Tren Teknologi dan Mikrokontroler pada Sistem Pakan Ikan Otomatis Berbasis IoT: Tinjauan Sistematis 2022–2026


  • Faiz Abdurrahman Mustofa * Mail Universitas Sains dan Teknologi Komputer, Jawa Tengah, Indonesia
  • Dani Sasmoko Universitas Sains dan Teknologi Komputer, Jawa Tengah, Indonesia
  • Sumaryanto Sumaryanto Universitas Sains dan Teknologi Komputer, Jawa Tengah, Indonesia
  • (*) Corresponding Author
Keywords: Automatic Feeding System; Internet of Things; Aquaculture; Microcontroller; Bibliometric Analysis; Technology Trends

Abstract

This study aims to analyze technology trends and microcontrollers in automatic fish feeding systems based on the Internet of Things (IoT) through a Systematic Literature Review (SLR) of publications from 2022–2026. The method used is SLR with the PRISMA 2020 protocol, bibliometric analysis assisted by VOSviewer, and content analysis. Data were taken from the ScienceDirect database, with an initial search yielding 200 articles for Research Question 1 (RQ1) and 50 articles for RQ2. After identification, screening, and eligibility assessment, 32 articles for RQ1 and 17 articles for RQ2 were included for further analysis. The results show a shift from simple timer-based systems toward intelligent architectures integrated with AI and machine learning, with IoT+AI dominating at 35%. The Arduino platform dominates at 60%, followed by ESP32/NodeMCU, which is increasing due to wireless connectivity. The most critical sensors are temperature (30%), pH (25%), and dissolved oxygen (25%), while feeder actuators dominate at 30%. Publications peaked in 2025 with 26 articles. For RQ2, Arduino family microcontrollers (Uno, Mega, Nano) are the most dominant, covering two-thirds of articles, but there is a gradual shift toward ESP32 and ESP8266. An important finding reveals a reporting gap where 70% of articles do not explicitly list hardware specifications, hindering research replication and technology adoption. This study provides a current technology trend map and evidence-based recommendations for researchers and farmers in selecting the most mature technology for sustainable automatic feeding systems in the aquaculture sector.

Downloads

Download data is not yet available.

References

Abdullah, A. F., Man, H. C., Mohammed, A., Karim, M. M. A., Yunusa, S. U., & Jais, N. A. B. M. (2024). Charting the aquaculture internet of things impact: Key applications, challenges, and future trend. Aquaculture Reports, 39, 102358. https://doi.org/https://doi.org/10.1016/j.aqrep.2024.102358

Ahmed, F., Bijoy, M. H. I., Hemal, H. R., & Noori, S. R. H. (2024). Smart aquaculture analytics: Enhancing shrimp farming in Bangladesh through real-time IoT monitoring and predictive machine learning analysis. Heliyon, 10(17), e37330. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e37330

Al Mamun, M. R., Ashik-E-Rabbani, M., Haque, M. M., & Upoma, S. M. (2024). IoT-based real-time biofloc monitoring and controlling system. Smart Agricultural Technology, 9, 100598. https://doi.org/https://doi.org/10.1016/j.atech.2024.100598

Alsehaimi, A., Houda, M., Waqar, A., Hayat, S., Ahmed Waris, F., & Benjeddou, O. (2024). Internet of things (IoT) driven structural health monitoring for enhanced seismic resilience: A rigorous functional analysis and implementation framework. Results in Engineering, 22, 102340. https://doi.org/https://doi.org/10.1016/j.rineng.2024.102340

Anmur, W. O. F., Saflia, I., & Habriani. (2025). Healthcare Access: A Hybrid Systematic Literature Review and Bibliometric Analysis. TIN: Terapan Informatika Nusantara, 6(3), 366–380.

Azhar, R. C. S., Anasari, T., Soekarno, G. A., Sumardi, & Suranto. (2026). Evaluasi Sistem Pendidikan Digital melalui Kepemimpinan Adaptif: Tinjauan SLR-PRISMA. TIN: Terapan Informatika Nusantara, 7(1), 294–305.

