Armi Susandi

Open dumpsites have been identified as significant sources of air emissions. This study analyzed the spatial and temporal variations of multicomponents in the Sarimukti dumpsite, Indonesia, and the health profiles of landfill workers. The analysis revealed that benzene, PM2.5, and NH3 levels...

Rice is the staple food for most Indonesians, making stable rice production central to national food security. Java and Sumatera, the two most populous and agriculturally important islands, together contribute around 75% of national rice output, yet a large share of paddy cultivation remains rainfed and reliant on traditional practices. This dependence on natural rainfall and temperature regimes heightens the sensitivity of rice production to climate variability and intensifies its vulnerability under climate change. This study assesses future risks to rice production in Java and Sumatera by analysing projected changes in extremely hot days and consecutive dry days (CDD) under multiple greenhouse gas emission scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Using downscaled projections from the NEX-GDPP CMIP6 dataset, the heat- and drought-stress evolution across two major planting seasons and the associated implications for rice yields is explored. The results show substantial increases in both hot days and CDD. Heat- and drought-stress intensify even under a low-emission pathway, which threaten Indonesia’s rice production. By linking projected changes in climate extremes with potential yield impacts, the study underscores the dual importance of determined mitigation and targeted adaptation to support climate-resilient rice production and safeguard Indonesia’s food security.

This study investigates the dynamics of water resources in the Citarum watershed during periods of weak La Niña, normal, and weak El Niño conditions occurring sequentially. The Citarum watershed serves various purposes, being utilized not only by seven (7) districts and two (2) cities in West Java, Indonesia but also as a source of raw water for drinking in the City of Jakarta. Using a time-series analysis of surface water data, data-driven (machine learning) methods, and statistical analysis methods, spatiotemporal predictions of surface water have been made. The surface water time series data (2017–2021), obtained from in situ instruments, are used to assess water resources, predict groundwater recharge, and analyze seasonal patterns. The results indicate that surface water follows a seasonal pattern, particularly during the monsoon season, corresponding to the groundwater recharge pattern. In upstream areas, water resources exhibit an increasing trend during both weak La Nina and weak El Niño, except for Jatiluhur Dam, where a decline is observed in both seasons. Machine learning predictions suggest that water levels and groundwater recharge tend to decrease in both upstream and downstream areas.

This study investigates the potential of integrating renewable energy and blue carbon ecosystems for coastal climate change mitigation and adaptation in Indonesia's upstream and downstream oil and gas industry. The urgency for the integration is also discussed, including climate change mitigation, energy security, technological innovation, social and environmental justice, and long-term planning and resilience. The study uses Army Design Methodology to identify the key challenges and opportunities of integrating renewable energy and blue carbon ecosystems. The findings suggest that the integration of renewable energy and blue carbon ecosystems can play a crucial role in coastal climate change mitigation and adaptation, particularly in Indonesia's oil and gas industry. The paper concludes with recommendations for further research and policy development in this area.

This study investigates the impact of environmental disasters on food systems in Indonesia and proposes strategies for environmental disaster reduction. The study utilizes a case study approach, analyzing the impact of environmental disasters on food systems in selected regions of Indonesia and proposing a mathematical function using an inverse function to map the food systems vulnerability index to the disaster capacity index. The study also considers social, economic, and cultural dimensions in the analysis to ensure the strategies are contextually appropriate. The analysis focuses on identifying successful strategies for environmental disaster reduction and quantitatively assessing their impact on food systems.The research highlights the importance of investing in adaptive and coping capacity to enhance food security and mitigate the impact of environmental disasters, with the potential to increase production by 3-5% in local areas in Indonesia. The study's findings have important implications for policy and practice, emphasizing the importance of integrating environmental disaster reduction strategies into national and regional development plans to ensure the resilience and sustainability of the food systems in Indonesia.

This study aims to compare the relationship between climate variables and rice productivity under different irrigation systems (irrigated and rainfed) in the clustering area on Java Island, Indonesia. This study used the clustering areas resulting from the previous study. The climate variables used are bias-corrected MERRA2 data from the period 1987–2017, cropped for Java Island. The rice productivity and reference evapotranspiration data used in this study are the results of the simulation of Aquacrop modeling. The result from the cluster method used tends to divide Java Island into 2 clusters with different altitudes (lowland and highland) areas. The results show that the correlation values between the precipitation variable and rice productivity from Aquacrop simulation (both irrigated or rainfed) in cluster 1 (dominated lowlands) are higher than in cluster 2 (dominated highlands), contrary to that the correlation values between the reference evapotranspiration variable with rice productivity from Aquacrop simulation (both irrigated or rainfed) are higher in cluster 2 (dominated highlands) areas, compared to cluster 1 areas (dominated lowlands). R-square values from response surface methodology (RSM) on the rainfed system in both clusters are higher than those on the irrigated system. This indicates that rainfed agriculture is highly dependent on climate variables, especially precipitation and reference evapotranspiration variables compared with the regular irrigated agricultural system. The RSM result also shows that climate variables significantly contribute to the variation of rice productivity generated by Aquacrop modeling in irrigated and rainfed systems and in all clusters.