• Korean Security Journal
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• no.61
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• pp.39-57
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• 2019

The executive authority of the Ministry of Public Administration and Security on the 'management of security of the government complexes' is not sufficiently secured only with the organization law, the Government Organization Act. It is needed to establish an administrative actions law, an individual law that sets detailed contents and limitations of the executive authority to be stipulated. The current regulation, Regulation on the Management of the Government Complexes which is a Presidential Decree, is a legal decree that lacks a legal basis. The decree does not match with the current constitutional framework and raises the issue of its legality. The regulation may have the characteristics as a public property management law so far as it stipulates such matters as supply and maintenance management for the complexes, acquisition and disposition of complexes, facilities management of complexes, etc. However, the regulation includes high authority actions by an administrative organization, such as facilities security and order maintenance including restriction and control of access. This makes the regulation have the characteristics of a public property policy act as well. To supplement the legal framework for this situation, it is needed to level up some of the provisions relating to protection and security management to the level of an act as they stipulate high authority actions by an administrative organization. Other matters in the Regulation on the Management of the Government Complexes such as provisions relating to supply and allocation of complexes, etc. may be maintained as they are. In addition, the protection officers (general service official) does not own legal authority and have limitations on securing the capability to deal with the situations on implementing the on-site protection duty. Therefore, it is needed for the protection officers to secure protection duty-related authority by stipulating in a law. The main contents of the law on the protection and security of the government complexes may be those matters providing reservations on the implementations of laws. These may include the limitation of rights of and charging obligations on the people such as restricting the actions of personnel in the complex, rights and obligations of protection personnels relating to their duties, use of weapons, training of protection personnel, penal provisions, etc. These legal reservations should be included in an individual act.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.45-69
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• 2016

Recently, sentiment analysis using open Internet data is actively performed for various purposes. As online Internet communication channels become popular, companies try to capture public sentiment of them from online open information sources. This research is conducted for the purpose of analyzing pulbic sentiment of Korean Top-10 companies using a multi-categorical sentiment lexicon. Whereas existing researches related to public sentiment measurement based on big data approach classify sentiment into dimensions, this research classifies public sentiment into multiple categories. Dimensional sentiment structure has been commonly applied in sentiment analysis of various applications, because it is academically proven, and has a clear advantage of capturing degree of sentiment and interrelation of each dimension. However, the dimensional structure is not effective when measuring public sentiment because human sentiment is too complex to be divided into few dimensions. In addition, special training is needed for ordinary people to express their feeling into dimensional structure. People do not divide their sentiment into dimensions, nor do they need psychological training when they feel. People would not express their feeling in the way of dimensional structure like positive/negative or active/passive; rather they express theirs in the way of categorical sentiment like sadness, rage, happiness and so on. That is, categorial approach of sentiment analysis is more natural than dimensional approach. Accordingly, this research suggests multi-categorical sentiment structure as an alternative way to measure social sentiment from the point of the public. Multi-categorical sentiment structure classifies sentiments following the way that ordinary people do although there are possibility to contain some subjectiveness. In this research, nine categories: 'Sadness', 'Anger', 'Happiness', 'Disgust', 'Surprise', 'Fear', 'Interest', 'Boredom' and 'Pain' are used as multi-categorical sentiment structure. To capture public sentiment of Korean Top-10 companies, Internet news data of the companies are collected over the past 25 months from a representative Korean portal site. Based on the sentiment words extracted from previous researches, we have created a sentiment lexicon, and analyzed the frequency of the words coming up within the news data. The frequency of each sentiment category was calculated as a ratio out of the total sentiment words to make ranks of distributions. Sentiment comparison among top-4 companies, which are 'Samsung', 'Hyundai', 'SK', and 'LG', were separately visualized. As a next step, the research tested hypothesis to prove the usefulness of the multi-categorical sentiment lexicon. It tested how effective categorial sentiment can be used as relative comparison index in cross sectional and time series analysis. To test the effectiveness of the sentiment lexicon as cross sectional comparison index, pair-wise t-test and Duncan test were conducted. Two pairs of companies, 'Samsung' and 'Hanjin', 'SK' and 'Hanjin' were chosen to compare whether each categorical sentiment is significantly different in pair-wise t-test. Since category 'Sadness' has the largest vocabularies, it is chosen to figure out whether the subgroups of the companies are significantly different in Duncan test. It is proved that five sentiment categories of Samsung and Hanjin and four sentiment categories of SK and Hanjin are different significantly. In category 'Sadness', it has been figured out that there were six subgroups that are significantly different. To test the effectiveness of the sentiment lexicon as time series comparison index, 'nut rage' incident of Hanjin is selected as an example case. Term frequency of sentiment words of the month when the incident happened and term frequency of the one month before the event are compared. Sentiment categories was redivided into positive/negative sentiment, and it is tried to figure out whether the event actually has some negative impact on public sentiment of the company. The difference in each category was visualized, moreover the variation of word list of sentiment 'Rage' was shown to be more concrete. As a result, there was huge before-and-after difference of sentiment that ordinary people feel to the company. Both hypotheses have turned out to be statistically significant, and therefore sentiment analysis in business area using multi-categorical sentiment lexicons has persuasive power. This research implies that categorical sentiment analysis can be used as an alternative method to supplement dimensional sentiment analysis when figuring out public sentiment in business environment.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Korean Business Review
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• v.17
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• pp.53-81
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• 2004

