Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Salmah ,Fredi Andria, Irfan Wahyudin

Judul : Implementation of Big Data Concept for Variability Mapping Control of Financing Assessment of Informal Sector Workers in Bogor City

Abstrak :
At present risks and uncertainties occur in protecting health for the community. This requires a national health insurance program that can guarantee health care costs. One of the program participants is a resident who works in the informal sector. This group is vulnerable as well as the potential for the implementation of health insurance programs. However, the level of participation of informal sector workers is still low, so an analysis of the constraints affecting it is needed. This study aims to identify categories of informal sector workers and analyze various obstacles faced by informal sector workers to become health insurance participants in the city of Bogor. The method used is the concept of big data with K-means clustering data mining techniques to group informal sector workers along with the constraints that exist in each of these groups. The results showed that there were 3 clusters with very low Social Security Administrator (BPJS) health ownership, namely cluster 1, cluster 3, and cluster 5. Each cluster had different constraints. Cluster 1 has constraints on the number of dependents it has, Cluster 3 has constraints on the gender side that are dominated by women, while Cluster 5 has constraints on the low-income side. Each cluster has a different obstacle resolution recommendation, namely for cluster 1 by registering workers in JKN contribution recipient (PBI) participants, cluster 2 by giving outreach to women who have only focused on men, and for clusters 5 by involving the community as a forum for the empowerment of informal sector workers.

Keywords Big Data Cluster Informal Worker Sector K-Means Clustering

Tahun : 2019 Media Publikasi : Jurnal Internasional

Kategori : Jurnal No/Vol/Tahun : - / 135 / 2019

ISSN/ISBN : 2392-2192

PTN/S : Universitas Pakuan Program Studi : MANAJEMEN

Bibliography :
References
[1] Arifianto, A. 2006. The New Indonesian Social Security Law: A Blessing or Curve for Indonesians?. ASEAN Economic Bulletin, Vol. 23, No. 1, pp. 57-74.
[2] Yustina, E.W., Budisarwo, J., Wiwoho, L.E. 2017. The Implementation of The National Health Insurance Based on Gotong-Royong Principle as the Efforts of Enhancing the Welfare. International Journal of Social Science and Humanity. Vol 7, No. 5.
[3] Andria, F., Kusnadi, N. 2018. Alternative Models of Contribution of Health Insurance Funds for Informal Sector Workers (Case Study in Bogor City). Pakuan Law Review Vol. 4, No 2, 175-215.
[4] Lestari, F.H., Djamaludin, M.D. 2017. Perception and Motivation of National Health Insurance Program Participation in Bogor. Journal of Consumer Sciences Vol. 2 No. 1, 39-50.
[5] Wang, Y., Kung, Lee, Ann., and Byrd, TA. 2016. Big Data Analytics : Understanding Its Capabilities and Potential Benefit for Healtcare Organization. Technological Forecasting & Social Change, An International Journal 126 (2016) 3-13.
[6] Thabrany, H., Gani, A., Pujianto, Mayanda, L., Mahlil, and Budi, B.S. 2003. Social Health Insurance in Indonesia: Current Status and the Plan for a National Health Insurance. Social Health Insurance Workshop, New Delhi: Who SEARO.
[7] Mboi, N. 2015. Indonesia: On the way the Universal Health Care. Health Systems & Reform, Vol. 1 (2), pp. 91-97.
[8] Odeyemi, I OA. 2014. Community-based health insurance programmes and the national health insurance scheme of Nigeria: challenges to uptake and integration. J of Equility in Health 13: 20.
[9] Shafie AA, Hassali MA. 2013. Willingness to pay for voluntary community-based health insurance: Findings from an exploratory study in the state of Penang, Malaysia. Social Science & Medicine 96 (2013) 272-276.
[10] Sims, J.M. 2018. Communities of Practice: Telemedicine and Online Medical Communities. Technological Forecasting & Social Change, vol. 126, p. 53-63.
[11] Milner, H. V., and Rudra, N. 2015. Globalization and the Political Benefits of the Informal Economy. International Studies Review, pp. 664-669.

