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This study proposes the development of a nonparametric regression model combined with geographically weighted regression. The regression model considers geographical factors and has a data pattern that does not follow a parametric form to overcome the problem of spatial heterogeneity and unknown regression functions. This study aims to model provincial food security index data in Indonesia with the GWSNR model, so finding the optimal knot point and the best geographic weighting is necessary. We propose the selection of optimal knot points using the Cross Validation (CV) and Generalized Cross Validation (GCV) methods. The optimal knot point will control the accuracy of the regression curve as we also consider the MSE value in showing the ability of the model. In addition, we determine the best geographic weighting and test the significance of the model parameters. We demonstrate the GWSNR model on food security index data. The best GWSNR model uses the Gaussian kernel weighting function and selects the optimal knot point as one-knot point based on the lowest CV and GCV values. Simultaneous and partial parameter test results show that there are 10 area classifications with different effects on each group of classification results. Some of the highlights of the proposed approach are:•The method is the development of a nonparametric regression model with geographic weighting, which combines nonparametric and spatial regression in modeling the national food security index.•There are three-knot points tested in nonparametric truncated spline regression and three geographic weightings in spatial regression. Then the optimal knot point and best bandwidth are determined using Cross Validation and Generalized Cross Validation.•This article will determine regional groupings in Indonesia in 2022 based on significant predictors in modeling the national food security index numbers.

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BACKGROUND: Work musculoskeletal disorder (WMSDs) are occupational health problems whose prevalence is still high in various countries. Ergonomic interventions are the most successful approach to reducing WMSDs. This study evaluated the effect of redesign traditional handloom on the work posture and musculoskeletal disorders of Samarinda Sarong traditional weavers. METHODS: The quasi-experiment has been carried out on 40 traditional weavers from February to September 2019 in Samarinda, Indonesia. The weaver using the new design handloom then evaluated its impact on work posture and WMSDs in the first 3 months and the second 3 months. Work posture was assessed using Rapid Upper Limb Assessment (RULA). WMSDs were assessed using a Nordic Body Map questionnaire. Data were analyzed using Friedman and Dunn's test. RESULTS: The RULA score decreased from 7 to 3 and 2, while the WMSD risk at "very high" level decreased from 12.5% to 7.5% and 2.5% and the WMSD at "high" risk level decreased from 87.5% to 10.0% and 5.0% following the introducing of the new design traditional handloom at 3 and 6 months, respectively. CONCLUSIONS: The new design of the traditional handloom on Sarong Samarinda female weavers has succeeded in improving work posture and reducing WMSDs.

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A cross-sectional study was conducted on 40 women weavers of Samarinda sarongs to identify the prevalence and risk factors of musculoskeletal disorders (MSDs). A Nordic body map, rapid upper limb assessment and anthropometric tools were used to plot the MSD severity, work posture and anthropometric dimensions of the weavers, respectively. The age, education background, working period and prolonged sitting position distributions of the weavers were collected by direct interview. Pearson's product-moment correlation was applied to identify correlations between the MSD prevalence and other parameters. An MSD prevalence of 80.5% was found among the women weavers, with the MSDs categorized as low, moderate and high in 15.0, 75.0 and 7.5% of the respondents, respectively. The MSD prevalence was significantly correlated with education background (p = 0.025), working period (p = 0.015), prolonged sitting hours (p = 0.032), work posture (p < 0.001) and weavers' anthropometry (p < 0.001).

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Cassava leaves contain albumin, fat, carbohydrates, vitamin A, vitamin B1, and fibers, thus having the potential to be used as an ingredient in production of cookies. The purpose of this research was to determine the optimum ratios between wheat flour, cassava puree and cassava leaves flour to produces cookies with good nutritional profile. This study was conducted as Completely Randomized Designs with five treatment, namely the ratios between wheat flour: cassava puree: cassava leaves flour (WF:CP:CLF) in grams of 50 : 50 : 0, 49 : 49 : 2, 48 : 48 : 4, 47 : 47 : 6 and 46 : 46 : 8. Each treatment was replicated thrice. Parameters observed were the nutritional content, sensory acceptance, antioxidant activity and glycemic index (GI) of the cookies. The data obtained were analysed with ANOVA, continued by LSD test at α=0.05 for treatment showed a significant effect. The result showed that the chemical characteristics of the cookies were 1.46-5.12, 0.23-2.10, 10.67-20.76, 1.20-4.26, 8.36-10.94 and 64.75-74.09% for moisture content, ash, fat, fibre, protein and carbohydrate, respectively. The energy and antioxidant activity (IC50) of the cookies was at the range of 426.31-480.30 kcal/100 g and 151-200 ppm. The most preferred sample was the one with WF : CP : CLF of 49 : 49 : 2 with a GI value of 77.4. This study showed that cassava leaves flour has potential as an alternative ingredient in improving the nutrition profile of cookies production.

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One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. The system can also provide recommendations for the treatment of identified diseases. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id.

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