Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor

Haerunnisa Basri; Purnawansyah Purnawansyah; Herdianti Darwis; Fitriyani Umar

doi:10.26905/jtmi.v9i2.10350

Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor

Haerunnisa Basri, Purnawansyah Purnawansyah, Herdianti Darwis, Fitriyani Umar

Abstract

The number of herbal plants in Indonesia is 30,000, but only about 1,200 plants are used in medicine. The large number of herbal plants makes it difficult for people to distinguish one type of herbal plant from another. From these conditions, this research has conducted tests to compare the performance of the K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) methods using Fourier Descriptor (FD) feature extraction on herbal plants, namely moringa (moringa oleifera) and katuk (sauropus androgynus). The amount of data used is 480 data using image conditions, namely dark and light images which are then divided into 20% testing data and 80% training data. Classification is done using the KNN method using 5 distance calculations (Euclidean, Chebyshev, Manhattan, Minkowski, and Hamming) and CNN with FD feature extraction. From the tests that have been carried out, it is found that the use of FD feature extraction for the KNN method produces the best performance on both light and dark image data. While the use of the CNN method, for dark image data, the best accuracy results are obtained with FD feature extraction and CNN. Meanwhile, for bright image data, the best performance accuracy results are obtained in the CNN method without going through feature extraction. Of these three methods, using FD and KNN feature extraction is more recommended because it produces 100% accuracy in moringa and katuk images with light and dark intensity.

Keywords

Fourier Descriptor; KNN; CNN; Herbal Leaves

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References

A. Herdiansah, R. I. Borman, D. Nurnaningsih, A. A. J. Sinlae, and R. R. Al Hakim, “KlasifikasiCitra Daun Herbal Dengan Menggunakan Backpropagation Neural Networks Berdasarkan Ekstraksi Ciri Bentuk,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 2, p. 388, Apr. 2022, doi: 10.30865/jurikom.v9i2.4066.

Haryono, K. Anam, and A. Saleh, “Autentikasi Daun Herbal Menggunakan Convolutional Neural Network dan Raspberry Pi (Authentication of Herbal Leaves Using Convolutional Neural Network and Raspberry Pi),” 2020.

Meiriyama, siska devella, and sandra mareza adelfi, “Klasifikasi Daun Herbal Fitur Bentuk Tekstur Metode KNN,” Jatisi, vol. 9, 2015.

Suastika yulia riska, laili cahyani, and muhammad imron rosadi, “Klasifikasi Mangga Berdasarkan Tulang Daun,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 2014.

F. Liantoni and H. Nugroho, “Klasifikasi Daun Herbal Menggunakan Metode Naïve Bayes Classifier Dan K-Nearest Neighbor,” SimanteC, vol. 5, no. 1, Dec. 2015.

Z. Abidin, Y. Fredyatama, P. Teknik Informasi, S. K. Tinggi Teknik Pati Jl Raya Pati-Trangkil,and P. Jawa Tengah, “Klasifikasi Daun Empon-Empo Menggunakan Metode Gray Level Co-Occurrence Matrix Dan Algoritma K-NN,” Jurnal Sains, Teknologi dan Industri, vol. 18, no. 02,pp. 261–267, 2021.

A. Anaya-Isaza and M. Zequera-Diaz, “Fourier Transform-Based Data Augmentation In Deep Learning For Diabetic Foot Thermograph Classification,” Biocybern Biomed Eng, vol. 42, no. 2, pp. 437–452, Apr. 2022, doi: 10.1016/j.bbe.2022.03.001.

L. Touiti, T. Kim, and Y. H. Jung, “Analysis of calcareous sand particle shape using fourier descriptor analysis,” International Journal of Geo-Engineering, vol. 11, no. 1, Dec. 2020, doi: 10.1186/s40703-020-00122-3.

