Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression


  • Sephia Pratista Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Alwis Nazir * Mail Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Iwan Iskandar Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Elvia Budianita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Iis Afrianty Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • (*) Corresponding Author
Keywords: Data Mining; Machine Learning; MAPE; Simple Linear Regression; Support Vector Regression

Abstract

Public Health Centers (Puskesmas) had a crucial role in furnishing society essential healthcare services and medication management. To preempt errors in stock management, a predictive approach is employed. This prediction methodology involves comparing Data Mining techniques utilizing the Simple Linear Regression algorithm and Machine Learning methodologies harnessing the Support Vector Regression algorithm. This research uses Paracetamol 500 mg and Cetirizine drug data from January 2020 to June 2023. The selection of these algorithms is motivated by the continuous nature of the data variables and their temporal span, spanning 42 months (period). The core aim of this study is to evaluate the magnitude of predictive errors using the Mean Absolute Percentage Error (MAPE) methodology. Implementing these methods was effectuated through the programming language Python with an 80%:20% partitioning of training and testing data. Drawing from experimental endeavors conducted concerning Paracetamol 500 mg, the utilization of the Simple Linear Regression algorithm, yields a MAPE score of 20.85%, categorized as 'Moderate,' whereas the application of the Support Vector Regression algorithm generates a MAPE of 18.39%, classified as 'Good.' Otherwise, experimentation on Cetirizine employing the Simple Linear Regression algorithm, employing an identical division of training and testing data, results in a MAPE of 18.39%, also classified as 'Good.' Meanwhile, resorting to the Support Vector Regression algorithm leads to a MAPE of 17.14%, falling under the 'Good' category. Based on the MAPE obtained, the Support Vector Regression algorithm has better prediction results than the Simple Linear Regression algorithm

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References

Dahlia dan Andri, “Implementasi Data Mining untuk Prediksi Persediaan Obat pada Puskesmas Kertapati menggunakan Regresi Linier Berganda,” JURNAL SISTEM DAN INFORMATIKA, hlm. 95–103, Nov 2020, doi: 10.30864/jsi.v15i2.331.

D. Suwardiyanto, M. Nur Shodiq, D. Hidayat Kusuma, dan T. Oktalita Sari, “Sistem Prediksi Kebutuhan Obat di Puskesmas Menggunakan Metode Least Square,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 4, no. 1, hlm. 75–80, Jan 2019, doi: 10.30591/jpit.v4i1.1085.

D. Abdianto, Elisawati, F. Tawakal, dan Masrizal, “Prediksi Stok Obat Menggunakan Metode Learning Vector Quantization Studi Kasus Puskesmas Dumai Barat,” Prosiding Seminar Sains Nasional dan Teknologi, vol. 1, no. 1, hlm. 68–74, 2021.

H. Noor, A. Dharmawati, dan T. Wahyu Qur, “Penerapan Algoritma K-Means Clustering Analysis Pada Kasus Penderita Hiv/Aids (Studi Kasus Kabupaten Banjar),” Technologia, vol. 12, no. 2, hlm. 72–76, Apr 2021.

Y. Andini, J. Tata Hardinata, dan Y. Pranayama Purba, “Penerapan Data Mining Terhadap Tata Letak Buku Di Perpustakaan Sintong Bingei Pematangsiantar Menggunakan Metode Apriori,” Jurnal Times, vol. XI, no. 1, hlm. 9–15, 2022, [Daring]. Tersedia pada: http://ejournal.stmik-time.ac.id

S. P. Dewi, N. Nurwati, dan E. Rahayu, “Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor,” Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, hlm. 639–648, Mar 2022, doi: 10.47065/bits.v3i4.1408.

N. Karolina, “Data Mining Pengelompokan Pasien Rawat Inap Peserta BPJS Menggunakan Metode Clustering (Studi Kasus : RSU.Bangkatan),” JOURNAL OF INFORMATION AND TECHNOLOGY UNIMOR, vol. 1, no. 2, hlm. 47–53, Sep 2021, [Daring]. Tersedia pada: www.kaputama.ac.id

Haryan, D. Nofriansyah, dan I. Mariami, “Implementasi Data Mining Untuk Pengelempokan Buku Di Perpustakaan Yayasan Nurul Islam Indonesia Baru Dengan Metode K-Means Clustering,” Jurnal CyberTech, vol. 1, no. 1, hlm. 1–12, Sep 2021, [Daring]. Tersedia pada: https://ojs.trigunadharma.ac.id/index.php/jct/index

M. Syauqi Haris, A. Naseh Khudori, dan W. Teja Kusuma, “Perbandingan Metode Supervised Machine Learning Untuk Prediksi Prevalensi Stunting Di Provinsi Jawa Timur,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 9, no. 7, hlm. 1571–1576, Des 2022, doi: 10.25126/jtiik.202296744.

