PENERAPAN ALGORITMA APRIORI DALAM MENINGKATKAN PRODUKSI BUDIDAYA PERIKANAN MENGGUNAKAN ASSOCIATION RULE

  • Tajrin Tajrin Universitas Prima Indonesia
  • David Ebenezer Frans Universitas Prima Indonesia
  • Nadia Damayanti Nainggolan Universitas Prima Indonesia
  • Jose Agustin Fernando Marbun Universitas Prima Indonesia
  • Sediaman Julianus Gulo Universitas Prima Indonesia

Abstract

Aquaculture production in North Sumatra needs to be increased in order to meet the increasing demand for aquaculture in the area from time to time. In order to optimize aquaculture production, recommendations are made for what types of aquaculture are most in demand by the community so that the North Sumatra Marine and Fisheries Service can prepare fishery supplies optimally. Recommendations for the provision of fish are carried out by utilizing data mining technology with an association rule algorithm. Processed data is fish sales history data. The result of this data processing is the combination of fish data that is most in demand by the community with a minimum amount of support of 40% to 3 itemset. Furthermore, the a priori algorithm is implemented at the marine and fisheries service to determine associations. By utilizing the results of this a priori algorithm analysis, the Department of Marine and Fisheries of North Sumatra can find out what types of aquaculture are the priorities for increasing production so that they can meet the needs of the community.

References

[1] S. Sumaizar, K. Sinaga, E. D. Siringo-ringo, and V. M. M. Siregar, “Determining Goods Delivery Priority for Transportation Service Companies Using SAW Method,” J. Comput. Networks, Archit. High Perform. Comput., vol. 3, no. 2, pp. 256–262, Nov. 2021, doi: 10.47709/cnahpc.v3i2.1154.

[2] S. H. Musti, D. Irmayani, and G. J. Yanris, “ANALYSIS OF THE ELECTRE METHOD IN DECISION SUPPORT SYSTEMS FOR DETERMINING AREAS OF EXPERTISE FOR,” Infokum, vol. 9, no. 2, pp. 184–190, 2021.

[3] T. Purnamasari, M. Nasution, and G. J. Yaris, “Analisis Minat Belajar Mahasiswa Pada Masa Perkuliahan Online Menggunakan Rougt Set,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. VII, no. 3, pp. 251–258, 2021, [Online]. Available: https://jurnal.stmikroyal.ac.id/index.php/jurteksi/article/view/1062

[4] S. Aisyah and W. Purba, “Aplikasi Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Menggunakan Metode Profile Matching,” J. Mahajana Inf., vol. 4, no. 2, pp. 16–20, 2019.

[5] A. T. Purba, “Sistem Pendukung Keputusan Dalam Penerimaan Mahasiswa Baru Dengan Metode Analytical Hierarchy Process (AHP),” J. Tekinkom, vol. 1, no. 1, pp. 1–7, 2018.

[6] V. Marudut, M. Siregar, S. Sonang, and E. Damanik, “Sistem Pendukung Keputusan Penentuan Pelanggan Terbaik Menggunakan Metode Weighted Product,” J. TEKINKOM, vol. 4, no. 2, pp. 239–244, 2021.

[7] S. Parsaoran Tamba, P. Wulandari, M. Hutabarat, M. Christina, and A. Oktavia, “Penggunaan Metode Topsis (Technique for Order Preference By Similarity To Ideal Solution) Untuk Menentukan Kualitas Biji Kopi Terbaik Berbasis Android,” J. Mantik Penusa, vol. 3, no. 1, pp. 73–81, 2019.

[8] H. Hertyana, “Sistem pendukung keputusan penentuan karyawan terbaik menggunakan metode saw studi kasus amik mahaputra riau,” Intra-Tech, vol. 2, no. 1, pp. 74–82, 2018, [Online]. Available: https://www.journal.amikmahaputra.ac.id/index.php/JIT/article/view/27

[9] B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices,” J. Financ. Data Sci., vol. 4, no. 3, pp. 183–201, 2018, doi: 10.1016/j.jfds.2018.04.003.

[10] U. Inyaem, “Construction Model Using Machine Learning Techniques for the Prediction of Rice Produce for Farmers,” 2018 3rd IEEE Int. Conf. Image, Vis. Comput. ICIVC 2018, pp. 870–874, 2018, doi: 10.1109/ICIVC.2018.8492883.

[11] S. Mehtab and J. Sen, “A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3502624.
[12] P. Yu and X. Yan, “Stock price prediction based on deep neural networks,” Neural Comput. Appl., vol. 32, no. 6, pp. 1609–1628, 2020, doi: 10.1007/s00521-019-04212-x.

[13] Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” J. Comput. Appl. Math., vol. 365, 2020, doi: 10.1016/j.cam.2019.112395.

[14] S. K. Singh and D. R. K. Dwivedi, “Data Mining: Dirty Data and Data Cleaning,” SSRN Electron. J., 2020, doi: 10.2139/ssrn.3610772.

[15] N. G. Ramadhan, F. D. Adhinata, A. Jala, T. Segara, and D. Putra, “Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression,” vol. 9, no. 2, pp. 251–256, 2022, doi: 10.30865/jurikom.v9i2.3979.

[16] C. M. Sitorus, A. Rizal, and M. Jajuli, “Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, Aug. 2020, doi: 10.28932/jutisi.v6i2.2672.

[17] A. Nurhopipah and U. Hasanah, “Dataset Splitting Techniques Comparison For Face Classification on CCTV Images,” vol. 14, no. 4, pp. 341–352, 2020.

[18] A. Ramdan, U. Siliwangi, N. Widyasono, U. Siliwangi, H. Mubarok, and U. Siliwangi, “Prediksi Jaringan TOR dan VPN menggunakan Algoritma K-Nearest Neighbour pada Trafik Darknet,” vol. 05, no. 01, pp. 21–35, 2022.

[19] R. S. Oktavian and S. Budi, “Analisis Dataset Google Playstore Menggunakan Metode Exploratory Data Analysis,” J. Strateg. Maranatha, vol. 2, no. 2, pp. 636–649, 2020.

[20] M. Nawawi, “Klasifikasi Tingkat Popularitas Siswa Berdasarkan Aktifitas Komunikasi Siswa Menggunakan Smartphone dengan Teknik Logistic Regression,” vol. 4, no. 1, pp. 978–979, 2018.

[21] E. N. Fauziyah and S. R. Nudin, “Sistem Pendukung Keputusan Penentuan Jurusan di SMKN 1 Pungging Menggunakan Gradient Boosting Tree,” vol. 3, pp. 42–50, 2021.
Published
2022-06-30
How to Cite
TAJRIN, Tajrin et al. PENERAPAN ALGORITMA APRIORI DALAM MENINGKATKAN PRODUKSI BUDIDAYA PERIKANAN MENGGUNAKAN ASSOCIATION RULE. Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 5, n. 1, p. 153-159, june 2022. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/510>. Date accessed: 08 july 2025. doi: https://doi.org/10.37600/tekinkom.v5i1.510.
Section
Articles