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All Journal Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Data Science Insights
Siti Sarah Maidin
INTI International University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Hybrid agile development phases: the practice in software projects as performed by software engineering team Norzariyah Yahya; Siti Sarah Maidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1738-1749

Abstract

The combination of scrum and waterfall is one of the software engineering teams that preferred hybrid agile models. The purpose of combining the two models is to leverage the advantages of each also to tailor the hybrid agile model to the needs of the project. However, to what extent are the phases, stages, and features of scrum and waterfall implemented in a software project remains unclear. Additionally, which phase will employ scrum, and when will waterfall be deemed optimal is also the arising question. This research adopted a qualitative study, and interviews are used as a data collection instrument. The interview is conducted based on an interview protocol, and thematic analysis is utilized to extract the themes from the interviews. This study investigates how the scrum and waterfall models are utilized in a software project, and three themes were identified in answering the research question. The findings indicate five development phases in a hybrid agile project and that waterfall is the preferable model in planning, while development is on scrum, and project testing and deployment could be either waterfall or scrum.
A Smart Kumbung For Monitoring and Controlling Environment in Oyster Mushroom Cultivation Based on Internet of Things Framework Rizky Aulia Rahman; Dadan Nur Ramadan; Sugondo Hadiyoso; Siti Sarah Maidin; Indrarini Dyah Irawati
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2248

Abstract

Oyster mushroom (Pleurotus Ostreatus) is a fungus-like plant that is often cultivated in Indonesian agriculture. Oyster mushroom is raised by manipulating environmental parameters, so that it can grow in the provided Kumbung. Oyster mushroom requires a temperature that is used, which ranges from 23°-28°C, for humidity used between 70% -90% and for light intensity it requires light of ±300 lux. In this study, a system was designed to carry out automatic monitoring and control in real time based on the Internet of Things (IoT) which integrates DHT22 humidity and temperature sensors, BH1750 light intensity sensors and NodeMCU as a microcontroller with measurement results sent to the Firebase database. In addition, a water pump connected to the sprayer nozzle is installed in this system to maintain the humidity of the oyster mushroom curd. From the test results, the system can work automatically to stabilize temperature, humidity, and light intensity according to the ideal parameters.
The need for an enhanced IoT-based malware detection model using Artificial Intelligence (AI) algorithm: A Review Siti Sarah Maidin; Norzariyah Yahya
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.6

Abstract

The interconnected world using technology has opened the door for cyberattacks. For example, the utilization of Internet of Things (IoT) devices has increased the exposure to malware attacks. The massive amount of data generated by the IoT devices leads to the possibility of infections in the network. Due to the diverse nature of the IoT devices and the ever-evolving nature of their environment, it can be challenging to devise very comprehensive security measures. Therefore, the application of Artificial Intelligence (AI) in detecting malware has gained attention as a suitable tool for detecting malware due to its strength in malware classification. This research aims to review malware detection in IoT devices using AI and its challenges.
Decision Support System Application in Disaster Management Li Yilin; Fu Zhaoji; Vijay Rathnam Kowthalam; Wu Guangfa; Yusrina Binti Abdul Rahim; Siti Sarah Maidin; Norzariyah Yahya
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.21

Abstract

Disasters such as earthquake, flood, fire, and tsunami result in catastrophic human suffering, loss of property and other negative consequences. The continues threats of future disasters enforce human to find best possible ways to detect and take premeasured actions based on calculated risks to reduce these negative impacts of disasters. 
An Adaptive Cuckoo Search Algorithm with Deep Learning for Addressing Cyber Security Problem J. Jeyaboopathiraja; Princess Mariajohn; Siti Sarah Maidin; Jing Sun
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.366

Abstract

IoT (Internet of Things) offers continued services to organizations by connecting systems, application and services using the medium of internet. They also leave themselves open to threats including virus attacks and software thefts where the risks of losing crucial information are high. These threats harm both the business’ finances and reputation. This work offers a combined Deep Learning strategy using Artificial Neural Networks that can assist in detecting illegal software and malware tainted files. The proposed cyber security architecture uses data mining techniques to forecast cyber-attacks and prepare Internet of Things for suitable countermeasures. This framework uses two phases namely detections and predictions. This paper proposes Adaptive Cuckoo Search Optimization-based Algorithms for cloud network routes. Adaptive Cuckoo Search Algorithm are a bio-inspired protocol based on cuckoo birds’ characteristics. Artificial Neural Networks classify assaults on cloud environments. The major goal of this work is to separate malicious servers from legitimate servers that are impacted by Denial of Service and Distributed Denial of Service assaults and thus safeguard server data and ensuring they are sent to legitimate servers. The outcome from this research proposed scheme shows better performances for protecting systems from cyber-attacks in terms of values for accuracy, Precision, Recall and F1-Measure when compared to existing algorithms.
Optimizing Survival Prediction in Children Undergoing Hematopoietic Stem Cell Transplantation through Enhanced Chaotic Harris Hawk Deep Clustering R. Arthi; G Maria Priscilla; Siti Sarah Maidin; Qingxue Yang
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.468

