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Accurate forecasting of consumer demand for goods is extremely important as it allows companies to provide the right amount of goods at the right time. Autoregressive integrated moving average (ARIMA) is a popular method for forecasting time series data, and previous studies have shown that ARIMA can produce fairly accurate forecasting results. On the other hand, the neural network method has advantages in detecting non-linear patterns in data. In addition to these methods, the hybrid method, which combines the ARIMA and neural network methods, was applied in this study. A comparison analysis was conducted to determine the best performing model. In this study, the neural network model was found to be the most accurate.

An industrial activity is never separated from potential hazards and risks that can lead to workplace accidents. A small accident could bring a major impact on a company. Woodworking industry is the type of industry with the highest accident rate in manufacturing sector. This research is focused on a company which take roles in selling furniture and building components made of high quality wood. In its practical history, the company has never conducted risk assessment within the company, which makes the company is probably at high risk and can be easily exposed to various potential hazards. Risk assessment is a necessary step to create a safe working environment for the company and for the people involved in it. By conducting risk assessment, the workplace accidents which possibly occur within the company can be reduced or even eliminated. This research is conducted by identifying hazards as the initial stage. And then risk analysis is performed using risk matrix to determine risk ranking of the identified hazards and risks. Once the risk ranking of each risk is determined, the risk is prioritized based on its urgency to be managed. According to risk ranking, production machines are mainly the source of risk within the company with the highest severity rate. In order to mitigate the risk, Job Safety Analysis for company’s production machines is organized so it can provide the most relevant output in the end. This research conduces recommendations for the company to reduce the potential hazards which possibly arise within the company.

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Road accidents are a major issue in Indonesia, and their number increases every year. Based on previous studies, mental fatigue is one of the biggest factors leading to road accidents and is majorly affected by mental workload. Driving duration is one of the factors that triggers mental fatigue. The prior literature cites electroencephalogram (EEG) measurement as the gold standard for measuring fatigue. However, there has been only limited study to examine the EEG indicators that are affected by driving duration, and the prior research still contains disagreements regarding the best EEG parameter for use in measuring fatigue. Therefore, this study aimed to evaluate the effect of driving duration on EEG fluctuation and determine the best EEG parameter related to fatigue. Seven participants were asked to spend three hours driving in a medium-fidelity simulator. A one-way ANOVA and correlation analysis were performed to measure the effect of driving duration on the EEG indicators and determine the correlation of the indicators. A Receiver Operating Characteristics (ROC) curve was also utilized to determine the variable with the greatest correlation with the subjective sleepiness indices. The results showed that at the end of three hours' driving, there was an increment in delta and theta activities, followed by a decrement in alpha and beta activities. In addition, the correlation of all bands was significant, with positive results for the alpha-beta and theta-delta bands, and a negative result in relation to each other. Furthermore, the results from the ROC curve revealed the Relative Power Ratio (RPR) of theta, the RPR of alpha, and the ratio of θ/α+β to be the best indicators among others, demonstrating a high degree of accuracy (above 85%).

KoinWorks is one of financial technology in Indonesia that running its businesses with online peer to peer lending model based on mobile application. The number of KoinWorks mobile application users is increasing however there are still some obstacles along the way. Canceled order rates of KoinWorks are still quite high. In 2017, there are only 7,5% of the total users who actually did the transaction. In addition, the result of a preliminary study conducted on 114 respondents shows that nearly 75% of the users find it difficult to use the application. This study aims to evaluate the usability of KoinWorks mobile application, to analyze the existing usability problem, and to achieve a better usability interface design. Usability is measured using both quantitative and qualitative measurements. The quantitative measurement comprises user performance metrics such as task success, time on task and error that measure effectiveness and efficiency of the mobile application as well as saccade on eye tracking. The qualitative measurements include the System Usability Scale questionnaire (SUS), Questionnaire for User Interface Satisfaction (QUIS) and Retrospective Think Aloud (RTA). This study shows that KoinWorks mobile application has a poor performance and usability. Interface redesign was proposed and resulted in a better performance and usability.

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