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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA INTEK: Jurnal Penelitian Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian kepada Masyarakat eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Rifki Wijaya
Telkom University, Bandung
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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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%.
DUNIA BARU PENDIDIKAN DI ERA METAVERSE UNTUK GURU SMA MUHAMMADIYAH CILEUNGSI Rifki Wijaya; Gamma Kosala; Tito Waluyo
Prosiding COSECANT : Community Service and Engagement Seminar Vol 2, No 2 (2022)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (225.399 KB) | DOI: 10.25124/cosecant.v2i2.18681

Abstract

Metaverse adalah salah satu kata yang sering didengar akhir-akhir ini. Metaverse telah merambah ke semua bidang termasuk diantaranya dunia pendidikan. Metaverse menjadi salah satu cara untuk melakukan pembelajaran interaktif. Media pembelajaran interaktif semakin berkembang di era sekarang menuju metaverse. Beberapa tools sudah banyak dikembangkan untuk mengemas pembelajaran semakin menarik untuk siswa. Salah satu aplikasi web yang cukup menarik adalah gather town. Aplikasi web ini menciptakan sebuah ruang digital dimana masingmasing individu baik siswa maupun guru dapat menciptakan avatarnya sendiri. Aplikasi ini pun memiliki banyak feature diantaranya video conference, chat, bahkan papan tulis digital. Guru, siswa dan semua yang terlibat dalam kegiatan belajar mengajar perlu mempersiapkan diri menghadapi teknologi ini. Teknologi ini sudah lama digunakan akan tetapi kemunculan covid19 menjadi waktu yang tepat dalam mengembangkan berbagai metode pembelajaran jarak jauh. Guru dan siswa perlu memiliki pemikiran yang sama mengenai metode pembelajaran metaverse ini sehingga bisa memiliki pemikiran yang sama dalam menghadapi era baru ini.Kata Kunci: Metaverse, Pembelajaran Interaktif, Pembelajaran Jarak jauh
Stress Detection Due to Lack of Rest Using Artificial Neural Network (ANN) Lukman Nurwahidin; Putu Harry Gunawan; Rifki Wijaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6642

Abstract

Currently, many people feel symptoms of stress due to lack of adequate rest. Which at this time the person will carry out activities that are very heavy both from tasks that are too heavy, work pressure that accumulates, and much more. People who experience stress symptoms sometimes don't know what causes stress. Through this research a learning machine will be made, using the Artificial Neural Network algorithm, will analyze heart rate data or BPM from 7 patient data per day, using a Fitbit smart watch will display several data such as falling asleep, waking up, REM (Rapid Eyes Movement) and, well, from the results of the data collected from the patients. Total data in this research are 36224. This research process will show the best accuracy results from several types of Artificial Neural Network algorithms. At the processing stage of the patient's heartbeat dataset, a comparison will be made between the types of Artificial Neural Network algorithms. The research will obtain the highest accuracy value of 81% from the results the Artificial Neural Network algorithm.
Comparative Analysis of ARIMA and LSTM Models for Predicting Physical Fatigue in Bandung Workers Kiki Dwi Prasetyo; Rifki Wijaya; Gia Septiana Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7282

Abstract

In today's era of rapid economic growth, there is an increasing demand for workers to increase productivity by working longer and harder. However, these demands often lead to irregular and excessive working hours, which can potentially lead to negative consequences, such as physical fatigue-a state in which the body feels tired after physical activity. Factors that influence this fatigue include age, gender, health conditions, workload and work environment. Physical fatigue poses a significant challenge in ensuring workplace safety, especially in the transportation and industrial sectors, as it can reduce overall performance, productivity and quality of work. In addition, physical fatigue also increases the likelihood of decision-making errors and workplace accidents. Predicting physical fatigue is crucial to addressing these challenges. Heart rate serves as a parameter to measure fatigue, given its proven efficacy as a marker to predict physical fatigue, which is derived from the electrocardiogram and regulated by the autonomic nervous system. This research utilizes two machine learning algorithms - ARIMA and LSTM - with heart rate (bpm) and number of steps as variables. Performance evaluation, using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), showed that the LSTM model outperformed the ARIMA model. The LSTM model showed better performance, with MSE of 0.1108 and RMSE of 0.3329, compared to the ARIMA model with MSE of 0.2397 and RMSE of 0.4895.
X Spotify Cares Clustering Analysis using K-Means and K-Medoids Pangestu, Citra; Shaufiah, Shaufiah; Wijaya, Rifki
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7279

