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University students are frequently preoccupied with their classes, assignments and extracurricular activities, particularly on campus. This may cause them to lose track of any events or programmes that have taken place on campus. Event or programme organizers at the UiTM Machang community often publicize their events by simply placing posters throughout the campus area or disseminating messages through social media like Whatsapp and Telegram. Due to their hectic schedules, students occasionally fail to see the posters and messaging, which in turn leads to a lack of attendees at the organizers' activities. By using predictive Software Development Life Cycle (SDLC) methodology, User-centered UiTM e-Event is developed to benefit both the students and event organizers within the UiTM Machang community for improving event management. By developing the system, students’ event information can be centralized, hence, ease the students to discover and engage with upcoming events. UiTM e-Event offers an efficient platform to promote their events, facilitating the attraction of a larger audience and encouraging greater participation among UiTM Machang users. This paper also highlights the users’ feedback regarding UiTM e-Event usability testing which result positive feedback from their testing result.

Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change.

COVID-19 gives a drastic change in our lifestyle. In education, it effects on motivation, routine and the dilemma in using certain application. The university has taken various proactive measures for ensuring the productivity of their stakeholders. Students and instructors are changing their teaching and learning style which this impact on learning technology usage while having online class. This paper shares COVID-19 story in terms of learning system development online which is challenging for learners and instructors. They need to adjust themselves so that the message is conveyed, and problem is solved during the online class. This paper looks at how the learners and instructors manage their online class by using an online prototype tool, Justinmind, for assisting them to learn system development during COVID-19. The learners have been asked to plan, gather, and design the user interface according to the requirements stated previously. This study found out that there are some difficulties in conducting system development class in online mode since there is a change of routine as well as teaching and learning style. However, the difficulties have been addressed by the utilization of the technology and thus creating more exciting journey for the class regardless of the pandemic era.

Purpose – Online learning is an essential platform to support varying learning conditions, anytime and anywhere. It helps connect online communities regardless of one’s geographical location and time zone. During the COVID-19 outbreak in Malaysia, the use of online learning platforms has become a crucial option for accomplishing learning objectives. During this challenging time, universities have been looking for suitable solutions to address issues regarding the online learning process. This paper discusses the challenges in online learning, as well as the importance of collaborative learning activities during the Movement Control Order (MCO). Challenges in emotion and changes of routine among students have been detected during the MCO imposed by the government of Malaysia. Methodology – An exploratory sequential approach was carried out in this study to obtain students’ feedback in terms of their emotions and routine changes during the outbreak of COVID-19 and the subsequent implementation of the MCO. Convenience sampling was used for this research, and in order to obtain feedback on online learning during the MCO an online interview was conducted with 42 students from the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA branch in the state of Kelantan. The feedback items were recorded and transcribed in Excel format. Next, sentiment analysis was carried out using Rapidminer software. Findings – The results showed that higher education institutions or universities had to play their part in providing a virtual learning environment as convenient as possible during the Movement Control Order period. The results also showed that educators had to ensure the success of online learning by maintaining the motivation of their students Significance – The findings in this study can also benefit educators and higher education institutions or universities in executing suitable teaching and learning plans during the COVID-19 outbreak.