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Klasifikasi Kualitas Tanah Berdasarkan Kandungan pH, Kelembapan, dan Suhu Menggunakan Algoritma K-Nearest Neighbors

  • Md Wira Putra Dananjaya1*
  • Gede Humaswara Prathama2
  • I Gusti Ngurah Darma Paramartha3
  • Putu Gita Pujayanti4
1,2,3,4 University of National Education image/svg+xml
* Corresponding Author: Md Wira Putra Dananjaya

Abstract

This study aims to analyze soil quality using the K-Nearest Neighbors (KNN) algorithm based on environmental parameters such as temperature, humidity, pH, and nutrient content (N, P, K). The dataset used consists of 660 entries covering 22 different classes describing soil types with varying characteristics. The KNN model was applied to classify soil quality, and the results were evaluated using the Confusion Matrix and Classification Report. The accuracy of the model obtained was around 61%, indicating potential improvements in the classification of some more difficult soil classes. The model performed better on certain classes such as kidney beans, chickpeas, and grapes, but was less than optimal on other classes such as watermelon and pomegranate. These results indicate class alignment in the dataset that affects model performance. This study contributes to the application of machine learning algorithms in agriculture, especially for soil quality monitoring. In the future, this study opens up opportunities for further improvements by using parameter optimization techniques and other more complex algorithms. Thus, the results of this study can be used as a basis for developing intelligent systems for more effective and efficient soil management.

Keywords

KNN Agriculture Classification

How to Cite

Citation Format
Dananjaya, M. W. P., Prathama, G. H., Paramartha, I. G. N. D., & Pujayanti, P. G. (2025). Klasifikasi Kualitas Tanah Berdasarkan Kandungan pH, Kelembapan, dan Suhu Menggunakan Algoritma K-Nearest Neighbors. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(4), 1437-1444. https://doi.org/10.35870/jtik.v9i4.4049

References

10 References
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Author Biographies

Md Wira Putra Dananjaya, University of National Education

Program Studi Bisnis Digital, Fakultas Ekonomi dan Bisnis, Universitas Pendidikan Nasional, Kota Denpasar, Provinsi Bali, Indonesia.

Gede Humaswara Prathama, University of National Education

3 Program Studi Teknologi Informasi, Fakultas Teknik dan Informatika, Universitas Pendidikan Nasional.

I Gusti Ngurah Darma Paramartha, University of National Education

Program Studi Teknologi Informasi, Fakultas Teknik dan Informatika, Universitas Pendidikan Nasional.

Putu Gita Pujayanti, University of National Education

Program Studi Bisnis Digital, Fakultas Ekonomi dan Bisnis, Universitas Pendidikan Nasional, Kota Denpasar, Provinsi Bali, Indonesia.

License

Copyright (c) 2025 Md Wira Putra Dananjaya, Gede Humaswara Prathama, I Gusti Ngurah Darma Paramartha, Putu Gita Pujayanti

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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