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 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2022): Journal La Multiapp" : 5 Documents clear
Comparative Analysis of Mammography Image Segmentation Strategies Areej Rebat Abed; Karim Hussein
Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i2.567

Abstract

Breast cancer is a serious medical problem that affects women all over the world, and it is one of the most well-known tumors that kill women. The specialists of Breast cancer Prefer to use imaging methods such as a mammography to speed up recovery and reduce the risk of breast cancer. An ROI describe the tumor will be retrieved from the image that is entered to detect a malignant tumor. One of the basic techniques used to classify breast cancer is segmentation. Segmentation may be difficult in the presence of noise, blurring or low contrast. Pre-processing aids in the removal of extraneous data from a picture or the enhancement of image contrast in the early stages. Classification is greatly influenced by segmentation. Recent research have presented automatic and semi-automated segmentation algorithms for extracting the region of interest (ROI), lesions, and masses to check for breast cancer. In this study provides high-level overview of approaches of segmentation, with a focus on mammography images from current research. The datasets that were available were discussed as well as the problems encountered during the segmentation operation for the identification of breast cancer.
Effect of Variable Thermal Conductivity and Viscosity on MHD Casson Nanofluid Flow Vertical Plate through Thermal Radiation Convective Temperature along with Velocity Slip Ramanuja Mani; A. Sudhaker; V. Nagradhika
Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i2.583

Abstract

This article presents the influences of connected variable thickness with created conductivity, nanofluid flow over a vertical level plate through convective smooth, with velocity slip boundary surroundings. The controlling vehicle nonlinear divided differential stipulations with the interrupt surroundings are non- dimensionalized. The reachable path of motion of certain existing differential conditions is then diminished to a set of joined nonlinear quintessential differential conditions utilizing convenience modify. Numerical outcomes are getting for dimensionless velocity, temperature, and nanoparticle quantity. It is discovered that the velocity increments, while each temperature and nanoparticle extent partrot with improved estimations of variable maximum conductivity and consistency. At the same time as the Dufour range and Soret, comprehensive range augmentation with working up the relative and the thing subject decompose as the Schmidt range tendencies while the temperature area decreases with extending Prandtl number and Dufour number correlations are executed with scattered facts virtually taking parent proper now the numerical outcomes. Surprising consideration is seen. Taking the entirety into account, the effects of essential parameters on fluid velocity, temperature, and focus on dispersion moreover as on the partition total mass, heat, and mass exchange figures are audited in detail. Also, this existing consideration can determine purposes in the method, which include nanofluid works out.
Neural Network Algorithm for Budget Expenditure Prediction in LPP RRI Gorontalo Rubiyanto Maku
Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i2.596

Abstract

In this Data Mining research, the researcher uses the Neural Network Algorithm to predict budget expenditures at LPP RRI Gorontalo, the goal is to find out how much cash spending at LPP RRI Gorontalo is on average in each month, so it will make it easier for the Treasurer to control cash disbursements in each month. month. Using 412 Expenditure Records Data from 2013 to 2021, the lowest RMSE value is at Hiden Layer 11, Training Cyle 400, Learning Rate 0.1 and Momentum 0.1 with RMSE 0.142. Prediction results look better because they are closer to Real Data, so Neural Networks can be used to predicting spending at LPP RRI Gorontalo.
Review of Parameters in Routing Protocols in Vehicular Ad-hoc Networks Intisar Mohsin Saadoon; Maha Ali Hussein; Farah Neamah Abbas
Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i2.604

Abstract

Vehicular Ad_hoc Network (VANET) is a sophisticated elegance of devoted cellular network that permits automobiles to intelligently communicate for different roadside infrastructure. VANETs bring with it some of demanding situations associated with Quality of Service (QoS) and performance. QoS relies upon on many parameters which includes packet transport ratio, bandwidth, postpone variance, records latency, etc. This paper, discuss numerous troubles associated with latency records, bandwidth usage, and transport of packet in VANETs. The demanding situations have been recognized in offering security, reliability and confidentiality of posted records. Finally, numerous packages of VANETs also are introduced in the modern computing scenario.
Stunting Classification in Children's Measurement Data Using Machine Learning Models Syahrial Syahrial; Rosmin Ilham; Zulaika F Asikin; St. Surya Indah Nurdin
Journal La Multiapp Vol. 3 No. 2 (2022): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v3i2.614

Abstract

The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue 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