R. Pratama, M. I. Herdiansyah, D. Syamsuar, dan A. Syazili, “Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 1, hlm. 96–104, Mar 2023, doi: 10.32736/sisfokom.v12i1.1507.

L. W. Chong, D. Rengasamy, Y. W. Wong, dan R. K. Rajkumar, “Load Prediction Using Support Vector Regression,” dalam TENCON 2017 - 2017 IEEE Region 10 Conference, IEEE, Nov 2017, hlm. 1069–1074. doi: 10.1109/TENCON.2017.8228016.

G. Lin, A. Lin, dan D. Gu, “Using Support Vector Regression and K-Nearest Neighbors For Short-Term Traffic Flow Prediction Based On Maximal Information Coefficient,” Inf Sci (N Y), vol. 608, hlm. 517–531, Agu 2022, doi: 10.1016/j.ins.2022.06.090.

A. A. Suryanto dan A. Muqtadir, “Penerapan Metode Mean Absolute Error (MEA) Dalam Algoritma Regresi Linear Untuk Prediksi Produksi Padi,” SAINTEKBU: Jurnal Sains dan Teknologi, vol. 11, no. 1, hlm. 78–83, 2019.

Harsiti, Z. Muttaqin, dan E. Srihartini, “Penerapan Metode Regresi Linier Sederhana Untuk Prediksi Persediaan Obat Jenis Tablet,” Jurnal Sistem Informasi , vol. 9, no. 1, hlm. 12–16, Mar 2022.

A. Suhaidi Batubara, H. Dafitri, dan I. Faisal, “Analysis Of Linear Regression And Trend Moment Methods In Predicting Sales Using Mape,” Jurnal Sistem Informasi dan Ilmu Komputer Prima, vol. 6, no. 1, hlm. 75–81, 2022, doi: https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2919.

T. Wahyudi dan D. Septya Arroufu, “Implementation Of Data Mining Prediction Delivery Time Using Linear Regression Algorithm,” Journal of Applied Engineering and Technological Science, vol. 4, no. 1, hlm. 84–92, 2022, doi: https://doi.org/10.37385/jaets.v4i1.918.

Nendi dan A. Wibowo, “Prediksi Jumlah Pengiriman Barang Menggunakan Kombinasi Metode Support Vector Regression, Algoritma Genetika dan Multivariate Adaptive Regression Splines,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 7, no. 6, hlm. 1169–1176, Des 2020, doi: 10.25126/jtiik.202072441.

D. Iskandar, W. Adi, dan S. Wibowo, “Data Mining Dalam Prediksi Jumlah Pasien Dengan Regresi Linear Dan Exponential Smoothing,” Jurnal Sistem Informasi dan Sains Teknologi, vol. 5, no. 1, hlm. 1–8, 2023.

R. Novita, I. Yani, dan G. Ali, “Sistem Prediksi untuk Penentuan Jumlah Pemesanan Obat Menggunakan Regresi Linier,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 2, no. 1, hlm. 62–70, Apr 2022.

A. Anggrawan, N. Azmi, U. Bumigora, dan I. Anthonyangrawan, “Prediksi Penjualan Produk Unilever Menggunakan Metode Regresi Linear Sales Prediction of Unilever Products using the Linear Regression Method,” Jurnal Bumigora Information Technology (BITe), vol. 4, no. 2, hlm. 123–132, Nov 2022, doi: 10.30812/bite.v4i2.2416.


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Article History
Submitted: 2023-09-09
Published: 2023-09-27
Abstract View: 443 times
PDF Download: 338 times
How to Cite
Pratista, S., Nazir, A., Iskandar, I., Budianita, E., & Afrianty, I. (2023). Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression. Building of Informatics, Technology and Science (BITS), 5(2), 456−465. https://doi.org/10.47065/bits.v5i2.4260
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