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All Journal JOIV : International Journal on Informatics Visualization Energi & Kelistrikan TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika eProceedings of Engineering
Cahyantari Ekaputri
Universitas Telkom
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Simultaneous Hydroponic Nutrient Control Automation System Based on Internet of Things Adidrana, Demi; Iskandar, Ade Rahmat; Nurhayati, Ade; Suyatno, -; Ramdhani, Mohamad; Adam, Kharisma Bani; Ardianto, Rizki; Ekaputri, Cahyantari
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.865

Abstract

Hydroponic is one of the solutions of gardening methods using water as a nutrition medium. Usually, maintaining hydroponic plant quality and water nutrients are done manually and require human efforts, such as the degree of acidity or wetness (pH), TDS (Total Dissolved Solids), and nutrient temperature. With the Internet of Things technology, we can automate hydroponic control by measuring the nutrients' TDS, pH, and temperature values and controlling water nutrition by pump nutrition needs for hydroponic plants. This research uses the NFT (Nutrient Film Technique) for the hydroponic system and uses lettuce as the nutrition parameter. The lettuce parameters are pH, TDS, and Water Temperature equal to the sensor we used in the proposed IoT system. The condition has 27 classifications, and we use this classification as a reference in decision-making, using the K-Nearest Neighbor (KNN) algorithm to activate the actuator. We improve the simultaneous actuator from previous research with specified intervals and duration to achieve ideal nutritional conditions. The other improvement is that we collect more data and more testing times. The accuracy was 91.2%, with k = 3. From the evaluation results, the accuracy of KNN is quite high and has an advantage, which has better accuracy than the other algorithms and can activate actuator simultaneously. We conclude that the hydroponic nutrient automation system using the Internet of Things method is ready for real planting use with this improvement.
Co-Authors - Suyatno Achmad Mahdiyatul Tajrie Ade Nurhayati, Ade Ade Rahmat Iskandar Afdalul Azmi Angga Rusdinar Ardianto, Rizki Burhanuddin Indra Calvien Pradiptha Giovannie Dania Chairunissa Demi Adidrana Ekki Kurniawan Erwin Susanto Fahmi Arif Maulana Fajar Trihatmoko Fernando Napitupulu Fikri Dzuraeka Putra Gorbi Prasetya Hardita Kurniawan Ig. Prasetya Dwi Wibawa Jeremiah Andreas Bargain Sihombing Juan Aldo Hasibuan Kharisma Bani Adam Lukman Pradita Mohamad Ramdhani Mohammad Ramdani Mohammad Ramdhani Muhamad Reza Muhamad Royhan Muhammad Alam Badri Muhammad Reza Muhammad Rifqi Azmi Ni Luh Andrea Maurilla Sarasvanya Poman Pangaribuan Porman Pangaribuan Radhiansyah Radhiansyah Ramdhan Nugraha Reza Habiib Aulia Rio Ari Saputra Rio Fernando Rizal Rinaldi Safira Kusuma Ramadhanti Sagahalled Ramadhan Sigit Yuwono Siti Halimah Sony Sumaryo Sudarmono Sasmono Sulthan Mahdy Ghandurdiar Trisna Nur Robby Wahyu Pamungkas Yusuf Nur Wijayanto Zulaikha Zulaikha