ATTRIBUTE SELECTION   F INDONESIAN TELEMATIC SERVICES MSMEs   FEASIBILITY ASSISTANCE, USING AHP

Tosida, Eneng Tita; Herdiyeni, Yeni

doi:10.28961/kursor.v8i2.67

Cited by 3 publications

(9 citation statements)

References 13 publications

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“…Based on this, the model results are in conformity with the real condition, because the Indonesian government is intensively developing and developing start-up based technology, especially start-up based on information and telecommunication technology (telematics). This strategy is carried out to strengthen the telematics field built from the SMEs telematics able to have a strong competitiveness [2], especially facing the AEC [12]. Finally, this decision support model base LVQ has a usefulness as a classification of the feasibility of Indonesian telematics services SMEs, receiving assistance using rules or rules that have been made and store data classification and its decisions.…”

Section: Discussionmentioning

confidence: 99%

“…The data set of the Telematics Services business obtained from the National Census in 2006 consisted of 8798 UMKM Telematics Services with attributes of 21 attributes (4 numeric attributes and 17 categorical attributes). Data for this classification have undergone pre-processing stages of data mining such as data cleansing and data integration conducted by previous research by [12]. The available data is unbalanced data on the key attribute.…”

Section: Methodsmentioning

confidence: 99%

“…For developing countries software and services generally have greater opportunities because they do not require large investments in research and production support equipment. This is mainly due to more software based on knowledgeable workforce [12]. Based on previous research by [11] the system has not built a system of determining the provision of telematics services assistance limited to SMEs data visualization of each region.…”

Section: Introductionmentioning

confidence: 99%

“…Based on previous research by [11] the system has not built a system of determining the provision of telematics services assistance limited to SMEs data visualization of each region. On the feasibility of assistance for Indonesian telematics services Micro Small Medium Enterprises (MSMEs) involve complex criteria consisting of 21 criteria [12]. The relationship between the criteria for the feasibility of the aid is non-linear, Indonesian telematics services SMEs therefore it can be approximated by artificial neural network method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Tosida¹,

Wihartiko²,

Lumesa³

2018

IJET

View full text Add to dashboard Cite

show abstract

“…Likewise, in the field of telematics, MSME telematics in quantity increased rapidly with the increasing needs of the Indonesian people for these telematics products and services. Therefore, the government continues to make various efforts to improve competitiveness in the field of telematics, especially in the face of free trade in the scope of Asia in 2015 [9], [10], [11]. Several studies related to the condition of Indonesian telematics MSMEs have been carried out [15](as well as efforts to increase their competitiveness through clustering model approaches [12], classification [13], [14], and hybrid mining),besides the development strategy of small and medium telematics industries [16].…”

Section: Introductionmentioning

confidence: 99%

Business Intelligence of Indonesian Telematics Human Resource: Optimization of Customer and Internal Balanced Scorecards

Tosida¹,

Wahyudin²,

Andria³

et al. 2020

Journal of Southwest Jiaotong University

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This study aims to build an Indonesian telematics human resources business intelligence based on the optimization of the balanced scorecard of the customer and internal aspects. The balanced scorecard for customer aspects is done through the concept of the data lake and needs of the telematics workforce, which refers to the largest job vacancy site in Indonesia. The internal aspect of the balanced scorecard is carried out through vocational high school clustering as a telematics workforce producer. Business intelligence applications are able to describe in real-time the needs of the telematics workforce. Moreover, this application is also made in a mobile version, so that the conditions make it easier for vocational high schools to find out trends in telematics workforce needs nationally. This business intelligence is integrated with the balanced scorecard in the internal aspects of vocational telematics high school. The results show that Indonesian vocational telematics high schools still have major constraints on laboratory facilities, but most vocational telematics high schools show optimism through the development of technopreneurial programs. This is in line with the government's program, which continues to launch a telematics-based start-up ecosystem growth program to be highly competitive in facing the Asian Economic Community and industrial revolution 4.0. The government needs to provide more intensive workshop program incentives to vocational high school teachers because this shows a significant effect on the absorption capacity of vocational high school graduates.

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Prospect Prediction Model Of Indonesian Telematics Medium Large Size Enterprises Using Deep Learning Approach

Tosida¹,

Wihartiko²,

Utep³

et al. 2021

ijeeit

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Analysis of business prospects is an important part of predicting a country's economic conditions. Currently, the prediction of prospects for medium-big sized enterprises (MLE) in the telematics sector has not been widely researched and represented as a factor of economic development in Indonesia. In fact, in accordance with the development of the Industrial Revolution 4.0, the telematics sector business is one of the pillars that is a priority to be developed in Indonesia. The main purpose of this study is to construct the prediction model for prospects in the Indonesian telematics LME sector using a deep learning approach. We used data from the 2016 National Economic Census as many as 2500 preprocessed data. The deep learning approach in this study used a multilayer perceptrón (MLP) architecture, 17 attributes, 3 hidden layers and 5 target classes. The attributes in question include province, business owner education, legal entity status, length of operation, business network, total assets, business lava, number of workers, difficulties, partnerships, marketing innovations, comparison of profit with the previous year, and development plans. The target class of prospects are excellent, good, neutral, bad and very bad. The optimal results were achieved in epoch 50 conditions with a learning reate of 0.2 and an accuracy rate of 98.80%. Based on the prediction model, this business prospect can be used as a reference for the development of MLE in the telematics sector in Indonesia. This prospect model still lacks visualization and attribute analysis that affects the classification of prospects for Indonesian telematics MLE. Research development opportunities can be carried out through the integration of the whitebox model in the deep learning model and complementing a web-based graphical user interface (GUI) to make it easier for stakeholders to develop strategies based on the strength of attributes that affect the prospects for MLE Telematics Indonesia. This is expected to boost the competitiveness of the prospects for Indonesian telematics MLE.

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ATTRIBUTE SELECTION F INDONESIAN TELEMATIC SERVICES MSMEs FEASIBILITY ASSISTANCE, USING AHP

Abstract: The assistance program had not absorbed optimally yet for telematics Micro Small Medium Enterprises (MSMEs

Cited by 3 publications

References 13 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

Business Intelligence of Indonesian Telematics Human Resource: Optimization of Customer and Internal Balanced Scorecards

Prospect Prediction Model Of Indonesian Telematics Medium Large Size Enterprises Using Deep Learning Approach

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