Published: 2025-10-01

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

DOI: 10.35870/jtik.v9i4.4049

Md Wira Putra Dananjaya, Gede Humaswara Prathama, I Gusti Ngurah Darma Paramartha, Putu Gita Pujayanti
  • Md Wira Putra Dananjaya: Universitas Pendidikan Nasional , Indonesia
  • Gede Humaswara Prathama: Universitas Pendidikan Nasional
  • I Gusti Ngurah Darma Paramartha: Universitas Pendidikan Nasional
  • Putu Gita Pujayanti: Universitas Pendidikan Nasional , Indonesia
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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

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How to Cite

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
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 9 No. 4 (2025)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2025

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Author Biographies
Md Wira Putra Dananjaya
Google Scholar ResearchGate ORCID

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

Gede Humaswara Prathama
Google Scholar ResearchGate ORCID

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

I Gusti Ngurah Darma Paramartha
Google Scholar ResearchGate ORCID

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

Putu Gita Pujayanti
Google Scholar ResearchGate ORCID

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

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