DOI: https://doi.org/10.14710/jtsiskom.2020.13353

Prediksi konsumsi beras menggunakan metode regresi linear pada sistem kotak beras cerdas

Rice consumption prediction using linear regression method for smart rice box system

Department of Informatics, Universitas Telkom, Indonesia

Received: 7 May 2019; Revised: 13 May 2020; Accepted: 25 May 2020; Published: 31 Oct 2020; Available online: 19 Oct 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
Currently, the smart rice box has applied the Internet of Things (IoT) but without prediction of rice runs out which shows the amount of rice consumption. This study applies linear regression to predict the rice runs out in an IoT-based smart rice box and analyzes its performance. The prediction used the dataset obtained by measuring a smart rice box equipped with a load cell weight sensor and Hx711 module. The weight sensor accuracy was an RMSE of between 56 and 170 grams. The linear regression method applied to the smart rice box to predict rice running out has an MSE value of 0.2588 with a prediction window of 43 days. An R-squared value of less than one is obtained with a predictive threshold of 24 days.
Fulltext View|Download Email colleagues
Keywords: ricebox; internet of things; linear regression; rice prediction
Funding: Universitas Telkom

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Statistics:
Related articles
Development of Web Interface for Room Monitoring System Speech Recognition and Analysis of Electrical Device Control Systems Using Arduino Uno Implementation of Policy Base Routing and Failover using Mikrotik Router to Distribute Internet Access in FMIPA Unnes Attitude stabilization control for quadrotor using self-tuning fuzzy-PD Sistem Informasi Inventory dan Peminjaman Barang pada Laboratorium Program Studi Sistem Komputer More related articles
Most cited articles
Sistem Monitoring Digital Penggunaan dan Kualitas Kekeruhan Air PDAM Berbasis Mikrokontroler ATMega328 Menggunakan Sensor Aliran Air dan Sensor Fotodiode Automatic Home Roof System on Smarthome by Using Arduino Application caching strategy based on in-memory using Redis server to accelerate relational data access Sistem Informasi Geografis Pariwisata Kota Semarang Decision Support System for Subsidizing the Repair Cost of Containers Damage Using Naive Bayes More cited articles
  1. C. C. Denardin, N. Boufleur, P. Reckziegel, L. P. da Silva, and M. Walter, “Amylose content in rice (Oryza sativa) affects performance, glycemic and lipidic metabolism in rats,” Ciência Rural, vol. 42, no. 2, pp. 381–387, 2012. doi: 10.1590/S0103-84782012005000002
  2. B. S. Priya, C. Kumaravelu, A. Gopal, and P. Stanley, “Classification of rice varieties using Near-Infra red Spectroscopy,” in 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development, Chennai, India, Jul. 2015, pp. 13–16. doi: 10.1109/TIAR.2015.7358524
  3. L. V. Arsyati, “Tempat penyimpanan beras elektronik berbasis mikrokontroler ATMega16,” Skripsi, Universitas Negeri Yogyakarta, Indonesia, 2013
  4. A. Riduansyah and A. Sonita, “Monitoring jumlah beras pada rice box dengan sensor berat dan sensor kelembaban berbasis mikrokontroler,” Jurnal Scientific and Applied Informatics, vol. 2, no. 2, pp. 159-164, 2019. doi: 10.36085/jsai.v2i2.196
  5. M. D. Nastiti, M. Abdurohman, and A. G. Putrada, “Smart shopping prediction on smart shopping with linear regression method,” in 7th International Conference on Information and Communication Technology, Kuala Lumpur, Malaysia, Jul. 2019, pp. 1-6. doi: 10.1109/ICoICT.2019.8835271
  6. M. Edward, K. Karyono, and H. Meidia, “Smart fridge design using NodeMCU and home server based on Raspberry Pi 3,” in 4th International Conference on New Media Studies, Yogyakarta, Indonesia, Nov. 2017, pp. 148–151. doi: 10.1109/CONMEDIA.2017.8266047
  7. S. M. Shaheed, M. S. B. Ilyas, J. A. Sheikh, and J. Ahamed, “Effective smart home system based on flexible cost in Pakistan,” in Fourth HCT Information Technology Trends, Al Ain, UAE, Oct. 2017, pp. 35–38. doi: 10.1109/CTIT.2017.8259563
  8. H. Ghayvat, J. Liu, A. Babu, E. E. Alahi, X. Gui, and S. C. Mukhopadhyay, “Internet of Things for smart homes and buildings: Opportunities and Challenges,” Journal of Telecommunication & the Digital Economy, vol. 3, no. 4, pp. 33–34, 2015. doi: 10.18080/ajtde.v3n4.23
  9. F. Ghassani, M. Abdurohman and A. G. Putrada, "Prediction of smarthphone charging using k-nearest neighbor machine learning," in Third International Conference on Informatics and Computing (ICIC), Palembang, Indonesia, Oct. 2018, pp. 1-4. doi: 10.1109/IAC.2018.8780497
  10. G. Ristanoski, W. Liu, and J. Bailey, “Time series forecasting using distribution enhanced linear regression,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Gold Coast, Australia, Apr. 2013, pp. 484–495. doi: 10.1007/978-3-642-37453-1_40
  11. C. Zhang and F. Wang, “Research on correlation analysis between test score and classroom attendance based on linear regression model,” in 2nd International Conference on Industrial Mechatronics and Automation, Wuhan, China, May 2010, pp. 545–548. doi: 10.1109/ICINDMA.2010.5538079
  12. P. P. Moletsane, T. J. Motlhamme, R. Malekian, and D. C. Bogatmoska, “Linear regression analysis of energy consumption data for smart homes,” in 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, Opatija, Croatia, May 2018, pp. 0395–0399. doi: 10.23919/MIPRO.2018.8400075
  13. A. M. Aprillia, “Pengukuran jarak terbatas menggunakan kombinasi sensor berat dan pegas tekan dengan tampilan pada perangkat smartphone,” PhD Thesis, Universitas Muhammadiyah Surakarta, Indonesia, 2017
  14. N. Golubovic, R. Wolski, C. Krintz, and M. Mock, “Improving the accuracy of outdoor temperature prediction by IoT devices,” in IEEE International Congress on Internet of Things, Milan, Italy, 2019, pp. 117–124. doi: 10.1109/ICIOT.2019.00030
  15. A. I. Abdul-Rahman and C. A. Graves, “Internet of things application using tethered msp430 to thingspeak cloud,” in IEEE Symposium on Service-Oriented System Engineering, Oxford, UK, Apr. 2016, pp. 352–357. doi: 10.1109/SOSE.2016.42
  16. K. Margi and S. Pendawa, “Analisa dan penerapan metode single exponential smoothing untuk prediksi penjualan pada periode tertentu (studi kasus: Pt. Media Cemara Kreasi),” in Seminar Nasional Teknologi dan Informatika, Kudus, Indonesia, Sept. 2015, pp. 259-266
  17. J. O. Rawlings, S. G. Pantula, and D. A. Dickey, Applied regression analysis: a research tool. Springer Science & Business Media, 2001

Last update:

No citation recorded.

Last update: 2021-10-23 10:04:48

No citation recorded.