Pemanfaatan Algoritma Levenberg-Marquardt untuk Analisis Prediksi Persentase Penduduk yang Melakukan Pengobatan Sendiri


  • Surya Darma * Mail STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Fahmi Firzada STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • (*) Corresponding Author
Keywords: Machine Learning; Health Independence; Self-Medication; Pematangsiantar City; Simalungun Regency

Abstract

Self-medication is a practice in which individuals use drugs or administer treatments without a doctor's prescription or medical supervision. This phenomenon has become a significant health issue in Indonesia, particularly in the city of Pematangsiantar and Simalungun Regency, where many residents tend to self-medicate without receiving adequate medical consultation. Therefore, the aim of this study is to analyze the predicted percentage of health independence development among residents who self-medicate in Pematangsiantar and Simalungun Regency using the Levenberg-Marquardt algorithm. The research data consists of time-series data on the percentage of residents self-medicating in Pematangsiantar and Simalungun Regency from 2018 to 2023, obtained from the Central Statistics Agency of North Sumatra. The analysis was conducted using five architecture models: 4-5-1, 4-10-1, 4-15-1, 4-20-1, and 4-25-1. The results show that the Levenberg-Marquardt algorithm with the 4-15-1 architecture model provided the best performance, with the lowest Mean Squared Error (MSE) value of 0.0268691174 compared to the other architecture models. This study is expected to assist local governments by providing information on the development of the percentage of residents who self-medicate in Pematangsiantar and Simalungun Regency, enabling them to formulate the best policies for improving public health in the region in the future. This research also contributes to the development of artificial intelligence-based health prediction methods, particularly for analyzing the percentage of self-medicating residents in complex and dynamic regional contexts.

Downloads

Download data is not yet available.

References

A. Quincho-lopez, C. A. Benites-Ibarra, M. M. Hilario-Gomez, R. Quijano-Escate, dan A. Taype-Rondan, “Self-medication practices to prevent or manage COVID-19 : A systematic review,” PLOS ONE, vol. 16, no. 11, hal. 1–12, 2021, doi: 10.1371/journal.pone.0259317.

D. Baracaldo-Santamaría, M. J. Trujillo-Moreno, A. M. Pérez-Acosta, J. E. Feliciano-Alfonso, C. A. Calderon-Ospina, dan F. Soler, “Definition of self-medication: a scoping review,” Therapeutic Advances in Drug Safety, vol. 13, hal. 1–14, 2022, doi: 10.1177/20420986221127501.

I. Ahmed, R. King, S. Akter, R. Akter, dan V. R. Aggarwal, “Determinants of antibiotic self-medication: A systematic review and meta-analysis,” Research in Social and Administrative Pharmacy, vol. 19, no. 7, hal. 1007–1017, 2023, doi: 10.1016/j.sapharm.2023.03.009.

N. Ahmed, S. Ijaz, S. Manzoor, dan S. Sajjad, “Prevalence of self-medication in children under-five years by their mothers in Yogyakarta city Indonesia,” Journal of Family Medicine and Primary Care, vol. 10, no. 8, hal. 2798–2803, 2021, doi: 10.4103/jfmpc.jfmpc_2457_20.

P. Ge dkk., “Self-medication in Chinese residents and the related factors of whether or not they would take suggestions from medical staff as an important consideration during self-medication,” Frontiers in Public Health, vol. 10, hal. 1–18, 2022, doi: 10.3389/fpubh.2022.1074559.

A. Carias, K. Orellana, W. Cruz, F. Rodriguez Rivas, D. Naira, dan P. Simons Morales, “Automedicación en pacientes mayores de 18 años en centros de salud de Honduras,” Journal of Pharmacy & Pharmacognosy Research, vol. 10, no. 2, hal. 218–226, 2022, doi: 10.56499/jppres21.1148_10.2.218.

M. Karatas, L. Eriskin, M. Deveci, D. Pamucar, dan H. Garg, “Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives,” Expert Systems with Applications, vol. 200, hal. 116912, 2022, doi: 10.1016/j.eswa.2022.116912.

M. Senbekov dkk., “The recent progress and applications of digital technologies in healthcare: A review,” International Journal of Telemedicine and Applications, vol. 2020, hal. 1–18, 2020, doi: 10.1155/2020/8830200.

J. P. Onnela, “Opportunities and challenges in the collection and analysis of digital phenotyping data,” Neuropsychopharmacology, vol. 46, no. 1, hal. 45–54, 2021, doi: 10.1038/s41386-020-0771-3.

S. Singh, P. Bansal, M. Hosen, dan S. K. Bansal, “Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM,” Resources Policy, vol. 80, no. 1, hal. 103159, 2023, doi: 10.1016/j.resourpol.2022.103159.

H. Dichtl, W. Drobetz, dan T. Otto, “Forecasting Stock Market Crashes via Machine Learning,” Journal of Financial Stability, vol. 65, no. 1, hal. 101099, 2023, doi: 10.1016/j.jfs.2022.101099.

B. A. Aderemi, T. O. Olwal, J. M. Ndambuki, dan S. S. Rwanga, “Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa,” Systems and Soft Computing, vol. 5, no. 1, hal. 200049, 2023, doi: 10.1016/j.sasc.2023.200049.

M. Akbarian, B. Saghafian, dan S. Golian, “Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran,” Journal of Hydrology, vol. 1, no. April, hal. 129480, 2023, doi: 10.1016/j.jhydrol.2023.129480.

