cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Urfan Taghiyev
Contact Email
u.taghiyev@newinera.com
Phone
-
Journal Mail Official
u.taghiyev@newinera.com
Editorial Address
Jl. Borong Raya Baru I, Makassar, South Sulawesi, Postal Code: 90233. Indonesia
Location
Unknown,
Unknown
INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol. 5 No. 1 (2024): Journal La Multiapp" : 6 Documents clear
The Applications based on Video Motion Magnification Techniques Amed, khalida; lbrahim, Abdul-Wahab Sami; Sadiq, Asmaa
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.814

Abstract

This research study's major goal is to present an important overview of recent work on applications based on Video Motion magnification (VMM) approaches during the course of the last 10 years. Over the past few years, video motion magnification (VMM) technologies have attracted a lot of attention and research, particularly as applications based on video motion have become more and more necessary. With an increase in the number of recommended procedures, surveying and evaluation become necessary. In this study, we will highlight how the survey was focused on several articles that used motion video augmentation techniques in their applications. We contrast these applications as well.
Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques Mtasher, Ashwaq Katham; Al-wakel, Esraa Hassan Jawad
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.843

Abstract

Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.
Prediction of Mental Health of Elementary School (SD) Students using the Decision Tree Algorithm with K-Fold CV testing in Bone Bolango Regency, Gorontalo Province. Liputo, Salahuddin; Tupamahu, Frangky
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.853

Abstract

Mental health is a fundamental component of the WHO definition of health, which means not only being free from disease but also being physically, mentally and socially healthy. Currently, mental health has become a major issue in modern society because if it is good it will enable us to realize our own potential, overcome the normal stresses of life, work productively, and be able to contribute to the society in which we live. In Indonesia, problems related to mental health are related to the lack of mental health detection tools. Meanwhile abroad, much research has been developed regarding mental health detection based on innovative technology using Machine Learning. This research aims to predict mental health using the Social Emotional Health Survey-Secondary (SEHS-S) as a prediction evaluation criterion using Machine Learning with the Decision Tree algorithm method with K-Fold CV testing. The sample in this research was elementary school students in Bone Bolango Regency, Gorontalo Province.
Artificial Intelligence and the Silent Pandemic of Antimicrobial Resistance: A Comprehensive Exploration Al Marjani, Mohammed F.; Mohammed, Rana K.; Ahmed, Ziad O.; Mohialden, Yasmin Makki
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.952

Abstract

The rise of antimicrobial resistance (AMR) in the 21st century has made it a worldwide disaster. Due to the fast spread of AMR illnesses and the lack of novel antimicrobials, the silent pandemic is well known. This issue requires a fast and meaningful response, not just speculation. To address this dilemma, deep learning (DL) and machine learning (ML) have become essential in many sectors. As a cornerstone of modern research, machine learning helps handle the many aspects of AMR. AI helps researchers construct clinical decision-support systems by collecting clinical data. These methods enable antimicrobial resistance monitoring and wise use. Additionally, AI applications help research new drugs. AI also excels at synergistic medicine combinations, providing new treatment methods. This paper summarizes our extensive study of AI and the silent epidemic of antibiotic resistance. Through deep learning and machine learning applications across multiple dimensions, we hope to contribute to the proactive management of AMR, moving away from its presentation as a future problem to present-day solutions.
Design of Forecasting for Perishable Product with Artficial Neural Network Sabhira, Chintya Salwa
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.1000

Abstract

Raw materials are an important part of the manufacturing industry, especially for raw materials that do not last long or have a lifespan. To be able to produce good products, the raw materials used must be of good quality. This happened to company XYZ which operates in the cereal and snack food industry. Inventory control is quite a big challenge for companies. In this year the company experienced losses due to a shortage of finished snacks products, due to finished goods being obsolete due to a lack of accuracy in forecasting snack demand. The research raised forecasting using the Artificial Neural Network method. ANN is known to be able to produce good accuracy values in predicting sales.
Analysis Planning Relay Layout with Computerized Relative Allocation of Facilities Technique Approach Marcelani, Rigel Saesaria; Joumil Aidil SZS; Yekti Condro Winursito
Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i1.1588

Abstract

PT XYZ is one of the manufacturing industries engaged in the production of palm oil processing. The products produced are cooking oil and margarine. So far, the implementation of activities has been hampered by the current layout conditions having a less efficient material handling path length, in addition, the distance between facilities is too far ± 150m which results in repeated transportation, this results in PT XYZ's material transfer activities being less effective. Therefore, this study aims to redesign the layout of production facilities in order to minimize material handling costs and raw material movement distances to be more effective. From these problems, the method used is the CRAFT (Computerized Relative Allocation of Facilities Techniques) method, this method can be done with the help of FTC (From to Chart), ARC (Activity Relation Chart) and ARD (Activity Relationship Diagram) calculations. The results of this study showed that the CRAFT algorithm succeeded in saving total material handling costs of Rp. 469,352.04 per day and reducing the total distance of movement by 67.15 meters. The reduction in material handling costs achieved was Rp 2,114,734.77 or around 81.84%, while the reduction in material handling distance reached 306.85 meters or around 82.05%.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2026): Journal La Multiapp Vol. 7 No. 1 (2026): Journal La Multiapp Vol. 6 No. 6 (2025): Journal La Multiapp Vol. 6 No. 5 (2025): Journal La Multiapp Vol. 6 No. 4 (2025): Journal La Multiapp Vol. 6 No. 3 (2025): Journal La Multiapp Vol. 6 No. 2 (2025): Journal La Multiapp Vol. 6 No. 1 (2025): Journal La Multiapp Vol. 5 No. 6 (2024): Journal La Multiapp Vol. 5 No. 5 (2024): Journal La Multiapp Vol. 5 No. 4 (2024): Journal La Multiapp Vol. 5 No. 3 (2024): Journal La Multiapp Vol. 5 No. 2 (2024): Journal La Multiapp Vol. 5 No. 1 (2024): Journal La Multiapp Vol. 4 No. 6 (2023): Journal La Multiapp Vol. 4 No. 3 (2023): Journal La Multiapp Vol. 4 No. 2 (2023): Journal La Multiapp Vol. 4 No. 1 (2023): Journal La Multiapp Vol. 3 No. 6 (2022): Journal La Multiapp Vol. 3 No. 5 (2022): Journal La Multiapp Vol. 3 No. 4 (2022): Journal La Multiapp Vol. 3 No. 3 (2022): Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp Vol. 3 No. 1 (2022): Journal La Multiapp Vol. 2 No. 6 (2021): Journal La Multiapp Vol. 2 No. 5 (2021): Journal La Multiapp Vol. 2 No. 4 (2021): Journal La Multiapp Vol. 2 No. 3 (2021): Journal La Multiapp Vol. 2 No. 2 (2021): Journal La Multiapp Vol. 2 No. 1 (2021): Journal La Multiapp Vol. 1 No. 6 (2020): Journal La Multiapp Vol. 1 No. 5 (2020): Journal La Multiapp Vol. 1 No. 4 (2020): Journal La Multiapp Vol. 1 No. 3 (2020): Journal La Multiapp Vol. 1 No. 2 (2020): Journal La Multiapp Vol. 1 No. 1 (2020): Journal La Multiapp More Issue