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All Journal Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Data Science Insights
Siti Sarah Maidin
INTI International University
Civil Engineering, Building, Construction & Architecture Computer Science & IT 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. 
Co-Authors Dadan Nur Ramadan Fu Zhaoji Indrarini Dyah Irawati Li Yilin Norzariyah Yahya Norzariyah Yahya Norzariyah Yahya Rizky Aulia Rahman Sugondo Hadiyoso Vijay Rathnam Kowthalam Wu Guangfa Yusrina Binti Abdul Rahim