H. Liu, T. Rashid, and M. Habes, “Cerebral Microbleed Detection Via Fourier Descriptor with Dual Domain Distribution Modeling,” in ISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings, Institute of Electrical and Electronics Engineers Inc., Apr. 2020. doi: 10.1109/ISBIWorkshops50223.2020.9153365.

S. E. Basri, D. Indra, H. Darwis, A. W. Mufila, L. B. Ilmawan, and B. Purwanto, “Recognition of Indonesian Sign Language Alphabets Using Fourier Descriptor Method,” in 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, Institute of

Electrical and Electronics Engineers Inc., Apr. 2021, pp. 405–409. doi:

1109/EIConCIT50028.2021.9431883.

F. Umar, H. Darwis, and ; Purnawansyah, “Fourier Descriptor on Lontara Scripts Handwriting Recognition,” ILKOM Jurnal Ilmiah, vol. 15, no. 1, pp. 193–200, 2023, doi: 10.33096/ilkom.v15i1.1040.193-200.

A. Huang, R. Xu, Y. Chen, and M. Guo, “Research on Multi-Label User Classification of Social Media Based on ML-KNN Algorithm,” Technol Forecast Soc Change, vol. 188, Mar. 2023, doi:10.1016/j.techfore.2022.122271.

Y. Ren, W. Wei, P. Zhu, X. Zhang, K. Chen, and Y. Liu, “Characteristics, Classification and KNN- Based Evaluation of Paleokarst Carbonate Reservoirs: A Case Study of Feixianguan Formation inNortheastern Sichuan Basin, China,” Energy Geoscience, vol. 4, no. 3, Jul. 2023, doi: 10.1016/j.engeos.2023.100156.

C. Wijaya, H. Irsyad, and W. Widhiarso, “Klasifikasi Pneumonia Menggunakan Metode K-Nearest Neighbor Dengan Ekstraksi GLCM,” 2020.

Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor

C. Paramita, E. Hari Rachmawanto, C. Atika Sari, and D. R. Ignatius Moses Setiadi, “Klasifikasi Jeruk Nipis Terhadap Tingkat Kematangan Buah Berdasarkan Fitur Warna Menggunakan K-Nearest Neighbor,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 4, no. 1, pp. 1–6, Jan. 2019,doi: 10.30591/jpit.v4i1.1267.

L. Farokhah and P. Korespondensi, “Implementasi K-Nearest Neighbor Untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 2019, doi: 10.25126/jtiik.202072608.

S. Zhang, J. Li, and Y. Li, “Reachable Distance Function for KNN Classification,” IEEE Trans Knowl Data Eng, 2022, doi: 10.1109/TKDE.2022.3185149.

A. Setiawan, “Perbandingan Penggunaan Jarak Manhattan, Jarak Euclid, dan Jarak Minkowski dalam Klasifikasi Menggunakan Metode KNN pada Data Iris,” Jurnal Sains dan Edukasi Sains, vol. 5, no. 1, pp. 28–37, May 2022, doi: 10.24246/juses.v5i1p28-37.

A. Ely Rahayu and A. Charis Fauzan, “Komparasi Jarak Euclidean dan Manhattan Pada Algoritma K-Nearest Neighbor Dalam Mendeteksi Penyakit Diabetes Mellitus,” Jurnal Sistem Komputer dan Informatika (JSON) Hal: 413−, vol. 419, no. 2, 2022, doi: 10.30865/json.v4i2.5046.

R. Kesuma Dinata, “Analisis Chebyshev Distance pada Algoritma K Nearest Neighbor dalam Sistem Klasifikasi Rumah Sakit,” 2022.

M. Azwar, S. Hidayat, F. Yudha, J. Informatika, P. Magister, and T. Industri, “Teknik Audio Forensik Dengan Metode Minkowski Untuk Pengenalan Rekaman Suara Pelaku Kejahatan,” Yogyakarta, May 2021.

