DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.84-88

Sistem inferensi fuzzy Mamdani untuk menentukan tingkat kualitas air pada kolam bioflok dalam budidaya ikan lele

Mamdani fuzzy inference system for mapping water quality level of biofloc ponds in catfish cultivation

Institut Teknologi Telkom Purwokerto, Indonesia

Received: 17 Dec 2018; Revised: 6 Jan 2020; Accepted: 18 Jan 2020; Available online: 5 Feb 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
The government has launched a program to increase the production of catfish by using biofloc ponds. The biofloc ponds can maintain the quality of water biologically to maximize the growth of fish. However, the level of water quality monitoring is generally only divided into good or bad categories so that it cannot represent the condition of fish growth. Therefore, this study aims to get the level of water quality (0–100 %) using the Mamdani fuzzy inference system (FIS) algorithm based on pH, temperature, and dissolved oxygen (DO). The level of water quality was correlated based on catfish growth conditions. The results showed that the range of values of the water quality level for each condition of catfish growth was 100 % for normal-living fish, 83–99 % for stunted fish growth, and < 83% for threatened fish. The FIS algorithm had 89.92 % of accuracy.
Fulltext View|Download Email colleagues
Keywords: Mamdani FIS; pH level; temperature monitoring; dissolved oxygen concentration; catfish biofloc
Funding: Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Indonesia

