Implementation of Self Organizing Map (SOM) as decision support: Indonesian telematics services MSMEs empowerment

Tosida, Eneng Tita; Maryana, Sufiatul; Thaheer, Hermawan; Hardiani,

doi:10.1088/1757-899x/166/1/012017

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…The LVQ model built on Susenas 2006 also shows that the attributes of business group types have a high impact in the relief process. This condition is in line with the results of research [13] which states that one of the characteristics of business clusters of telematics services that are eligible to be provided has a characteristic of high business improvement. This is shown from the type of business group Computer Settings Services and Internet businesses.…”

Section: Discussionsupporting

confidence: 85%

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Learning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES

Tosida¹,

Wihartiko²,

Lumesa³

2018

IJET

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Implementation of Learning Vector Quantization (LVQ) Algorithm for classification of Indonesia telematics service is designed and created as a classification system to support the decision of grant aid for Small Medium Enterprises (SMEs). Based on the test results, the LVQ algorithm has the best accuracy (93.11%) when compared with ID3 algorithm (64%) and C45 (62%) for telematics data of National Census of Economic (Susenas 2006). The data is still valid and relevant for use in this research because in Indonesia census data is done every 10 years and there is no update of data until now. LVQ implementation results are applied to a web-based decision support system to predict the provision of assistance for Indonesian telematics services SMEs. Unlike the C45 and ID3 algorithms, the LVQ algorithm generates the weight of a neural network where it difficult to know which attributes are most influential for decision making. But in this study LVQ able to show good performance through the analysis of the relevance of existing conditions by comparing it with the weight value produced by the model that are implemented in a web-based decision support system

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Section: Discussionsupporting

confidence: 85%

“…RS-boosting and multi boosting). [13] applies credit scoring using non parametric method for Peruvian microfinance industry. The model used is neural network multi layer perceptron (MLP) which compared its performance with linear discriminant analysis (LDA) approach, quadratic discriminant analysis (QDA) and logistic regression (LR).…”

Section: Introductionmentioning

confidence: 99%

Learning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES

Tosida¹,

Wihartiko²,

Lumesa³

2018

IJET

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“…SC and DBI were used to evaluate the results of user clustering. In order to realize the analysis of telecommunication service cluster, Tosida et al [14] proposed a self-organizing mapping algorithm based on artificial neural network system, clustered telecommunication service facilities, and used DBI as the evaluation index of clustering results. In order to provide data for the construction of wind power plants, according to the wind speed in Turkey, Yesilbudak et al [15] used K-Means clustering algorithm to divide the regions, and selected the best number of clusters according to the change of SC.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, in the calculation process of Sim(i,j), Len(I) and Len(J) are used as a coefficient to expand the calculated value of Wcd'(i) and Wcd'(j), thereby increasing the gap between the numerator and the denominator in the formula of DBI. The specific calculation processes of Sim'(i,j) is shown in formulas (14).…”

Section: Redefinition Of Davies-bouldin Indexmentioning

confidence: 99%

Analysis and improvement of evaluation indexes for clustering results

Zhong¹,

Zhang²,

Jia³

2020

EAI Endorsed Transactions on Collaborative Computing

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Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three indexes. (1) Keep the number of clusters and the objects in the cluster unchanged. When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged. When changing the objects in the cluster, the three indexes will change tinily. This shows that the three indexes unable to evaluate the clustering results very well. Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes -NSC, NDBI and NCHI. Through testing on standard data sets, three new indexes can better evaluate clustering results.

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“…Roadmap for Telematics Industry Development(Deprin, 2010) The first step of the research methode was in-depth study and analysis related to condition factors that include potential, problems and challenges faced by the business group of telematics, through the optimization of research resultsTosida et al (2015) andTosida et al(2016). The very important condition factor was studied as the first step in drafting the competency model.…”

mentioning

confidence: 99%

Strategi Pengembangan Industri Kecil Menengah Telematika Di Indonesia

Tosida¹,

Andria

Wahyudin

et al. 2019

j. penelit. pos inform.

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The telematics industry is included in the national industry development policy. Electronic and Telematic Industries are projected to grow twofold by 2025. The telematics industry is also included in the Nine Priority SMEs for development. The telematics industry is even part of the creative industry that absorbs about 13,000 workers. The national telematics industry is grouped into five groups, namely the office equipment industry, software, animation, games, and embedded. The strategy for developing Telematics SMEs is based on the position of the industry in its strength map. The five industry groups have been mapped into 4 developing quadrants. The office equipment industry is included in quadrant 1, with an expansive strategy such as increasing production and market share. The game and embedded industries map into quadrant 2, the strategy developed is proactive such as strengthening promotion and innovation. The software industry enters quadrant 3, the strategy developed is consolidation, such as strengthening human resources, infrastructure, and business institutions. While the animation industry entered quadrant 4, the strategy developed was to defensive, namely managing production cost efficiency, strengthening the domestic market, and increasing the competency of human resources. Human resource development has received special attention in the development of telematics SMEs. The skills of human resources in the telematics business may not be doubted, but recognition of competencies is a distinguishing factor in efforts to provide quality assurance to customers.

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Implementation of Self Organizing Map (SOM) as decision support: Indonesian telematics services MSMEs empowerment

Cited by 8 publications

References 6 publications

Learning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES

Learning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES

Analysis and improvement of evaluation indexes for clustering results

Strategi Pengembangan Industri Kecil Menengah Telematika Di Indonesia

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