A. Pratama Agustin and A. Charis Fauzan, “Implementation Of K-Nearest Neighbor With Minkowski Distance For Early Detection Of Covid-19 In CT-Scan Images Of The Lungs Abstrak,” 2022. [Online]. Available:https://ui.adsabs.harvard.edu/abs/2020arXiv200313865Y/abstract.

K. Manfield, A. Pasaribu, R. E. Saputra, and C. Setianingsih, “Sistem Informasi Monitoring Bencana Alam Dari Data Media Sosial Menggunakan Metode K-Nearest Neighbor Natural Disaster Monitoring Information System From Social Media Data Using K-Nearest Neighbor

Method,” 2021.

K. F. Margolang, M. M. Siregar, S. Riyadi, and Z. Situmorang, “Analisa Distance Metric Algoritma K-Nearest Neighbor Pada Klasifikasi Kredit Macet,” Journal of Information System Research (JOSH), vol. 3, no. 2, pp. 118–124, Feb. 2022, doi: 10.47065/josh.v3i2.1262.

Y. N. Gulo, “Penerapan Algoritma Hamming Distance Untuk Pencarian Teks Pada Aplikasi Ensiklopedia Indonesia,” JoGTC: Journal Global Tecnology Computer, vol. 1, no. 2, pp. 50–54,2022.

H. A. Abu Alfeilat et al., “Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review,” Big Data, vol. 7, no. 4. Mary Ann Liebert Inc., pp. 221–248, Dec. 01,

doi: 10.1089/big.2018.0175.

A. A. Thant and S. M. Aye, “Euclidean, Manhattan and Minkowski Distance Methods for Clustering Algorithms,” Int J Sci Res Sci Eng Technol, pp. 553–559, Jun. 2020, doi:10.32628/ijsrset2073118.

Biswaal Avijeet, “Top 10 Deep Learning Algorithms You Should Know in 2023,” Simpli learn,2023. https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm(accessed Feb. 03, 2023).

H. A. Pratiwi, M. Cahyanti, and M. Lamsani, “Implementasi Deep Learning Flower ScannerMenggunakan Metode Convolutional NeuralNetwork,” Sebatik, vol. 25, no. 1, Jun. 2021, doi:10.46984/sebatik.v25i1.1297.

N. H. Harani, C. Prianto, and M. Hasanah, “Deteksi Objek Dan Pengenalan Karakter Plat Nomor Kendaraan Indonesia Menggunakan Metode Convolutional Neural Network (CNN) Berbasis

Python,” 2019.

S. A. Dainamang, N. Hayatin, and D. R. Chandranegara, “Analisis Sentimen Media Sosial Twiiter terhadap RUU Omnibus Law dengan Metode Naive Bayes dan Particle Swarm Optimization,” Komputika : Jurnal Sistem Komputer, vol. 11, no. 2, pp. 211–218, Aug. 2022, doi:

34010/komputika.v11i2.6037.

A. Mulyanto, E. Susanti, F. Rosi, Wajiran, and B. R. Indra, “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Penerapan Convolutional Neural Network (CNN) pada Pengenalan Aksara Lampung Berbasis Optical Character Recognition (OCR) Agus Mulyanto #1 , Erlina Susanti #2 , Farli Rosi #3 , Wajiran #4 , Rohmat Indra Borman #5,” JEPIN : Jurnal Edukasi dan Penelitian Informatika, vol. 7, Apr. 2021, [Online]. Available: https://colab.research.google.com.

S. Passura Backar, H. Darwis, and W. Astuti, “Hybrid Fourier Descriptor Naïve Bayes dan CNN pada Klasifikasi Daun Herbal,” vol. 8, no. 2, 2023.

N. Khasanah, R. Komarudin, N. Afni, Y. I. Maulana, and A. Salim, “Skin Cancer Classification Using Random Forest Algorithm,” SISFOTENIKA, vol. 11, no. 2, p. 137, May 2021, doi: 10.30700/jst.v11i2.1122.

DOI: https://doi.org/10.26905/jtmi.v9i2.10350