Abstract

The rise of social media platforms, particularly Twitter, has transformed how individuals express opinions and concerns. Companies, like Spotify, leverage platforms such as Twitter for customer support and feedback gathering. This research delves into the world of Spotify Cares tweets using K-Means and K-Medoids clustering methods, aiming to enhance customer support analysis. The study employs the silhouette coefficient and the Davies-Bouldin Index (DBI) to evaluate clustering quality. With an extensive dataset covering more than 3 million Twitter customer service interactions, including 29,479 notes specific to Spotify Cares, this investigation uncovered latent patterns and themes. The versatility of K-Means and K-Medoids, proven effective in a wide range of applications, is highlighted. Therefore, K-means and K-medios were implemented in this research. The results show that K-Means, with 10 clusters (K = 10), with a DBI value of 1.76, shows moderate dispersion, indicating the potential for improvements for better segmentation precision. In contrast, K-Medoids, with 2 clusters (K = 2) and a lower DBI of 1.48, present a clearer and more compact clustering structure. This implies simplified customer categories, which is beneficial for targeted support. In conclusion, although both methods have strengths and weaknesses, K-Medoids with two clusters emerges as a promising method for Spotify Cares, offering cohesive customer groupings for efficient intervention. Future research efforts could focus on refining parameters and exploring the complex relationships between response time, sentiment analysis, and customer satisfaction to achieve a more nuanced analysis.
Integrating Heart Rate, Gyro, and Accelero Data for Enhanced Emotion Detection Post-Movie, Post-Music, and While-Music Setyawan, Hendy; Wijaya, Rifki; Kosala, Gamma
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7634

Abstract

The study investigates the integration of heart rate, gyro, and accelerometer data to enhance emotion recognition across different scenarios like post-movie, post-music, and during music listening. Recognizing the limitations of solely using heart rate data, the research combines gyroscopic and accelerometer data to provide a more comprehensive understanding of emotional responses. Employing machine learning algorithms, notably support vector machines, the aim is to develop robust models for real-time emotion recognition. Conducting rigorous experimental protocols involving motion sensor data from smartwatches, a user study with 50 participants examines emotional responses during activities such as movie-watching and music listening. The dataset includes data from accelerometers, gyroscopes, and heart rate sensors, with additional evaluation metrics to assess the effectiveness of the proposed method in detecting emotional states. The findings demonstrate significant effectiveness of an innovative neural network (NN) method in determining post-activity emotional states, with accuracies ranging from 59.0% to 83.4%, depending on the activity and context. Although NN accuracy is slightly lower compared to other methods like random forest and logistic regression, the differences are not significant, especially when compared to logistic regression. Overall, the research aims to advance emotion recognition technology for applications in human-computer interaction contexts.
Optimizing Emotion Recognition with Wearable Sensor Data: Unveiling Patterns in Body Movements and Heart Rate through Random Forest Hyperparameter Tuning Nur, Zikri Kholifah; Wijaya, Rifki; Wulandari, Gia Septiana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7761

Abstract

This research delves into the utilization of smartwatch sensor data and heart rate monitoring to discern individual emotions based on body movement and heart rate. Emotions play a pivotal role in human life, influencing mental well-being, quality of life, and even physical and physiological responses. The data were sourced from prior research by Juan C. Quiroz, PhD. The study enlisted 50 participants who donned smartwatches and heart rate monitors while completing a 250-meter walk. Emotions were induced through both audio-visual and audio stimuli, with participants' emotional states evaluated using the PANAS questionnaire. The study scrutinized three scenarios: viewing a movie before walking, listening to music before walking, and listening to music while walking. Personal baselines were established using DummyClassifier with the 'most_frequent' strategy from the sklearn library, and various models, including Logistic Regression and Random Forest, were employed to gauge the impacts of these activities. Notably, a novel approach was undertaken by incorporating hyperparameter tuning to the Random Forest model using RandomizedSearchCV. The outcomes showcased substantial enhancements with hyperparameter tuning in the Random Forest model, yielding mean accuracies of 86.63% for happy vs. sad and 76.33% for happy vs. neutral vs. sad.
Comparative Prediction of Physical Fatigue Patterns in Bandung, Indonesia Workers using CNN and ANN Ardiansyah, Muhammad Fikri Raihan; Wijaya, Rifki; Wulandari, Gia Septiana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7280