Bhavsar, D., Limbasia, B., Mori, Y., Imtiyazali Aglodiya, M., & Shah, M. (2023). A comprehensive and systematic study in smart drip and sprinkler irrigation systems. Smart Agricultural Technology, 5, 100303. https://doi.org/https://doi.org/10.1016/j.atech.2023.100303

Chiu, M.-C., Yan, W.-M., Bhat, S. A., & Huang, N.-F. (2022). Development of smart aquaculture farm management system using IoT and AI-based surrogate models. Journal of Agriculture and Food Research, 9, 100357. https://doi.org/https://doi.org/10.1016/j.jafr.2022.100357

Dong, Y., & Hansen, H. (2023). Development and design of an affordable field scale weighing lysimeter using a microcontroller system. Smart Agricultural Technology, 4, 100147. https://doi.org/https://doi.org/10.1016/j.atech.2022.100147

Fahim, A. Al, Chowdhury, T. A., Kader, M. A., & Khandaker, M. U. (2025). Solar-powered automated fish-feeding boat: A cost-effective and sustainable solution for aquaculture. Aquacultural Engineering, 111, 102592. https://doi.org/https://doi.org/10.1016/j.aquaeng.2025.102592

Gladju, J., Kamalam, B. S., & Kanagaraj, A. (2022). Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology, 2, 100061. https://doi.org/https://doi.org/10.1016/j.atech.2022.100061

Jamroen, C., Yonsiri, N., Odthon, T., Wisitthiwong, N., & Janreung, S. (2023). A standalone photovoltaic/battery energy-powered water quality monitoring system based on narrowband internet of things for aquaculture: Design and implementation. Smart Agricultural Technology, 3, 100072. https://doi.org/https://doi.org/10.1016/j.atech.2022.100072

Jamshidi, B., Khabbaz Jolfaee, H., Mohammadpour, K., Seilsepour, M., Dehghanisanij, H., Hajnajari, H., Farazmand, H., & Atri, A. (2025). Internet of things-based smart system for apple orchards monitoring and management. Smart Agricultural Technology, 10, 100715. https://doi.org/https://doi.org/10.1016/j.atech.2024.100715

Jose, J. A. C., Chu, T. S. C., Jacob, L. H. M., Rulloda, L. A. R., Ambrosio, A. Z. M. H., Sy, A. C., Vicerra, R. R. P., Choi, A. E. S., & Dadios, E. P. (2025). An automated small-scale aquaponics system design using a closed loop control. Environmental Challenges, 19, 101127. https://doi.org/https://doi.org/10.1016/j.envc.2025.101127

Kiropoulos, K., Bibi, S., Ampatzoglou, A., & Tsipouras, M. (2026). Blockchain-enabled precision agriculture: A scoping review of data infrastructures and decision-support systems. Smart Agricultural Technology, 14, 102385. https://doi.org/https://doi.org/10.1016/j.atech.2026.102385

Malkani, P., Mani, I., Sahoo, P. K., Parray, R. A., Swain, S. S., K.R., A., Dharmender, Chowdhury, M., Rathod, S. K., Al-Qahtani, W. H., Salem, A., Elbeltagi, A., & Elwakeel, A. E. (2024). Development and evaluation of a sensor enabled smart herbicide applicator for inter row crops. Smart Agricultural Technology, 9, 100649. https://doi.org/https://doi.org/10.1016/j.atech.2024.100649

Martypasha, M., Justicio, S., Zakaria, M. H. A., Devari, B. S. M., Farhan, R. F., & Putra, F. R. S. (2026). Implementasi Tata Kelola Teknologi Informasi dalam Transformasi Digital Manajemen Sumber Daya Manusia: Tinjauan Literatur. TIN: Terapan Informatika Nusantara, 7(2), 863–876.

Morchid, A., Muhammad Alblushi, I. G., Khalid, H. M., El Alami, R., Sitaramanan, S. R., & Muyeen, S. M. (2024). High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing. Journal of the Saudi Society of Agricultural Sciences. https://doi.org/https://doi.org/10.1016/j.jssas.2024.02.001

Quispe, M., Chiotti, D., Del-Águila, J., Quino, G., & Jara Alegria, E. (2026). Automatic pellet positioner with switched fuzzy-PD control for a smart shrimp feeder. Smart Agricultural Technology, 13, 101863. https://doi.org/https://doi.org/10.1016/j.atech.2026.101863

Rahman, T., Ornob, M. D. R. R., Jannat, M., Hafiz, F., & Talukder, M. S. (2026). FishNetBot: An IoT and Deep Learning-Based Automated Fish Sorting Robot. Array, 32, 101236. https://doi.org/https://doi.org/10.1016/j.array.2026.101236

Rajak, P., Ganguly, A., Adhikary, S., & Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research, 14, 100776. https://doi.org/https://doi.org/10.1016/j.jafr.2023.100776

Rastegari, H., Nadi, F., Lam, S. S., Ikhwanuddin, M., Kasan, N. A., Rahmat, R. F., & Mahari, W. A. W. (2023). Internet of Things in aquaculture: A review of the challenges and potential solutions based on current and future trends. Smart Agricultural Technology, 4, 100187. https://doi.org/https://doi.org/10.1016/j.atech.2023.100187

