Design and Construction of a Sweet Starfruit Harvest Monitoring and Prediction System Using the ANFIS Model and LoRa Communication
DOI:
https://doi.org/10.55927/esa.v5i4.21Keywords:
ANFIS, Sweet Starfruit, LoRa, Monitoring System, Smart FarmingAbstract
This research aims to develop an IoT-based environmental monitoring and yield prediction system for starfruit plants using LoRa communication and the ANFIS model. The system is designed using two devices, namely an ESP32-based data capture device with DHT22, BH1750, soil pH, and soil moisture sensors, and an ESP32-S3-based data processing device that receives data via LoRa, displays information on an OLED, sends data to Blynk, and runs a simulation of yield prediction. The monitored parameters include air temperature, air humidity, light intensity, soil pH, and soil moisture. Test results show that the system is able to monitor environmental data in real-time, stable LoRa communication up to 300 m with a delay of 100–150 ms, and the ANFIS model produces RMSE and MAE of 0.3. This system has the potential to support data-driven cultivation decisions.
References
Ali, Z., Henna, S., Akhunzada, A., Raza, M., & Kim, S. (2019). Performance evaluation of LoRaWAN for green Internet of Things. IEEE Access, 7, 164102–164112. https://doi.org/10.1109/ACCESS.2019.2943720
Ampatzidis, Y., & Partel, V. (2019). UAV-based high throughput phenotyping in citrus utilizing multispectral imaging and artificial intelligence. Remote Sensing, 11(4), 410. https://doi.org/10.3390/rs11040410
Ariani, A., Supriyono, S., & Prasetyo, D. (2020). Sistem monitoring suhu dan pencahayaan berbasis Internet of Things. Jurnal Teknologi dan Sistem Komputer, 8(2), 120–127.
Asnamawati, L., Herawati, I., Rasoki, T., Faridha, I., & Nurmalia, A. (2024). Using RITX soil technology and weather sensors to increase rice production: How farmers adopt innovation? Jurnal Agroqua: Media Informasi Agronomi dan Budidaya Perairan, 21(2), 480–497. https://doi.org/10.32663/ja.v21i2.4179
Batong, A., Murdiyat, P., & Kurniawan, A. (2020). Analisis kelayakan LoRa untuk jaringan komunikasi sistem monitoring listrik di Politeknik Negeri Samarinda. Poligrid, 1(2), 55. https://doi.org/10.46964/poligrid.v1i2.602
Bijanto, B., Nugroho, S., Fredyatama, Y., Fahrani, D., & Proborini, E. (2023). Perancangan dan implementasi sistem smartfarming menggunakan Arduino Uno dan modul ESP8266. Jurnal Disprotek, 14(1), 77–90. https://doi.org/10.34001/jdpt.v14i1.3924
Ellahi, R., Khan, M. A., & Hussain, F. (2022). Mean absolute error-based evaluation for predictive modelling. Journal of Computational Methods, 10(1), 1–10.
Fakharchian, M. (2023). Root mean square error analysis for prediction models. Journal of Data Science and Intelligent Systems, 2(1), 44–52.
Godinho, E., Caneppele, F., & Floriano, C. (2024). Application of neuro-fuzzy to the elephant grass production process: A systematic bibliographic review. Research, Society and Development, 13(2), e6613244927. https://doi.org/10.33448/rsd-v13i2.44927
Jafari, R., Contreras, M., Yu, W., & Gegov, A. (2020). Applications of fuzzy logic, artificial neural network and neuro-fuzzy in industrial engineering. https://doi.org/10.1007/978-3-030-45402-9_2
Khakim, E., Hermawan, A., & Avianto, D. (2023). Implementasi correlation matrix pada klasifikasi dataset wine. JIKO (Jurnal Informatika dan Komputer), 7(1), 158. https://doi.org/10.26798/jiko.v7i1.771
Kusumah, R., Abdurohman, M., & Putrada, A. (2020). Basement flood control with adaptive neuro fuzzy inference system using ultrasonic sensor. International Journal on Information and Communication Technology, 5(2), 11. https://doi.org/10.21108/ijoict.2019.52.482
Merdekawan, A., & Sari, P. (2022). Studi awal rancang bangun indoor farming monitoring system berbasis IoT dengan protokol websocket. Jurnal Ilmu Komputer dan Agri-Informatika, 9(2), 189–198. https://doi.org/10.29244/jika.9.2.189-198
Moghaddam, M., Ali, M., & Rahman, F. (2022). Sugeno fuzzy inference system for decision-making in complex systems. International Journal of Fuzzy Systems, 24(3), 1120–1133.
