Identifikasi Tingkat Pemakaian Obat Menggunakan Metode Fuzzy C-Means
Article Sidebar
Published:
Dec 31, 2021
Keywords:
Clustering, Data Mining, Fuzzy C Means, Medicine, Puskesmas
Main Article Content
Abstract
Medicine is one of the irreplaceable components in health services that can help in treating sick people. Planning for drug needs is one of the important aspects in drug management, because it affects the procurement, distribution and use of drugs in health care units. Planning the right drug needs will make procurement effective and efficient so that it is in accordance with the needs of health services with guaranteed quality and can be obtained when needed. Puskesmas is one of the health services that is managed under the District and City Health Offices. However, in reality there are still obstacles in the process of drug procurement at the Puskesmas so that it has not yet achieved excellent service related to the availability of drug services. Clustering in Data Mining can be used to analyze the use of drugs, planning and controlling drugs at the Puskesmas. The method that will be used in this research is the Fuzzy C-Means algorithm, which is the most widely used and relatively successful unsupervised machine learning method among many fuzzy clustering algorithms. The purpose of this study was to categorize drug data which can be used as a reference in making decisions in planning and controlling medical supplies at the puskesmas. Based on 501 Pharmacy Monthly LPLPO data records in October 2020-February 2021, the results obtained in cluster one are 179 types of drugs which are included in the low level of use, cluster 2 there are 18 types of drugs that are included in the moderate level of use and cluster 3 as many as 4 types. drugs that are included in the high level of use.
Article Details
How to Cite
Rusnedy, H., Nurcahyo, G. W., & Sumijan, S. (2021). Identifikasi Tingkat Pemakaian Obat Menggunakan Metode Fuzzy C-Means. Jurnal Informasi Dan Teknologi, 3(4), 196-201. https://doi.org/10.37034/jidt.v3i4.152
Issue
Section
Articles
References
[1] Firdaus, R. D., Laksana, T. G., & Ramadani, R. D. (2019). Pengelompokan Data Persediaan Obat Menggunakan Perbandingan Metode K-Means Dengan Hierarchical Clustering Single Linkage. Journal of Informatics, Information System, Software Engineering and Applications (INISTA), 2(1), 33–48. https://doi.org/HTTPS://DOI.ORG/10.20895/INISTA.V2I1.
[2] Husein, A. M., Harahap, M., Aisyah, S., Purba, W., & Muhazir, A. (2018). The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data. Journal of Physics: Conference Series, 978(1), 012019. https://doi.org/10.1088/1742-6596/978/1/012019.
[3] Anton, A. (2020). Data Mining Implementation with Clustering Techniques for Drug Inventory Information in Antonius Hospital Pontianak. Eduma : Mathematics Education Learning and Teaching, 9(1), 86. https://doi.org/10.24235/eduma.v9i1.4011.
[4] Jothi, N., Rashid, N. A., & Husain, W. (2015). Data Mining in Healthcare - A Review. Procedia Computer Science, 72, 306–313. https://doi.org/10.1016/j.procs.2015.12.145.
[5] Zhang, Y., Zhou, Z., & Si, Y. (2019). When more is less: What explains the overuse of health care services in China? Social Science and Medicine, 232(March), 17–24. https://doi.org/10.1016/j.socscimed.2019.04.018.
[6] Ding, Y., & Fu, X. (2016). Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing, 188, 233–238. https://doi.org/10.1016/j.neucom.2015.01.106.
[7] Bisilisin, F. Y., & Naatonis, R. N. (2021). Pengelompokkan Jenis Rumput Laut Menggunakan Fuzzy C-Means Berbasis Citra. Jurnal Manajemen Informatika & Sistem Informasi, 4(1). https://doi.org/https://doi.org/10.36595/misi.v4i1.212.
[8] Rustiyan, R., & Mustakim, M. (2018). Penerapan Algoritma Fuzzy C Means untuk Analisis Permasalahan Simpanan Wajib Anggota Koperasi. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(2), 171. https://doi.org/10.25126/jtiik.201852605.
