IJNMT (International Journal of New Media Technology)

The Potential of “GENIUS”: Deep Learning Integrated Application to Fight Obesity

Putu Nindya Krisnadewi Rahadi, Ananda Eka Raharja, Andi Ahmad Haidir — Mon, 15 Jul 2024 05:49:42 +0000

Lifestyle changes regarding food consumption and sedentary lifestyle has led to increase prevalence of obesity worldwide, including in Indonesia. Obesity as a risk factor for various diseases has become an urgent issue considering that currently available therapies have not shown optimal results in overcoming this problem. The "GENIUS" application is present as a body types analysis system and program recommendations for obesity therapy. The purpose of writing this paper is to find out the potential, construction mechanism, and operating mechanism of the application. The methodology of writing this paper is literature review, based on secondary data from databases such as Google Scholar, PubMed, and ScienceDirect. The construction mechanism of the application includes process of collecting dataset, creating the application and deep learning system, and launching the application. The technology utilized in the application involves image processing deep learning and recurrent neural networks, enabling it to generate outputs suit to each individual's needs and provide appropriate program recommendations. Through the "GENIUS" application, users can also consult with medical professionals, receive recommendations, and record clinical data progress in a single digital application accessible via smartphones. The application also provides an interesting sub-feature in the form of reward points given to users for using the application's features. The implementation of the application involves the quadruple helix model. The benefits of the application encompass the fields of health and knowledge, aiming to prevent obesity in order to foster an intelligent generation and achieve a healthy Indonesia.

Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors

Yudha Mulyana, Muhamad Akrom, Gustina Alfa Trisnapradika — Mon, 15 Jul 2024 05:50:14 +0000

This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts.

Convolutional Neural Network Implementation in BISINDO Alphabet Sign Language Recognition System

Aning Aning Kinanti, Donny Maulana, Edora Edora — Mon, 15 Jul 2024 05:50:55 +0000

This research develops a system for recognizing finger spelling gestures in Indonesian Sign Language (BISINDO) using Convolutional Neural Network (CNN). The objective of this research is to apply the Convolutional Neural Network (CNN) method to the BISINDO finger spelling gesture recognition system to improve its accuracy. The method employed is Convolutional Neural Network (CNN), an effective method for processing image data for pattern recognition. Based on the test results, the system demonstrates that the developed CNN model is capable of recognizing BISINDO finger spelling gestures with an accuracy of 97.5%. This indicates that the BISINDO finger spelling gesture recognition system performs well in pattern recognition. The implementation of the system for real-time prediction via a web interface using Flask also enhances its accessibility. However, there is still room for improvement, particularly in recognizing one of the 26 letters that has not been predicted accurately. For further development, it is recommended to consider collecting a larger dataset and incorporating more complex gesture variations to improve recognition accuracy.

Implementation of Support Vector Machine Method for Twitter Sentiment Analysis Related to Cancellation of u-20 World Cup in Indonesia

Muhammad Armanda, Fenina Adline Twince Tobing — Mon, 15 Jul 2024 05:51:46 +0000

The cancellation of the U-20 world cup in Indonesia in 2023 has become a hot debate among the Indonesian people because the reasons for the cancellation are still unclear. The number of pro and con opinions uploaded by the Indonesian people on twitter social media makes these opinions can be used as data to assess opinions which are divided into three categories, namely positive, negative and neutral. After being divided into three categories, sentiment analysis will then be carried out using the SVM method and comparing linear, polynomial and rbf kernels to get the best performance of existing kernels in the support vector machine method. By using confusion matrix to measure the performance of the classification, accuracy, precision, recall and f1-score can be assessed. It was found that the 80:20 data ratio had the highest accuracy of the linear, polynomial, rbf kernel and the rbf kernel had better results than the linear and polynomial kernels, namely Accuracy 78.15%, F1-Score, 76.30%, Precision 77.37% and Recall 75.58%. In addition, the data obtained also succeeded in analyzing Indonesian texts that were input externally and categorized into positive, neutral and negative. From the results that have been obtained, the support vector machine method has been successfully implemented in sentiment analysis of the U-20 world cup cancellation in Indonesia in 2023 on twitter social media

Avia Saga: A Gamified Mobile-Based Learning Management System

Putra Aldo Oswald, Dennis Gunawan — Mon, 15 Jul 2024 05:52:11 +0000

The usage of Learning Management Systems (LMS) has increased since the Covid-19 pandemic. LMS have drawbacks despite the advantages they provide. To fully support the advantages they provide, students must be motivated and involved. Adding gamification to the LMS is one way to potentially solve this issue. The MDA framework and Octalysis are combined in this research's gamification approach. The application, named Avia Saga, was designed and built using Flutter and Spring Boot as a mobile application. A trial of the application was conducted with 38 students majoring in Informatics. The evaluation of the application was done using the Hedonic-Motivation System Adoption Model (HMSAM) with a Likert scale. The research results revealed a 7% increase in the behavioral intention to use category, suggesting a greater inclination for reusing the application, and an 11.7% increase in the immersion category, indicating elevated sentiments of users being carried away by the ambiance while using the application.