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p-Index From 2021 - 2026
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All Journal Journal of System and Computer Engineering Journal of Engineering, Electrical and Informatics
Lisna Junita Pairunan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Utilization of Voice Recognition in the Development of Android-Based Medical Emergency Response Applications Suryaningsih Patandung; Yohanis Padallingan; Srivan Palelleng; Plesmi Plesmi; Lisna Junita Pairunan
Journal of Engineering, Electrical and Informatics Vol. 5 No. 3 (2025): October: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i3.6672

Abstract

Medical emergencies can occur at any time and often without warning. Conditions such as heart attacks, strokes, seizures, or severe accidents require immediate assistance from others. However, many victims are unable to request help due to physical limitations and delays in accessing emergency services, especially among the elderly and people with disabilities. Along with technological advances, medical emergency response systems can be developed through Android-based applications. Limited mobility in emergency situations makes voice recognition technology a suitable solution, using common emergency voice commands such as “help” or “pain.” This study aims to design and implement an application capable of rapidly sending emergency alerts through an emergency button or voice command activation, while also displaying the user's real-time location using Google Maps. The system was developed using the Waterfall method, which includes requirements analysis, system design, implementation, testing, and maintenance. Application testing was conducted using black-box testing to evaluate functionality and a User Acceptance Test (UAT) involving 15 respondents based on a Likert scale. The results indicate that all application features function properly, with a user acceptance level of 91.44%, categorized as very good. Therefore, the Tolong Kini application is considered effective and beneficial in supporting faster medical emergency response.
Prediction of Protein Content of Shredded Goldfish Based on Physical Characteristics and Processing Process Using Random Forest Regression Method Irene Devi Damayanti; Muhammad Sofwan Adha; Lisna Junita Pairunan
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2270

Abstract

Shredded goldfish is a processed fishery product that has high nutritional value, especially in its protein content. This study aims to predict the protein content in shredded goldfish based on the physical characteristics of the ingredients (moisture, ash, fat, and crude fiber content) and processing parameters (temperature and frying time) using the Random Forest method. The data used consisted of 10 samples of proximate analysis results and were divided into training data (67%) and test data (33%). The model was evaluated using MAE, MSE, RMSE, and R-squared metrics. The evaluation results showed that the model produced an MAE of 0.5649, MSE of 0.5409, RMSE of 0.7354, and R² of 0.0898. The low R² value indicates that the model is still not optimal in explaining variations in the target data. The prediction of protein levels for new data with certain characteristics resulted in a value of 20.16%, which is still within the range of actual values. This research shows the potential of using machine learning methods in predicting the nutritional value of food products, although increased accuracy is still needed through additional data and exploration of other models. It is recommended that the frying temperature is 155°C to 160°C and the frying time is 11 minutes to 13 minutes to maintain optimal protein levels.
Co-Authors Irene Devi Damayanti Muhammad Sofwan Adha Plesmi Plesmi Srivan Palelleng Suryaningsih Patandung Yohanis Padallingan