Implementation Of The Support Vector Machine Method In Determining The Best Quality Of Sap
Keywords:
Rubber Trees, Support Vector Machine, Data MiningAbstract
Rubber trees (Hevea brasiliensis) are the main source of natural rubber and play an important role in Indonesia's industry. Determining the quality of rubber sap is a challenge for companies, as traditional manual processes are time-consuming and prone to human error. PT Poly Kencana Raya, a company in Besitang, North Sumatra, currently still uses conventional methods in determining the quality of rubber latex it produces. This research aims to design a web-based system with the application of the Support Vector Machine (SVM) method to facilitate the determination of rubber latex quality. SVM was chosen as a classification method because of its ability to determine the optimal hyperplane that can separate data from two different classes, namely feasible and unfit. The built system utilizes the main criteria such as tree age, tapping time, moisture content, color, and texture in determining the quality of the sap. Implementation. This study used 120 samples of test data, with accurate prediction results on 111 data, resulting in an accuracy rate of 92.5%. This decision support system is expected to increase efficiency and accuracy in rubber sap selection and support the development of rubber production quality in Indonesia. This research also opens up opportunities for further development by adding other classification methods for accuracy comparison or adding training data to optimize prediction results.
Keywords: Rubber Trees, Support Vector Machine, Data Mining
References
[1] A. Anriani, I. F. Astuti, and F. Agus, “Model Penentuan Kualitas Getah Karet Menggunakan Metode Multi Attributive Border Approximation Area Comparison (Mabac),” IKRA-ITH Inform. J. Komput. dan Inform., vol. 8, no. 2, pp. 34–43, 2024, doi: 10.37817/ikraith-informatika.v8i2.2954.
[2] N. W. S. Agustini, D. Priadi, and R. V. Atika, “Profil Kimia dan Aktivitas Antibakteri Fraksi Aktif Nannochloropsis sp. sebagai Senyawa Penghambat Bakteri Penyebab Gangguan Kesehatan Mulut,” J. Pascapanen dan Bioteknol. Kelaut. dan Perikan., vol. 17, no. 1, p. 19, 2022, doi: 10.15578/jpbkp.v17i1.781.
[3] F. O. Dayera, Musa Bundaris Palungan, “G-Tech : Jurnal Teknologi Terapan,” G-Tech J. Teknol. Terap., vol. 8, no. 1, pp. 186–195, 2024, [Online]. Available: https://ejournal.uniramalang.ac.id/index.php/g-tech/article/view/1823/122
[4] N. I. Ningrum, A. Azanuddin, and D. Suherdi, “Sistem Pendukung Keputusan Menentukan Kualitas Getah Karet Menggunakan Metode COPRAS,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 4, p. 374, 2022, doi: 10.53513/jursi.v1i4.5346.
[5] D. Aldo, “Rekomendasi Kualitas Getah Karet Terbaik Berbasiskan Sistem Pendukung Keputusan dengan Metode MAUT,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1592–1602, 2022, doi: 10.47065/bits.v4i3.2631.
[6] I. Ahmad, S. Samsugi, and Y. Irawan, “Implementasi Data Mining Sebagai Pengolahan Data,” J. Teknoinfo, vol. 16, no. 1, p. 46, 2022, [Online]. Available: http://portaldata.org/index.php/portaldata/article/view/107
[7] E. Suryati, Styawati, and A. A. Aldino, “Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model Word2vec Text Embedding Dan Algoritma Support Vector Machine (SVM),” J. Teknol. Dan Sist. Inf., vol. 4, no. 1, pp. 96–106, 2023, [Online]. Available: https://doi.org/10.33365/jtsi.v4i1.2445
[8] W. R. U. Fadilah, D. Agfiannisa, and Y. Azhar, “Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine,” Fountain Informatics J., vol. 5, no. 2, p. 45, 2020, doi: 10.21111/fij.v5i2.4449.
[9] D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Azhari Putra Sayani, Safwandi, Fajriana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright Notice
Authors published in this journal agree to the following terms:
1. The copyright of each article is retained by the author (s).
2. The author grants the journal the first publication rights with the work simultaneously licensed under the Creative Commons Attribution License, allowing others to share the work with an acknowledgment of authorship and the initial publication in this journal.
3. Authors may enter into separate additional contractual agreements for the non-exclusive distribution of published journal versions of the work (for example, posting them to institutional repositories or publishing them in a book), with acknowledgment of their initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (For example in the Institutional Repository or on their website) before and during the submission process, as this can lead to productive exchanges, as well as earlier and larger citations of published work.
5. Articles and all related material published are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License.
