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Journal La Multiapp
Published by Newinera Publisher
ISSN : 27163865     EISSN : 27211290     DOI : https://doi.org/10.37899/journallamultiapp
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering
International Journal La Multiapp peer reviewed, open access Academic and Research Journal which publishes Original Research Articles and Review Article, editorial comments etc in all fields of Engineering, Technology, Applied Sciences including Engineering, Technology, Computer Sciences, Architect, Applied Biology, Applied Chemistry, Applied Physics, Material Engineering, Civil Engineering, Military and Defense Studies, Photography, Cryptography, Electrical Engineering, Electronics, Environment Engineering, Computer Engineering, Software Engineering, Electromechanical Engineering, Transport Engineering, Mining Engineering, Telecommunication Engineering, Aerospace Engineering, Food Science, Geography, Oil & Petroleum Engineering, Biotechnology, Agricultural Engineering, Food Engineering, Material Science, Earth Science, Geophysics, Meteorology, Geology, Health and Sports Sciences, Industrial Engineering, Information and Technology, Social Shaping of Technology, Journalism, Art Study, Artificial Intelligence, and other Applied Sciences.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol. 4 No. 6 (2023): Journal La Multiapp" : 4 Documents clear
Innovative Design of Solar Benches for Public Spaces: Renewable Energy with Arduino Integration Kango, Riklan; Pongtularan, Ezra Hartarto; Ain, Mohamad Isram M.
Journal La Multiapp Vol. 4 No. 6 (2023): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v4i6.850

Abstract

This research aims to design and implement an innovative park bench that combines solar panel technology with the Arduino platform. The methodology of this research consists of designing the integration of solar panels with Arduino components that are assembled integrated on the physical bench. In the analysis phase, measurements of solar energy production were conducted under various light and weather conditions. The performance of the system in battery charging and fulfillment of energy needs was evaluated. Environmental data from Arduino sensors were analyzed to illustrate the effect of environmental conditions on system operation. The results showed that the solar bench produced higher energy during daytime and reduced during nighttime conditions. The system can supply energy for purposes such as lighting and charging electronic devices. The voltage and current data at night show inefficiency in charging the solar panel, while the cell phone charger condition works as long as the battery is above 15%. In addition, environmental analysis through Arduino sensors revealed a correlation between light intensity and energy production and usage. These findings can be used to optimize the operation of the smart bench system based on changing environmental conditions. In conclusion, this solar bench design has the potential to reduce environmental impacts and support the use of renewable energy in urban public spaces, with the potential to increase public awareness of sustainable energy
Enhancing User Authentication with Facial Recognition and Feature-Based Credentials Mohialden, Yasmin Makki; Hussien, Nadia Mahmood; Ali, Doaa Muhsin Abd
Journal La Multiapp Vol. 4 No. 6 (2023): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v4i6.903

Abstract

This research proposes a novel and trustworthy user authentication method that creates individualized and trusted credentials based on distinctive facial traits using facial recognition technology. The ability to easily validate user identification across various login methods is provided by this feature. The fundamental elements of this system are face recognition, feature extraction, and the hashing of characteristics to produce usernames and passwords. This method makes use of the OpenCV library, which is free software for computer vision. Additionally, it employs Hashlib for secure hashing and Image-based Deep Learning for Identification (IDLI) technology to extract facial tags. For increased security and dependability, the system mandates a maximum of ten characters for users and passwords. By imposing this restriction, the system increases its resilience by reducing any possible weaknesses in its defense. The policy also generates certificates that are neatly arranged in an Excel file for easy access and management. To improve user data and provide reliable biometric authentication, this study intends to create and implement a recognition system that incorporates cutting-edge approaches such as face feature extraction, feature hashing, and password creation. Additionally, the system has robust security features using face recognition.
Analysis of Blended Learning Development in Distance Learning in Variation of Borg & Gall and Addie Models Untoroseto, Dedi; Triayudi, Agung
Journal La Multiapp Vol. 4 No. 6 (2023): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v4i6.973

Abstract

With the success of blended learning and the use of online media on learning outcomes and from article search results, it shows that there have been many articles that contain blended learning and various media uses, and reviews are needed about it by reviewing existing articles or commonly called literature reviews. Borg & Gall and ADDIE models. The Borg & Gall model and ADDIE are two teaching models used in colleges and universities. ADDIE stands for Analyze, Design, Development, Implementation, and Evaluation. In the Borg & Gall model, the steps taken are research and information. Research and information is used to collect information about the need for learning evaluation instruments for learning media development courses for students. In the ADDIE model the steps taken are the same as the original which includes aspects of Analyze, Design, Development, Implementation, and Evaluation. Thus, what is needed in this development is a reference about the product procedure to be developed. The description of the development model of Borg and Gall, described as follows; Educational research and development (R&D) is the process used to develop and validate educational products. The validity of interactive blended learning is: (1) according to expert reviews the content of metrics shows a good category (92%), (2) according to expert reviews learning design is in the good category (88%), (3) according to expert reviews learning media shows a good category (86%), Thus, this interactive blended eLearning does not need to be revised and can be used for further research.
Prediction of Elementary School Students' Mental Health using Decision Tree Algorithm with K-Fold Cross-Validation in Bone Bolango Regency, Gorontalo Province Liputo, Salahuddin; Tupamahu, Franky; Hasyim, Wahyudin; Sabiku, Sri Ariyanti; Parman, Rahmawaty; Hanapi, Aan
Journal La Multiapp Vol. 4 No. 6 (2023): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v4i6.1005

Abstract

Mental health is a fundamental component of the World Health Organization's definition of health, encompassing not only freedom from illness but also well-being in physical, mental, and social dimensions. In today's modern society, mental health has become a paramount issue, as its soundness enables individuals to realize their own potential, cope with normal life pressures, work productively, and contribute effectively to their communities. In Indonesia, mental health-related challenges are associated with the absence of a reliable mental health detection tool. Conversely, abroad, there has been a substantial amount of research focused on innovative technology-based mental health detection using Machine Learning. This study aims to predict mental health using the Social Emotional Health Survey-Secondary (SEHS-S) as the evaluation criterion for prediction through Machine Learning. The Decision Tree algorithm is employed, and the prediction model is tested using K-Fold Cross-Validation, resulting in 8 folds with an accuracy rate of 78.61%.

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