Predictive Analysis of Avocado Ripeness Level Using the Logistic Regression Algorithm
DOI:
https://doi.org/10.51179/ilka.v3i1.46Abstract
Determining the ripeness level of avocado fruit is an important factor in distribution, marketing, and consumption processes. Conventional ripeness assessment is often subjective and dependent on human experience, which can lead to inconsistent results. This study aims to develop an avocado ripeness prediction system using the Logistic Regression algorithm based on physical and visual fruit characteristics. The dataset consists of 1,250 avocado samples with features including firmness, color attributes, tapping sound, weight, and fruit size. Data preprocessing involved cleaning, normalization of numerical features using StandardScaler, and categorical feature transformation using one-hot encoding. The experimental results show that the proposed model achieved an accuracy of approximately 77% in classifying avocado ripeness into ripe and unripe categories, indicating that Logistic Regression is a lightweight and efficient approach for numerical-based ripeness prediction systems.
Downloads
References
[1] I. Sutoyo, “IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI DATA PESERTA DIDIK,” vol. 14, no. 2, 2018, [Online]. Available: www.bsi.ac.id
[2] H. Annur, “KLASIFIKASI MASYARAKAT MISKIN MENGGUNAKAN METODE NAÏVE BAYES,” 2018.
[3] A. Samosir, M. Hasibuan, W. E. Justino, and T. Hariyono, “Komparasi Algoritma Random Forest, Naïve Bayes dan K-Nearest Neighbor Dalam klasifikasi Data Penyakit Jantung”.
[4] D. Armiady and I. M. R, “Klasifikasi Kualitas Buah Pisang Berdasarkan Citra Buah Menggunakan Stochastic Gradient Descent,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 2, 2023.
[5] W. F. Hidayat, T. Asra, and A. Setiadi, “Klasifikasi Penyakit Daun Kentang Menggunakan Model Logistic Regression,” Indonesian Journal on Software Engineering (IJSE), vol. 8, no. 2, pp. 173–179, 2022, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ijse
[6] H. Hikmayanti Handayani, K. Ahmad Baihaqi, and U. Buana Perjuangan Karawang, “Implementasi Algoritma Logistic Regression Untuk Klasifikasi Penyakit Stroke,” 2023.
[7] D. Najwa Ardelia, H. Desfianty Arifin, S. Daniswara, A. Puspita Sari, P. Studi Informatika -UPN, and J. Timur Jl Raya Rungkut Madya Gunung Anyar -Surabaya, “Klasifikasi Harga Ponsel Menggunakan Algoritma Logistic Regression.”
[8] A. Adrian, I. Verawati, ) Program, and S. Informatika, “Analisis Performa Logistic Regression dan Random Forest dalam Klasifikasi Kelayakan Penerimaan Kredit,” 2025. [Online]. Available: https://subset.id/index.php/IJCSR
[9] I. R. Muslem and T. M. Johan, “KLIK: Kajian Ilmiah Informatika dan Komputer Klasifikasi Citra Ikan Menggunakan Algoritma Convolutional Neural Network dengan Arsitektur VGG-16,” Media Online), vol. 4, no. 2, pp. 978–985, 2023, doi: 10.30865/klik.v4i2.1209.
[10] A. Hamzah, E. Susanti, and R. M. Lestari, “SUPPORT VECTOR MACHINE (SVM)”.
[11] M. Noer, F. Hidayat, J. K. Zaini Mun’im, P. N. Jadid, and J. Timur, “KLASIFIKASI BUAH ALPUKAT BERDASARKAN TEKSTUR BUAH MENGGUNAKAN METODE BACKPROPAGATION BERBASIS IMAGE PROCESSING,” 2023. [Online]. Available: http://e-journal.stmiklombok.ac.id/index.php/jireISSN.2620-6900
[12] A. Ayaz Mirani, M. Suleman Memon, R. Chohan, A. Ali Wagan, and M. Qabulio, “Machine Learning In Agriculture: A Review,” LUME, vol. 10, p. 5, 2021, [Online]. Available: www.ijstr.org
[13] C. H. H. Jannah, I. Muslem, and D. Azmi, “Jurnal Ilmu Komputer Aceh Klasifikasi Plat Nomor Kenderaan Bedasarkan Wilayah Tertentu Menggunakan Algoritma Optical Character Recognition,” Jurnal Ilmu Komputer Aceh, Oct. 2025, [Online]. Available: https://jurnal.fikompublisher.com/ilka/article/view/16
[14] Gareth. James, Daniela. Witten, Trevor. Hastie, and Robert. Tibshirani, An introduction to statistical learning : with applications in R. Springer : Springer Science+Business Media, 2017.
