Judul: Utilization of Himawari-8 Hotspot Data for Land and Forest Fires Tracing, Utilization of Himawari-8 Hotspot Data for Land and Forest Fires Tracing
Abstrak: The use of hotspots in the early warning system for forest and land fires has been applied and is considered successful. Currently, there are regulations governing the use of hotspots in the above context, as stated in regulation No. P.8/Menlhk/Setjen/Kum.1/3/2018 concerning Fixed Procedures for Field Checking of Hotspot Information and/or Forest and Land Fires Information. However, over time, currently in Indonesia hotspot data has also been used to trace fire propagation as one of the evidences of the cause (source of fire) in cases of forest and land fires. Hotspot data used for tracking forest and land fires generally comes from the Terra/Aqua (MODIS), NOAA (AVHRR), and SNPP (VIIRS) satellites. The three satellites have a recording frequency of 12 – 24 hours per day so as to produce satellite data with low temporal resolution, even though currently there is hotspot data available with a temporal resolution of 10 minutes, namely hotspots from AHI (Advanced Himawari Imager) data from the Himawari-8 satellite. This study examined the use of AHI (Himawari-8) hotspot data compared to MODIS, AVHRR, and VIIRS for forest and land fires tracing in Seruyan Hilir, Tanjung Puting National Park, and the area between the Barito and Negara rivers. This study begins with the identification of the time period of the fire’s incidence on the Sentinel-2. Forest and land fires indicator has been observed from the appearance of flames, burn scar, and plumes of smoke. Furthermore, Hotspots were selected at a confidence level ≥80%. This analysis was based on the detection time series of hotspots. The study showed that AHI (Himawari-8) hotspots could be used to see fire propagation and explain the phenomenon of hotspot leaps (hotspot discontinuity). In addition, AHI (Himawari-8) hotspots can also be used to detect fires that rapidly spread and easily extinguish in low biomass areas. Based on these results, the use of AHI (Himawari-8) hotspots can be used in tracing the initial source of fire in cases of forest and land fires.
Keyword: resolusi temporal, Himawari-8, rambatan kebakaran hutan dan lahan

Judul: Utilizing Random Forest for Hotspot Estimation in Peatlands
Abstrak: Titik panas atau hotspot dapat menjadi salah satu indikator tahap awal terjadinya kebakaran hutan dan lahan gambut yang juga menjadi salah satu permasalahan yang cukup masif terjadi di Indonesia. Penentuan hotspot sebagai identifikasi awal kebakaran hutan dan lahan gambut dapat menggunakan teknologi machine learning, salah satunya adalah Random Forest. Penelitian ini bertujuan mengestimasi hotspot menggunakan model Random Forest, serta melihat keakuratan model dan partisi indeks spektral terhadap estimasi hotspot. Penggunaan Random Forest dalam penelitian ini memanfaatkan berbagai indeks spektral sebagai prediktor terhadap estimasi hotspot di wilayah KHG Sungai Kahayan-Sungai Sebangau, Kalimantan Tengah, meliputi NDVI, NDMI, NDWI, EVI, EVI2, NBR, dan NBR2 yang diekstrak dari data citra satelit Sentinel-2A. Model Random Forest memanfaatkan indeks spektral untuk mengestimasi kelas pada setiap lokasi hotspot, meliputi kelas low, nominal, dan high. Model menunjukkan performa yang cukup rendah pada kelas low yang berada di kisaran 30%, sebaliknya model menunjukkan performa yang cukup baik dalam mengestimasi hotspot pada kelas nominal dan high yang berada di kisaran 60-70%. Nilai overall accuracy (OA) juga menunjukkan hasil yang cukup tinggi pada data testing dan validasi, yaitu mencapai 65% dengan indikasi model tidak mengalami overfitting. Tingkat partisi indeks spektral tertinggi adalah NBR2 dan partisi indeks terendah adalah EVI1. Estimasi hotspot dengan random forest juga menunjukkan indikasi adanya bias akibat class imbalance. Penentuan hotspot menggunakan model Random Forest diharapkan dapat berkontribusi terhadap kajian mengenai pemantauan kebakaran hutan dan lahan gambut., Hotspots can serve as early indicators of forest and peat fires, which are significant issues frequently occurring in Indonesia. Identifying hotspots as an early warning of forest and peat fires can be effectively conducted using machine learning technology, particularly the Random Forest algorithm. This study aims to estimate hotspots using the Random Forest model and assess the accuracy of the model and the spectral index partitions in estimating hotspots. The application of Random Forest in this research leverages various spectral indices as predictors for hotspot estimation in the KHG Sungai Kahayan-Sungai Sebangau area, Central Kalimantan. The indices used include NDVI, NDMI, NDWI, EVI, EVI2, NBR, and NBR2, all extracted from Sentinel-2A satellite imagery data. Random Forest model classifies each hotspot location into low, nominal, and high categories based on spectral indices. The model demonstrates lower performance for the low category, with an accuracy of approximately 30%, but shows better performance for the nominal and high categories, with accuracies ranging from 60% to 70%. The overall accuracy (OA) of the model is relatively high for both testing and validation datasets, reaching 65%, indicating that the model does not suffer from overfitting. The highest spectral index partition was NBR2, while the lowest was EVI1. The estimation of hotspots using the Random Forest model also indicated potential bias due to class imbalance. Utilizing the Random Forest model for hotspot detection is expected to contribute to the study of monitoring and management of forest and peat fires.
Keyword: hotspot, forest and peat fires, peatland, random forest, spectral indices

Judul: Optimization of Alginate-Chitosan Microcapsules Formation for Leydig Cells Encapsulation
Abstrak: Hypogonadism is a clinical condition characterized by low concentrations of testosterone. It is caused by malfunction of Leydig cells in producing testosterone. Encapsulation method will protect the transplanted cells from the immune system rejection. The aims of this study was to optimize the formation of microcapsules for Leydig cells encapsulation. The microcapsules were made of alginate and chitosan as the first and the second coating agents, respectively. The result showed that the minimum concentration of alginate was 1.5% (w/v) with a viscosity of 33.8 cPs, resulted spherical microcapsules with diameters of 230-270 µm. The optimum concentration of chitosan as the second coating agent was 0.5% (w/v), resulted spherical microcapsule with mechanical stability in 4 hours. Leydig cells can be trapped inside the capsule with a density that proportional with concentration of cells used in the encapsulation.
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