The fear of ghosts is a common thing that can be managed as a social condition that turns out to have an impact on the continuity of forest maintenance. Applying a qualitative approach supported by in-depth interview methods, observation, and literature study. This research does not attempt to prove the existence of ghosts or discuss the psychological conditions of people who fear ghosts. The main finding of this research is the reality of the reproduction of stories and experiences of fear of ghosts, as well as the implementation of traditions or rituals related to community activities in the forest. Stories of fear of ghosts with various forms and versions of naming not only enrich the cultural life of the community but also encourage social conditioning in the form of togetherness to agree on the fear of ghosts as a means of creating a social system in order to carry out activities in the forest. The social system is identified in the form of pamali traditions or things that should not be done in the forest, balian rituals to eliminate or treat ghost disturbances, and besoyong rituals to utilize forest products, which then have an impact on the awareness to respect the continuity of these rituals and tradition. So, even though the fear of ghosts can be overcome psychologically and disappear quickly, the reality of respect for the social system related to the forest can still survive. In addition, ghost stories’ reproduction continues to be rolled out and adapted to the times. In turn, ghosts and forest rituals continue to be conditioned into a social system that has implications for forest conservation.

In June 2023, the European Union (EU) enacted the Regulation on Deforestation-Free Products (EUDR), which requires agricultural products to enter and leave its territory free from deforestation. The regulations apply to seven commodities: cattle, cocoa, coffee, oil palm, rubber, soya, wood, and their derivate products grown or raised on land subject to deforestation or forest degradation will be banned from entering the EU market. EUDR will have a significant impact on Vietnam’s Exports of Agricultural Products. Coffee, rubber, wood, and wood products are the main industries in Vietnam affected by this regulation, as the country exports a substantial portion of these products to EU markets. This article examines the impacts of the European Union Deforestation Regulation on Vietnam’s coffee supply chains, discusses possible unintended effects on coffee farmers and farming households, and explores strategies to mitigate these negative impacts while highlighting specific challenges that may arise. The results of this study contribute to a better understanding and management of Vietnam’s agricultural exports, particularly in the coffee sector. Additionally, the article gives some recommendations for improving Vietnam’s laws and policies on deforestation-free products.

This research is based on the condition of the ever-rampant events of illegal logging perpetrated by companies in various areas in Indonesia and Malaysia. The issue of corporate illegal logging happened due to a concerning level of conflict of interest between companies, the government, and local societies due to economic motives. this paper aims to analyze the law enforcement on corporate illegal logging in Indonesia and Malaysia as well as the law enforcement on corporate illegal logging that is based on sustainable forestry. this research used the normative legal approach that was supported by secondary data in the forms of documents and cases of illegal logging that happened in Indonesia and Malaysia. this paper employed the qualitative analysis. Results showed that Indonesia had greater commitment and legal action than Malaysia because Indonesia processed more illegal logging cases compared to Malaysia. But mere commitment is not enough as the illegal logging ratio in Indonesia compared to timber production is 60%. meanwhile, in Malaysia, it is 35%. This shows that the ratio of law enforcement in Malaysia is more effective when comparing the rate of illegal logging and timber production. The phenomenon of forest destruction in Indonesia happened due to a disharmonic situation or an improper social relationship between society, the regional government, the forestry sector, business owners, and the law-enforcing apparatus. The sustainable forest-based law enforcement concept against corporate illegal logging is carried out through the integration approach that involves various parties in both countries.

Brunei Darussalam is a small Sultanate country with diverse forest cover. One of them would be Mangrove Forest. As it has four main administrative districts, Temburong would be the chosen case study area. The methods of collecting data for this article are by collecting secondary data from official websites and the map in this article (Figure 1) are showing the forest cover in Brunei Darussalam as of 2020. The aim of this article is to explain the mangrove forest especially at the Temburong District. As for the objectives, it would to be able to show the different types of forests in Temburong, hoping in ability to explain the different subtypes of mangroves forest and to explain in general the green jewel of Brunei Darussalam. Temburong has become the second highest tree coverage in Brunei Darussalam of 124 kha as of 2010, while the mangrove forest covering about 66% of total mangrove forest of 12,164 km² out of 18,418 hectares. Mangrove forest has seven subtypes: Bakau species, Nyireh bunga, Linggadai, Nipah, Nipah-Dungun, Pedada and Nibong. Selirong Forest Reserve and Labu Forest Reserve are the two-mangrove forest reserves in Brunei Darussalam at Temburong District. Forest cover in Brunei Darussalam are 3800 hectares as of 2020 and has lost its tree coverage of 1.17 kha and one of the reasons would be forest fire and the tree cover loss due to fire is around 197 ha and the district that has lost its tree cover mostly was at Belait District of total 13.4 kha between the year 2001 until 2022.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.