The purpose of this study is to understand the realities and the subject of environmental management for small and medium-sized companies in Gwangwon area, through surveying the present status as to acquiring the certification of ISO14001, and to seek for a plan to facilitate environmental management. Given summarizing key results, those are as follows. First, while the number of companies in our country which acquired the certification of ISO14001, amounts to 1,215 businesses as of April of 2003, the number of small and medium-sized companies in Gwangwon area which obtained the certification of ISO14001 reached only 26 businesses, the lowest level among metropolitan municipalities. Second, for the reason that companies who didn't acquire the certification, strive not to receive the certification, it did present the point that' costs to be needed in acquiring and maintaining the certification are larger than practical benefit. Third, the biggest reason for either companies which did not acquire the certification of ISO14001 or companies which did (try to) acquire the certification of ISO1400, was, enhancement of a corporate image,' and the effect after a company who obtained the certification introduced the environmental management system, was also shown to be 'the improvement of a corporate image.' Fourth, many companies who acquired the certification of ISO1400 pointed out the response related to 'burden on document creation and costs' and 'lack of manpower' as problems when introducing the environmental management system. On the basis of major results of a study as the above, given presenting the subject and a plan for activating the environmental management of small and medium-sized companies in Gwangwon area, those are as follows. First, because most of companies who did not obtain the certification of ISO1400 have low recognition of ISO14001, it needs continuous and positive publicity, education and a training system. Second, it requires to carry out an educational program to nurture professional manpower due to lack of manpower relevant to environmental management, to expand payment of subsidies, to open exclusive-charge department and consulting contact, to have the relevant information be database and to develop software. Third, in order to make the certification obtained through inexpensive costs and simple procedures, it needs to positively consider the creation of public approval system for a small and medium-sized company, group approval system, industrial-complex approval system, and others.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Proceedings of the Korean Reliability Society Conference
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• 2001.06a
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• pp.277-278
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• 2001

