Machine Learning Penyortiran Buah Naga Menggunakan Algoritma K-Means Berbasis Internet of Things Menggunakan Platform Blynks

Authors

  • Wanda Ramadan STMIK Subang
  • Aa Zezen Zenal Abidin Universitas Mandiri
  • Usep Tatang Suryadi Universitas Mandiri
  • Yuli Murdianingsih Universitas Mandiri
  • Muhammad Faizal Universitas Mandiri
  • Suhendri Suhendri Universitas Mandiri
  • Carkiman Carkiman Universitas Mandiri
https://doi.org/10.47561/jtik.v18i1.285
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Abstract


One of the stages in the process of managing agricultural and plantation products is to carry out a structured division of products to determine the quality of the harvest. Sorting is done by observing the skin quality, fruit weight, and the quantity of the harvest. The quality of dragon fruit is determined by various parameters, including age and maturity (color index), size, and weight. As one of the commodities favored by many people, dragon fruit requires a sorting process (selection), because the market demands uniformity in the dragon fruit. Selection is usually done according to separation principles, such as: different weights, different shapes, different surface properties, different densities, different color textures, and different maturities. In the manual sorting process, humans have limitations in performing sensory tasks with large capacity and long working hours.

Based on this issue, the author is interested in creating a tool called the Internet of Things-Based Dragon Fruit Sorting Machine Learning Using the K-Means Algorithm on the Blynk Platform. The methodologies used by the author include literature study, documentation, data mining, system analysis, system design, system development, and system testing.

The Internet of Things-Based Dragon Fruit Sorting Machine Learning Using the K-Means Algorithm on the Blynk Platform, which the author worked on, was successfully implemented using a Load Cell sensor to measure weight and a TCS230 sensor to determine color. Additionally, the TCS3200 sensor can detect color accurately. The data obtained by the tool can be clustered using the K-Means algorithm correctly through 7 iterations with BCV=2096.84, WCV=442563.35, and a Ratio of 211.



Keywords: K-Means Algorithm, Blynk, Dragon Fruit, Internet of Things

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References

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Published

2025-04-01

How to Cite

[1]
W. Ramadan, “Machine Learning Penyortiran Buah Naga Menggunakan Algoritma K-Means Berbasis Internet of Things Menggunakan Platform Blynks”, JTIK, vol. 18, no. 1, pp. 1-14, Apr. 2025.

Issue

Vol. 18 No. 1 (2025): April

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Articles

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Copyright (c) 2025 Wanda Ramadan, Aa Zezen Zenal Abidin, Usep Tatang Suryadi, Yuli Murdianingsih, Muhammad Faizal, Suhendri, Carkiman

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