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All Journal HAYATI Journal of Biosciences JOIV : International Journal on Informatics Visualization Aptisi Transactions on Technopreneurship (ATT)
Zainuddin, Nurul Hila
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Social Sciences

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High-Resolution Downscaling with Interpretable Relevant Vector Machine: Rainfall Prediction for Case Study in Selangor Abdul Rashid, Raghdah Rasyidah; Milleana Shaharudin, Shazlyn; Filza Sulaiman, Nurul Ainina; Zainuddin, Nurul Hila; Mahdin, Hairulnizam; Mohd Najib, Summayah Aimi; Hidayat, Rahmat
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.8.2.2700

Abstract

Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling. We conducted a study to determine the effectiveness of Relevant Vector Machine (RVM), one of the machine learning approaches, in outperforming existing statistical methods in downscaling historical rainfall data in the complex terrain of Selangor, Malaysia. While machine learning eliminates the requirement for manual feature selection when extracting significant information from predictor fields, considering multiple pivotal factors is essential. These factors include identifying relevant atmospheric features contributing to rainfall, addressing missing data, and developing a significant model to predict daily rainfall intensity using appropriate machine-learning techniques. The Principal Component Analysis (PCA) technique was employed to choose relevant environmental variables as input for the machine learning model, and various imputation methods were utilized to manage missing data, such as mean imputation and the KNN algorithm. To assess the performance of the RVM-based rainfall model, we collected a dataset from the Department of Irrigation and Drainage Malaysia. We used Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) as evaluation metrics. This study concluded that Relevance Vector Machine (RVM) models are suitable for forecasting future rainfall since they can support large rainfall extremes and generate reliable daily rainfall estimates based on rainfall extremes. In this study, the RVM model was employed to determine a predictive association between predictand variables and predictors.
Assessing Climate Factors and Cyanobacterial Abundance on Microcystins Prediction Using Artificial Neural Network: A Case Study in Malaysia’s Drinking Water Reservoir Ahmad, Nurul Awatif; Sinang, Som Cit; Zainuddin, Nurul Hila; Abdul Rajak, Noorazrin
HAYATI Journal of Biosciences Vol. 33 No. 1 (2026): January 2026
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.33.1.204-218

Abstract

Toxic cyanobacterial blooms often lead to contamination with cyanotoxins, particularly microcystins. This study aims to examine microcystins persistence in a selected public water supply system and predict their concentration at various points based on climate factors and cyanobacterial abundance. Using the Enzyme-Linked Immunosorbent Assay (ELISA) method, microcystins concentrations were quantified at various points of the water supply system, including the raw water intake, reservoir, water treatment plant outlet, and distribution system. The highest microcystins concentration was detected at the reservoir with a mean concentration of 2.63 μg/L. An artificial neural network (ANN) model was developed to predict microcystins concentration. Rainfall, temperature, chlorophyll-a, phycocyanin (BGA-PC), and mcyE gene copy numbers were used as inputs, while microcystins concentrations at various water sampling points served as outputs of the multilayer perceptron ANN. Using the Statistical Package for the Social Sciences (SPSS, ver. 29), three networks with scaled conjugate gradient, sigmoid functions, and one hidden layer with 4 to 13 neurons were trained and validated to determine the best configuration that fits the observed data. The result shows a satisfactory prediction at the reservoir (Point 2) with low values of error (root mean square error = 0.065) and high coefficient values (R2 = 0.894) between experimental and predicted values, which are below the maximum value of the actual concentrations. Phycocyanin (BGA-PC) and chlorophyll-a had the most positive effects in predicting microcystins concentrations. These results indicate that ANN modelling can be a reliable tool for predicting microcystins contamination in drinking water reservoir.
Leveraging A Hybrid Machine Learning Model for Enhanced Cyberbullying Detection Syafariani, Fenny; Lola, Muhamad Safiih; Mutalib, Sharifah Sakinah Syed Abd; Nasir, Wan Nuraini Fahana Wan; Hamid, Abdul Aziz K. Abdul; Zainuddin, Nurul Hila
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.536

Abstract

Cyberbullying is a form of bullying that occurs through digital technology on various social media platforms. This issue has become critical, particularly when it involves racial statements that can threaten community harmony. Many researchers worldwide are working on solutions for automatic hate speech and cyberaggression detection using different machine learning models. This study aims to introduce a novel hybrid method for detecting cyberbullying, utilizing a combination of Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), collectively referred to as SVM-LDA. The methodology involves integrating SVM and LDA techniques. The models efficiency was assessed using various metrics, offering a comparative analysis of the hybrid model against individual machine learning models. The results show that the proposed hybrid model achieved 96.1% accuracy and outperformed single machine learning models on the Twitter dataset. The hybrid model also demonstrated robustness in handling imbalanced classes for cyberbullying detection. The proposed SVM-LDA hybrid approach shows significant potential in effectively detecting cyberbullying, even in cases of class imbalance. This model offers a more robust solution compared to traditional single machine learning models in detecting cyberaggression.
Co-Authors Abdul Rajak, Noorazrin Abdul Rashid, Raghdah Rasyidah Ahmad, Nurul Awatif Filza Sulaiman, Nurul Ainina Hairulnizam Mahdin, Hairulnizam Hamid, Abdul Aziz K. Abdul Milleana Shaharudin, Shazlyn Mohd Najib, Summayah Aimi Muhamad Safiih Lola Mutalib, Sharifah Sakinah Syed Abd Nasir, Wan Nuraini Fahana Wan R Fenny Syafariani Rahmat Hidayat Sinang, Som Cit