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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science The Indonesian Journal of Computer Science
Suhandi, Vartin
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Personal Training with Tai Chi: Classifying Movement using Mediapipe Pose Estimation and LSTM Suhandi, Vartin; Santoso, Handri
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5536

Abstract

This research aims to tackle challenges in the practice of Tai Chi Bafa Wubu (BWTC), where limited access to trained instructors and daily schedules hinder training consistency. The proposed approach combines Human Pose Estimation technology using Mediapipe with Long Short-Term Memory (LSTM) models to classify BWTC movements. This method utilizes video datasets collected from the internet and augmented to train LSTM models, focusing on An, Ji, and Zhou movements. Experimental results show that the model can predict movements with high accuracy in training and direct user trials. The development of these techniques facilitates more effective self-training in Tai Chi, leveraging advanced AI technology to improve movement supervision and user movement interpretation accuracy. This study not only offers a practical solution to enhance Tai Chi training efficiency and accessibility but also explores the potential application of pose estimation technology and machine learning in broader sports movement monitoring and evaluation. It is expected that this research will make a significant contribution to health and fitness by enabling individuals to independently practice Tai Chi with technological guidance, promoting better mental and physical health among the general public.
Implementation of K-Means, Hierarchical, and BIRCH Clustering Algorithms to Determine Marketing Targets for Vape Sales in Indonesia Laurenso, Justin; Jiustian, Danny; Fernando, Felix; Suhandi, Vartin; Rochadiani, Theresia Herlina
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.4871

Abstract

In today's era, smoking is a common thing in everyday life. Along with the development of the times, an innovation emerged, namely the electric cigarette or vape. Electric cigarettes or vapes use electricity to produce vapor. The e-cigarette business is very promising in today's business world due to the consistent increase in market demand. However, determining the target buyer is one of the things that is quite important in determining the success of a business. In this analysis, the background of each region in Indonesia has different diversity; therefore, observation of data is needed to find out which regions in Indonesia have the potential to increase marketing based on profits (margins) to support the target market analysis process so that companies do not suffer losses and increase business success. In this study, the analysis will be carried out using vape quantity, margin, and purchasing power data in each region, which is processed using 3 algorithms: K-Means, Hierarchical, and BIRCH. The results of the clustering of the three algorithms produce two clusters. The K-means, Hierarchical, and BIRCH algorithms produce the same clusters: a potential cluster consisting of 18 cities and a non-potential cluster consisting of 45 cities. To see the performance of the model results, an evaluation was carried out using the Silhouette score, Davies Bouldin, Calinski Harabasz, and Dunn index, which obtained results of 0.765201, 0.376322, 315.949434, and 0.013554. From these results, it can be concluded that the clustering results are not too good and not too bad because the greater the Silhouette Score, Calinski Harabasz, and Dunn Index value, the better the clustering results while for Davies Bouldin the smaller the value means the better the clustering results.
Machine Health in a Click: A Website for Real-Time Machine Condition Monitoring Rochadiani, Theresia Herlina; Santoso, Handri; Aprilia, Novia Pramesti; Laurenso, Justin; Suhandi, Vartin
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3592

Abstract

Globalization in the current digital era has made it easier to use information technology to obtain fast and accurate information. One source of information is a website that can be used to monitor machine conditions in the industry. A good machine maintenance strategy is needed to maintain and increase machine productivity. Therefore, this research aims to build a website to monitor machine conditions in real-time. The machine condition is monitored using sushi sensors to track parameters such as temperature, acceleration, and velocity. Deep learning analysis is then used to identify anomalies in the machine. Using the SCRUM method, this website was successfully built. From the results of tests carried out using unit testing and integrated testing, every feature on this website can run well and according to user needs.
Co-Authors Aprilia, Novia Pramesti Fernando, Felix Jiustian, Danny Laurenso, Justin Rochadiani, Theresia Herlina Santoso, Handri