[15] P. O. Akinwumi, S. Ojo, T. I. Nathaniel, J. Wanliss, O. Karunwi, and M. Sulaiman, “Evaluating machine learning models for stroke prediction based on clinical variables,” Front. Neurol., vol. 16, 2025, doi: 10.3389/fneur.2025.1668420.
[16] I. H. Ikasari, P. Rosyani, and R. Amalia, “Klasifikasi Jenis Buah Menggunakan Metode CNN,” RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 2, pp. 5451–5458, Jul. 2025, doi: 10.31004/riggs.v4i2.1271.
[17] G. Honestya, M. Sajida, and A. Ramadhanu, “Klasifikasi Jenis Daun Herbal Menggunakan Metode Logistic Regression dan Decision Tree Classifier Berdasarkan Fitur (Warna dan Bentuk)”.
[18] F. B. Setiawan, C. B. Adipradana, and L. H. Pratomo, “Fruit Ripeness Classification System Using Convolutional Neural Network (CNN) Method,” PROtek : Jurnal Ilmiah Teknik Elektro, vol. 10, no. 1, p. 46, Jan. 2023, doi: 10.33387/protk.v10i1.5549.
[19] M. Knott, F. Perez-Cruz, and T. Defraeye, “Facilitated machine learning for image-based fruit quality assessment,” J. Food Eng., vol. 345, May 2023, doi: 10.1016/j.jfoodeng.2022.111401.
[20] A. Hamzah, E. Susanti, and R. M. Lestari, “SUPPORT VECTOR MACHINE (SVM)”.
[21] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey.”
[22] L. Luthfi, R. Imam Muslem, D. Armiady, S. Sriwinar, R. Fajri, and I. Iqbal, “Analysis of CNN Method for Image Classification of Coconut Ripeness Levels,” in 2023 Eighth International Conference on Informatics and Computing (ICIC), IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/ICIC60109.2023.10381964.
[23] A. Caban, K. K. April Gregorio, K. V Macam, M. S. Rosario Puzon, C. S. Santillan, and M. C. Santillan, “CLASSIFICATION OF NIPA FRUIT USING ARTIFICIAL NEURAL NETWORK,” Asian Journal of Multidisciplinary Studies, vol. 5, no. 1, 2022.
[24] D. M. Bulanon and T. Kataoka, “Fruit detection system and an end effector for robotic harvesting of Fuji apples,” 2010. [Online]. Available: http://www.cigrjournal.org
[25] N. Utami Putri and E. Redi Susanto, “Klasifikasi Jenis Kayu Menggunakan Support Vector Machine Berdasarkan Ciri Tekstur Local Binary Pattern,” CYBERNETICS, vol. 4, no. 02, pp. 93–100, 2020.
[26] J. Liu, J. Sun, Y. Wang, X. Liu, Y. Zhang, and H. Fu, “Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects,” Jun. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/foods14122137.
[27] F. D. Pramakrisna, F. D. Adhinata, and N. A. F. Tanjung, “Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression,” Teknika, vol. 11, no. 2, pp. 90–97, Jun. 2022, doi: 10.34148/teknika.v11i2.466.
[28] B. Tri Putra, E. Yulianingsih, S. Informasi, S. Teknologi, and U. Bina Darma, “Analisis Tingkat Akurasi Prediksi Gejala COVID-19 Dengan Menggunakan Metode Logistic Regression dan Support Vector Machine”, [Online]. Available: http://github.com/nshomron/covidpred.
[29] W. T. Setiadi, D. Jollyta, and E. B. Setiawan, “Seminar Nasional Informatika (SENATIKA) Prosiding Senatika 2025 Prediksi Risiko Diabetes Menggunakan Model Regresi Logistik dan Random Forest.”
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Aceh Journal of Computer Science
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This journal is an open access journal that provides direct, barrier-free national access to the full text of all published articles without any cost to readers or their institutions. Readers are entitled to read, download, copy, distribute, print, search, or link to the full text of all articles in the ILKA Journal. This journal provides open access to its content on the principle that making research freely available to the public supports greater global knowledge exchange.
