Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Computer Engineering, Electronics and Information Technology
Rahmawati Rahmawati
PT Bisa Artificial Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Performance Evaluation of the Transfer Learning Model in Car Vehicle Detection Dafa Fidini Asqav; Rahmawati Rahmawati; Monica Marito Manurung
Journal of Computer Engineering, Electronics and Information Technology Vol 2, No 1 (2023): COELITE: Volume 2, Issue 1, 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/coelite.v2i1.59716

Abstract

Congestion is a common problem that occurs in big cities. It is necessary to make an ITS (Intelligent Transportation System) using computer vision to monitor street density by counting the number of passing vehicles. The basis of vehicle counting is vehicle detection. This study will compare the performance of vehicle detection models in order to make it easier to determine which model is suitable for implementation. The dataset used is the Vehicle Detection Image Set. The models used are InceptionV3, Xception, MobileNet, MobileNet V2, VGG 16, VGG 19, Efficientnet (B7), and Efficientnet V2 (L). The results show that Inception, Xception, Mobilenet, and VGG19 are the models with the highest test accuracy. The VGG 16 model has the shortest training duration while the Efficientnet V2 (L) has the longest training duration.
Comparison of Machine Learning and Deep Learning Algorithms for Classification of Breast Cancer Puji Ayuningtyas; Rahmawati Rahmawati; Akhmad Miftahusalam
Journal of Computer Engineering, Electronics and Information Technology Vol 2, No 2 (2023): COELITE: Volume 2, Issue 2, 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/coelite.v2i2.59717

Abstract

Statistical data from the American Cancer Society which shows that breast cancer ranks first with the highest number of cases of all types of cases of malignant tumors (cancer) worldwide. through a data mining process that is used to extract information and data analysis, a classification process can be carried out to carry out further analysis of the pattern of a data. The dataset used in this study is the Breast Cancer Wisconsin (Diagnostic) Dataset obtained from UCI Machine Learning. The purpose of this study is to compare five algorithms, namely Logistic Regression, K Neighbors Classifier (KNN), Decision Tree Classifier, Deep Neural Network, Genetic Algorithm. The results showed that deep neural network algorithms and multilayer perceptron-genetic algorithms get 96% accuracy, logistic regression algorithms have 96% accuracy, then KNN with 94%, and decision tree classifier with 92%.
Co-Authors Akhmad Miftahusalam Dafa Fidini Asqav Monica Marito Manurung Puji Ayuningtyas