Nasirudin, H., & Pangaribowo, T. (2022). Prototype automasi greenhouse dengan monitoring menggunakan smartphone dan ESP32 berbasis Internet of Things. Jurnal Teknologi Elektro, 13(3), 194. https://doi.org/10.22441/jte.2022.v13i3.010
Nazir, N., Suliansyah, I., & Chaniago, I. (2017). Pengaruh kondisi tanah terhadap pertumbuhan tanaman hortikultura. Jurnal Ilmiah Mahasiswa Pertanian, 2(1), 21–30. https://doi.org/10.17969/jimfp.v2i1.2149
Nurshiyam ‘Izami, S. (2025). Rancang bangun sistem pemantauan untuk prediksi hasil panen tanaman belimbing manis menggunakan model ANFIS dan komunikasi LoRa [Tugas akhir, Universitas Negeri Malang].
Nurwahyudin, R., & Rintyarna, B. (2023). Optimasi waktu pemaparan cahaya monokromatik terhadap produktivitas mikrogreens pakcoy melalui sistem Internet of Things. Prosiding Seminar Nasional Pembangunan dan Pendidikan Vokasi Pertanian, 4(1), 604–611. https://doi.org/10.47687/snppvp.v4i1.684
Pagano, A., Croce, D., Tinnirello, I., & Vitale, G. (2023). A survey on LoRa for smart agriculture: Current trends and future perspectives. IEEE Internet of Things Journal, 10(4), 3664–3679. https://doi.org/10.1109/JIOT.2022.3230505
Rahardjo, P. (2022). Sistem penyiraman otomatis menggunakan sensor kelembaban tanah berbasis mikrokontroler Arduino Mega 2560 pada tanaman mangga harum manis Buleleng Bali. Majalah Ilmiah Teknologi Elektro, 21(1), 31. https://doi.org/10.24843/mite.2022.v21i01.p05
Rahman, A., et al. (2021). Adaptive morphology of Averrhoa carambola in tropical climates. Asian Journal of Agricultural Research, 15(3), 112–120.
Restiatun, R., Udi, K., & Rosyadi, R. (2023). Pengaruh pertumbuhan sektor pertanian, jumlah pekerja sektor pertanian dan nilai tukar petani terhadap tingkat kemiskinan perdesaan di Indonesia. Jurnal Ekonomi Pembangunan, 12(1), 42–53. https://doi.org/10.23960/jep.v12i1.977
Satria, A., Badri, R., & Safitri, I. (2023). Prediksi hasil panen tanaman pangan Sumatera dengan metode machine learning. Digital Transformation Technology, 3(2), 389–398. https://doi.org/10.47709/digitech.v3i2.2852
Septyan, R., et al. (2020). Morphological characterization of sweet starfruit (Averrhoa carambola). Journal of Tropical Horticulture, 3(2), 45–52.
Shahrosi, M., Harijanto, A., & Nuraini, L. (2023). Rancang bangun prototype sistem monitoring suhu, kelembaban dan intensitas cahaya pada tanaman cabai berbasis IoT. STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 7(3), 300. https://doi.org/10.30998/string.v7i3.15139
Walid, M., Hafiah, S., & Bakir, B. (2019). Penerapan wireless sensor networks (WSN) untuk sistem pemantauan sawah tadah hujan. Nusantara Journal of Computers and Its Applications, 4(2), 110. https://doi.org/10.36564/njca.v4i2.149
Wijaya, A., Santoso, H., & Pratama, R. (2019). Min-Max normalization for improving machine learning model performance. Jurnal Teknologi Informasi dan Ilmu Komputer, 6(4), 405–412.
Wulandari, P., Rahima, P., & Hadi, S. (2020). Rancang bangun sistem penyiraman otomatis berbasis Internet of Things pada tanaman hias sirih gading. Bumigora Information Technology, 2(2), 77–85. https://doi.org/10.30812/bite.v2i2.886
Yuniarto, R., Putra, A., & Kurniawan, B. (2023). Internet of Things-based monitoring system for smart environments. Jurnal Sistem Cerdas, 6(1), 33–42.
Zhang, P., Chen, X., Li, S., Zhang, C., & Hu, Y. (2023). Development of the Internet of smart orchard things based on multi-sensors and LoRa technology. Intelligent and Converged Networks, 4(4), 342–354. https://doi.org/10.23919/icn.2023.0028
Zhar, M. I., & Mahmudy, W. F. (2018). Prediksi curah hujan menggunakan metode adaptive neuro fuzzy inference system (ANFIS). Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(11), 4932–4939. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3139
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