KLASIFIKASI KEMATANGAN PISANG BERDASARKAN CITRA WARNA KULIT MENGGUNAKAN DECISION TREE DAN SUPPORT VECTOR MACHINE DENGAN INTEGRASI YOLOV8

Authors

  • Deva Gitisari Universitas Bina Sarana Informatika
  • Restu Putri Nisrina Universitas Bina Sarana Informatika
  • Nayla Natania Putri Universitas Bina Sarana Informatika
  • Sujiliani Heristian Universitas Bina Sarana Informatika
  • Veti Apriana Universitas Bina Sarana Informatika
  • Rame Santoso Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.21927/ijubi.v8i2.6488

Keywords:

Kematangan Pisang, Machine learning, Support Vector Machine, Decision Tree, YOLOv8, Pengolahan Citra

Abstract

 

Di Indonesia, panen pisang sering dilakukan sebelum buah mencapai kematangan fisiologis. Akibatnya, seringkali pisang yang belum matang beredar di pasaran. Tujuan dari penelitian ini adalah untuk mengevaluasi akurasi dua algoritma Machine Learning, yaitu Decision Tree dan Support Vector Machine (SVM) untuk menentukan tingkat kematangan pisang dengan  menggunakan dataset 6000 gambar pisang yang dikategorikan unripe, ripe, overripe, dan rotten. Dataset dipecah dalam rasio 80:20 untuk data latih dan data uji. Kemudian, metrik akurasi, presisi, recall, dan skor F1 digunakan untuk menguji. Hasil pengujian menunjukkan algoritma SVM memiliki akurasi tertinggi 92%, melampaui Decision Tree yang memiliki akurasi 82%. Model SVM Terbaik kemudian dikombinasikan dengan YOLOv8 untuk identifikasi kematangan pisang secara real-time menggunakan kamera. Penelitian ini memberikan kontribusi dengan menunjukkan efektivitas kombinasi HSV-SVM serta implementasi real-time menggunakan YOLOv8 menawarkan solusi praktis untuk pemantauan kualitas pisang secara otomatis.

Author Biographies

Deva Gitisari, Universitas Bina Sarana Informatika

Undergraduate student of Informatics, Universitas Bina Sarana Informatika. Interested in data science and machine learning research.

Restu Putri Nisrina, Universitas Bina Sarana Informatika

Undergraduate student of Informatics, Universitas Bina Sarana Informatika. Interested in data science and machine learning research.

Nayla Natania Putri, Universitas Bina Sarana Informatika

Undergraduate student of Informatics, Universitas Bina Sarana Informatika. Interested in data science and machine learning research.

Sujiliani Heristian, Universitas Bina Sarana Informatika

A lecturer in Informatics at Universitas Bina Sarana Informatika. Her research interests are in image processing and machine learning, with a particular focus on the development and implementation of intelligent systems for real-world applications.

Veti Apriana, Universitas Bina Sarana Informatika

A lecturer at the Department of Informatics, Universitas Bina Sarana Informatika. She teaches the Computer Science Research course and has research interests in research methodology, information system development, and the application of technology in education.

Rame Santoso, Universitas Bina Sarana Informatika

A lecturer at the Department of Informatics, Universitas Bina Sarana Informatika. He teaches the Artificial Intelligence course and has research interests in artificial intelligence, machine learning, and data processing. His/Her research focuses on the application of intelligent algorithms for solving real-world problems.

References

D. Andriansyah, E. Mufida, N. Iriadi, R. S. Anwar, and L. K. Rahayu, “Klasifikasi Kualitas Buah Pisang Berdasarkan Waktu Panen dan Tingkat Kematangan Menggunakan Metode SVM & KNN,†SATIN - Sains dan Teknol. Inf., vol. 10, no. 1, pp. 147–156, 2023, doi: 10.33372/stn.v9i2.1000.

Rendika Efando and Anisya Sonita, “Klasifikasi Tingkat Kematangan Buah Matoa Menggunakan Metode PCA dan KNN Berdasarkan Warna RGB,†JSAI (Journal Sci. Appl. Informatics), vol. 8, no. 2, pp. 337–346, Jun. 2025, doi: 10.36085/jsai.v8i2.7851.

Tjokorda Istri Agung Pandu Yuni Maharani, I Gusti Agung Indrawan, Gede Dana Pramitha, Christina Purnama Yanti, and I Made Marthana Yusa, “Classification of Cavendish Banana Ripeness With CNN Method,†Indones. J. Data Sci., vol. 6, no. 2, pp. 203–210, Jul. 2025, doi: 10.56705/ijodas.v6i2.259.

Y. A. Nisa, C. A. Sari, E. H. Rachmawanto, and N. Mohd Yaacob, “Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN),†SinkrOn, vol. 8, no. 4, pp. 2568–2578, Oct. 2023, doi: 10.33395/sinkron.v8i4.12961.

A. W. Hastungkoro, A. D. Putro Wicaksono, and Y. Diah Rosita, “Klasifikasi Kualitas dan Kematangan Pisang Cavendish Menggunakan Convolutional Neural Network,†J. SAINTEKOM, vol. 14, no. 2, pp. 185–194, Sep. 2024, doi: 10.33020/saintekom.v14i2.686.

