Implementation of Apriori Data Mining Algorithm on Medical Device Inventory System

Meidar Hadi Avizenna, R Arri Widyanto, Dwi Kusuma Wirawan, Teguh Adhi Pratama, Amandha Shafa Nabila


The pattern of the need for drugs and medical devices in various hospitals has a tendency to be repeated and similar in a relatively long period of time, especially in one particular department, because the cases found are often similar or even similar. Ensuring the availability of stock in each departmental depot is very vital, because the procurement of medical devices must go through a certain process and time, so that cases of critical rheumatism often occur but the equipment needed at depositors does not meet the standards. need or run from inventory and must indent first. By calculating the trend of demand patterns and needs using an algorithm (Apriori Association) in the dataset, a rule is formed that in the pattern of dependence between itemsets that have supporting criteria in the form of 33.3% support and 85% Confidence, where the items that appear are items with frequency of occurrence and associations so that it can be taken into consideration to ensure the availability of drugs and medical devices.

Article Metrics

Abstract: 0 Viewers PDF: 0 Viewers


Data Mining; Association Rules; Apriori Algorithm; Medical tools

Full Text:



O. Agboola et al., “A review on the impact of mining operation: Monitoring, assessment and management,” Results Eng., vol. 8, no. October, p. 100181, 2020, doi: 10.1016/j.rineng.2020.100181.

P. K. Mishra, M. Bolic, M. C. E. Yagoub, and R. F. Stewart, “RFID technology for tracking and tracing explosives and detonators in mining services applications,” J. Appl. Geophys., vol. 76, pp. 33–43, 2012, doi: 10.1016/j.jappgeo.2011.10.004.

S. Xu and H. K. Chan, “Forecasting medical device demand with online search queries: A big data and machine learning approach,” Procedia Manuf., vol. 39, no. 2019, pp. 32–39, 2019, doi: 10.1016/j.promfg.2020.01.225.

A. M. Cruz, “Evaluating record history of medical devices using association discovery and clustering techniques,” Expert Syst. Appl., vol. 40, no. 13, pp. 5292–5305, 2013, doi: 10.1016/j.eswa.2013.03.034.

J. H. Gruenhagen, R. Parker, and S. Cox, “Technology diffusion and firm agency from a technological innovation systems perspective: A case study of fatigue monitoring in the mining industry,” J. Eng. Technol. Manag. - JET-M, vol. 62, no. August, p. 101655, 2021, doi: 10.1016/j.jengtecman.2021.101655.

L. Li, “Real time auxiliary data mining method for wireless communication mechanism optimization based on Internet of things system,” Comput. Commun., vol. 160, no. June, pp. 333–341, 2020, doi: 10.1016/j.comcom.2020.06.021.

P. T. Bich Thao, S. Pimonsree, K. Suppoung, S. Bonnet, A. Junpen, and S. Garivait, “Development of an anthropogenic atmospheric mercury emissions inventory in Thailand in 2018,” Atmos. Pollut. Res., vol. 12, no. 9, p. 101170, 2021, doi: 10.1016/j.apr.2021.101170.

M. A. Sayed, X. Qin, R. J. Kate, D. M. Anisuzzaman, and Z. Yu, “Identification and analysis of misclassified work-zone crashes using text mining techniques,” Accid. Anal. Prev., vol. 159, no. August 2020, p. 106211, 2021, doi: 10.1016/j.aap.2021.106211.

T. wahyuningsih, “Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice’s Coefficient,” J. Appl. Data Sci., vol. 2, no. 2, pp. 45–54, 2021, doi: 10.47738/jads.v2i2.31.

Y. N. Chi, “Modeling and Forecasting Long-Term Records of Mean Sea Level at Grand Isle, Louisiana: SARIMA, NARNN, and Mixed SARIMA-NARNN Models,” J. Appl. Data Sci., vol. 2, no. 2, pp. 1–13, 2021, doi: 10.47738/jads.v2i2.27.

S. Shadroo and A. M. Rahmani, “Systematic survey of big data and data mining in internet of things,” Comput. Networks, vol. 139, pp. 19–47, 2018, doi: 10.1016/j.comnet.2018.04.001.

