Indonesia is known as an agricultural country. This means that most of the population work in the agricultural sector related to food. However, food insecurity still occurs in Indonesia. With the COVID-19 pandemic, the Food and Agriculture Organization (FAO) stated that there was a threat of food scarcity which had an impact on food insecurity conditions. This would undermine the second goal of the SDGs, which is to end hunger and create sustainable agriculture. The purpose of this study was to determine the spatial pattern of food insecurity in each province in Indonesia using the bicluster method. The data used are data from Susenas and Sakernas by BPS in 2019. Several studies show that the bicluster method with the CC algorithm shows that each province group has a different characteristic pattern. In the bicluster approach, the researcher runs parameter tuning to select the best parameter based on the Mean Square Residual in Volume (MSR / V). The CC algorithm tries to get a bicluster with a low MSR value, therefore the best parameter is the one that produces the smallest MSR / V value, in this study the smallest MSR / V is 0,01737 with δ = 0,01. The application of the CC biclustering algorithm to the food insecurity structure in Indonesia results in 5 bicluster. Bicluster 1 consists of 15 provinces with 8 variables, Bicluster 2 consists of 10 provinces with 5 variables, Bicluster 3 consists of 3 provinces with 7 variables, Bicluster 4 consists of 4 provinces with 4 variables and Bicluster 5 consists of 2 provinces with 5 variables. Biculster 4 represents a cluster of food insecurity areas with the characteristics of the bicluster P0, P1, P2 and calorie consumption of less than 1400 KKAL.

During 2016-2019, Yayasan Bumi Manira-Studio Driyamedia Bandung (the institutions where authors work) involved in cooperation with the dairy coops KPSBU (Koperasi Peternak Sapi Bandung Utara) in Lembang-West Java and KPBS (Koperasi Peternak Bandung Selatan) in Pangalengan-West Java to develop their animal health and extension staff capacities in communication and extension. The staff consist of: para-medics, animal health, extension officers and veterinarians involved in trainings in technical subjects, communication and extension methods to advice farmers in better way. The analysis of this article aims to evaluate and review the communication pattern of those staffs. The capacity building methodology developed is Participatory Learning and Action (PLA) method as trainings of coop staff using “action-reflection-action” learning system. The result of evaluation and review showed that the progress of the project indicate of positive situation because they have potential to be came good officer and very enthusiastic. Most problems of communication and extension in KPBS and KPSBU are similar: using too much one-way communication, less implementation of adult education, and less usage of various learning media. Therefor they need more participatory atmosphere, more adult learning policy, and more flexible management system. The board also can push synergize among sections

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The first goal of the SDGs is to end poverty in any form. The COVID-19 pandemic has greatly affected several economic indicators, especially absolute poverty, especially in Sulawesi Island, which has increased poverty indicators, leading to the movement of values between districts/cities.  The grouping will show similar characteristics of absolute variable poverty. By the Fuzzy method clustering, each observation has a degree of membership so that from the degree of membership can be identified which areas have vulnerable to move from one cluster to another. Grouping using fuzzy algorithms will get an overview of districts of concern to the government during the pandemic so that the variable indicators of absolute poverty do not worsen due to the pandemic. Comparison with the absolute variables of poverty in 2019 and 2020 in the headcount index (P0), Poverty Gap Index (P1), and Poverty Severity Index (P2) in districts/cities on the island of Sulawesi based on silhouette coefficients shows that optimum clusters formed as many as 2 clusters, with a coefficient of 0.57 and 0.60 respectively. Cluster 1 has characteristics including areas with absolute poverty rates that tend to be more prosperous than cluster 2 in the 2019 and 2020 data groups on the island of Sulawesi. The fuzzy algorithm detects areas prone to displacement from cluster 1 to cluster 2, namely Bombana, Bone, Sangihe Islands, South Konawe, and Siau Tagulandang Biaro in 2019 and Bombana, Bone, Sangihe, and Maros Islands in 2020. The COVID-19 pandemic in March 2020 has not had much impact on the macro indicators of poverty seen in the transfer of membership from 2019 to 2020, which only occurred to 3 districts that changed, namely bolaang mongondouw and konawe selatan from cluster 1 to cluster 2 and Maros from cluster 2 to cluster 1.