Analisis Perkembangan Produksi Tanaman Biofarmaka (Obat) di Indonesia Menggunakan Algoritma Resilient

Indra Satria(1*), Azwar Anas Manurung(2), Mhd Ali Hanafiah(3),

(1) Universitas Asahan, Kisaran, Indonesia
(2) Universitas Asahan, Kisaran, Indonesia
(3) Sekolah Tinggi Ilmu Ekonomi Bina Karya, Tebing Tinggi, Indonesia
(*) Corresponding Author

Abstract


Biofarmaka (medicinal plants) in Indonesia play a crucial role in the pharmaceutical industry's development, providing natural resources for drug research, and supporting the utilization of traditional herbal remedies for public health. This research aims to analyze the development of biofarmaka plant production in Indonesia through predictions. This is essential for strategic planning, resource management, and future pharmaceutical industry development, ensuring an adequate supply of raw materials and supporting sustainable growth in the bio-pharmaceutical sector. The research dataset comprises biofarmaka plant production data in Indonesia by plant type, from 2018 to 2022, obtained from the Indonesian Central Statistics Agency. The research employs the Resilient algorithm, a machine learning technique. Architectural models used include 3-5-1, 3-10-1, 3-15-1, and 3-20-1. Among the four models, the 3-5-1 model is selected as the best due to its higher accuracy of 100%, and a lower Mean Squared Error (MSE) of 0.0023021, indicating the successful application of the Resilient algorithm in predicting the development of biofarmaka plant production in Indonesia.

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References


I. Sulistiyawati, G. Anggraeni, and T. Setyaningtyas, ‘Membangun Desa Sehat Mandiri dengan Pengembangan Produk Olahan Tanaman Biofarmaka menjadi Obat Herbal di Desa Sirkandi Banjarnegara’, Jurnal Madaniya, vol. 4, no. 4, pp. 1356–1367, 2023, doi: 10.53696/27214834.558.

I. G. A. A. H. Triandini, A. Anri, Y. Mulyani, R. Ziska, C. A. Muhtar, and I. G. A. S. Wangiyana, ‘Implementasi Konsep Merdeka Belajar Kolaboratif Melalui Pengolahan Tanaman Biofarmaka Galaktagog Di Kota Mataram’, SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan, vol. 7, no. 1, pp. 83–89, 2023, doi: 10.31764/jpmb.v7i1.12496.

D. Purliantoro and I. Ayesha, ‘Data Mining K-Means Clusterization Using the Davies Bouldin Index Based on Arima Forecasting Results of Biopharmaco Crop Production in Indonesia Province’, Journal of Scientech Research and Development, vol. 5, no. 1, pp. 580–594, 2023, doi: 10.56670/jsrd.v5i1.181.

I. Adi nugroho and R. Pinnusa, ‘Standar Pengembangan Produk Tumbuhan Obat di Pulau Jawa’, STANDAR: Better Standard Better Living, vol. 1, no. 6, pp. 14–17, 2022.

S. Widayati and L. Marliyah, ‘Sosialisasi Pemanfaatan Tanaman Obat Keluarga Bagi Masyarakat’, MANGGALI: Jurnal Pengabdian dan Pemberdayaan Masyarakat, vol. 3, no. 1, pp. 99–109, 2023, doi: 10.31331/manggali.v3i1.2441.

B. Wahyuddin and R. Sidi, ‘Pengaturan dan Dampak Hukum Produk Obat Herbal dalam Upaya Pemenuhan Hak Kesehatan di Indonesia’, JIIP - Jurnal Ilmiah Ilmu Pendidikan, vol. 6, no. 9, pp. 6754–6762, 2023, doi: 10.54371/jiip.v6i9.2817.

W. Saputra, T. Tulus, M. Zarlis, R. W. Sembiring, and D. Hartama, ‘Analysis Resilient Algorithm on Artificial Neural Network Backpropagation’, Journal of Physics: Conference Series, vol. 930, no. 1, p. 012035, 2017, doi: 10.1088/1742-6596/930/1/012035.

W. Saputra, A. P. Windarto, and A. Wanto, ‘Analysis of the Resilient Method in Training and Accuracy in the Backpropagation Method’, The IJICS (International Journal of Informatics and Computer Science), vol. 5, no. 1, pp. 52–56, 2021, doi: 10.30865/ijics.v5i1.2922.

