for the macro average and micro average, a precision of 0.840 for the macro average and 0.838 for the micro average, and a recall of 0.840 for the macro average and 0.838 for the micro average. Based on the results of analysis, it was found that Indonesian people tend to be dissatisfied with existing delivery services.
Implementation of FP-Growth Algorithms for Promo Package Determination in a Scooter Motorcycle Workshop Business I Gusti Ngurah Bagus Picessa Kresna Mandala; I Ketut Adi Purnawan; I Made Agus Dwi Suarjaya
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9499

Abstract

This study applies the FP-Growth algorithm to design bundled promotions for a scooter motorcycle accessory store and workshop in Denpasar, Bali. FP-Growth was chosen for its efficiency in mining frequent itemsets without generating candidate sets. From 23,381 transaction records (January-August 2024), the algorithm identified 16 association rules using a minimum support of 1% and confidence of 50%. These rules were selected based on lift values and product relevance. One notable example is the association between "BAUT TITANIUM GR5 M10 X 60" and "BAUT TITANIUM GR5 M8X50", which had a lift of 47.814, indicating a very strong co-purchase relationship. These high-lift combinations present valuable opportunities for bundling and targeted point-of-sale offers. The algorithm performed efficiently, with a runtime of just 0.1354 seconds and 402.6 MB of memory usage. Bundles based on these associations were presented to customers, and feedback was collected through a Customer Satisfaction (CSAT) survey involving 56 recent buyers. The survey yielded a high CSAT score of 83.93%, demonstrating customer satisfaction with the bundles’ relevance and appeal. These results confirm that FP-Growth can effectively inform promotional strategies by identifying strong product pairings that align with actual purchasing behavior. Strategically promoting such bundles not only enhances customer experience but also encourages multi-item purchases. This data-driven bundling approach is practical and profitable for medium-sized retail businesses, ultimately supporting the goal of increasing the Average Order Value.
Analisis Sentimen Publik Terkait Kekerasan Seksual di Indonesia dengan Algoritma Naïve Bayes dan SVM Nalista, Ni Made Naila; Mandenni, Ni Made Ika Marini; Suarjaya, I Made Agus Dwi
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8556

Abstract

Semakin meningkatnya kasus kekerasan seksual yang terjadi di Indonesia, dan media sosial merupakan ruang bagi masyarakat Indonesia untuk mengekspresikan pendapat. The increasing number of sexual violence cases in Indonesia, along with the role of social media as a space for the public to express their opinions, forms the basis for this research. The study aims to classify various types of public sentiment expressed on X (formerly Twitter) and Instagram comments by applying two algorithms for comparison: Naïve Bayes and SVM. Several processes carried out, including data collection from social media, data preprocessing, manual labeling, and the implementation of both algorithms on the processed dataset. The data sources utilized are posts written in Indonesian on X (Twitter) and Instagram, focusing on issues of sexual violence in Indonesia. The sentiment analysis results were grouped into three main categories: positive, negative, and neutral. The outcomes show that SVM achieved an accuracy of 82.17% using an 80:20 data split without applying GridSearch for optimization. The SVM results outperformed those of Naïve Bayes, which achieved an accuracy of 78.92%. This investigation leads to the conclusion that SVM is more optimal in analyzing public sentiment related to sexual violence in Indonesia compared to Naïve Bayes. The sentiment analysis results from social media regarding sexual violence in Indonesia show that the majority of sentiments are neutral, with the dataset being dominated by informative content, case reports without emotional expression, and off-topic comments
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