Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal PROCEEDING IC-ITECHS 2014
Abdul Razak, Nor Hafizah
Unknown Affiliation
Computer Science & IT Education

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

Found 2 Documents
Search

 1 
Early Detection on Company Bankruptcy: a Comparison of Neural Networks and Logistic Regression Ahmad Shukri, Muhammad Fairus; Abdul Razak, Nor Hafizah; Mat Din, Mazura
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1678

Abstract

Detecting firm insolvency at an early stage is crucial for financial analysis and risk management. This study compares the efficacy of two widely used bankruptcy prediction techniques: Neural Networks (NN) and Logistic Regression (LR). We evaluate each approach based on its accuracy, computing efficiency, and interpretability, aiming to identify a suitable predictive model that aligns with specific objectives, data characteristics, and the need for interpretability in financial decision-making. This research indicates that NN provides superior prediction accuracy but is accompanied by increased computing demands and reduced interpretability. In contrast, LR offers more speed, requires fewer processing resources, and provides explicit understanding of variable correlations; however, it may not perform well with intricate and nonlinear data. This study confirms the significance of choosing a suitable predictive model that balances competing demands of accuracy, efficiency, and interpretability in financial decision-making.
Deep Learning for Meal Recognition and Calorie Estimation Ahmad Fariz, Ahmad Nabil Bin; Abdul Razak, Nor Hafizah; Mat Din, Mazura
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1679

Abstract

Accurate calorie estimates from foods are prerequisite for diet following and health monitoring. Manual calorie estimations according to age-old methods mostly tend to be inaccurate. This paper proposes the use of convolutional neural networks (CNNs) for precise identification from food images and prediction of meal calories to solve the concern. Therefore, the objective is to create a model capable of recognizing foodstuff besides estimating their caloric content. Developing a model that could correctly identify food ingredients and calculate their energy value through training and testing was important in this project. Our aim here was to verify the accuracy of the model using systematic reviewing means as well as an interface where it can be tested. A dataset of 1,337 high-quality images divided into 12 culinary classes cake, hamburger, noodles, spaghetti, pizza, chicken curry, croissant, French fries, fried chicken, roast chicken, lobster nasi goreng, and waffle was obtained from Roboflow Universe and used for this project. The selection of technique which is YOLO (You Only Look Once) model architecture and flow design because it proved to be highly efficient for real-time object recognition.
Co-Authors Ahmad Fariz, Ahmad Nabil Bin Ahmad Shukri, Muhammad Fairus Mat Din, Mazura