Klasifikasi Kunyit dan Temulawak dengan VGG16 dan Fuzzy Tsukamoto Berbasis Android

Muhammad Rizki Setyawan; Fajar Rahardika Bahari Putra; Ahmad Ilham; Dimas Adi Suseno

doi:10.30865/jurikom.v12i3.8696

Authors

Muhammad Rizki Setyawan Universitas Muhammadiyah Sorong, Sorong
Fajar Rahardika Bahari Putra Universitas Muhammadiyah Sorong, Sorong https://orcid.org/0009-0008-4086-060X
Ahmad Ilham Universitas Muhammadiyah Sorong, Sorong
Dimas Adi Suseno Universitas Muhammadiyah Sorong, Sorong

DOI:

https://doi.org/10.30865/jurikom.v12i3.8696

Keywords:

Turmeric, Temulawak, VGG-16, Fuzzy_Tsukamoto, Android

Abstract

Indonesia has a very rich biodiversity, including various medicinal plants that are highly financially beneficial and health-promoting. Among these medicinal plants, temulawak and turmeric are the two most popular rhizomes widely used in traditional medicine as well as the herbal industry. However, because the shape and color of these two plants are very similar, it is often difficult to distinguish between them, especially for laypeople and new industry workers. This research developed an Android-based application that can effectively and accurately distinguish between temulawak and turmeric to address this issue. For this application, the Convolutional Neural Network (CNN) architecture of the VGG-16 model is used along with the Tsukamoto fuzzy method as an additional layer. The trials conducted on the developed model using test data showed an accuracy rate of 0.97, a recall value of 0.98, and an F1 score of 0.97. Meanwhile, the blackbox testing shows that this application functions stably without technical issues, making it ready for use. Additionally, blackbox testing shows that the system can function stably without any issues, making it suitable for real-world use

References

D. A. Widiyastuti, A. Ajizah, L. Nurtamara, N. Huda, and M. Afdal, “Pemberdayaan Ekonomi Skala Rumah Tangga Melalui Pembuatan Jamu Bubuk Rempah Temulawak, Jahe, Kunyit, dan Sereh,” J. Pengabdi. ILUNG (Inovasi Lahan Basah Unggul), vol. 3, no. 2, p. 369, 2023, doi: 10.20527/ilung.v3i2.10298.

A. Z. Pramesthi, F. Purnamasari, and I. Setiawan, “Eksplorasi Keanekaragaman Hayati Tanaman Konsumsi di Pekarangan Desa Sumber RW 07 Surakarta Integrasi Fungsi Pangan dan Pengobatan,” vol. 4, pp. 7865–7880, 2024, doi: https://doi.org/10.31004/innovative.v4i6.

A. Wilapangga, D. Rahmat, and R. Rachmaniar, “Formulasi dan Evaluasi Sediaan Gel Nanopartikel Ekstrak Temulawak (Curcuma xanthorrhiza) sebagai Tabir Surya,” Indones. J. Pharm. Educ., vol. 3, no. 1, pp. 26–32, 2023, doi: 10.37311/ijpe.v3i1.18854.

M. Y. Zamzam, Yuniarti Fayla, and Yossi Onirika Anggraeni, “UJI AKTIVITAS ANTIOKSIDAN KOMBINASI EKSTRAK KULIT BUAH NAGA MERAH (Hylocereus polyrhizus) DAN EKSTRAK KUNYIT(Curcuma longa L.) DENGAN METODE DPPH,” Med. Sains J. Ilm. Kefarmasian, vol. 8, no. 1, pp. 85–96, 2023, doi: https://doi.org/10.37874/ms.v8i1.546.

M. Mayasari, D. Iskandar Mulyana, M. Betty Yel, and S. Tinggi Ilmu Komputer Cipta Karya Informatika Jl Raden, “Komparasi Klasifikasi Jenis Tanaman Rimpang Menggunakan Principal Component Analiysis, Support Vector Machine, K-Nearest Neighbor Dan Decision Tree,” J. Tek. Inform. Kaputama, vol. 6, no. 2, pp. 644–655, 2022.

A. Arifin, J. Hendyli, and D. E. Herwindiati, “Klasifikasi Tanaman Obat Herbal Menggunakan Metode Support Vector Machine,” Comput. J. Comput. Sci. Inf. Syst., vol. 5, no. 1, p. 25, 2021, doi: 10.24912/computatio.v1i1.12811.

A. Effendi and Yantri Komala Dewi, “Analisis Bibliometrik Perancangan Arsitektur Dengan Kecerdasan Buatan,” SARGA J. Archit. Urban., vol. 17, no. 1, pp. 48–63, 2023, doi: https://doi.org/10.56444/sarga.v17i1.219.

G. A. Sandag and J. Waworundeng, “Identifikasi Foto Fashion Dengan Menggunakan Convolutional Neural Network (CNN),” CogITo Smart J., vol. 7, no. 2, pp. 305–314, 2021, doi: https://doi.org/10.31154/cogito.v7i2.340.305-314.

S. Salsabila, “Implementasi Algoritma Convolutional Neural Network Untuk Mendeteksi Pola Gerak Tangan Dalam Permainan Pong Ball Dengan Ai,” Method. J. Tek. Inform. dan Sist. Inf., vol. 9, no. 2, pp. 8–11, 2023, doi: https://doi.org/10.46880/mtk.v9i2.1995.

