Involving domain experts in the construction of specific domain ontology / Syerina Azlin Md Nasir, Nor Laila Md Noor and Wan Fairos Wan Yaacob

Most works suggested the involvement of domain experts when handling and dealing with specific-domain knowledge. Therefore, the construction of ontology requires active participation from domain experts especially in preserving the authenticity of digital artifacts, or specifically in this study, the batik artifacts. In this study, two main components of ontology mainly structure and content were identified. For each component, an expert has been chosen to evaluate the ontology syntactically and semantically. Both experts were then being interviewed to gain their rich and contextualised insights. The results show that the participation from domain experts in the early stage of the construction leads to the development of ontology, in this case, Malaysian Batik Heritage Ontology (MBHO) with defined classes and properties. This stage is really crucial in ensuring the interpretation of cultural information can be done properly and accordingly. The developed ontology could later be used by others to further enhance Malaysian textile ontology

Decision Tree Model for Non-Fatal Road Accident Injury

Non-fatal road accident injury has become a great concern as it is associated with injury and sometimes leads to the disability of the victims. Hence, this study aims to develop a model that explains the factors that contribute to non-fatal road accident injury severity. A sample data of 350 non-fatal road accident cases of the year 2016 were obtained from Kota Bharu District Police Headquarters, Kelantan. The explanatory variables include road geometry, collision type, accident time, accident causes, vehicle type, age, airbag, and gender. The predictive data mining techniques of decision tree model and multinomial logistic regression were used to model non-fatal road accident injury severity. Based on accuracy rate, decision tree with CART algorithm was found to be more accurate as compared to the logistic regression model. The factors that significantly contribute to non-fatal traffic crashes injury severity are accident cause, road geometry, vehicle type, age and collision type

Tracking employment trends in Malaysia using text mining technique

The Covid-19 pandemic has changed the world we live in today. In particular, Movement Control Orders (MCOs) that have been deployed nationwide also have an indirect impact on the job creation. With the large number of graduates who have graduated and those who do not have a job will make it even more difficult to get a job. This study attempts to investigate the employment trends during the pandemic in Malaysia by extracting job advertisements randomly from JobStreet website from September to October 2020. A sample of 1050 documents was analysed using text mining technique on two driving factors, job title and location. The results reveal that the highest number of positions offered are managers and the place that offered the most jobs was in Kuala Lumpur followed by Selangor. Further analysis is performed using K-Mediods Clustering to cluster the job titles against the location to illustrate the employment trends in Malaysia, which resulted in similar outcomes

Construction of batik heritage ontology through automated mapping

The wave of ontology has spread drastically in the cultural heritage domain.The impact can be seen from the growing number of cultural heritage web information systems, available textile ontology and harmonization works with the core ontology, CIDOC CRM.The aim of this study is to provide a base for common views in automating the process of mapping between revised TMT Knowledge Model and CIDOC CRM. In this study, manual mapping was conducted to find similar or overlapping concepts which are aligned to each other in order to achieve ontology similarity.Although there are several problems encountered during mapping process, the result shows an instant view of the classes which are found to be easily mapped between both models

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

The machine learning approach has been widely used in many areas of studies, including the tourism sector. It can offer powerful estimation for prediction. With a growing number of tourism activities, there is a need to predict tourists’ classification for monitoring, decision making, and planning formulation. This paper aims to predict visitors to totally protected areas in Sarawak using machine learning techniques. The prediction model developed would be able to identify significant factors affecting local and foreign visitors to these areas. Several machine learning techniques such as k-NN, Naive Bayes, and Decision Tree were used to predict whether local and foreign visitors’ arrival was high, medium, or low to these totally protected areas in Sarawak, Malaysia. The data of local and foreign visitors’ arrival to eighteen totally protected areas covering national parks, nature reserves, and wildlife centers in Sarawak, Malaysia, from 2015 to 2019 were used in this study. Variables such as the age of the park, distance from the nearest city, types of the park, recreation services availability, natural characteristics availability, and types of connectivity were used in the model. Based on the accuracy measure, precision, and recall, results show Decision Tree (Gain Ratio) exhibited the best prediction performance for both local visitors (accuracy = 80.65) and foreign visitors (accuracy = 84.35%). Distance to the nearest city and size of the park were found to be the most important predictors in predicting the local tourist visitors’ park classification, while for foreign visitors, age, type of park, and the natural characteristics availability were the significant predictors in predicting the foreign tourist visitors’ parks classification. This study exemplifies that machine learning has respectable potential for the prediction of visitors’ data. Future research should consider bagging and boosting algorithms to develop a visitors’ prediction model

Spatio-Temporal Clustering of Sarawak Malaysia Total Protected Area Visitors

Based on data of visitors to national parks, nature reserves and wildlife sanctuaries in Sarawak, this study’s objective is to use the spatial and temporal analysis to describe the underlying trend and temporal pattern of local and foreign visitors and ultimately infer the temporal distribution of visitors to 18 different TPAs. The second aim of the study is to cluster the visitors according to the location of TPAs using Wards hierarchical clustering method. By comparing average monthly visitors’ count, we observed that the average number of monthly visitors significantly reflects the distribution concentration of visitors based on the spatial map. Findings indicate that the monthly distributions of local and foreign visitors differ according to different TPAs. The spatial and temporal analysis found that local visitors’ arrival is high at the end of the year while foreign visitors showed significant arrival during the months of July, August and September. The Wards minimum variance method was able to cluster TPAs local and foreign visitors into very high, high, medium and low visitor area. This study provides additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors and improve the overall visitors’ experience. The findings of the study are also important to respective local authorities in providing information for planning and monitoring tourism in TPAs. Consecutively, this will ensure sustainability of TPAs resources while protecting their biodiversity