D. E. Harmadji, S. Solikhin, U. Yudatama, and A. Purwanto, ‘Prediksi Produksi Biofarmaka Menggunakan Model Fuzzy Time Series dengan Pendekatan Percentage Change dan Frequency Based Partition’, Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 10, no. 1, pp. 173–184, 2023, doi: 10.25126/jtiik.20231016267.

Z. A. N. Azizah, I. Cholissodin, and L. Muflikhah, ‘Prediksi Hasil Panen Tanaman Biofarmaka di Indonesia dengan Menggunakan Metode Extreme Learning Machine’, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer (J-PTIIK), vol. 6, no. 11, pp. 5331–5338, 2022.

S. Dahlia and D. Rahmi, ‘Peramalan Produksi Tanaman Biofarmaka di Provinsi Riau dengan Metode Sarimax’, Jurnal Pendidikan Ilmiah Transformatif, vol. 7, no. 12, pp. 266–273, 2023.

M. Riedmiller and H. Braun, ‘RPROP - A Fast Adaptive Learning Algorithm’, The International Symposium on Computer and Information Science VII, vol. 1, no. 4, pp. 4–10, 1992, doi: 10.1007/978-1-4419-1603-7_12.

W. Saputra, J. T. Hardinata, and A. Wanto, ‘Resilient method in determining the best architectural model for predicting open unemployment in Indonesia’, IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–7, 2020, doi: 10.1088/1757-899X/725/1/012115.

Apriliyah and A. W. W. M, Wayan Firdaus, ‘Perkiraan Penjualan Beban Listrik Menggunakan Jaringan Syaraf Tiruan Resilent Backpropogation (RPROP)’, Jurnal Kursor, vol. 4, no. 2, pp. 41–47, 2008, doi: 10.1089/fpd.2015.2079.

B. P. Statistik, ‘Produksi Tanaman Biofarmaka Menurut Jenis Tanaman, 2018 - 2022’, Tabel Statistik Pertanian, Kehutanan, Perikanan, 2023. https://www.bps.go.id/id/statistics-table?subject=557# (accessed Dec. 14, 2023).

A. A. Manurung, I. Satria, and A. Wanto, ‘JST : 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, pp. 802–815, 2023, doi: 10.30645/jurasik.v8i2.658.

P. Parulian et al., ‘Analysis of Sequential Order Incremental Methods in Predicting the Number of Victims Affected by Disasters’, Journal of Physics: Conference Series, vol. 1255, no. 012033, pp. 1–6, 2019, doi: 10.1088/1742-6596/1255/1/012033.

I. M. Muhamad, S. A. Wardana, A. Wanto, and A. P. Windarto, ‘Algoritma Machine Learning untuk penentuan Model Prediksi Produksi Telur Ayam Petelur di Sumatera’, vol. 1, no. 4, pp. 126–134, 2022.

E. Siregar, H. Mawengkang, E. B. Nababan, and A. Wanto, ‘Analysis of Backpropagation Method with Sigmoid Bipolar and Linear Function in Prediction of Population Growth’, Journal of Physics: Conference Series, vol. 1255, no. 012023, pp. 1–6, 2019, doi: 10.1088/1742-6596/1255/1/012023.

R. Sinaga, M. M. Sitomorang, D. Setiawan, A. Wanto, and A. P. Windarto, ‘Akurasi Algoritma Fletcher-Reeves untuk Prediksi Ekspor Karet Remah Berdasarkan Negara Tujuan Utama’, Journal of Informatics Management and Information Technology, vol. 2, no. 3, pp. 91–99, 2022, doi: 10.47065/jimat.v2i3.170.

Y. Andriani, H. Silitonga, and A. Wanto, ‘Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia’, Register - Jurnal Ilmiah Teknologi Sistem Informasi, vol. 4, no. 1, pp. 30–40, 2018.

M. Mahendra, R. C. Telaumbanua, A. Wanto, and A. P. Windarto, ‘Akurasi Prediksi Ekspor Tanaman Obat , Aromatik dan Rempah-Rempah Menggunakan Machine Learning’, KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 6, pp. 207–215, 2022.

A. Wanto, N. L. W. S. R. Ginantra, S. Hendraputra, I. O. Kirana, and A. R. Damanik, ‘Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model’, Matrik: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer, vol. 22, no. 1, pp. 51–82, 2022, doi: 10.30812/matrik.v22i1.1826.

Safruddin, E. Efendi, R. M. Ch, and 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, pp. 473–483, 2023, doi: 10.30865/mib.v7i1.5524.




DOI: https://doi.org/10.30645/brahmana.v5i1.285

DOI (PDF): https://doi.org/10.30645/brahmana.v5i1.285.g282

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