Central Sulawesi Province in Indonesia has great potential for horticultural commodities, namely local red onion Palu. In the current climate change, local farmers are still watering plants in the conventional way. The automatic watering system simplifies the work of local farmers. This device uses a soil moisture sensor as a soil moisture detector and Arduino as a program brain. This study aims to determine the position of soil moisture sensor, the optimal length of watering time and analyze the quality of data stored. The experiment was carried out using a Completely Randomized Design (CRD). The position of the soil moisture sensor was analyzed by Profile Analysis. The optimal length of watering time was determined by Analysis of Variance (ANOVA) and Least Significant Difference (LSD). The quality of data stored was determined by a number of missing values and frequency of watering. The results showed that in soil planting media the position of soil moisture sensor had no significant effect, while in others planting media (water and combination of water and soil) the position of the sensor had a significant effect. The optimal watering time was 3 seconds. The stored data has low quality in terms of missing values and lack of consistency.

Time series model on multiple objects could be univariate and multivariate model. The more objects used in multivariate model would decrease precision of forecast for each object. One way to overcome this problem used univariate models for each object. However, univariate models for each object became inefficient in time. Therefore, clustering performed on objects so that model became efficient. The objective of this research is to study results of predicting and forecasting model with and without clustering. Model and forecasting used time series regression model on broad proportion of plant-disturbing organism attack and planting area of food crops in Indonesia. Clustering of time series data was same as clustering in general, but the distance and method should be able to accommodate time series data structure which was dynamic in time. Evaluation of prediction and forecast mean average percentage error (MAPE) show that forecast of model was performed with clustering as good as forecast of model for each object. However, prediction of model with clustering was not as good as the prediction of model for each object so that the prediction of broad proportion of plant-disturbing organism attack only served as an indicator of the arrival populations of plant-disturbing organism.

This paper develops how the hierarchical clustering analysis uses multivariate variables with spatial dependence on macro social-economic indicator data in Regency/City Central Sulawesi Province. Macro social-economic indicator data used in this paper are the number of criminal cases, per capita expenditure, population density, and Human Development Index of Regency/City of Central Sulawesi Province in 2018.To answer this question, Macro social-economic indicator data was reduced to a new variable using principal component analysis. The new variable was used to identify spatial dependency using the Moran index test. Spatial weight, that meets the Moran index test on the alternative hypothesis (there is a spatial dependency between locations), was used as the spatial dependency distance. Cluster analysis using two distance including variable and spatial dependency distance. The results showed that neighboring Regency/City are in the same cluster (spatial dependency occasion). So that there are five clusters Regency/City in Central Sulawesi Province.

The spread of HIV/AIDS in Central Sulawesi is centralized and spreads in certain districts or cities so that there are indications of a spatial effect in the spread of HIV/AIDS. Geographically Weighted Negative Binomial Regression (GWNBR) is one of the right solutions for modelling the relationship between response variables and explanatory variables on counted data that is local for each observation location with overdispersion and the influence of location or spatial aspects on the data. Spatial aspects can be caused by geographic, socio-cultural, economic conditions, as well as different people’s knowledge between regions. Overdispersion is a condition where the variance of the data is greater than the average data. This study aims to determine the GWNBR model and the factors that influence the number of HIV/AIDS cases in the province of Central Sulawesi. The obtained result based on GWNBR model shown that the districts with the most number of districts that have the most significant similarities in variables are divided into nine subdistrict groups. Factor affecting the number of HIV/AIDS cases in Central Sulawesi Province in 2018 was population density.

Domestic passengers are objects whose travel / flight transportation services only cover the domestic area. The increase or decrease in the number of domestic passengers is usually influenced by the occurrence of intervention. This research uses the intervention analysis. Intervention analysis is the time series analysis to model data that is determined by the presence of an intervention. Intervention analysis is one of the time series analysis to model data that are affected by the occurrence of a particular event in a short period of time, such as accidents, natural disasters, and promotions. This research is used to establish intervention model with pulse function of passengers of domestic Sultan Hasanuddin Airport. The result of the research were obtained the model Seasonal ARIMA .There were 6 intervention times during 2006 - 2018, by entering the intervention order b = 0, s = 0, and r = 1 based on the smallest AIC value is -303,66 with MAPE value is 6,1023. Keywords : Domestic passanger, Seasonal ARIMA, MAPE, Intervention analysis, pulse function