PERAN KENDALI GLUKOSA TERHADAP KADAR VITAMIN D SERUM PADA PASIEN DIABETES MELITUS TIPE 2 DI MALANG

Background:Diabetes mellitus (DM) is a chronic metabolic disorder due to disruption insulin work, resulting in an increase of glucose concentration in the blood, or commonly called hyperglycemia. Hyperglycemia can also cause vitamin D serum interference because the micronutrients are involved in insulin signaling and action, also responsible in insulin sensitivity and secretion.This study will assess the effect of glucose control upon vitamin D serum levels of type 2 DM patients.Method:This research was analytic observational with cross sectional study using post-test only control group with research subjects (respondents) human male and female patients with diabetes mellitus without complications with an age range above 40 years. The research variables were classified into 2 groups, namely controlled group and uncontrolled group. Each control group of data respectively will depend on the vitamin D serum level using the Human 25-Dihydroxy Vitamin D (25-OH-D) ELISA kit. Data were analyzed using Independent T-Test followed by Pearson correlation test with a significance level of p<0.05.Result:Glucose control has no role in serum Vitamin D levels (p=0.754). This happened allegedly due to an error in the process of Vitamin D serum examination, and because of errors in the management of serum samples before use.Conclusion:Glucose control has no role in serum vitamin D levels towards patients with DM type 2.Keyword:glucose control, vitamin D serum, type 2 diabetes

Credit decision support methodology for Micro, Small and Medium Enterprises (MSMEs) : Indonesian cases

This research is aimed at constructing an objective and accurate credit risk assessment method for Micro, Small and Medium Enterprises in Indonesia. Credit data of three sample banks is structured using eXtensible Markup Language and Database Structure Model for analysis purposes. Selected data mining techniques are applied to perform credit risk classification based on quantitative and text-based qualitative information. This fills the gap in previous studies where text-based qualitative information was excluded from the models

Reformulation of the Role of Regional Development Banks as Agents of Regional Catalyst: The Case of Indonesia

Regional development banks (RDB) in Indonesia are constitutionally mandated to be the economic and social catalyst&nbsp;for local development, a role which requires banks to finance &lsquo;unprofitable&rsquo; projects. The incompatible functions are resulted in&nbsp;reservations within RDB to maximize their resource allocation potential. This paper is aimed at evaluating RDB&rsquo;s catalyst role&nbsp;and at proposing a refinement to the current role which would enable RDBs to achieve their expected goals. This research uses&nbsp;descriptive statistics of RDBs&rsquo; loan performance from 2012 to 2017 to establish RDBs&rsquo; performance in economic and social areas.&nbsp;Accounts included in this research are third-party funds, productive loans, loan deposit ratio and small business loans. It is evident&nbsp;from the secondary data that RDBs have issues in contributing to local economy generator. Next, data from interviews with senior&nbsp;management team of four RDB&rsquo;s are analysed to identify critical pillars for formulation of RDBs&rsquo; role. Referring to Thorne&rsquo;s and&nbsp;Du Toit&rsquo;s framework for development banks (2009) the reformulation of RDBs&rsquo; catalyst role starts from stipulating their role in&nbsp;an exclusive and well-defined operating environment. This will allow RDBs to synergize their operations with local development&nbsp;programs. From governance and financial performance perspectives, RDBs can prepare efforts to make an initial public offering&nbsp;as part of a strategy for increasing capital, structuring corporate governance, and enhance corporate value

Assessment of micro loan payment using structured data mining techniques: the case of Indonesian People' credit bank

Providing financial service to Micro, Small and Medium Enterprises (MSMEs) in Indonesia presents a challenge for small rural banks such as People’ Credit Banks. These banks are required to infer risks about customers’ loan repayment from structured (quantitative, financial) and unstructured (qualitative, non-financial) type of credit information. In this study, the complex nature of credit related information is contextualised and represented in domain specific way using the eXtensible Markup Language (XML). An approach that enables the application of wider selections of data mining techniques on XML data is utilized. Experiments are performed using real world credit data obtained from an Indonesian bank. The results demonstrate the potential of the approach to generate reliable and valid patterns useful for evaluation of existing lending policy

Incorporating qualitative information for credit risk assessment through frequent subtree mining for XML

Credit risk assessment has been one of the most appealing topics in banking and finance studies, attracting both scholars’ and practitioners’ attention for some time. Following the success of the Grameen Bank, works on credit risk, in particular for Small Medium Enterprises (SMEs), have become essential. The distinctive character of SMEs requires a method that takes into account quantitative and qualitative information for loan granting decision purposes. In this chapter, we first provide a survey of existing credit risk assessment methods, which shows a current gap in the existing research in regards to taking qualitative information into account during the data mining process. To address this shortcoming, we propose a framework that utilizes an XML-based template to capture both qualitative and quantitative information in this domain. By representing this information in a domain-oriented way, the potential knowledge that can be discovered for evidence-based decision support will be maximized. An XML document can be effectively represented as a rooted ordered labelled tree and a number of tree mining methods exist that enable the efficient discovery of associations among tree-structured data objects, taking both the content and structure into account. The guidelines for correct and effective application of such methods are provided in order to gain detailed insight into the information governing the decision making process. We have obtained a number of textual reports from the banks regarding the information collected from SMEs during the credit application/evaluation process. These are used as the basis for generating a synthetic XML database that partially reflects real-world scenarios. A tree mining method is applied to this data to demonstrate the potential of the proposed method for credit risk assessment

Understanding the use of knowledge sharing tools

This paper investigates the drivers of the use of knowledge-sharing tools through the lens of task– technology fit (TTF), the role of social factors, and the cognitive and affective mechanisms. Data were collected from 294 knowledge workers and analyzed using partial least squares. This study found that cognition (i.e., perceived usefulness) and positive affect play an important role in mediating the effect of TTF, social influence, and trust on users’ behavioral intention. Interestingly, the role of negative affect as a mediator between sociotechnical factors and behavioral intention is considered not significant by the knowledge workers. This research has practical implications for organizations that are planning or reviewing their knowledge-sharing initiatives