M. A. F. Ros–Tonen, J. J. Aggrey, D. P. Somuah, and M. Derkyi, “Human insecurities in gold mining: A systematic review of evidence from Ghana,” Extr. Ind. Soc., no. April, p. 100951, 2021, doi: 10.1016/j.exis.2021.100951.

M. Sharma, S. Joshi, and K. Govindan, “Issues and solutions of electronic waste urban mining for circular economy transition: An Indian context,” J. Environ. Manage., vol. 290, no. October 2020, p. 112373, 2021, doi: 10.1016/j.jenvman.2021.112373.

P. Zerbino, A. Stefanini, and D. Aloini, “Process science in action: A literature review on process mining in business management,” Technol. Forecast. Soc. Change, vol. 172, no. July, p. 121021, 2021, doi: 10.1016/j.techfore.2021.121021.

N. Martin et al., “Recommendations for enhancing the usability and understandability of process mining in healthcare,” Artif. Intell. Med., vol. 109, no. July, 2020, doi: 10.1016/j.artmed.2020.101962.

C. V. Valderrama, E. Santibanez-González, B. Pimentel, A. Candia-Véjar, and L. Canales-Bustos, “Designing an environmental supply chain network in the mining industry to reduce carbon emissions,” J. Clean. Prod., vol. 254, 2020, doi: 10.1016/j.jclepro.2019.119688.

S. Kosai, U. Takata, and E. Yamasue, “Natural resource use of a traction lithium-ion battery production based on land disturbances through mining activities,” J. Clean. Prod., vol. 280, p. 124871, 2021, doi: 10.1016/j.jclepro.2020.124871.

H. Estiri et al., “Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations,” Patterns, vol. 1, no. 4, p. 100051, 2020, doi: 10.1016/j.patter.2020.100051.

C. Ricciardi et al., “Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center,” Comput. Methods Programs Biomed., vol. 189, p. 105343, 2020, doi: 10.1016/j.cmpb.2020.105343.

G. Cho, H.-M. Park, W.-M. Jung, W.-S. Cha, D. Lee, and Y. Chae, “Identification of candidate medicinal herbs for skincare via data mining of the classic Donguibogam text on Korean medicine,” Integr. Med. Res., vol. 9, no. 4, p. 100436, 2020, doi: 10.1016/j.imr.2020.100436.

A. Pickens and S. Sengupta, “Benchmarking Studies Aimed at Clustering and Classification Tasks Using K-Means , Fuzzy C-Means and Evolutionary Neural Networks,” Mach. Learn. Knowl. Extr., no. 3, pp. 695–719, 2021.

R. Khamisy-farah et al., “Big Data for Biomedical Education with a Focus on the COVID-19 Era : An Integrative Review of the Literature,” Int. J. Environ. Res. Public Heal., no. Viii, pp. 1–16, 2021.

A. P. Sousa et al., “Using data mining to assist in predicting reproductive outcomes following varicocele embolization,” J. Clin. Med., vol. 10, no. 16, 2021, doi: 10.3390/jcm10163503.

G. Karakatsoulis and K. Skouri, “Optimal reorder level and lot size decisions for an inventory system with defective items,” Appl. Math. Model., vol. 92, pp. 651–668, 2021, doi: 10.1016/j.apm.2020.11.025.

F. Tepolt, K. Montag Schafer, and J. Budd, “Standardization of medication inventory in an urban family medicine clinic,” J. Am. Pharm. Assoc., vol. 61, no. 4, pp. e242–e248, 2021, doi: 10.1016/j.japh.2021.03.001.

J. B. Raja and S. C. Pandian, “PSO-FCM based data mining model to predict diabetic disease,” Comput. Methods Programs Biomed., vol. 196, 2020, doi: 10.1016/j.cmpb.2020.105659.

H. Wang, X. Tan, Z. Huang, B. Pan, and J. Tian, “Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks,” Artif. Intell. Med., vol. 105, no. August 2019, p. 101859, 2020, doi: 10.1016/j.artmed.2020.101859.


  • There are currently no refbacks.


Journal of Applied Data Sciences

2723-6471 (Online)
Published by Bright Publisher
Puri Mersi Baru, Jl.Martadireja II, Gang Sitihingil 3 Blok A No 2, Purwokerto Timur, Jawa Tengah
Website :
Email :

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0