Abstract

This research explores the impact of physical fatigue on task performance and evaluates the effectiveness of Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) in predicting fatigue levels. Physical fatigue, as a critical factor influencing performance and safety, serves as a signal for the body's need for rest. Utilizing a smartwatch with heart rate sensors, this study applies ANN for subjective fatigue assessments and CNN for time series analysis. With a structured approach encompassing data collection, preprocessing, and model training, a confusion matrix evaluates the model's performance. Results indicate an accuracy of 92.4% for the ANN model with an RMSE of 0.275, while the CNN model achieves an accuracy of 85.46% with an RMSE of 0.381. These findings affirm the effectiveness of both models in predicting fatigue, providing valuable insights for future research and emphasizing the importance of comprehensive data analysis for a nuanced understanding of individual performance (Number of data: 149,796 from 6 subjects).
Sentimen Analysis Social Media for Disaster using Naïve Bayes and IndoBERT Anugerah, Sri Mulyani; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 11 No 1 (2024): April 2024
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v11i1.4771

Abstract

The rapid advancement of information and communication technology has resulted in a significant surge in data, especially text data from social media platforms. This paper presents a sentiment analysis approach using IndoBERT and Naïve Bayes algorithms to classify sentiment related to natural disasters, specifically from a dataset of tweets derived from social media platform X. The focus of this research is to categorize tweets as positive and negative sentiment to provide useful insights in improving disaster response and management, with a focus on tweets related to earthquakes, floods, and the eruption of Mount Merapi. The goal is to assist the government in allocating aid more efficiently and understanding public sentiment during disasters. The methodology used includes data collection, data preparation, labeling, categorization, word weighting using tf-idf, data separation, and classification using Naïve Bayes and IndoBERT algorithms. The results showed that IndoBERT achieved 91% accuracy, while Naïve Bayes achieved 74% accuracy. The study highlights the potential of sentiment analysis in improving disaster preparedness and more effective response strategies.
Analyzing Public Sentiments on Disaster Relief Efforts Through Social Media Data Fakhruddin, Muhammad Rafi; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 11 No 1 (2024): April 2024
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v11i1.4773

Abstract

Social media has become a source of quick but not necessarily accurate information. Especially in social media X, which is often used to share information. This research aims to conduct sentiment analysis on posts related to natural disasters that aim to maximize assistance to victims of natural disasters. This research takes datasets from tweets on social media X, the data will be labeled into positive and negative. And then the preprocessing process will be carried out, in this study, categorization will be carried out on each tweet related to the category, then the data will be divided into training and testing. Then the Term Frequency-Inverse Document Frequency (TF-IDF) feature is used to assist in reducing the weight of words that often appear in the dataset, The next step involves designing a system with a focus on applying the Support Vector Machine (SVM) Polynomial Kernel algorithm which becomes a classifier which will later be used to find the best hyperline or decision boundary that divides each review into two classes, namely positive tweets and negative tweets. Then obtained with a value of Precision of 86.49%, Recall 99.21%, F1-Score 92.42%, and Accuracy of 87.01%. This research is expected to provide involvement in making a fast and effective decision for victims of natural disasters.
Co-Authors Anugerah, Sri Mulyani Ardiansyah, Muhammad Fikri Raihan Citra Pangestu Fakhruddin, Muhammad Rafi Gamma Kosala Gia Septiana Wulandari Ginting, Dewi Swarni Br Khalid Kiki Dwi Prasetyo Komara, Riza Aria Lukman Nurwahidin M Darfyma Putra Moch Arif Bijaksana Muhammad Rahman Ali Natha, Si Gede Ngurah Chandra Adi Naufal , Dziban Nur, Zikri Kholifah Padilah, Rifki Prasti Eko Yunanto Putti, Fasya Hanifah Putu Harry Gunawan Rachdian Habi Yahya Ramadhan, Adam Zahran Rambe , Muhammad Anugra Rizky Setyawan, Hendy Shaufiah . Tito Waluyo Tito Waluyo Purboyo Tjokorda Agung Budi Wirayuda Warih Maharani