[13] Saraswathi, K., Ganesh, B.V. 2015. A survey on data mining trends, applications and techniques. Discovery 30(135), 383-389.
[14] Sanchez, D., Martin-Bautista, M.J., Blanco, I., Justicia de la Torre. 2008. Text Knowledge Mining: An Alternative to Text Data Mining. Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy.
[15] Feng, C., Pan, D., Jiafu, W., Dhaqiang, Z., Athanasios, V.V., Xiaohui, R. 2015. Data Mining for the Internet of Things: Literature Review and Challenges. International Journal of Distributed Sensor Networks. Vol. 11, Issue: 8. http://dx.doi.org/10.1155/2015/431047
[16] Smita, S. Priti. 2014. Use of Data Mining in Various Field: A Survey Paper. IOSR Journal of Computer Engineering Vol, 16(3), pp. 18-21.
[17] Brijesh, K.B. 2011. Mining Educational Data to Analyze Students Performance. International Journal of Advanced Computer Science and Applications 2 (6).
[18] Shital, H.B., Nirav, B. 2016. Data Mining Techniques and Trends – A Review. Global Journal for Research Analysis, Vol. 5(5), pp. 252-254
[19] Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., Vasilakos, A. V. 2016. Big Data: From Beginning to Future. International Journal of Information Management, Vol. 36, pp. 1231-1247.
[20] Barry, L. 2010. An introduction to data mining and other techniques for advanced analytics. Data and Digital Marketing Practice, 12(2), 137–153. doi:10.1057/dddmp.2010.35.
[21] Sohrab Md, M., Mostafizer Md, R., Nasim Md, A. 2012. Improvement of K-means clustering algorithm with better initial centroids based on weighted average. Electrical & Computer Engineering (ICECE), 2012 7th International Conference.
[22] Bangoria, B.M. 2014. Enhanced K-Means Clustering Algorithm to Reduce Time Complexity for Numeric Values. International Journal of Computer Science and Information Technologies 5 (1), 876-879.
[23] Andria, F., Tosida, E.T., Kusnadi, N. and Andriani, S. 2019. Prediction Model of Health Insurance membership for Informal Workers. American Journal of Humanities and Social Sciences Research Vol. 3, Issued 4 (April 2019), pp-236-246.
[24] Jyoti, Y., Monika, S. 2013. A Review of K-mean Algorithm. International Journal of Engineering Trends and Technology Vol. 4, Issue. 7, pp. 2972-2976.
[25] Yu, Z., Haghighat, F., Fung, B.C.M. 2016. Advances and Challenges in Building Engineering and Data Mining Applications for Energy-Efficient Communities. Sustainable Cities and Society, Vol. 25, pp. 33-38.
[26] Tosida, E.T., Hairlangga, O., Amirudin, F., and Ridwanah, M. 2018. Application of Decision Rules for Empowering of Indonesian Telematics Service SMEs. IOP Conference Series: Materials Sciences and Engineering 332 (1), 012018.

[27] Rolindrawan, Djoni. 2015. The Impact of BPJS Health Implementation for Poor and Near Poor on the Use of Health Facility. Procedia - Social and Behavioral Sciences 211, 550-559.
[28] Sparrow, R., Budiyati, S., Yumna, A., Warda, N., Suryahadi, A., Bedi, A.S. 2017. Sub-national Health Care Financing Reforms in Indonesia. Health Policy and Planning, Vol. 32, pp. 91-101.
[29] Mahendradhata, Y., Trisnantoro, L., Listyadewi, S., Soewondo, P., Harimurti, P., Prawira, J. 2017. The Republic of Indonesia Health System Review. Health System in Transition Vol. 7, No. 1.
[30] Ichikawa, D., Saito, T., and Oyama, H. 2017. Impact of Prediction Health-Guidance Candidates Using Massive Health Check-up Data: A Data-driven Analysis. International Journal of Medical Informatics, vol. 106, pp. 32-36.
[31] Dhanachandra, N., Manglem, K., Chanu, Y. J. 2015. Image Segmentation using K-Means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, Vol. 54, pp. 764-771.
[12] Igudia, E., Ackrill, R., Coleman, S., Dobson, C. 2016. Determinants of the informal economy of an emerging economy: a multiple indicator, multiple causes (MIMIC) approach. International Journal of Entrepreneurship and Small Business, Vol. 28 (2/3), pp. 154-177.

