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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Community Service Seminar and Community Engagement (COSECANT)
Rifki Wijaya
Telkom University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering 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 Citra Pangestu; Shaufiah Shaufiah; Rifki Wijaya
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.
Co-Authors Citra Pangestu Gamma Kosala Gia Septiana Wulandari Kiki Dwi Prasetyo Lukman Nurwahidin Putu Harry Gunawan Rachdian Habi Yahya Shaufiah Shaufiah Tito Waluyo Warih Maharani