The successful operation of a product In service depends upon the effective provision of logistic support in order to achieve and maintain the required levels of performance and customer satisfaction. Logistic support encompasses the activities and facilities required to maintain a product (hardware and software) in service. Logistic support covers maintenance, manpower and personnel, training, spares, technical documentation and packaging handling, storage and transportation and support facilities.The cost of logistic support is often a major contributor to the Life Cycle Cost (LCC) of a product and increasingly customers are making purchase decisions based on lifecycle cost rather than initial purchase price alone. Logistic support considerations can therefore have a major impact on product sales by ensuring that the product can be easily maintained at a reasonable cost and that all the necessary facilities have been provided to fully support the product in the field so that it meets the required availability. Quantification of support costs allows the manufacturer to estimate the support cost elements and evaluate possible warranty costs. This reduces risk and allows support costs to be set at competitive rates.Integrated Logistic Support (ILS) is a management method by which all the logistic support services required by a customer can be brought together in a structured way and In harmony with a product. In essence the application of ILS:- causes logistic support considerations to be integrated into product design;- develops logistic support arrangements that are consistently related to the design and to each other;- provides the necessary logistic support at the beginning and during customer use at optimum cost.The method by which ILS achieves much of the above is through the application of Logistic Support Analysis (LSA). This is a series of support analysis tasks that are performed throughout the design process in order to ensure that the product can be supported efficiently In accordance with the requirements of the customer.The successful application of ILS will result in a number of customer and supplier benefits. These should include some or all of the following:- greater product uptime;- fewer product modifications due to supportability deficiencies and hence less supplier rework;- better adherence to production schedules in process plants through reduced maintenance, better support;- lower supplier product costs;- Bower customer support costs;- better visibility of support costs;- reduced product LCC;- a better and more saleable product;- Improved safety;- increased overall customer satisfaction;- increased product purchases;- potential for purchase or upgrade of the product sooner through customer savings on support of current product.ILS should be an integral part of the total management process with an on-going improvement activity using monitoring of achieved performance to tailor existing support and influence future design activities. For many years, ILS was predominantly applied to military procurement, primarily using standards generated by the US Government Department of Defense (DoD). The military standards refer to specialized government infrastructures and are too complex for commercial application. The methods and benefits of ILS, however, have potential for much wider application in commercial and civilian use. The concept of ILS is simple and depends on a structured procedure that assures that logistic aspects are fully considered throughout the design and development phases of a product, in close cooperation with the designers. The ability to effectively support the product is given equal weight to performance and is fully considered in relation to its cost.The application of ILS provides improvements in availability, maintenance support and longterm 3ogistic cost savings. Logistic costs are significant through the life of a system and can often amount to many times the initial purchase cost of the system.This study provides guidance on the minimum activities necessary to Implement effective ILS for a wide range of commercial suppliers. The guide supplements IEC60106-4, Guide on maintainability of equipment Part 4: Section Eight maintenance and maintenance support planning, which emphasizes the maintenance aspects of the support requirements and refers to other existing standards where appropriate. The use of Reliability and Maintainability studies is also mentioned in this study, as R&M is an important interface area to ILS.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.11-21
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• 2015

As growing interest in Human-Computer Interaction(HCI), research on HCI has been actively conducted. Also with that, research on Natural User Interface/Natural User eXperience(NUI/NUX) that uses user's gesture and voice has been actively conducted. In case of NUI/NUX, it needs recognition algorithm such as gesture recognition or voice recognition. However these recognition algorithms have weakness because their implementation is complex and a lot of time are needed in training because they have to go through steps including preprocessing, normalization, feature extraction. Recently, Kinect is launched by Microsoft as NUI/NUX development tool which attracts people's attention, and studies using Kinect has been conducted. The authors of this paper implemented hand-mouse interface with outstanding intuitiveness using the physical features of a user in a previous study. However, there are weaknesses such as unnatural movement of mouse and low accuracy of mouse functions. In this study, we designed and implemented a hand mouse interface which introduce a new concept called 'Virtual monitor' extracting user's physical features through Kinect in real-time. Virtual monitor means virtual space that can be controlled by hand mouse. It is possible that the coordinate on virtual monitor is accurately mapped onto the coordinate on real monitor. Hand-mouse interface based on virtual monitor concept maintains outstanding intuitiveness that is strength of the previous study and enhance accuracy of mouse functions. Further, we increased accuracy of the interface by recognizing user's unnecessary actions using his concentration indicator from his encephalogram(EEG) data. In order to evaluate intuitiveness and accuracy of the interface, we experimented it for 50 people from 10s to 50s. As the result of intuitiveness experiment, 84% of subjects learned how to use it within 1 minute. Also, as the result of accuracy experiment, accuracy of mouse functions (drag(80.4%), click(80%), double-click(76.7%)) is shown. The intuitiveness and accuracy of the proposed hand-mouse interface is checked through experiment, this is expected to be a good example of the interface for controlling the system by hand in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.1
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• pp.44-50
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• 2013

