Perbandingan Multi Algoritma Klasifikasi dan Tuning Parameter untuk Prediksi Ketergantungan Skincare Berbasis Streamlit
-
Aneira Vicentiya Kuncoro
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Laila Maulin Ni’mah Universitas Dian Nuswantoro, Semarang, Indonesia
- Edi Faisal Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
The use of skincare products in Indonesia has increased significantly along with the increasing public awareness of the importance of skincare, but this also raises indications of dependence behaviour that needs to be anticipated, especially in young age groups. This research aims to build a skincare dependency prediction system based on demographic, psychological and behavioural attributes collected through an online survey. In addition, a comparison of five classification algorithms-Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression-was conducted to determine the best model that is most accurate and efficient in predicting the dependency tendency. The data obtained was processed through normalisation and categorical feature transformation with One-Hot Encoding, then evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that the Decision Tree algorithm provided the best performance with accuracy reaching 87% and excellence in model interpretability. The model was then implemented in the form of an interactive web application based on Streamlit that allows users to make predictions independently and in real-time. The contribution of this research is the availability of a prediction system that supports education and wiser decision-making in the use of skincare, as well as opening up opportunities for the utilisation of machine learning technology for other issues.
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References
A. Arianto, J. Reveny, Nazliniwaty, and H. Bangun, Formulasi dan Teknologi Kosmetik Perawatan Kulit. Medan: USUpress (Penerbit Universitas Sumatera Utara), 2022. [Online]. Available: https://usupress.usu.ac.id
Badan Pengawas Obat dan Makanan Republik Indonesia, Peraturan Badan Pengawas Obat dan Makanan Nomor 12 Tahun 2023 tentang Pengawasan Pembuatan dan Peredaran Kosmetik. Jakarta, 2023. [Online]. Available: https://peraturan.bpom.go.id
P. Minerva, Kosmetik Perawatan Kulit (Skin Care Cosmetics), First Edition. Padang: CV. Berkah Prima, 2022. [Online]. Available: https://sinta.kemdikbud.go.id/authors/profile/6187973/?view=books
Tim Redaksi UNAIR News, “Perilaku Konsumtif Perempuan untuk Produk Perawatan Kulit,” Surabaya, Oct. 2024. [Online]. Available: https://unair.ac.id/perilaku-konsumtif-perempuan-untuk-produk-perawatan-kulit/
J. Lee, H. Yoon, S. Kim, C. Lee, J. Lee, and S. Yoo, “Deep learning-based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions,” Journal of Cosmetic Dermatology (JoCD), vol. 23, no. 6, pp. 2066–2077, Jun. 2024, doi: 10.1111/jocd.16218.
A. Annur Rohman, G. Alfa Trisnapradika, and K. Kunci, “Perbandingan Algoritma NBC, SVM, Logistic Regression untuk Analisis Sentimen Terhadap Wacana KaburAjaDulu di Media Sosial X,” Building of Informatics, Technology and Science (BITS), vol. 7, no. 1, 2025, doi: 10.47065/bits.v7i1.7261.
I. I. Purnomo and A. Andie, “Perancangan Sistem Evaluasi Tingkat Kompetensi Alumni dengan Algoritma Support Vector Machine (SVM) Linear Method Berbasis Framework Streamlit,” Jurnal Informatika Universitas Pamulang, vol. 8, no. 3, pp. 407–412, Sep. 2023, doi: 10.32493/informatika.v8i3.33766.
T. Ayhan and T. Uçar, “Determining customer limits by data mining methods in credit allocation process,” International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 2, pp. 1910–1915, Apr. 2022, doi: 10.11591/ijece.v12i2.pp1910-1915.
I. T. Ali, Y. Rahayu, and A. Setiawan, “Web Application Based on Machine Learning for Diabetes Detection using Microstrip Resonator and Streamlit,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI), vol. 6, no. 3, pp. 2656–8624, 2024, doi: 10.35882/ijeeemi.v6i3.2.
R. S.P., S. M., and J. R., “Streamlit-based Web Application for Parkinson’s Detection using Machine Learning,” Journal of Artificial Intelligence and Capsule Networks (JAICN), vol. 6, no. 4, pp. 466–478, Jan. 2025, doi: 10.36548/jaicn.2024.4.006.
I. T. Rahmawati and D. Alita, “Perbandingan Algoritma SVM, Random Forest, KNN untuk Analisis Sentimen Terhadap Overclaim Skincare pada Media Sosial X,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6782.
P. Aulia Azhar, M. Arya Pratama, and R. Fitriani, “Prediksi Harga Mobil Audi Bekas Menggunakan Model Regresi Linear dengan Framework Streamlit,” Journal of Technology and Informatics (JoTI), vol. 6, no. 1, pp. 22–28, Oct. 2024, doi: 10.37802/joti.v6i1.763.
Y. Yadin and D. Ayu Megawaty, “Perbandingan Algoritma K-Nearest Neighbor dan Support Vector Machine Pada Pengenalan Pola Tanda Tangan Digital,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6982.
G. Ayu Syafarina and I. Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, “Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi,” METIK JURNAL, vol. 7, no. 2, p. 2023, 2023, doi: 10.47002/metik.v7i2.680.
E. Arya Pranata, F. Budiman, and D. Kurniawan, “Analisis Sentimen Ulasan Mobile JKN pada Playstore dengan Perbandingan Akurasi Algoritma Naïve Bayes dan SVM,” Building of Informatics, Technology and Science (BITS), vol. 7, no. 1, 2025, doi: 10.47065/bits.v7i1.7334.
H. Hilmi Zain, R. Maulana Awannga, and W. Isti Rahayu, “Perbandingan Model Svm, Knn Dan Naïve Bayes Untuk Analisis Sentiment Pada Data Twitter: Studi Kasus Calon Presiden 2024,” JIMPS: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah, vol. 8, no. 3, pp. 2083–2093, 2024, doi: 10.24815/jimps.v8i3.25342.
D. Hartama et al., “Perbandingan Algoritma Decision Tree, ID3, dan Random Forest dalam Klasifikasi Faktor-Faktor yang Mempengaruhi Karier Mahasiswa Ilmu Komputer,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK), vol. 6, no. 1, 2025, doi: https://doi.org/10.35870/jimik.v6i1.1113.
R. Sukma Andarujaya and R. R. Suryono, “Perbandingan Kinerja Algoritma Random Forest, KNN, dan SVM dalam Analisis Sentimen Cryptocurrency,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6572.
N. G. Ramadhan and A. Khoirunnisa, “Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine,” Jurnal Media Informatika Budidarma (MIB), vol. 5, no. 4, p. 1580, Oct. 2021, doi: 10.30865/mib.v5i4.3347.
L. Kencono and D. Darwis, “Perbandingan Algoritma NBC, SVM dan Random Forest untuk Analisis Sentimen Implementasi Starlink pada Media Sosial X,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6813.
H. D. Purnomo, T. Gonsalves, E. Mailoa, F. Y. Santoso, and M. R. Pribadi, “Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network,” Jurnal Rekayasa Sistem dan Teknologi Informasi (RESTI), vol. 8, no. 3, pp. 340–345, Jun. 2024, doi: 10.29207/resti.v8i3.5730.
J. Rusman, B. Z. Haryati, and A. Michael, “Optimisasi Hiperparameter Tuning pada Metode Support Vector Machine untuk Klasifikasi Tingkat Kematangan Buah Kopi,” Jurnal Komputer dan Informatika, vol. 11, no. 2, pp. 195–202, Oct. 2023, doi: 10.35508/jicon.v11i2.12571.
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