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Detail Karya Ilmiah Dosen

Salmah ,Fredi Andria, Irfan Wahyudin

Judul	:	Implementation of Big Data Concept for Variability Mapping Control of Financing Assessment of Informal Sector Workers in Bogor City
Abstrak	:	At present risks and uncertainties occur in protecting health for the community. This requires a national health insurance program that can guarantee health care costs. One of the program participants is a resident who works in the informal sector. This group is vulnerable as well as the potential for the implementation of health insurance programs. However, the level of participation of informal sector workers is still low, so an analysis of the constraints affecting it is needed. This study aims to identify categories of informal sector workers and analyze various obstacles faced by informal sector workers to become health insurance participants in the city of Bogor. The method used is the concept of big data with K-means clustering data mining techniques to group informal sector workers along with the constraints that exist in each of these groups. The results showed that there were 3 clusters with very low Social Security Administrator (BPJS) health ownership, namely cluster 1, cluster 3, and cluster 5. Each cluster had different constraints. Cluster 1 has constraints on the number of dependents it has, Cluster 3 has constraints on the gender side that are dominated by women, while Cluster 5 has constraints on the low-income side. Each cluster has a different obstacle resolution recommendation, namely for cluster 1 by registering workers in JKN contribution recipient (PBI) participants, cluster 2 by giving outreach to women who have only focused on men, and for clusters 5 by involving the community as a forum for the empowerment of informal sector workers. Keywords Big Data Cluster Informal Worker Sector K-Means Clustering
Tahun	:	2019	Media Publikasi	:	Jurnal Internasional
Kategori	:	Jurnal	No/Vol/Tahun	:	- / 135 / 2019
ISSN/ISBN	:	2392-2192
PTN/S	:	Universitas Pakuan	Program Studi	:	MANAJEMEN
Bibliography	:	References [1] Arifianto, A. 2006. The New Indonesian Social Security Law: A Blessing or Curve for Indonesians?. ASEAN Economic Bulletin, Vol. 23, No. 1, pp. 57-74. [2] Yustina, E.W., Budisarwo, J., Wiwoho, L.E. 2017. The Implementation of The National Health Insurance Based on Gotong-Royong Principle as the Efforts of Enhancing the Welfare. International Journal of Social Science and Humanity. Vol 7, No. 5. [3] Andria, F., Kusnadi, N. 2018. Alternative Models of Contribution of Health Insurance Funds for Informal Sector Workers (Case Study in Bogor City). Pakuan Law Review Vol. 4, No 2, 175-215. [4] Lestari, F.H., Djamaludin, M.D. 2017. Perception and Motivation of National Health Insurance Program Participation in Bogor. Journal of Consumer Sciences Vol. 2 No. 1, 39-50. [5] Wang, Y., Kung, Lee, Ann., and Byrd, TA. 2016. Big Data Analytics : Understanding Its Capabilities and Potential Benefit for Healtcare Organization. Technological Forecasting & Social Change, An International Journal 126 (2016) 3-13. [6] Thabrany, H., Gani, A., Pujianto, Mayanda, L., Mahlil, and Budi, B.S. 2003. Social Health Insurance in Indonesia: Current Status and the Plan for a National Health Insurance. Social Health Insurance Workshop, New Delhi: Who SEARO. [7] Mboi, N. 2015. Indonesia: On the way the Universal Health Care. Health Systems & Reform, Vol. 1 (2), pp. 91-97. [8] Odeyemi, I OA. 2014. Community-based health insurance programmes and the national health insurance scheme of Nigeria: challenges to uptake and integration. J of Equility in Health 13: 20. [9] Shafie AA, Hassali MA. 2013. Willingness to pay for voluntary community-based health insurance: Findings from an exploratory study in the state of Penang, Malaysia. Social Science & Medicine 96 (2013) 272-276. [10] Sims, J.M. 2018. Communities of Practice: Telemedicine and Online Medical Communities. Technological Forecasting & Social Change, vol. 126, p. 53-63. [11] Milner, H. V., and Rudra, N. 2015. Globalization and the Political Benefits of the Informal Economy. International Studies Review, pp. 664-669. [13] Saraswathi, K., Ganesh, B.V. 2015. A survey on data mining trends, applications and techniques. Discovery 30(135), 383-389. [14] Sanchez, D., Martin-Bautista, M.J., Blanco, I., Justicia de la Torre. 