R. Rakholia, Q. Le, B. Quoc Ho, K. Vu, dan R. Simon Carbajo, “Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam,” Environment International, vol. 173, no. January, hal. 107848, 2023, doi: 10.1016/j.envint.2023.107848.

D. Galeano dan A. Paccanaro, “Machine learning prediction of side effects for drugs in clinical trials,” Cell Reports Methods, vol. 2, no. 12, hal. 100358, 2022, doi: 10.1016/j.crmeth.2022.100358.

R. Garriga dkk., “Machine learning model to predict mental health crises from electronic health records,” Nature Medicine, vol. 28, no. 6, hal. 1240–1248, 2022, doi: 10.1038/s41591-022-01811-5.

A. Alanazi, “Using machine learning for healthcare challenges and opportunities,” Informatics in Medicine Unlocked, vol. 30, no. March, hal. 100924, 2022, doi: 10.1016/j.imu.2022.100924.

M. Chua dkk., “Tackling prediction uncertainty in machine learning for healthcare,” Nature Biomedical Engineering, vol. 7, hal. 711–718, 2023, doi: 10.1038/s41551-022-00988-x.

L. Rubinger, A. Gazendam, S. Ekhtiari, dan M. Bhandari, “Machine learning and artificial intelligence in research and healthcare,” Injury, vol. 54, no. 3, hal. S69–S73, 2023, doi: 10.1016/J.INJURY.2022.01.046.

R. Chen, B. O. Petrazzini, W. A. Malick, R. S. Rosenson, dan R. Do, “Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 44, no. 2, hal. 491–504, 2024, doi: 10.1161/ATVBAHA.123.320331.

M. Hobensack, J. Song, D. Scharp, K. H. Bowles, dan M. Topaz, “Machine learning applied to electronic health record data in home healthcare: A scoping review,” International Journal of Medical Informatics, vol. 170, no. February, hal. 104978, 2023, doi: 10.1016/j.ijmedinf.2022.104978.

K. Arumugam, M. Naved, P. P. Shinde, O. Leiva-Chauca, A. Huaman-Osorio, dan T. Gonzales-Yanac, “Multiple disease prediction using Machine learning algorithms,” Materials Today: Proceedings, vol. 80, no. 3, hal. 3682–3685, Jan 2023, doi: 10.1016/j.matpr.2021.07.361.

A. Hennebelle, H. Materwala, dan L. Ismail, “HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System,” Procedia Computer Science, vol. 220, hal. 331–338, 2023, doi: 10.1016/j.procs.2023.03.043.

I. S. Purba dkk., “Accuracy Level of Backpropagation Algorithm to Predict Livestock Population of Simalungun Regency in Indonesia,” in Journal of Physics: Conference Series, 2019, hal. 012014. doi: 10.1088/1742-6596/1255/1/012014.

A. Wanto dan J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0,” IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, hal. 012114, 2020, doi: 10.1088/1757-899X/725/1/012114.

A. Wanto, S. Defit, dan A. P. Windarto, “Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana,” RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, hal. 254–264, 2021, doi: 10.29207/resti.v5i2.3031.

I. Zuhrufillah, F. Anggraini, dan R. Dewantara, “Peramalan Jumlah Kasus Baru HIV Menurut Provinsi Menggunakan Machine Learning dengan Teknik Levenberg-Marquardt,” Journal of Computer System and Informatics (JoSYC), vol. 3, no. 4, hal. 212–221, 2022, doi: 10.47065/josyc.v3i4.2172.

A. Anas Manurung, I. Satria, dan A. Wanto, “Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient,” Jurnal Riset Sistem Informasi Dan Teknik Informatika (JURASIK), vol. 8, no. 2, hal. 802–815, 2023, [Daring]. Tersedia pada: https://tunasbangsa.ac.id/ejurnal/index.php/jurasik

Safruddin, E. Efendi, R. M. Ch, dan A. Wanto, “Pemanfaatan Algoritma BFGS Quasi-Newton untuk Melihat Potensi Perkembangan Luas Tanaman Kopi di Pulau Sumatera,” Jurnal Media Informatika Budidarma, vol. 7, no. 1, hal. 473–483, 2023, doi: 10.30865/mib.v7i1.5524.

I. Zuhrufillah, F. Anggraini, dan R. Dewantara, “Peramalan Jumlah Kasus Baru HIV Menurut Provinsi Menggunakan Machine Learning dengan Teknik Levenberg-Marquardt,” Journal of Computer System and Informatics (JoSYC), vol. 3, no. 4, hal. 212–221, 2022, doi: 10.47065/josyc.v3i4.2172.

A. A. Manurung, I. Satria, dan A. Wanto, “JST: Prediksi Perkembangan Produksi Tanaman Sayuran Dalam Upaya Pemenuhan Gizi Masyarakat dengan Algoritma Resilient,” Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), vol. 8, no. 2, hal. 802–815, 2023, doi: 10.30645/jurasik.v8i2.658.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Pemanfaatan Algoritma Levenberg-Marquardt untuk Analisis Prediksi Persentase Penduduk yang Melakukan Pengobatan Sendiri

Dimensions Badge
Article History
Submitted: 2024-10-29
Published: 2024-11-30
Abstract View: 25 times
PDF Download: 19 times
Section
Articles