Machine Learning (ML) has been used for a long time and has gained wide attention over the last several years. It can handle a large amount of data and allow non-linear structures by using complex mathematical computations. However, traditional ML models do suffer some problems, such as high bias and overfitting. Therefore, this has resulted in the advancement and improvement of ML techniques, such as the bagging and boosting approach, to address these problems. This study explores a series of ML models to predict the water quality classification (WQC) in the Kelantan River using data from 2005 to 2020. The proposed methodology employed 13 physical and chemical parameters of water quality and 7 ML models that are Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Random Forest and Gradient Boosting. Based on the analysis, the ensemble model of Gradient Boosting with a learning rate of 0.1 exhibited the best prediction performance compared to the other algorithms. It had the highest accuracy (94.90%), sensitivity (80.00%) and f-measure (86.49%), with the lowest classification error. Total Suspended Solid (TSS) was the most significant variable for the Gradient Boosting (GB) model to predict WQC, followed by Ammoniacal Nitrogen (NH₃N), Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). Based on the accurate water quality prediction, the results could help to improve the National Environmental Policy regarding water resources by continuously improving water quality.

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.

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.

Stong Geopark is a prominent natural attraction site in Kelantan, characterized by its rich biodiversity and variety of plant and animal species, catering to diverse visitor interests such as wildlife enthusiasts, explorers, and tourists. Geoguides play a vital role in helping visitors comprehend the significance of the geopark's geological features, the interconnectedness of the ecosystem, and the knowledge of indigenous communities. However, the tourism industry faces challenges in meeting the requirements for various forms of tourism services, including geoguiding. This study aims to profile the geo guides at Stong Geopark and better understand their areas of expertise. The research employs a quantitative approach, surveying 39 geoguides, to provide a comprehensive profile encompassing their age, English proficiency, income distribution, tour frequency, and identified areas of expertise. The findings reveal three distinct clusters of geoguides' areas of expertise: Cluster 1 - Nature and Adventure, Cluster 2 - Nature, and Cluster 3 - Adventure. The study also highlights the need for the majority of geo guides to receive additional training in English communication skills and continuous professional development courses. Overall, this study offers valuable insights into the profiles of geoguides at Stong Geopark, emphasizing the importance of supporting and developing local geoguides' expertise to enhance the effectiveness of geopark management and visitor satisfaction. Furthermore, the study provides a spatial understanding of Stong Geopark by highlighting the locations of active geo guides about the distribution of geosites. This offers a comprehensive overview of the geopark's geography. The findings can inform policymakers, tourism authorities, and geopark management in developing targeted strategies for capacity building and professionalization of geo guides, ultimately contributing to the sustainable development of Stong Geopark as a thriving ecotourism destination.

This study aims to uncover recent trends and themes in text mining research, focusing on online news and annual report data sources. These sources are rich in content, real-world context, and domain-specific information, making them crucial for text mining. The primary research questions guiding this study are: 1) What are the emerging trends in text mining research applied to online news and annual reports? 2) What are the themes generated based on systematic literature review and text mining technique? To address this question, the study systematically reviews a large number of related studies using the PRISMA review protocol and text mining techniques from the SCOPUS and Web of Science databases. After thorough evaluation, 34 selected articles were analyzed. The PRISMA review protocol ensures transparency and completeness in reporting the review process through its standardized approach for systematic reviews. Additionally, this systematic review explores advancements in text mining techniques such as document clustering and topic modeling, which have facilitated the identification and extraction of relevant evidence from vast amounts of textual data. The study’s findings identified four primary themes (text mining/text analytics, machine learning, deep learning, ensemble methods) with 19 sub-themes related to each theme’s methodology when applying the PRISMA protocol. By utilizing a text mining techniques, five topics were uncovered based on article keywords (text mining, text analytics, machine learning, deep learning, and ensemble) and ten topics emerged based on article abstracts. Underlying both approaches is the consistent recognition of four main areas: text mining/text analytics, machine learning, deep learning, and ensemble methods. This systematic review offers a comprehensive overview of recent text mining research and the emerging trends in this field. It highlights the importance of systematic reviews in synthesizing existing literature and identifying areas for future research.