Klasifikasi Jenis Kismis Menggunakan Teknik Data Mining
DOI:
https://doi.org/10.31599/ryvqk945Kata Kunci:
Classification, Data Mining, RaisinsAbstrak
Raisins are one of the processed grape products that are often found in the market, raisins are one of the processed grape products by drying. The color and quality of raisins are usually determined by the type of grape and the drying process. To assess the quality of raisins, many methods can be used, one of which is the traditional method carried out by humans manually. However, since traditional methods are considered to tend to take a long time and errors often occur due to human error. Currently, machine vision systems can be used to assess the quality of raisins. In addition to assessing the quality of raisins, this method can also be used to identify and classify raisins. One way to classify raisins is to use data mining with classification algorithms. This research applies 5 data mining classification algorithms namely naïve bayes, decision tree, random forest, neural network and SVM. From the modeling results of the five algorithms, the neural network algorithm has the highest accuracy of 86.81%.
Unduhan
Referensi
Anggraini, R. A. (2023). Algoritma Naïve Bayes Dengan Backward Elimination Pada Dataset Breast Cancer. Jurnal Kajian Ilmiah, 23(1), 87–94.
Anggraini, R. A., Widagdo, G., Budi, A. S., & Qomaruddin, M. (2019). Penerapan Data Mining Classification untuk Data Blogger Menggunakan Metode Naïve Bayes. Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 7(1), 47. https://doi.org/10.26418/justin.v7i1.30211
ÇINAR, İ., KOKLU, M., & TAŞDEMİR, Ş. (2020). Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods. Gazi Journal of Engineering Sciences, 6(3), 200–209. https://doi.org/10.30855/gmbd.2020.03.03
Darmawan, R., Indra, I., & Surahmat, A. (2022). Optimalisasi Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) Pada Analisis Sentimen Terhadap Official Account Ruang Guru di Twitter. Jurnal Kajian Ilmiah, 22(2), 143–152. https://doi.org/10.31599/jki.v22i2.1130
Feng, L., Zhu, S., Zhang, C., Bao, Y., Gao, P., & He, Y. (2018). Variety identification of raisins using near-infrared hyperspectral imaging. Molecules, 23(11). https://doi.org/10.3390/molecules23112907
Guo, J., Chen, C., Chen, C., Zuo, E., Dong, B., Lv, X., & Yang, W. (2022). Near-infrared spectroscopy combined with pattern recognition algorithms to quickly classify raisins. Scientific Reports, 12(1), 1–8. https://doi.org/10.1038/s41598-022-12001-1
Khojastehnazhand, M., & Ramezani, H. (2020). Machine vision system for classification of bulk raisins using texture features. Journal of Food Engineering, 271(September 2019), 109864. https://doi.org/10.1016/j.jfoodeng.2019.109864
Maryam, M., & Ariono, H. W. (2022). Sistem Pakar Pengklasifikasi Stadium Kanker Serviks Berbasis Mobile Menggunakan Metode Decision Tree. Jurnal Kajian Ilmiah, 22(3), 267–278. https://doi.org/10.31599/jki.v22i3.1368
Pamuji, F. Y., & Ramadhan, V. P. (2021). Komparasi Algoritma Random Forest dan Decision Tree untuk Memprediksi Keberhasilan Immunotheraphy. Jurnal Teknologi Dan Manajemen Informatika, 7(1), 46–50. https://doi.org/10.26905/jtmi.v7i1.5982
Pandji, B. Y., Indwiarti, I., & Rohmawati, A. A. (2019). Perbandingan Prediksi Harga Saham dengan model ARIMA dan Artificial Neural Network. Indonesia Journal on Computing (Indo-JC), 4(2), 189–198. https://doi.org/10.21108/indojc.2019.4.2.344
Riadi, I., Umar, R., & Aini, F. D. (2019). Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm). ILKOM Jurnal Ilmiah, 11(1), 17–24. https://doi.org/10.33096/ilkom.v11i1.361.17-24
Saidi, I. A., & Wulandari, F. E. (2021). Pengeringan Sayuran Dan Buah -buahan. Umsida. https://doi.org/https://doi.org/10.21070/2019/978-602-5914-67-6
Tarakci, F., & Ozkan, I. A. (2021). Comparison of classification performance of kNN and WKNN algorithms. Selcuk University Journal of Engineering Sciences 20(02): 32-37, 2021, 20(02), 32–37.
Yajun, Z., Yang, Y., Ma, C., & Jiang, L. (2022). Identification of multiple raisins by feature fusion combined with NIR spectroscopy. Plos One. https://doi.org/https://doi.org/10.1371/journal.pone.0268979