The persons in charge of management request diagnosis and prescription to tree hospitals in order to get consultation about the problems like blight that occur in landscape tree management. This study aims to analyze what the main problems and questions raised by landscape gardeners are and those concerned in landscape tree management. This is done by investigating landscape tree-related questions and answers uploaded on the online consultation boards of the plant diagnostic centers approved in Korea including the Seoul National University Plant Clinic, the Chungbuk National University Plant Hospital, and the Kangwon Diagnostic Center. As a result, those concerned in landscape occupied the most as 81.4% among the questioners. However, only 11.5% did explain the plant management history or surrounding environment, which is essential for landscape tree diagnosis when asking questions. This shows that those concerned in landscape lack basic knowledge or interest about plant diagnosis. Among 263 questions about landscape trees, questions about physiological damage included 94 cases that were the most taking up 35.8%. Moreover, the next were damage by insects and damage by disease in order. It is thought that due to the characteristics of physiological problems that occur by various sorts of stress and with no signs, they tend to request diagnosis or prescription the most. The most frequent reasons for physiological damage are water stress and temperature stress. About damage by disease, there exist many types of diseases, and there are many complex damages accompanied by physiological causes. About damage by insects, the most common include damage by moths. In consideration of this result, universities or technician training centers should provide education for landscape tree management so that landscape technicians and students can acquire essential knowledge and information about landscape tree management and increase their interest in it. In particular, it is necessary to provide profound learning opportunities for plant physiology, and the technicians should make efforts themselves. In addition, it is needed to build organizations to which they can ask technical questions about landscape planting and management in order to understand landscape industry in general and the actual status of landscape planting technique and the actual field. Moreover, to elevate systemicity and expertise in the area of landscape tree management not yet equipped with the foundation, it is needed to cultivate the technicians intensively and conduct research by those concerned both in academic and industrial circles.

• Journal of Korean Society of Forest Science
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• v.94 no.4 s.161
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• pp.214-225
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• 2005

The mountain village development program in Korea started in the mountain villages, the 45.9% of total land and one of the typical marginal region, from 1995 to achieve the equilibrium development of national land and the sustainable mountain development in Chapter 13 in Agenda 21, and it has been accelerated to increase the happiness and the quality of life of mountain community residents through the expansion by province and the improvement of related laws and regulations. This study has been aimed to analyze the response of main stakeholder's -mountain village residents and local government officials - on mountain villages development, and to provide the future plan as community development. The survey and interview data were collected from the mountain villages which already developed 59 villages and developing 15 villages in 2003. The mountain village development program has achieved the positive aspects as community development plan in the several fields, - the voluntary participation of residents, the establishment of self-support spirit as the democratic civilians, the development of base of income increasement, the creation of comfortable living environment, the equilibrium development with the other regions. Especially the mountain residents and local government officials both highly satisfy with the development of base of income increasement and the creation of comfortable living environment which are the main concerns to both stakeholder. However through the mountain development program, it is not satisfied to increase the maintenance of local community and the strengthening of traditional value of mountain villages. Also to improve the sustainable income improvement effects, it is necessary to develop the income items and technical extension which good for the each region. In the decentralization era, it is necessary for local government should have the more active and multilateral activities for these. With this, the introduction of methods which the mountain community people and the local government officials could co-participate in the mountain villages' development from the initial stages and the renovation of related local government organizations and the cooperatives will be much helpful to the substantiality of mountain development program. Also it is essential for the assistance of central government to establish the complex plan and the mountain villages network for all mountain area and the exchange of information, the education and training of mountain villages leader who are the core factor for the developed mountain villages maintenance, the composition of national mountain villages representatives. In case the development proposals which based on the interests of the main stakeholder's on mountain community could be positively accepted, then the possibility of the mountain village development as one of community development will be successfully improved in future.