Southeast Asia Development Research

The Contextual Relation between Ideology and Political Violence: Khmer Rouge

2025-05-22T10:52:19+08:00

During the Cold War era in the world, the ideological approaches of the East and the West driven by the bipolar system directly influenced the state systems. The Soviet Union’s communist system, in particular, made a significant impact in Asia, notably due to the dominant role of the People’s Republic of China. With the critical role of China, the communist regime stood out as a distinctive ideology in the Asian territories. The exploitation efforts by imperial powers such as the United States, France and the United Kingdom in Asia led to torture and oppression, resulting in the repugnance and hatred of people. This repugnance not only sparked a revolutionary movement in Vietnam but also gave rise to terrorist activities in the Cambodian territories. Founded during the Pol Pot regime, the Khmer Rouge violent regime led to a brutal mass murder of millions of people especially in Cambodia under the Communist regime. The present study focuses on the massacre of the Vietnamese people by the Communist Pol Pot violent regime under the communist regime. Thus, the contextual relationship between ideology and political violence is tried to be proved with the data obtained from this case study, elite interviews and other secondary sources.

Did Russia’s Soft Power Really Work? An Initial Survey of Russia’s Soft Power Attraction in Indonesia

2025-05-08T08:31:15+08:00

Russia has been trying to change its image in global politics since the demise of the Soviet Union. One of the most popular tools the Russian government used to do this is soft power resources. The exploration of Russia’s soft power can be seen from the Concept of the Foreign Policy of the Russian Federation in 2016, which places more emphasis on the use of soft power instruments in the implementation of foreign policy. This article attempts to explore and understand the type and use of soft power resources of Russia in Indonesia. By using a constructive perspective and descriptive statistical methods, this article will capture how the Indonesian young generation perceives the type and the use of Russia’s soft power resources in Indonesia. However, this study’s data, collected in 2018, precedes the significant geopolitical shifts following Russia’s 2022 military actions in Ukraine, which may have altered global perceptions. Our results showed that Russian soft power resources emanated from Russian culture and Cold War historical remnants have been successfully converted into soft power attractions for foreign audiences in Indonesia. It indicates a shared understanding of the respondents about global multipolarity in which Russia was perceived as a balancing great power of US domination.

Future Scenarios for CO2 Sequestration by Oil Palm in Southeast Asia Versus Other Regions to Reduce Climate Change

2025-06-24T15:12:38+08:00

Carbon dioxide (CO2) emissions from fossil fuels are a significant contributor to climate change. Concentrations of CO2 were in balance when emissions were controlled by the photosynthetic capabilities of organisms on land and in oceans. Palm oil is a valuable commodity, and vast plantations of oil palm (OP) have been created, especially in Malaysia and Indonesia, which have involved destroying rainforests and growing palms on peat soil, which increases CO2 emissions. However, OPs are effective at sequestrating CO2, and growing OPs on degraded land may allow sequestration to combat climate change. Future scenarios for CO2 sequestration are presented in this report by employing a CLIMEX computer programme, climate models, and narratives to determine optimal future sequestration. High levels of CO2 sequestration by OP will be maintained generally until 2070, but this will decrease dramatically by 2100. Parts of Malaysia and Indonesia will have significantly greater sequestration than others. Some novel regions of high sequestration may occur in Paraguay and Uganda. Overall, it cannot be assumed that the OP will continue to sequester CO2 in the same places where it currently grows well. The modelling provides a basis for making decisions regarding where to grow OP for CO2 sequestration in the future. More modelling of future OP growth is required, focusing on the CO2 sequestration potential.

Palm, Rubber and Rice Crops Classification and Diagnostic Using CNN Approach and NDVI in Thailand

2025-05-19T17:51:49+08:00

To support agricultural development in Thailand, accurate data collection and analysis of land use are essential. Understanding the spatial distribution and growth patterns of key crops enables better planning and resource allocation. This study proposes a deep learning-based approach for land cover classification, specifically targeting three significant crops: rice, rubber, and palm. A Convolutional Neural Network (CNN) is employed to classify satellite imagery into these three categories. The datasets used in this research are derived from high-resolution Pleiades satellite imagery and consist of three independent datasets, each containing 200,000 image tiles of 100x100 pixels. For each crop type, a dedicated CNN model are trained and optimized, achieving classification accuracies exceeding 90%. After prediction, a post-processing step is implemented to merge tile-level classifications into continuous land cover maps. This enables a clearer spatial visualization of crop distribution. Furthermore, a clustering algorithm is applied to identify individual agricultural fields, which facilitates further analysis. Vegetation health and maturity are assessed using the Normalized Difference Vegetation Index (NDVI), from which the approximate age of the crops is inferred. These parameters are then used to estimate the potential agricultural yield or production for each field. To validate the approach, a large area of 100 square kilometers is analyzed, and the model’s classification results are compared against manually labeled reference data provided by the Thailand Space Agency. The comparison reveals a classification discrepancy of -12% for palm crops and approximately -20% for both rice and rubber, demonstrating the model's high potential for scalable crop monitoring.