Application of Data Mining for Optimal Drug Inventory in a Hospital

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Dewi Sahputri Siringo-Ringo Razana Baringin Daud Tambunan Dian Yulizar Tri Agustina Daulay Amir Mahmud Husein

Abstract

The Hospital is a health care institution that conducts complete individual health services that provide inpatient, outpatient and emergency services. Drug inventory management is one thing that is very important for the survival of hospitals, management of the supply of medical equipment that is not optimal including medicines will have an impact on medical services as well as economically, because 70% of hospital revenue comes from drugs. In this study we propose data mining with a focus on contributions to the comparison of the K-Means and K-Nearest Neighbor (KNN) algorithms for disease classification, then the classification results are carried out mapping the correlation of diseases with drugs using Apriori, based on the results of testing the K-Means algorithm more accurately compared KNN in the Apriori method to find the relationship of disease with drugs based on the value of support, trust, support value, trust is expected to be a reference for drug purchase recommendations so that there is no excess or emptiness of the drug.

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SIRINGO-RINGO, Dewi Sahputri et al. Application of Data Mining for Optimal Drug Inventory in a Hospital. SinkrOn, [S.l.], v. 4, n. 1, p. 207-214, oct. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10236>. Date accessed: 21 nov. 2019. doi: https://doi.org/10.33395/sinkron.v4i1.10236.
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References

Alimjan, Gulnaz, et al. 2018. “A New Technique for Remote Sensing Image Classification Based on Combinatorial Algorithm of SVM and KNN.” International Journal of Pattern Recognition and Artificial Intelligence, vol. 32, no. 7, pp. 1–23.

Arora, Preeti, and Shipra Varshney. “Analysis of K-Means and K-Medoids Algorithm For Big Data.” Procedia - Procedia Computer Science, vol. 78, no. December 2015, Elsevier Masson SAS, 2016, pp. 507–12, doi:10.1016/j.procs.2016.02.095.

Dep Kes RI, 2016. Keputusan Menteri Kesehatan Republik Indonesia Tentang Standar Pelayanan Kefarmasian Di Rumah Sakit, Jakarta

Harahap, M., Husein, A. M., Aisyah, S., Lubis, F. R., & Wijaya, B. A. (2018, April). Mining association rule based on the diseases population for recommendation of medicine need. In Journal of Physics: Conference Series (Vol. 1007, No. 1, p. 012017). IOP Publishing.

Hermanto, et al. 2019. Comparison of Naïve Bayes Algorithm, C4.5 and Random Forest for Service Classification Ojek Online. Vol. 3, no. 2.

Husein, A. M., Harahap, M., Aisyah, S., Purba, W., & Muhazir, A. (2018, March). The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data. In Journal of Physics: Conference Series (Vol. 978, No. 1, p. 012019). IOP Publishing.

Khotimah, Bain Khusul, et al. 2016. A Genetic Algorithm for Optimized Initial Centers K-Means Clustering in SMEs. Journal of Theoretical and Applied Information Technology, vol. 90, no. 1, pp. 23–30.

Kodati, Sarangam, et al. 2019. Soft Computing and Signal Processing. Vol. 898, Springer Singapore.

Manuel, Ricky. 2017. Analisa Penentuan Skala Prioritas Obat Berdasarkan Klaster Penyakit Menggunakan Fuzzy C-Means (Studi Kasus : Kecamatan Sirimau Kota Ambon).

Ramadhana, Fanny. 2019. Klasifikasi Data Rekam Medis Berdasarkan International Statistical Classification of Diseases and Related Health Problem (ICD-10) Menggunakan Algoritma K-Nearest Neighbor (K-NN).

Rani, Nisha. 2015. Improving the Performance of Apriori Algorithm by Combining with Clustering Techniques. Vol. 3, no. 2, pp. 13–15.

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