2008. Text Knowledge Mining: An Alternative to Text Data Mining. Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15-19, 2008, Pisa, Italy. [15] Feng, C., Pan, D., Jiafu, W., Dhaqiang, Z., Athanasios, V.V., Xiaohui, R. 2015. Data Mining for the Internet of Things: Literature Review and Challenges. International Journal of Distributed Sensor Networks. Vol. 11, Issue: 8. http://dx.doi.org/10.1155/2015/431047 [16] Smita, S. Priti. 2014. Use of Data Mining in Various Field: A Survey Paper. IOSR Journal of Computer Engineering Vol, 16(3), pp. 18-21. [17] Brijesh, K.B. 2011. Mining Educational Data to Analyze Students Performance. International Journal of Advanced Computer Science and Applications 2 (6). [18] Shital, H.B., Nirav, B. 2016. Data Mining Techniques and Trends – A Review. Global Journal for Research Analysis, Vol. 5(5), pp. 252-254 [19] Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., Vasilakos, A. V. 2016. Big Data: From Beginning to Future. International Journal of Information Management, Vol. 36, pp. 1231-1247. [20] Barry, L. 2010. An introduction to data mining and other techniques for advanced analytics. Data and Digital Marketing Practice, 12(2), 137–153. doi:10.1057/dddmp.2010.35. [21] Sohrab Md, M., Mostafizer Md, R., Nasim Md, A. 2012. Improvement of K-means clustering algorithm with better initial centroids based on weighted average. Electrical & Computer Engineering (ICECE), 2012 7th International Conference. [22] Bangoria, B.M. 2014. Enhanced K-Means Clustering Algorithm to Reduce Time Complexity for Numeric Values. International Journal of Computer Science and Information Technologies 5 (1), 876-879. [23] Andria, F., Tosida, E.T., Kusnadi, N. and Andriani, S. 2019. Prediction Model of Health Insurance membership for Informal Workers. American Journal of Humanities and Social Sciences Research Vol. 3, Issued 4 (April 2019), pp-236-246. [24] Jyoti, Y., Monika, S. 2013. A Review of K-mean Algorithm. International Journal of Engineering Trends and Technology Vol. 4, Issue. 7, pp. 2972-2976. [25] Yu, Z., Haghighat, F., Fung, B.C.M. 2016. Advances and Challenges in Building Engineering and Data Mining Applications for Energy-Efficient Communities. Sustainable Cities and Society, Vol. 25, pp. 33-38. [26] Tosida, E.T., Hairlangga, O., Amirudin, F., and Ridwanah, M. 2018. Application of Decision Rules for Empowering of Indonesian Telematics Service SMEs. IOP Conference Series: Materials Sciences and Engineering 332 (1), 012018. [27] Rolindrawan, Djoni. 2015. The Impact of BPJS Health Implementation for Poor and Near Poor on the Use of Health Facility. Procedia - Social and Behavioral Sciences 211, 550-559. [28] Sparrow, R., Budiyati, S., Yumna, A., Warda, N., Suryahadi, A., Bedi, A.S. 2017. Sub-national Health Care Financing Reforms in Indonesia. Health Policy and Planning, Vol. 32, pp. 91-101. [29] Mahendradhata, Y., Trisnantoro, L., Listyadewi, S., Soewondo, P., Harimurti, P., Prawira, J. 2017. The Republic of Indonesia Health System Review. Health System in Transition Vol. 7, No. 1. [30] Ichikawa, D., Saito, T., and Oyama, H. 2017. Impact of Prediction Health-Guidance Candidates Using Massive Health Check-up Data: A Data-driven Analysis. International Journal of Medical Informatics, vol. 106, pp. 32-36. [31] Dhanachandra, N., Manglem, K., Chanu, Y. J. 2015. Image Segmentation using K-Means Clustering Algorithm and Subtractive Clustering Algorithm. Procedia Computer Science, Vol. 54, pp. 764-771. [12] Igudia, E., Ackrill, R., Coleman, S., Dobson, C. 2016. Determinants of the informal economy of an emerging economy: a multiple indicator, multiple causes (MIMIC) approach. International Journal of Entrepreneurship and Small Business, Vol. 28 (2/3), pp. 154-177.
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