ac

Determining Tilapia Quality Using the Fuzzy Logic

Authors

  • Maulana Iqbhal Prayogha Slamet Universitas Muhammadiyah Sidoarjo
  • Hindarto Hindarto Universitas Muhammadiyah Sidoarjo
  • Sumarno Sumarno Universitas Muhammadiyah Sidoarjo

DOI:

10.47709/cnahpc.v5i1.2016

Keywords:

Tilapia, Fuzzy Logic, Mamdani

Dimension Badge Record



Abstract

Fish are living things with the highest trophic level in a body of water. Fish are living creatures that live in aquatic environments in fresh, brackish and marine waters. Tilapia is one of the leading commodities of aquaculture and is a widely used freshwater fish, and its production is quite high. Fuzzy logic is a logic that deals with the concept of partial truth, where classical logic states that everything can be expressed in binary terms (0 or 1). Various theories in the development of fuzzy logic show that fuzzy logic can be used to model various systems. A very adaptable and data-tolerant approach is mamdani fuzzy. Therefore, in this research, a mamdani fuzzy model will be developed to evaluate the quality of tilapia. This research uses fuzzy logic to evaluate the quality of tilapia based on its texture, taste, and size. In this study, researchers modeled 4 fuzzy variables, with 3 inputs (texture, taste, and size), 1 output (price), and a total of 4 fuzzy variables. The MIN IMPLICATION function was used in the inference procedure in the fuzzy operator application. Next, the MAX approach is used in the compilation of all fuzzy outputs. Then comes the affirmation, also known as defuzzification which is done using the Centroid method. The results show with 10 trial data it is seen that the higher the input value of size, the higher the output value of price. 

Downloads

Download data is not yet available.
Google Scholar Cite Analysis
Abstract viewed = 198 times

References

Adeyi, A. J., Adeyi, O., Ajayi, O. K., Oke, E. O., Ogunsola, A. D., & Oyelami, S. (2021). Fuzzy-logic modelling for quality prediction of smoked Tilapia (Oreochromis niloticus) fish. Nigerian Journal of Technology, 40(5), 810–816.

Amalia, S., Andari, R., Kartiria, K., & Putra, P. E. (2021). PROTOTYPE SISTEM KONTROL DAN MONITORING SUHU SERTA KETINGGIAN AIR PADA KOLAM BUDIDAYA IKAN MENGGUNAKAN LOGIKA FUZZY. RADIAL: Jurnal Peradaban Sains, Rekayasa Dan Teknologi, 9(1), 23–38.

Arfiati, D., Farkha, K., & Anugerah, D. P. (2022). IKAN NILA (Oreochromis niloticus). Media Nusa Creative (MNC Publishing).

Chen, Z., & Feng, A. (2020). The quality evaluation method of tilapia fillets stored at 3 and? 2° C based on fractal dimension changes. Journal of Food Process Engineering, 43(7), e13407.

Dowlati, M., de la Guardia, M., & Mohtasebi, S. S. (2012). Application of machine-vision techniques to fish-quality assessment. TrAC Trends in Analytical Chemistry, 40, 168–179.

Fauzia, S. R., & Suseno, S. H. (2020). Resirkulasi Air untuk Optimalisasi Kualitas Air Budidaya Ikan Nila Nirwana (Oreochromis niloticus). Jurnal Pusat Inovasi Masyarakat, 2(5), 887–892.

Kristiantya, Y. N., Setiawan, E., & Prasetio, B. H. (2021). Sistem Kontrol dan Monitoring Kualitas Air pada Kolam Ikan Air Tawar menggunakan Logika Fuzzy berbasis Arduino. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer E-ISSN, 2548, 964X.

Martawijaya, I. E. I. (2019). Bisnis di Rumah Sendiri Pembenihan Gurami Dalam Galon. PT Penerbit IPB Press.

Pazouki, E. (2021). A practical surface irrigation design based on fuzzy logic and meta-heuristic algorithms. Agricultural Water Management, 256, 107069.

Puspita, Y. D., & Rahmawan, G. (2021). Pengaruh Harga, Kualitas Produk dan Citra Merek terhadap Keputusan Pembelian Produk Garnier. Jurnal Sinar Manajemen, 8(2), 98–104.

Radhitya, M. L., & Sudipa, G. I. (2020). PENDEKATAN Z-SCORE DAN FUZZY DALAM PENGUJIAN AKURASI PERAMALAN CURAH HUJAN. SINTECH (Science and Information Technology) Journal, 3(2), 149–156.

Raharjo, M. R., Saputra, R. E., Harjupa, W., & Fathrio, I. (2021). Perancangan Prediktor Hujan Deras Menggunakan Metode Logika Fuzzy Mamdani. E-Proceeding of Engineering, 8(5), 6557–6565.

Roobab, U., Fidalgo, L. G., Arshad, R. N., Khan, A. W., Zeng, X., Bhat, Z. F., Bekhit, A. E. A., Batool, Z., & Aadil, R. M. (2022). High?pressure processing of fish and shellfish products: Safety, quality, and research prospects. Comprehensive Reviews in Food Science and Food Safety, 21(4), 3297–3325.

Tutuhatunewa, A. (2021). Application of fuzzy logic in organoleptic tests (case study on fish floss products). AIP Conference Proceedings, 2360(1), 40004.

Wiratama, I. K., Fuadi, T. M., Pratiwi, E. Y. R., Kurniawan, D., & Sudipa, I. G. I. (2022). Selection Participants Of Science Olympic In Elementary School Using Fuzzy–Profile Matching Method. Jurnal Mantik, 6(2), 1850–1858.

Downloads

ARTICLE Published HISTORY

Submitted Date: 2023-01-18
Accepted Date: 2023-01-18
Published Date: 2023-01-22

How to Cite

Slamet, M. I. P., Hindarto, H. ., & Sumarno, S. . (2023). Determining Tilapia Quality Using the Fuzzy Logic. Journal of Computer Networks, Architecture and High Performance Computing, 5(1), 67-74. https://doi.org/10.47709/cnahpc.v5i1.2016