Analisis Perbandingan Kinerja Algoritma Apriori, FP-Growth dan Eclat dalam menemukan Pola Frekuensi pada Dataset INA-CBG'S

Eka Wahyu Pujiharto; Kusrini Kusrini; Asro Nasiri

doi:10.31154/cogito.v9i2.547.340-354

Authors

Eka Wahyu Pujiharto amikomyogyakarta
Kusrini Kusrini
Asro Nasiri

DOI:

https://doi.org/10.31154/cogito.v9i2.547.340-354

Keywords:

Analisis Perbandingan, Apriori, FP-Growth, Eclat

Abstract

Every health facility such as hospitals, clinics and community health centers that collaborate with BPJS is required to make funding claims for health care for patients using INACBG's (Indonesian - Case Based Groups) rates. INACBG's tariff is a service package that is based on disease diagnosis groupings using ICD-10 codes. This research aims to find frequency patterns in the INA-CBG's dataset, especially combinations of diagnoses, in order to find out what combinations of diagnoses frequently appear for further evaluation by health facility management. This research compares the performance of the Apriori, FP-Growth and Eclat algorithms. The accuracy values of the Lift Ratio and Rule Association of the three algorithms obtained the same value, but the computing time and memory usage of the Eclat Algorithm is more than the Apriori and Fp-Growth Algorithms, so it can be concluded that the FP-Growth and Apriori Algorithms are more suitable to be used as solutions in found frequency patterns in INACBG's dataset.

References

Kementrian Kesehatan Republik Indonesia, “INA-CBG’S untuk Optimalkan Pelayanan BPJS Kesehatan,” 2014, [Online]. Available: https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20140108/539538/ina-cbgs-untuk-optimalkan-pelayanan-bpjs-kesehatan/

Peraturan Presiden Republik Indonesia Nomor 12, “Tentang Jaminan Kesehatan,” 2013.

J. Suntoro, “Data Mining : Algoritma dan Implementasi dengan Pemrograman PHP,” vol. PT Elex Me, 2019.

A. A. Khairul Amri, Alwis Nazir, Elin Haerani, Muhammad Affandes, Reski Mai Candra, “Penerapan Data Mining Dalam Mencari Pola Asosiasi Data Tracer Study Menggunakan Equivalence Class Transformation (ECLAT),” no. Tek. Inform. UIN Sultan Syarif Kasim Riau, 2022.

E. S. M. Andi Ilhamsyah Idris, Eliyah A M Sampetoding, Valian Yoga Pudya Ardhana, Irene Maritsa, Adrisumatri Sakri, Hidayatullah Ruslan, “Comparison of Apriori, Apriori-TID and FP-Growth Algorithms in Market Basket Analysis at Grocery Stores,” vol. Hasanuddin, 2022, doi: DOI 10.30865/ijics.v6i2.4535.

Dr. V Srinadh, “Evaluation of Apriori, FP growth and Eclat association rule mining algorithms,” Int. J. Health Sci. (Qassim)., doi: https://doi.org/10.53730/ijhs.v6nS2.6729.

S. S. Putthiporn Thanathamathe, “Discovering Future Earnings Patterns through FP-Growth and ECLAT Algorithms with Optimized Discretization,” Emerg. Sci. J., no. ISSN 2610-9182, 2022.

L. Z. A. M. Moch Syahrir, “Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems,” Bumigora Univ. Indones. J. Manaj. Teknol. dan Inform., 2023, doi: http://dx.doi.org/10.31940/matrix.v13i2.52-67.

T. A. P. Karina Intan Indradewi, “Penerapan Association Rule Dalam Bidang Kesehatan Pada Rumah Sakit X,” Univ. Islam Indones. Semin. Nas. IENACO, no. ISSN : 2337-4349, 2019.

W. P. Nola Ritha, Eka Suswaini, “Penerapan Association Rule Menggunakan Algoritma Apriori Pada Poliklinik Penyakit Dalam (Studi Kasus: Rumah Sakit Umum Daerah Bintan),” J. Sains dan Inform., no. e-ISSN: 2598-5841.

V. Robu and V. D. Dos Santos, “Mining frequent patterns in data using apriori and eclat: A comparison of the algorithm performance and association rule generation,” 2019 6th Int. Conf. Syst. Informatics, ICSAI 2019, no. Icsai, pp. 1478–1481, 2019, doi: 10.1109/ICSAI48974.2019.9010367.

Islamiyah, P. L. Ginting, N. Dengen, and M. Taruk, “Comparison of Priori and FP-Growth Algorithms in Determining Association Rules,” ICEEIE 2019 - Int. Conf. Electr. Electron. Inf. Eng. Emerg. Innov. Technol. Sustain. Futur., pp. 320–323, 2019, doi: 10.1109/ICEEIE47180.2019.8981438.

luthfi taufiq E. Kusrini, “Algoritma Data Mining,” no. Penerbit Andi, Yogyakarta, 2009.

trivusi.web.id, “Algoritma Apriori: Pengertian, Cara Kerja, Kelebihan, dan Kekurangannya,” 2022, [Online]. Available: https://www.trivusi.web.id/2022/08/algoritma-apriori.html

Jeff Heaton, “Comparing dataset characteristics that favor the Apriori Eclat or FP-Growth frequent itemset mining algorithms,” SECON IEEE, 2016.

D. Samuel, “Penerapan Stuktur FP-Tree dan Algoritma FP-Growth dalam Optimasi Penentuan Frequent Itemset,” no. Program Studi Teknik Informatika, Sekolah Teknik Elektro dan Informatika, Institut Teknologi Bandung., 2008.

R. T. Kabir S, Ripon S, Rahman M, “Knowledge-based data mining using the semantic web. International Conference on Applied Computing,” Comput. Sci. Comput. Eng., 2014.

R. Mustofa and Irhamah, “Topic Discovery pada Jurnal-jurnal di IEEE Explore menggunakan Association Rule Mining dengan Pendekatan Closed Frequent Itemset,” Ejurnal.Its.Ac.Id, vol. .8, no. 2, 2019.

M. Fauzy, K. R. S. W, and I. Asror, “Penerapan Metode Association Rule Menggunakan Algoritma Apriori pada Simulasi Prediksi Hujan Wilayah Kota Bandung,” vol. 3Fakultas, 2015.

B. Santosa, “Data Mining Teori dan Aplikasi : Teknik Pemanfaatan Data untuk keperluan Bisnis,” Yogyakarta Graha Ilmu, p. 2007, 2007.