[9] Abdellahoum, H., Mokhtari, N., Brahimi, A., & Boukra, A. (2021). CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach. Expert Systems with Applications, 166, 114063. https://doi.org/10.1016/j.eswa.2020.114063.
[10] Zhao, Z., Zhao, J., Song, K., Hussain, A., Du, Q., Dong, Y., … Yang, X. (2020). Joint DBN and Fuzzy C-Means unsupervised deep clustering for lung cancer patient stratification. Engineering Applications of Artificial Intelligence, 91(February), 103571. https://doi.org/10.1016/j.engappai.2020.103571.
[11] Tuya, & Chun-sheng, Z. (2020). Research of Mongolian Heat Syndrome Medicine Prescription Classification Method Based on Fuzzy C-means Algorithm. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, 2020-Octob, 305–309. https://doi.org/10.1109/ICSESS49938.2020.9237750.
[12] Taslim, T., & Fajrizal, F. (2016). Penerapan algorithma k-mean untuk clustering data obat pada puskesmas rumbai. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 7(2), 108–114. https://doi.org/10.31849/digitalzone.v7i2.602.
[13] Gustientiedina, G., Adiya, M. H., & Desnelita, Y. (2019). Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan. Jurnal Nasional Teknologi Dan Sistem Informasi, 5(1), 17–24. https://doi.org/10.25077/teknosi.v5i1.2019.17-24.
[14] Bara, E. H., Pranoto, Y. A., & Ariwibisono, F. . (2020). Pengelompokan Data Obat Menggunakan Metode K-Means Clustering Pada Upt Puskesmas Kondoran Kec. Sangalla’. JATI (Jurnal Mahasiswa Teknik Informatika), 4(2), 92–97. https://doi.org/10.36040/jati.v4i2.2720.
[15] Safhi, H. M., Frikh, B., & Ouhbi, B. (2019). Assessing reliability of Big Data Knowledge Discovery process. Procedia Computer Science, 148, 30–36. https://doi.org/10.1016/j.procs.2019.01.005.
[16] Shikhli, M. S. E., & Hammad, A. M. (2018). Data Acquisition Model for Analyzing Schedule Delays Using KDD (Knowledge Discovery and Datamining). In ACM International Conference Proceeding Series (pp. 67–74). https://doi.org/10.1145/3233347.3233366.
[17] Bathla, G., Aggarwal, H., & Rani, R. (2018). A novel approach for clustering big data based on mapreduce. International Journal of Electrical and Computer Engineering, 8(3), 1711–1719. https://doi.org/10.11591/ijece.v8i3.pp1711-1719.
[18] Nidheesh, N., Nazeer, K. A. A., & Ameer, P. M. (2017). An enhanced deterministic K-means clustering algorithm for cancer subtype prediction from gene expression data. Computers in Biology and Medicine, 91, 213–221. https://doi.org/10.1016/j.compbiomed.2017.10.014.
[19] Afrina, & Muthmainnah. (2018). Sistem Pendukung Keputusan Penerimaan Beasiswa pada Rumah Zakat dengan Metode Fuzzy Analytical Network Proces (FANP) di Baitul Mal Kabupaten Aceh Utara. Jurnal Sistem Informasi, 2(1), 15–24.
[20] Putri, A. D., & Effendi. (2017). Fuzzy Logic Untuk Menentukan Lokasi Kios Terbaik Di Kepri Mall Dengan Menggunakan Metode Sugeno. Edik Informatika, 3(1), 49–59. https://doi.org/10.22202/ei.2016.v3i1.1517.
[21] Cao, S. J., Ding, J., & Ren, C. (2020). Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment’s safety and health. Sustainable Cities and Society, 59(April), 102190. https://doi.org/10.1016/j.scs.2020.102190.
[22] Hou, W. hui, Wang, Y. ting, Wang, J. qiang, Cheng, P. F., & Li, L. (2020). Intuitionistic fuzzy c-means clustering algorithm based on a novel weighted proximity measure and genetic algorithm. International Journal of Machine Learning and Cybernetics, 12(3), 859–875. https://doi.org/10.1007/s13042-020-01206-3.