Abstract

Cancer can impact individuals of all ages, including both children and adults. Diagnosing the pediatric cancer can be challenging due to its rarity. Typically, it is not recommended to screen for pediatric cancer as it may lead to potential harm to the children. One of the specialized treatments for pediatric cancer is Hematopoietic Stem Cell Transplant (HSCT). HSCT performs replacement of existing one’s blood cells with the donor’s bone marrow healthy cells. However, forecasting the survival rates following the pediatric HSCT is crucial and poses challenges in early detection. Many machine learning algorithms have been developed to predict the risk of transplant outcomes which depends on the type of disease or patient’s comorbidity. In this work, the enhancement of survival prediction for children who have undergone hematopoietic stem cell transplantation (HSCT) is achieved through the introduction of a deep learning model that is based on behavioral characteristics. The primary aim of this model is to identify and differentiate between the patterns of malignancy, non-malignancy, and hematopoietic conditions within the dataset of bone marrow transplant patients. The existing unsupervised machine learning algorithms, performs clustering of instances with the randomly selected centroids, which often results in local optima and early convergence affects the accuracy rate. Hence, the present approach introduces Chaotic mapping Harris Hawk Optimization (CHHO) in order to enhance the conventional k-means clustering procedure due to its significantly reduced computational complexity. To understand the pattern of the bone marrow transplant dataset, the deep clustering model with its ability of auto encoder and decoder, discriminates the labelled instanced. With the inferred knowledge proposed CHHO with Deep clustering Model (CHHO-DCM) performs the effective clustering of instances with the advantage of both local and global optimization. The simulation outcomes have substantiated the effectiveness of the suggested CHHO-DCM model as it attains the highest level of precision when compared to the prevailing clustering models in predicting the survival of pediatric patients during Hematopoietic Stem Cell Transplantation (HSCT).s enduring HSCT.
Speech Enhancement using Sliding Window Empirical Mode Decomposition with Median Filtering Technique Poovarasan Selvaraj; Siti Sarah Maidin; Qingxue Yang
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.470

Abstract

The Empirical Mode Decomposition is raising significant interest since its first introduction among the nineties. The attention in varied fields such as medical engineering, space analysis, hydrology, synthetic aperture measuring, speech enhancement, watermarking and etc. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by subsequently the least degraded IMF. Hereafter, in this article, SWEMD method is enhanced by using Sliding Window (SW) procedure. This research work has come SDG goals for health and well-being and also this research work concentrated on hearing aid application using noise level adjustment. In this SWEMDH method, the calculation of EMD is performed based on the small and sliding window along with the time axis. For each component, the total of sifting iterations is unwavering by decomposition of many signal windows by standard algorithm and calculating the average amount of sifting steps for each component. The median filter used for removed nonlinear components of this work. SWEMDH technique removed for low frequency Noisy Components. The speech quality was evaluation by the performance matrices of Mean Square Error, Perceptual evaluation of speech quality, signal to noise ratio, peak signal to noise ratio. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.
Empirical Study of the Correlation between Social Media Content and Health Issues among College Students Using Machine Learning M. Hemalatha; Siti Sarah Maidin; Jing Sun
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.365

Abstract

This study analyzes the effect of social media content on college student addiction using data science techniques. It aims to examine the correlation between different types of social media content and addictive behavior in college students. The research methodology used is non-probability sampling with a sample size of 587 college students in Tamil Nadu, India. The study uses statistical tools such as correlation analysis, regression analysis, one-way ANOVA, and Friedman ranking test to analyze the data collected. The findings suggest that the factors influencing social media addiction are positively correlated with the health issues faced by college students. The study indicates that demographic variables such as age, gender, year in college, and place of living may play a role in shaping an individual's perception of social media addiction. The results of the study can inform the development of interventions and prevention strategies to reduce social media addiction among college students. The study recommends a multi-pronged approach to address the root causes of addiction and provide students with the tools and resources they need to manage their social media use and promote their physical and mental health.
Current and Future Trends for Sustainable Software Development: Software Security in Agile and Hybrid Agile through Bibliometric Analysis Siti Sarah Maidin; Norzariyah Yahya; Muhammad Ashraf bin Fauri Fauzi; Normi Sham Awang Abu Bakar
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.473

Abstract

The industrial growth of digitalized era has given rise to a growing concern in software development. The present research investigates the prevailing and projected patterns in sustainable software development, especially those related to process innovation, with a particular emphasis on software security within Agile and Hybrid Agile approaches, employing bibliometric analysis. However, a comprehensive understanding of the security concerns of both agile and hybrid agile is crucial and needs further garnered. However, it is expected that a thorough comprehension of the hybrid agile model landscape would uncover various themes encompassing its implementation. The analysis aims to provide a comprehensive overview of the current, present, and future state of software security for agile and hybrid agile. The study employed a bibliometric approach to gather a total of 1593 journals from the Web of Science (WOS) database. This study utilizes co-citation and co-word analysis techniques to identify the most significant articles, delineate the fundamentals framework, and provide a prognosis for future development. The present investigation has successfully discovered four distinct co-citation and three distinct co-word clusters. This study offers valuable insights regarding the software security in agile and hybrid agile. The increasing evolution of the software ecosystem necessitates the prioritization of bridging the gap between agility and security. This paper provides a detailed roadmap for scholars and practitioners who are navigating this intersection
Co-Authors Dadan Nur Ramadan Fu Zhaoji G Maria Priscilla Indrarini Dyah Irawati J. Jeyaboopathiraja Jing Sun Jing Sun Li Yilin M. Hemalatha Muhammad Ashraf bin Fauri Fauzi Normi Sham Awang Abu Bakar Norzariyah Yahya Norzariyah Yahya Norzariyah Yahya Poovarasan Selvaraj Princess Mariajohn Qingxue Yang R. Arthi Rizky Aulia Rahman Sugondo Hadiyoso Vijay Rathnam Kowthalam Wu Guangfa Yusrina Binti Abdul Rahim