Since the concept of the industry cluster was popularized by Porter in 1990, many countries try to improve the competitiveness through industry sector. Not only companies who take part in the cluster but also academic institutes, government agencies, associations, and supportive industries. The more actors involved in the cluster the more knowledge were distributed among the member of cluster. Although, many literatures about cluster explained how knowledge is important for the cluster development. But, there is no specific knowledge management methodology or system for the cluster. This study is concerned about knowledge exchange in the cluster by using knowledge engineering methodology to analyze, model and design Knowledge Management System (KMS). At the end of this study, KMS will be studied the possibilities of implementing in the handicraft cluster in Thailand as our case study. This paper present methodology and primary result from knowledge engineering. Then the KMS architecture was proposed as the result of the preliminary study in this paper.

Knowledge-based economy forces companies in every country to group together as a cluster in order to maintain their competitiveness in the world market. The cluster development relies on two key success factors which are knowledge sharing and collaboration between the actors in the cluster. Thus, our study tries to propose a knowledge management system to support knowledge management activities within the cluster. To achieve the objectives of the study, ontology takes a very important role in the knowledge management process in various ways; such as building reusable and faster knowledge-bases and better ways of representing the knowledge explicitly. However, creating and representing ontology creates difficulties to organization due to the ambiguity and unstructured nature of the source of knowledge. Therefore, the objectives of this paper are to propose the methodology to capture, create and represent ontology for organization development by using the knowledge engineering approach. The handicraft cluster in Thailand is used as a case study to illustrate our proposed methodology.

A challenge for organizations is to increase employee performance and motivation, since the most crucial asset of every organization is manpower. Many companies and factories have started implementing online training platforms under the concept of “Massive Open Online Courses (MOOCs)” in their workplace to foster employee performance. Previously, the mobile application called “HSC MOOC” which is provided by the Halal Science Center, Chulalongkorn Univer-sity, Thailand functioned as a solution that encourages self-learning on online platforms at companies in Thailand. However, the main barrier or risk that occurs when implementing an online platform is the user’s motivation, since the dropout rate is considered as a serious issue regarding MOOCs. Thus, incentive and re-ward were added to online training programs which aimed to motivate employees. Many types of rewards were provided for employees who had met their own company’s expectations. Recently, psychology research papers have illustrated that non-monetary rewards seem to provide greater results on the side of employee’s motivation. However, not all types of non-monetary rewards provide positive impact on employee’s motivation. Therefore, the aim of this research is to present the effect of different non-monetary rewards on employee performance. Ninety volunteer employees from a food manufacturing company in Chiang Mai, Thailand participated in this research. The experiment was divided into two sections. The first section aimed to measure the motivation of employees which based on different non-monetary rewards. The questionnaire for measuring Valence, Instrumentality, and Expectancy variables (VIE theory) was deployed to test employee motivation in 3 different groups; “Tangible Non-Monetary Re-wards”, “Social Non-Monetary Rewards” and “Job Related Non-Monetary Re-wards”. The test consisted of 10 items using a 5-point Likert scale. The second experiment aimed to reveal which type of non-monetary reward is the most suitable for motivating employees in participating and completing the course in MOOCs. Participants in different groups were assigned to learn via MOOCs on their mobile device within a period of 30 days. Different types of non-monetary rewards were provided only for participants who had completed certain conditions in MOOCs. The overall results showed that the group of tangible non-monetary rewards reached the significant highest score on the VIE questionnaire and over 60% of participants exposed to tangible non-monetary rewards completed the course’s conditions in MOOCs.

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Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.