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Pembuatan Aplikasi Antar-Jemput Laundry Berbasis Web Service pada Platform Android Teka-teki Unsur Kimia sebagai Media Pembelajaran Kimia Interaktif bagi siswa SMA Kelas X Berbasis Android Expert System for Diagnosis of Plant Pest and Disease Horticulture with Forward and Backward Chaining Inference Perancangan Sistem Informasi Akademik Berbasis Mobile Web Studi Kasus di Program Studi Sistem Komputer Universitas Diponegoro Data Mining using Apriori Algorithm for Product Recommendation for Customers More articles
Most cited articles
Design of wireless sensor networks (WSN) to monitor temperature and humidity using nrf24l01 SMS Security Improvement using RSA in Complaints Application on Regional Head Election’s Fraud Web Monitoring System of pH Level, Temperature and Color on River Water using Wireless Sensor Network People counter on CCTV video using histogram of oriented gradient and Kalman filter methods Pengembangan Aplikasi Multimedia untuk Pembelajaran Satelit Astronomi NASA dengan Teknologi Augmented Reality Berbasis Android More cited articles
  1. -, “Laporan Tahunan Direktorat Produksi Tahun 2013,” Direktorat Jenderal Perikanan Budidaya, KKP, Jakarta, 2014
  2. -, “Laporan kinerja 2016,” Direktorat Jenderal Perikanan Budidaya, KKP, Jakarta, 2016
  3. N. Adharani, K. Soewardi, A. D. Syakti, and S. Hariyadi, “Manajemen kualitas air dengan teknologi bioflok: studi kasus pemeliharaan ikan lele (clarias sp.),” Jurnal Ilmu Pertanian Indonesia, vol. 21, no. 1, pp. 35-40, 2016. doi: 10.18343/jipi.21.1.35
  4. F. A. Pratama, N. Afiati, and A. Djunaedi, “Kondisi kualitas air kolam budidaya dengan penggunaan probiotik dan tanpa probiotik terhadap pertumbuhan ikan lele sangkuriang (clarias sp) di Cirebon, Jawa Barat,” Journal of Management of Aquatic Resources, vol. 5, no. 1, pp. 38-45, 2016
  5. D. Rachmawati, I. Samidjan, and H. Setyono, “Manajemen kualitas air media budidaya ikan lele sangkuriang (clarias gariepinus) dengan teknik probiotik pada kolam terpal di desa vokasi Reksosari, kecamatan Suruh, kabupaten Semarang,” Pena Akuatika, vol. 12, no. 1, pp. 24–32, 2015
  6. A. Setiawan, R. Ariqoh, P. Tivani, L. Pipih, and I. Pudjiastuti, “Bioflokulasi sistem teknologi budidaya lele tebar padat tinggi dengan kapasitas 1m3/750 ekor dengan flock forming bacteria,” Jurnal Inovasi Teknik Kimia, vol. 1, no. 1, pp. 45-49, 2016
  7. R. Pramana, “Perancangan sistem kontrol dan monitoring kualitas air dan suhu air pada kolam budidaya ikan,” Sustainable, vol. 7, no. 1, pp. 13-23, 2018. doi: 10.31629/sustainable.v7i1.435
  8. M. Cholilulloh and D. Syauqy, “Implementasi metode fuzzy pada kualitas air kolam bibit lele berdasarkan suhu dan kekeruhan,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 5, pp. 1813-1822, 2017
  9. A. J. Kuswinta, I. G. P. W. Wedashwara, and I. W. A. Arimbawa, “Implementasi IoT cerdas berbasis inference fuzzy Tsukamoto pada pemantauan kadar pH dan ketinggian air dalam akuaponik,” Journal of Computer Science and Informatics Engineering, vol. 3, no. 1, pp. 65-74, 2019. doi: 10.29303/jcosine.v3i1.245
  10. D. M. Sihotang, “Penentuan kualitas air untuk perkembangan ikan lele sangkuriang menggunakan metode fuzzy SAW,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 7, no. 4, pp. 372-376, 2018. doi: 10.22146/jnteti.v7i4.453
  11. G. Mazenda, A. A. Soebroto, and C. Dewi, “Implementasi fuzzy interference system (FIS) metode Tsukamoto pada sistem pendukung keputusan penentuan kualitas air sungai,” Journal of Environmental Engineering and Sustainable Technology., vol. 1, no. 2, pp. 92-103, 2014. doi: 10.21776/ub.jeest.2014.001.02.4
  12. D. C. Rini, Y. Farida, N. Ulinnuha, G. Andriani, and L. Mahfiroh, “Aplikasi fuzzy inference system dengan metode Mamdani untuk menentukan status gizi balita di kota Surabaya,” Jurnal Matematika, vol. 1, no. 1, pp. 1-6, 2019
  13. B. H. Purnowo and Y. Wibowo, “Aplikasi fuzzy inference system untuk menentukan lokasi pengembangan sentra peternakan rakyat (SPR) sapi potong di kabupaten Jember,” Agrointek, vol. 12, no. 1, pp. 1-15, 2018. doi: 10.21107/agrointek.v12i1.3241
  14. A. S. Mubarak, D. A. Satyari, and R. Kusdarwati, “Korelasi antara konsentrasi oksigen terlarut pada kepadatan yang berbeda dengan skoring warna Daphnia spp.,” Jurnal Ilmiah Perikanan dan Kelautan, vol. 2, no. 1, pp. 45-50, 2019. doi: 10.20473/jipk.v2i1.11665
  15. Produksi benih ikan lele dumbo (clarias gariepinus x c. fuscus) kelas benih sebar, SNI: 01 - 6484.4 - 2000, 2000
  16. K. Oktafianto and F. Nimah, “Analisis perbandingan penentuan waktu simpan beras bansos rastra menggunakan fuzzy inference system (FIS) metode fuzzy Tsukamoto dan fuzzy Mamdani,” Jurnal Matematika, vol. 1, no. 1, pp. 45-54, 2019

Last update:

  1. Fountains Height Measurement Accuracy With Mamdani Fuzzy Inference System Algorithm

    Retno Devita, Ruri Hartika Zain, Ipriadi. Journal of Physics: Conference Series, 1783 (1), 2021. doi: 10.1088/1742-6596/1783/1/012009

Last update: 2024-04-17 10:26:03

  1. Fountains Height Measurement Accuracy With Mamdani Fuzzy Inference System Algorithm

    Retno Devita, Ruri Hartika Zain, Ipriadi. Journal of Physics: Conference Series, 1783 (1), 2021. doi: 10.1088/1742-6596/1783/1/012009