[23] Swindiarto, V. T. P., Sarno, R., & Novitasari, D. C. R. (2018). Integration of Fuzzy C-Means Clustering and TOPSIS (FCM-TOPSIS) with Silhouette Analysis for Multi Criteria Parameter Data. In Proceedings - 2018 International Seminar on Application for Technology of Information and Communication: Creative Technology for Human Life, iSemantic 2018 (pp. 463–468). IEEE. https://doi.org/10.1109/ISEMANTIC.2018.8549844.
[24] Jia, Z., Cai, Y., Chen, Y., & Zeng, W. (2018). Regionalization of water environmental carrying capacity for supporting the sustainable water resources management and development in China. Resources, Conservation and Recycling, 134(January), 282–293. https://doi.org/10.1016/j.resconrec.2018.03.030.
[25] Hidayati, R., Zubair, A., Pratama, A. H., & Indana, L. (2021). Analisis Silhouette Coefficient pada 6 Perhitungan Jarak K-Means Clustering. Techno.Com, 20(2), 186–197. https://doi.org/10.33633/tc.v20i2.4556.
[2] Husein, A. M., Harahap, M., Aisyah, S., Purba, W., & Muhazir, A. (2018). The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data. Journal of Physics: Conference Series, 978(1), 012019. https://doi.org/10.1088/1742-6596/978/1/012019.
[3] Anton, A. (2020). Data Mining Implementation with Clustering Techniques for Drug Inventory Information in Antonius Hospital Pontianak. Eduma : Mathematics Education Learning and Teaching, 9(1), 86. https://doi.org/10.24235/eduma.v9i1.4011.
[4] Jothi, N., Rashid, N. A., & Husain, W. (2015). Data Mining in Healthcare - A Review. Procedia Computer Science, 72, 306–313. https://doi.org/10.1016/j.procs.2015.12.145.
[5] Zhang, Y., Zhou, Z., & Si, Y. (2019). When more is less: What explains the overuse of health care services in China? Social Science and Medicine, 232(March), 17–24. https://doi.org/10.1016/j.socscimed.2019.04.018.
[6] Ding, Y., & Fu, X. (2016). Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing, 188, 233–238. https://doi.org/10.1016/j.neucom.2015.01.106.
[7] Bisilisin, F. Y., & Naatonis, R. N. (2021). Pengelompokkan Jenis Rumput Laut Menggunakan Fuzzy C-Means Berbasis Citra. Jurnal Manajemen Informatika & Sistem Informasi, 4(1). https://doi.org/https://doi.org/10.36595/misi.v4i1.212.
[8] Rustiyan, R., & Mustakim, M. (2018). Penerapan Algoritma Fuzzy C Means untuk Analisis Permasalahan Simpanan Wajib Anggota Koperasi. Jurnal Teknologi Informasi Dan Ilmu Komputer, 5(2), 171. https://doi.org/10.25126/jtiik.201852605.
[9] Abdellahoum, H., Mokhtari, N., Brahimi, A., & Boukra, A. (2021). CSFCM: An improved fuzzy C-Means image segmentation algorithm using a cooperative approach. Expert Systems with Applications, 166, 114063. https://doi.org/10.1016/j.eswa.2020.114063.
[10] Zhao, Z., Zhao, J., Song, K., Hussain, A., Du, Q., Dong, Y., … Yang, X. (2020). Joint DBN and Fuzzy C-Means unsupervised deep clustering for lung cancer patient stratification. Engineering Applications of Artificial Intelligence, 91(February), 103571. https://doi.org/10.1016/j.engappai.2020.103571.
[11] Tuya, & Chun-sheng, Z. (2020). Research of Mongolian Heat Syndrome Medicine Prescription Classification Method Based on Fuzzy C-means Algorithm. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, 2020-Octob, 305–309. https://doi.org/10.1109/ICSESS49938.2020.9237750.
[12] Taslim, T., & Fajrizal, F. (2016). Penerapan algorithma k-mean untuk clustering data obat pada puskesmas rumbai. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 7(2), 108–114. https://doi.org/10.31849/digitalzone.v7i2.602.