R. E. Saragih, Y. Pernando, A. J. Santoso, Y. Roza, M. KH, and I. Verdian, “Optimizing the Sorting of Barangan Bananas Through Hybrid and Transfer Learning Approaches,†no. April, 2025, doi: 10.24507/icicel.19.04.459.

R. Musa and M. Akbar, “Identification of Formaldehyde Bananas using Learning Vector Quantization,†Indones. J. Inf. Syst., vol. 3, no. 2, pp. 160–170, Feb. 2021, doi: 10.24002/ijis.v3i2.4110.

A. Sattar, M. A. M. Ridoy, A. K. Saha, H. M. H. Babu, and M. N. Huda, “A comprehensive approach to detecting chemical adulteration in fruits using computer vision, deep learning, and chemical sensors,†Intell. Syst. with Appl., vol. 23, no. June, p. 200402, Sep. 2024, doi: 10.1016/j.iswa.2024.200402.

R. Kosasih, S. Sudaryanto, and A. Fahrurozi, “Classification of six banana ripeness levels based on statistical features on machine learning approach,†Int. J. Adv. Appl. Sci., vol. 12, no. 4, pp. 317–326, Dec. 2023, doi: 10.11591/ijaas.v12.i4.pp317-326.

M. I. Khalif and L. Edi Prabowo, “Penerapan Model Algoritma Unsupervised Learning untuk Klasterisasi Tingkat Kenyamanan Ruang Tidur berdasarkan Faktor Lingkungan,†J. Teknol. Inf. dan Ilmu Komput., vol. 12, no. 2, pp. 311–320, Apr. 2025, doi: 10.25126/jtiik.2025129456.

H. A. Ramadhan and S. Sriani, “Klasifikasi Tingkat Kematangan Buah Salak Sidempuan Menggunakan K-Nearest Neighbor pada Ruang Warna HSV,†INSOLOGI J. Sains dan Teknol., vol. 4, no. 4, pp. 997–1012, Aug. 2025, doi: 10.55123/insologi.v4i4.6298.

P. Astuti, “Klasifikasi Kualitas Buah Apel Dengan Algoritma K-Nearest Neighbor (K-NN) Menggunakan Bahasa Pemrograman Python,†Comput. Sci., vol. 4, no. 2, pp. 127–132, Jul. 2024, doi: 10.31294/coscience.v4i2.3328.

Y. Jumaryadi, “Klasifikasi Penyakit Kardiovaskular Menggunakan Algoritma Decision Tree C4.5,†J. Ilm. FIFO, vol. 17, no. 1, pp. 1–6, Jun. 2025, doi: 10.22441/fifo.2025.v17i1.001.

Y. Amrozi, D. Yuliati, A. Susilo, N. Novianto, and R. Ramadhan, “Klasifikasi Jenis Buah Pisang Berdasarkan Citra Warna dengan Metode SVM,†J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 3, pp. 394–399, Dec. 2022, doi: 10.32736/sisfokom.v11i3.1502.

M. Juventus Dappa Deke, T. Atha Anastasya, A. Diani Putri Saka, and E. Yulia Puspaningrum, “ANALISIS PENGARUH METODE EKSTRAKSI FITUR CITRA BATIK TERHADAP KINERJA KLASIFIKASI SVM,†JATI (Jurnal Mhs. Tek. Inform., vol. 9, no. 5, pp. 7516–7523, Jul. 2025, doi: 10.36040/jati.v9i5.14752.

Y. Yuliska and K. U. Syaliman, “Literatur Review Terhadap Metode, Aplikasi dan Dataset Peringkasan Dokumen Teks Otomatis untuk Teks Berbahasa Indonesia,†IT J. Res. Dev., vol. 5, no. 1, pp. 19–31, Jul. 2020, doi: 10.25299/itjrd.2020.vol5(1).4688.

D. Septhya et al., “Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru,†MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 15–19, May 2023, doi: 10.57152/malcom.v3i1.591.

M. K. Risqi, I. W. D. Prastya, and M. J. Vikri, “Comparison of Decision Tree Algorithms and Support Vector Machine (SVM) In Depression Classification In Students,†Eduvest - J. Univers. Stud., vol. 5, no. 4, pp. 4557–4567, May 2025, doi: 10.59188/eduvest.v5i4.51108.

A. A. Rasjid, B. Rahmat, and A. N. Sihananto, “Implementasi YOLOv8 Pada Robot Deteksi Objek,†J. Technol. Syst. Inf., vol. 1, no. 3, pp. 1–9, Jul. 2024, doi: 10.47134/jtsi.v1i3.2969.

Downloads

Published

2025-12-23

Issue

Vol. 8 No. 2 (2025): Indonesian Journal of Business Intelligence (IJUBI)

Section

Articles

License

Copyright (c) 2026 Indonesian Journal of Business Intelligence (IJUBI)

Creative Commons License

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

COPYRIGHT TRANSFER FORM

The copyright to this article is transferred to Alma Ata University Press if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to AAU Press. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.

We declare that:

1. This paper has not been published in the same form elsewhere.

2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.

3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.

Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to AAU Press. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.

The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

Retained Rights/Terms and Conditions

 

1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.

2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the AAU Press copyright notice are indicated, the copies are not used in any way that implies AAU Press endorsement of a product or service of any employer, and the copies themselves are not offered for sale.

3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.