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Abstract

Ministry of Marine Affairs and Fisheries (KKP) noted that Indonesia produced 56,539 tons of gourami fish in the second quarter of 2022 High market demand and economical selling prices encourage farmers to cultivate gourami fish. In cultivating gourami fish there are several obstacles, for example, disease caused by poor water quality. Water quality is the main parameter in the success of gourami fish farming. This research aims to develop a water quality monitoring system based on the Internet of Things. The system prototype uses a temperature sensor (DS18B20), Ph sensor (dfrobot SEN0161), turbidity sensor (dfrobot SEN0189), flowmeter, and ultrasonic sensor (JSN-SR04) as input. The Arduino Mega R3 microcontroller is the processor and the Oled module (SSD1306) is the output. Thingboard is a cloud server that functions as sensor data monitoring. Temperature sensor testing results (DS18B20) average error 0.48%, Ph(dfrobot SEN0161) sensor testing average error 0.64%, ultrasonic sensor testing (JSN-SR04) average error 7.83%, testing Turbidity sensors can measure the level of water turbidity. Next, the water quality parameter data is processed using the Naïve Bayes algorithm method for classifying the water quality of gourami ponds. The results of this classification obtained an accuracy of 99.94% a Kappa Statistics value of 0.9989 and a Mean Absolute Error of 0.0003

Keywords

Internet of Things Naïve Bayes Monitoring Classification

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References

  1. A. Wantoro, H. Sulistiyani, Y. Yuniarthe, A. Setya Putra, A. Candra Widyawati, And N. Putra Wicaksono, “Sistem Pakar Diagnosis Penyakit Kutu Ikan Gurami (Argunus Indicus) Menggunakan Metode Naive Bayes,” Jurnal Komputasi, Vol. 10, No. 1, Pp. 13–22, 2022, Doi: Http://Dx.Doi.Org/10.23960%2fkomputasi.V10i1.2956.
  2. Kementrian Kelautan Dan Perikanan, “Rilis Data Kelautan Dan Perikanan Triwulan Ii Tahun 2022,” Rennisca Ray Damanti. Accessed: Apr. 03, 2023. [Online]. Available: Https://Sosek.Info/Wp-Content/Uploads/2023/02/Rilis-Data-Kelautan-Dan-Perikanan-Triwulan-Ii-Tahun-2022-1.Pdf
  3. A. Khumaidi And A. Hidayat, “Identification Of Causes Of Mass Death Of Gurami Fish (Osphronemus Gouramy) In Gurami Fish Cultivation Sentra, Desa Beji, Kedung Banteng District, Banyumas District, Central Java,” Journal Of Aquaculture Science, Vol. 3, No. 2, 2018, Doi: 10.31093/Joas.V3i2.53.
  4. A. R. Saraswati, Y. Saintika, A. N. A. Thohari, And A. R. Iskandar, “Sistem Pakar Diagnosis Penyakit Ikan Gurami (Osphronemus Goramy) Menggunakan Case Based Reasoning,” Jurnal Teknologi Informasi Dan Ilmu Komputer, Vol. 7, No. 4, 2020, Doi: 10.25126/Jtiik.2020701953.
  5. Y. Sudianto, “Desain Arsitektur Iot Untuk Budidaya Gurami,” Jurnal Teknologi Informasi Dan Ilmu Komputer, Vol. 8, No. 1, 2021, Doi: 10.25126/Jtiik.0813529.
  6. W. H. Islamy, “Sistem Monitoring Kualitas Air Budidaya Gurami Berbasis Arduino Menggunakan Metode Weighted Product,” Jurnal Mahasiswa Teknik Informatika, Vol. 3, No. 1, 2019.
  7. A. Supriyanto, A. Noor, And Y. Prastyaningsih, “Purwarupa Sistem Monitoring Kualitas Air Pada Kolam Ikan Air Tawar Berbasis Aplikasi Web Mobile,” 84 Ultimatics, Vol. Xi, No. 2, 2019.
  8. I. Erlangga Prasetya, S. Achmadi, And D. Rudhistiar, “Penerapan Iot (Internet Of Things) Untuk Sistem Monitoring Air Dan Controlling Pada Kolam Ikan Gurami Berbasis Website,” Jati (Jurnal Mahasiswa Teknik Informatika), Vol. 6, No. 2, 2023, Doi: 10.36040/Jati.V6i2.5400.
  9. A. Angdresey, L. Sitanayah, And T. M. I. Sumajow, “A Real-Time Water Quality And Quantity Monitoring System For Aquarium,” In Acm International Conference Proceeding Series, 2021. Doi: 10.1145/3489088.3489090.
  10. A. Firmansyah, D. Syauqy, And B. H. Prasetio, “Implementasi Algoritma Naive Bayes Pada Sistem Monitoring Dan Klasifikasi Kualitas Air Akuarium Ikan Mas Koki,” 2022.
  11. R. Marten, S. Tumangger, And N. Hidayat, “Komparasi Metode Data Mining Support Vector Machine Dengan Naive Bayes Untuk Klasifikasi Status Kualitas Air,” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, Vol. 3, No. 10, 2019.
  12. I. Alfitra Salam Et Al., “Application Of Naïve Bayes For Classification Of Criteria For Potable Water With The Crisp-Dm Method,” Lppm Universitas Bina Sarana Informatika, Vol. 25, No. 1, 2023.
  13. F. K. Fikriah, M. Burhanis Sulthan, N. Mujahidah, And Moh. Khoirur Roziqin, “Naïve Bayes Untuk Klasifikasi Penyakit Daun Bawang Merah Berdasarkan Ekstraksi Fitur Gray Level Cooccurrence Matrix (Glcm),” Jurnal Komtika (Komputasi Dan Informatika), Vol. 6, No. 2, 2022, Doi: 10.31603/Komtika.V6i2.7925.
  14. A. Kristiyanto And A. F. Zulfikar, “Deteksi Kebocoran Lpg Berbasis Iot Menggunakan Metode Fuzzy,” Jurnal E-Bisnis, Sistem Informasi, Teknologi Informasi, Vol. Xvi, No. 6, 2021.
  15. A. Kristiyanto, “Smart Aquarium Iot System Dengan Metode Fuzzy Untuk Klasifikasi Kualitas Air Berdasarkan Suhu, Ph, Dan Kekeruhan,” Smart Comp: Jurnalnya Orang Pintar Komputer, Vol. 12, No. 4, Pp. 929–940, 2023, Doi: https://Doi.Org/10.30591/Smartcomp.V12i4.5080.