[13] Gustientiedina, G., Adiya, M. H., & Desnelita, Y. (2019). Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan. Jurnal Nasional Teknologi Dan Sistem Informasi, 5(1), 17–24. https://doi.org/10.25077/teknosi.v5i1.2019.17-24.
[14] Bara, E. H., Pranoto, Y. A., & Ariwibisono, F. . (2020). Pengelompokan Data Obat Menggunakan Metode K-Means Clustering Pada Upt Puskesmas Kondoran Kec. Sangalla’. JATI (Jurnal Mahasiswa Teknik Informatika), 4(2), 92–97. https://doi.org/10.36040/jati.v4i2.2720.
[15] Safhi, H. M., Frikh, B., & Ouhbi, B. (2019). Assessing reliability of Big Data Knowledge Discovery process. Procedia Computer Science, 148, 30–36. https://doi.org/10.1016/j.procs.2019.01.005.
[16] Shikhli, M. S. E., & Hammad, A. M. (2018). Data Acquisition Model for Analyzing Schedule Delays Using KDD (Knowledge Discovery and Datamining). In ACM International Conference Proceeding Series (pp. 67–74). https://doi.org/10.1145/3233347.3233366.
[17] Bathla, G., Aggarwal, H., & Rani, R. (2018). A novel approach for clustering big data based on mapreduce. International Journal of Electrical and Computer Engineering, 8(3), 1711–1719. https://doi.org/10.11591/ijece.v8i3.pp1711-1719.
[18] Nidheesh, N., Nazeer, K. A. A., & Ameer, P. M. (2017). An enhanced deterministic K-means clustering algorithm for cancer subtype prediction from gene expression data. Computers in Biology and Medicine, 91, 213–221. https://doi.org/10.1016/j.compbiomed.2017.10.014.
[19] Afrina, & Muthmainnah. (2018). Sistem Pendukung Keputusan Penerimaan Beasiswa pada Rumah Zakat dengan Metode Fuzzy Analytical Network Proces (FANP) di Baitul Mal Kabupaten Aceh Utara. Jurnal Sistem Informasi, 2(1), 15–24.
[20] Putri, A. D., & Effendi. (2017). Fuzzy Logic Untuk Menentukan Lokasi Kios Terbaik Di Kepri Mall Dengan Menggunakan Metode Sugeno. Edik Informatika, 3(1), 49–59. https://doi.org/10.22202/ei.2016.v3i1.1517.
[21] Cao, S. J., Ding, J., & Ren, C. (2020). Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment’s safety and health. Sustainable Cities and Society, 59(April), 102190. https://doi.org/10.1016/j.scs.2020.102190.
[22] Hou, W. hui, Wang, Y. ting, Wang, J. qiang, Cheng, P. F., & Li, L. (2020). Intuitionistic fuzzy c-means clustering algorithm based on a novel weighted proximity measure and genetic algorithm. International Journal of Machine Learning and Cybernetics, 12(3), 859–875. https://doi.org/10.1007/s13042-020-01206-3.
[23] Swindiarto, V. T. P., Sarno, R., & Novitasari, D. C. R. (2018). Integration of Fuzzy C-Means Clustering and TOPSIS (FCM-TOPSIS) with Silhouette Analysis for Multi Criteria Parameter Data. In Proceedings - 2018 International Seminar on Application for Technology of Information and Communication: Creative Technology for Human Life, iSemantic 2018 (pp. 463–468). IEEE. https://doi.org/10.1109/ISEMANTIC.2018.8549844.
[24] Jia, Z., Cai, Y., Chen, Y., & Zeng, W. (2018). Regionalization of water environmental carrying capacity for supporting the sustainable water resources management and development in China. Resources, Conservation and Recycling, 134(January), 282–293. https://doi.org/10.1016/j.resconrec.2018.03.030.
[25] Hidayati, R., Zubair, A., Pratama, A. H., & Indana, L. (2021). Analisis Silhouette Coefficient pada 6 Perhitungan Jarak K-Means Clustering. Techno.Com, 20(2), 186–197. https://doi.org/10.33633/tc.v20i2.4556.