Ratan, R., M, A., Kamilya, D., & Nagarajan, V. (2026). Artificial intelligence and digital innovations in precision aquaculture: Advancements, applications, and future directions. Franklin Open, 15, 100567. https://doi.org/https://doi.org/10.1016/j.fraope.2026.100567

Rejeb, A., Rejeb, K., & Keogh, J. G. (2025). The nexus of IoT and aquaculture: A bibliometric analysis. Applied Food Research, 5(1), 100838. https://doi.org/https://doi.org/10.1016/j.afres.2025.100838

Shi, J., Yu, X., Zhao, Y., Zuo, D., He, T., Du, F., & Hua, L. (2026). Power Amplifier Based on MOS Transistor and Microcontroller. Procedia Computer Science, 285, 1254–1260. https://doi.org/https://doi.org/10.1016/j.procs.2026.07.304

Sitranata, J. S., Channing Teng, J. G., Prasetya, I. H., Yesmaya, V., Tanuar, E., Nafi’ah, R., & Azizan, S. A. (2025). Aqualyze: Smart Monitoring Water Quality through IoT-Enabled Sensors in RAS. Procedia Computer Science, 269, 502–511. https://doi.org/https://doi.org/10.1016/j.procs.2025.08.302

Songpayome, P., Sutin, S., Sukmak, W., Wanthong, U., Limchoowong, N., Sricharoen, P., & Musik, P. (2024). A prototype pond water management system (dissolved oxygen, pH and temperature) for giant freshwater prawn farming in Pak Phanang, Southern Thailand. Heliyon, 10(10), e31231. https://doi.org/https://doi.org/10.1016/j.heliyon.2024.e31231

Subeesh, A., & Chauhan, N. (2026). Agricultural digital twin for smart farming: A review. Green Technologies and Sustainability, 4(2), 100299. https://doi.org/https://doi.org/10.1016/j.grets.2025.100299

Sung, W.-T., Tofik Isa, I. G., & Hsiao, S.-J. (2023). An IoT-Based Aquaculture Monitoring System Using Firebase. Computers, Materials and Continua, 76(2), 2179–2200. https://doi.org/https://doi.org/10.32604/cmc.2023.041022

Tsipianitis, D., Filippou, M., Lavidas, K., & Komis, V. (2025). Real-time Monitoring of IoT-based Educational Aquatic Microecosystem. Procedia Computer Science, 257, 801–808. https://doi.org/https://doi.org/10.1016/j.procs.2025.03.103

Ubina, N. A., Lan, H.-Y., Cheng, S.-C., Chang, C.-C., Lin, S.-S., Zhang, K.-X., Lu, H.-Y., Cheng, C.-Y., & Hsieh, Y.-Z. (2023). Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT). Smart Agricultural Technology, 5, 100285. https://doi.org/https://doi.org/10.1016/j.atech.2023.100285

Vasileiou, M., & Vlontzos, G. (2025). A hybrid remotely operated underwater vehicle for maintenance operations in aquaculture: Practical insights from Greek fish farms. Computers and Electronics in Agriculture, 232, 110045. https://doi.org/https://doi.org/10.1016/j.compag.2025.110045

Yang, H., Feng, Q., Xia, S., Wu, Z., & Zhang, Y. (2025). AI-driven aquaculture: A review of technological innovations and their sustainable impacts. Artificial Intelligence in Agriculture, 15(3), 508–525. https://doi.org/https://doi.org/10.1016/j.aiia.2025.01.012

Yang, H., Wang, X., Shi, Y., Wang, J., Jia, B., Zhou, C., & Ye, H. (2025). Research on a precise feeding method for fry in recirculating aquaculture systems. Aquaculture Reports, 45, 103083. https://doi.org/https://doi.org/10.1016/j.aqrep.2025.103083

Yang, Q., Li, Z., Tang, J., Liu, Y., Guo, T., & Wen, Y. (2026). LEAF: A Lightweight Edge Agent Framework with Expert SLMs for the Industrial Internet of Things. Computers, Materials and Continua, 87(2). https://doi.org/https://doi.org/10.32604/cmc.2025.074384


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Tren Teknologi dan Mikrokontroler pada Sistem Pakan Ikan Otomatis Berbasis IoT: Tinjauan Sistematis 2022–2026

Dimensions Badge
Article History
Published: 2026-10-31
Abstract View: 18 times
Section
Articles