Judul: Analisis Asosiasi Indikator Iklim dengan Hotspot di Indonesia Menggunakan Heterogeneus Correlation Map (HCM)
Abstrak: One of the major land and forest fires occurred in 1997, it was estimated that around 13 million hectares of land and forest were burned. According to experts, land and forest fires in that year were not an ordinary occurrence, but the impact of climate change caused by the El Nino-Southern Oscillation (ENSO). ENSO can cause dry season is longer than usual and increasing the chances of hotspots occurring. Therefore, knowledge of the ENSO phenomenon is important in land and forest fire management. Apart from the ENSO, there are several indicators that can have an impact on climate change in Indonesia, such as the Indian Ocean Dipole (IOD), monsoon winds, and Sea Surface Temperature (SST). Aim of this research is to analyze how ENSO and IOD as indicators of global climate, and monsoon winds and local SST as indicators of local climate associated with land and forest fires in Indonesia. This research used Singular Value Decomposition (SVD) analysis which is presented in the Heterogenous Correlation Map (HCM). The data used for analyzed are hotspot data as indicator of land and forest fires, the Nino 3.4 index as ENSO indicator, DMI as IOD indicator, and the monthly average SST in the Karimata Strait and the Java Sea as indicator of local SST. As a consequence of seasonal climate patterns and annual variability in Indonesia, most land and forest fires in Indonesia occur during the dry season in the middle of the year. These incidents occur frequently in the provinces of South Kalimantan, East Kalimantan, Central Kalimantan, West Kalimantan, Lampung and South Sumatra. However, there were also episodes of land and forest fires at the beginning of the year in several areas, such as in Riau province. The nino 3.4 is more associated with hotspots in the provinces of South Kalimantan, East Kalimantan and Central Kalimantan, while DMI is more associated with hotspots in the provinces of Jambi, Lampung, South Sumatra, West Kalimantan, South Kalimantan and Central Kalimantan. AUSMI with an east wind pattern is a pattern associated with hotspots in Indonesia, especially in years where zonal winds enter an upward phase more slowly. The decrease in SST in the Karimata Strait is associated with hotspots in the coastal part of Riau province that occurred at the beginning of the year, and SST in the Java Sea is associated with hotspots in the provinces of Lampung, South Sumatra, Jambi, West Kalimantan, Central Kalimantan, South Kalimantan, and East Kalimantan., Salah satu kebakaran lahan dan hutan yang besar terjadi pada tahun 1997, diperkirakan sekitar 13 juta Ha lahan dan hutan yang terbakar. Menurut para ahli kebakaran lahan dan hutan pada tahun tersebut bukan kejadian biasa, melainkan dampak dari adanya perubahan iklim yang disebabkan El Nino-Southern Oscillation (ENSO). ENSO dapat menyebabkan musim kemarau yang lebih panjang daripada biasanya, sehingga peluang munculnya titik api lebih besar. Oleh karena itu, pengetahuan tentang fenomena ENSO menjadi penting dalam pengelolaan kebakaran lahan dan hutan. Selain ENSO, ada beberapa indikator yang bisa berdampak terhadap perubahan iklim di Indonesia seperti Indian Ocean Dipole (IOD), monsoon wind, serta Suhu Permukaan Laut (SPL). Tujuan dalam
Keyword: El Nino-Southern Oscillation, Indian Ocean Dipole, Land and Forest Fire, Monsoon Wind, Sea Surface Temperature

Judul: Characterization of Global Indicator  Regression Model on The Joint Pattern of Hotspot Distribution and Climate  Indicator Prediction in Kalimantan
Abstrak: Fenomena global seperti El Nino Southern Oscillation (ENSO) dan Indian Ocean Dipole (IOD) dapat meningkatkan risiko kebakaran hutan dan lahan (Karhutla). Salah satu indikator yang dapat digunakan untuk mengidentifikasi Karhutla adalah hotspot. Penelitian ini bertujuan mengembangkan model regresi indikator global terhadap pola bersama sebaran hotspot dan prediksi indikator iklim dan menganalisis pengaruh fenomena ENSO dan IOD terhadap hotspot di Kalimantan dengan menggunakan data bulanan dari 2001-2020. Metode optimasi digunakan untuk memaksimalkan koefisien determinasi model, sedangkan Principal Component Regression (PCR) digunakan untuk membuat model regresi. Model prediksi yang dihasilkan dapat digunakan untuk memprediksi hotspot pada bulan berikutnya menggunakan informasi masing-masing indikator dalam 2 bulan terakhir. Validasi model menggunakan 3 tahun data sebagai testing set dan 17 tahun sebagai training set menghasilkan model regresi yang memberikan koefisien determinasi lebih dari 0,5 pada data testing sebesar 31,58%. Kontribusi tertinggi terhadap parahnya musim kemarau dan jumlah hotspot di Kalimantan dipengaruhi oleh fenomena ENSO yang disusul dengan IOD., Global phenomena such as the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) can increase the risk of forest and land fires (Karhutla). One indicator that can be used to identify forest and land fires is a hotspot. This study aims to develop a global indicator regression model on the joint pattern of hotspot distribution and climate indicator prediction and analyze ENSO and IOD phenomena on hotspots in Kalimantan using monthly data from 2001-2020. The optimization method is employed to maximize the coefficient of model determination and the Principal Component Regression (PCR) is employed to create a regression model. The result of this study can be applied to predict hotspots in the following month using the information of each indicator in the last 2 months. The model validation using 3 years of data as a testing set and 17 years as a training set produces a regression model that provides a coefficient of determination of more than 0,5 on the testing data of 31,58%. The highest contribution to the severity of the dry season and the number of hotspots in Kalimantan is influenced by the ENSO phenomenon followed by IOD.
Keyword: ENSO, Hotspot, IOD, PCR, Rainfall

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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