M. H. V. Sinaga, M. Albirra, and M. F. Sidiq, “Klasifikasi Gambar Pemandangan dengan Kecerdasan Buatan Berbasis CNN,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 8, no. 2, pp. 412–417, 2024, doi: https://10.35870/jtik.v8i2.1424.

C. Hu, B. B. Sapkota, J. A. Thomasson, and M. V. Bagavathiannan, “Influence of image quality and light consistency on the performance of convolutional neural networks for weed mapping,” Remote Sens., vol. 13, no. 11, 2021, doi: 10.3390/rs13112140.

A. S. Irohoto Nozomi, “PREDIKSI PRODUKSI DAN PENJUALAN MENGGUNAKAN METODE FUZZY TSUKAMOTO,” vol. 1, no. 1, pp. 469–477, 2023, doi: https://doi.org/10.62833/embistek.v3i2.

J. M. Jonson Manurung, B. S. Bosker Sinaga, P. M. H. Paska Marto Hasugian, L. Logaraj, and S. R. Sethu Ramen, “Analisis Algoritma C4.5 Dan Fuzzy Sugeno Untuk Optimasi Rule Base Fuzzy,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 5, no. 2, pp. 166–171, 2022, doi: https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v5i2.2488.

S. Bagas Valentino, “Klasifikasi Kualitas Daging Marmer Berdasarkan Citra Warna Daging Menggunakan Metode Convolutional Neural Network,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 125–129, 2023, doi: 10.36040/jati.v7i1.6128.

M. Yusuf, R. Ruimassa, A. I. Tawainella, and D. Maharani, “J-Icon : Jurnal Informatika dan Komputer KLASIFIKASI KUALITAS BERAS MENGGUNAKAN CONVOLUTIONAL NEURAL The importance of rice as a food commodity in Asia , especially in Indonesia , lies not only in its role as the main source of carbohydrates , but also in,” vol. 12, no. 2, pp. 186–192, 2024, doi: 10.35508/jicon.v12i2.18004.

A. F. R. Muh. Fajrin Bakri, Muhammad Fajar B, Gebby Indriani, “Penentuan Jumlah Produksi Roti Pada Toko Roti Kayla Menggunakan Fuzzy Logic Metode Tsukamoto,” J. Deep Learn. Comput. Vis. Digit. Image Process., vol. 2, no. 1, pp. 29–43, 2024, doi: https://doi.org/10.61255/decoding.v2i1.300.

S. Arif Dwi Saputra, Ragil Wijianto Adhi, “Pengembangan Aplikasi Tamu Wajib Lapor Di Desa Karangsalam Baturaden Berbasis Android,” Informatics Comput. Eng. J., vol. 3, no. 1, pp. 79–87, 2023, doi: https://doi.org/10.31294/icej.v3i1.1783.

I. Salamah, S. Humairoh, and S. Soim, “Implementasi Convolutional Neural Network Pada Alat Klasifikasi Kematangan dan Ukuran Buah Nanas Berbasis Android,” INOVTEK Polbeng - Seri Inform., vol. 8, no. 2, p. 243, 2023, doi: 10.35314/isi.v8i2.3413.

A. Safitri, A. Azzahra, and S. T. Kurnia, “Pengendalian Untuk Mengoptimalkan Produksi Mie Pada Warung Mie Pedas Dengan Menggunakan Logika Fuzzy Berbasis Metode Tsukamoto,” J. Deep Learn. Comput. Vis. Digit. Image Process., vol. 2, no. 1, pp. 21–28, 2024, doi: https://doi.org/10.61255/decoding.v2i1.316.

G. W. Intyanto, “Klasifikasi Citra Bunga dengan Menggunakan Deep Learning: CNN (Convolution Neural Network),” J. Arus Elektro Indones., vol. 7, no. 3, p. 80, 2021, doi: https://doi.org/10.19184/jaei.v7i3.28141.

A. Mufidatuzzainiya and M. Faisal, “Penggunaan Teknik Transfer Learning pada Metode CNN untuk Pengenalan Tanaman Bunga,” vol. 10, no. 2, pp. 195–206, 2025.

R. Soekarta, N. Nurdjan, and A. Syah, “Klasifikasi Penyakit Tanaman Tomat Menggunakan Metode Convolutional Neural Network (CNN),” Insect (Informatics Secur. J. Tek. Inform., vol. 8, no. 2, pp. 143–151, 2023, doi: doi.org/10.33506/insect.v8i2.2356.

F. R. B. Putra, M. R. Setyawan, and L. J. F. Rendra Soekarta, Nabila, “Implementasi Deep Learning Menggunakan Cnn Untuk Klasifikasi Tingkat Kematangan Buah Jeruk Berbasis Android,” vol. 8, no. 1, pp. 36–43, 2023, doi: 10.51544/jurnalmi.v9i2.5462.

M. Rizki Setyawan, “Sistem Pakar Deteksi Penyakit Kambing Menggunakan Certainty Factor Berbasis Android,” J. MAHAJANA Inf., vol. 8, no. 1, pp. 36–43, Jun. 2023, doi: 10.51544/jurnalmi.v8i1.4008.