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All Journal JOIV : International Journal on Informatics Visualization
Kamaludin, Hazalila
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Computer Science & IT

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Journal : joiv international journal on informatics visualization

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Rainfall-Runoff Modeling Using Artificial Neural Network for Batu Pahat River Basin Zulkiflee, Nurul Najihah; Mohd Safar, Noor Zuraidin; Kamaludin, Hazalila; Jofri, Muhamad Hanif; Kamarudin, Noraziahtulhidayu; Rasyidah, -
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2704

Abstract

This research delves into the effectiveness of Artificial Neural Networks with Multilayer Perceptron (ANN-MLP) and Nonlinear AutoRegressive with eXogenous inputs (NARX) models in predicting short-term rainfall-runoff patterns in the Batu Pahat River Basin. This study aims to predict river water levels using historical rainfall and river level data for future intervals of 1, 3, and 6 hours. Data preprocessing techniques, including the management of missing values, identification of outliers, and reduction of noise, were applied to enhance the accuracy and dependability of the models. This study assessed the performance of the models for ANN-MLP and NARX by comparing their effectiveness across various forecast timeframes and evaluating their performance in different scenarios. The findings of the study revealed that the ANN-MLP model showed robust performance in short-term prediction. On the contrary, the NARX model exhibited higher accuracy, particularly in capturing intricate temporal relationships and external impacts on river behavior. The ANN-MLP produces 99% accuracy for 1-hour prediction, and NARX yields 98% accuracy with 0.3245 Root Mean Squared Error and 0.1967 Mean Absolute Error. This study makes a valuable contribution to hydrological forecasting by presenting a rigorous and precise modeling methodology.
Intervention Strategies through Interactive Gamification E-Learning Web-Based Application to Increase Computing Course Achievement Mohd Safar, Noor Zuraidin; Kamaludin, Hazalila; Ahmad, Masitah; Jofri, Muhammad Hanif; Wahid, Norfaradilla; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.1001

Abstract

This study aims to help students improve their knowledge capability based on their active participation through gamification. Gamification is one of the newer methods of education that has the potential to improve student learning. This research looked into gamification's efficacy in student engagement and learning retention during teaching and learning sessions for computer science or information technology courses. The assessment involved in this study is through Pre-Test and Post-Test through instructional intervention by adapting interactive Quizizz gamification e-learning web-based application. The flow of research works begins with a survey of the problem, pre-intervention analysis, and action was taken during the intervention, ending with the implementation and observation phase. The pre and post-analysis of test results and questionnaires were accomplished and discussed. Fifty-six respondents participate in this study. Results show that 87% of the respondents have increased their percentage of marks. In the pre-test result, 56% of the respondents achieved below the 55 marks, while in the post-test, it reduced to 14%. Adoption of other gamification applications, a larger target demographic, and the addition of computer science or information technology courses will help improve the study in the future.
Intelligence Eye for Blinds and Visually Impaired by Using Region-Based Convolutional Neural Network (R-CNN) Yee, Lee Ruo; Kamaludin, Hazalila; Safar, Noor Zuraidin Mohd; Wahid, Norfaradilla; Abdullah, Noryusliza; Meidelfi, Dwiny
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.735

Abstract

Intelligence Eye is an Android based mobile application developed to help blind and visually impaired users to detect light and objects. Intelligence Eye used Region-based Convolutional Neural Networks (R-CNN) to recognize objects in the object recognition module and a vibration feedback is provided according to the light value in the light detection module. A voice guidance is provided in the application to guide the users and announce the result of the object recognition. TensorFlow Lite is used to train the neural network model for object recognition in conjunction with extensible markup language (XML) and Java in Android Studio for the programming language. For future works, improvements can be made to enhance the functionality of the Intelligence Eye application by increasing the object detection capacity in the object recognition module, add menu settings for vibration intensity in light detection module and support multiple languages for the voice guidance.
Co-Authors Abdullah, Noryusliza Ahmad, Masitah Dwiny Meidelfi Gusman, Taufik Jofri, Muhamad Hanif Jofri, Muhammad Hanif Mohd Safar, Noor Zuraidin Noraziahtulhidayu Kamarudin, Noraziahtulhidayu Rasyidah, - Safar, Noor Zuraidin Mohd Wahid, Norfaradilla Yee, Lee Ruo Zulkiflee, Nurul Najihah