The purpose of this research is to determine the unwanted condition as a strategic criterion in measuring the economic resilience of a city. A new approach in determining economic resilience was developed to overcome the weaknesses of the index method commonly used internationally. Based on the output of this research, the development priority program for each city becomes distinctive depending on the status of the city’s economic resilience. Quality improvement programs are used for cities that do not have resilience and retention programs for cities that already have economic resilience. Five piecewise linear regression parameters are applied to identify a statistical model between Income per capita and Pc as a concern variable and modifier variable, and a Z. Model is tested massively involving all 514 cities in Indonesia from 2015 to 2019, covering the components of the modifier variable: local revenue (PAD), poverty, unemployment and concern variable; GRDP and population. The value of the Fraction of variance unexplained (FVU) of the model is 40%. This value is obtained using the Rosenbrock Pattern Search estimation method with a maximum number of iterations of 200 and a convergence criterion of 0.0001. The FVU area is a condition of uncertainty and unpredictability, so that people will avoid this area. This condition is chaotic and declared as an unwanted condition. The chaotic area is located in the value of U_Z less than IDR 5,097,592 and P_c < P_c (U_Z) = 27,816,310.68, and thus the coordinates of the chaotic boundary area is (5,097,592: 27,816,310.68). FVU as a chaotic area is used as the basis for stating whether or not a city falls into unwanted conditions. A city is claimed not to be economically resilient if the modifier variable Z is in a chaotic boundary.

In this study, we use restlessness as an input for a rice crisis indicator, since restlessness rather than rice price provides a comparable year-to year context. We outline the significant increase in the use of unprecedented restlessness (UR) as an indicator for rice crises. The UR approach involves a precedence analysis, in contrast with the existing approach, the price shock analysis. We test UR as a new indicator for rice crises at the national level, which can be applied in Asia and other countries around the world where rice is the staple food. Strong indicators point out the effectiveness of strategic government programs and are able to assess solutions and detect rice crises, while weak indicators are only reliable in detecting whether or not there has been a crisis. UR is tested across 43 countries using two new statistics: success probability (SP) and constraint probability (CP). As a consequence of SP and CP calculations, a large number of IMR control charts for UR analysis are constructed to provide evidence that UR is a strong indicator. The optimum validity measurement result is achieved with SP = 8/26 = 0.31 and CP = 8/14 = 0.57. This means that the UR detects and is followed by only 31% of riot events. Since the value of SP is less than 0.6, we can conclude that the UR indicator is not considered valid as an indicator of rice crises at the national level. The values of CP and SP are determined subjectively as equal to 0.6. This is the main cause of the emergence of new problems in the calibration of UR as an indicator of rice crises. The subjective success criteria trigger a question regarding why the value is 0.6, for which there is no scientific justification. Based on this background, we continue to objectively establish success criteria for UR validity. After conducting a risk analysis involving a crisis recovery cost (CRC) to crisis anticipation cost (CAC) ratio, it is found that the probability of the CRC-to-CAC ratio having values greater than 7 is 0.76, which means the CRC-to-CAC ratio tends to be higher than 7. Objectively, it is concluded that UR, which has been defined as rice crisis indicator at the national level, is an important indicator.

The similarity of values shown by the two methods of reading paper specifications, namely Quality Control System (QCS) and QC Lab (controlling offline in Quality Control lab) to the attention of PT Bukit Muria Jaya (BMJ). The QCS is an online control process while QC Lab is offline control process. The company wants to change the offline control process online, but there is no analysis to show whether the output given by both methods is the same. The study involved Grade A paper measured at once by five paper specification parameters, i.e. paper weight, porosity, CaCo3 content, opacity, and paper strength. However, after normality testing was found, the data is not multivariate normal distribution. Therefore, this study aims to answer the problem with a multivariate free distribution procedure. The study was conducted in two stages, firstly by looking at the median equality of both outputs of the paper specification reading method, and the second is by looking at the similarity of characteristic capability. A preliminary study was conducted to see whether the two methods of reading the Grade A paper specifications had the same median. This test is done by Kruskal Wallis Multivariate method. The results of the analysis received a null hypothesis which means the median of the two outputs generated QCS and QC Lab for each Grade A paper are equal. Furthermore, based on a capability comparison analysis where the value of this capability is derived from the sample without observation out of control, out of control process control is assisted in the range of T2 Hotelling Control Free Distribution. It can be concluded that Grade A paper capability of QCS does not show the same result as QC Lab, i.e. Porosity variable, where Porosity is capable in QC Lab but not QCS, so manual process control for Grade A paper cannot yet be replaced online.

Infant mortality rate is one of several other indicators that are very susceptible in order to illustrate the mark of public health problems. This indicator is substantial to know the cause of the deaths as well as how success the maternal and child health program. This study aims to determine the characteristics of the causes of infant deaths based on age groups and sub-districts in the city of Bandung using multiple correspondence analysis. The results of the study concluded that asphyxia, early neonatal age groups provide significant positive dependence on the sub-district of Bojongloa Kaler, Bojongloa Kidul, Cicendo, Cidadap, Lengkong. Asphyxia, infant age groups provide significant positive dependence on sub-district of Cibeunying Kidul, Cinambo, Kiara Condong. Low birth weight, early neonatal age groups provide significant positive dependence on sub-district of Andir, Rancasari. Congenital abnormalities, early neonatal age groups provide significant positive dependence on sub-district of Babakan Ciparay, Batununggal, Sukajadi. Congenital abnormalities, advanced neonatal age groups provide significant positive dependence on sub-district of Bandung Kidul. Congenital abnormalities and infant age groups provide a significant positive dependence on sub-district of Coblong. Pneumonia, the early neonatal age group provide a significant positive dependence on the sub-district of Bandung Kulon. Sepsis, infant age groups provide significant positive dependence on sub-district of Bandung Wetan.