Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs
Keywords:
Classification, Support Vector Machine, Perceptron, Sigmoid, Work Program
Abstract
Government agencies are required to mobilize every aspect of publication which is carried out every year which must be accounted for and also carried out for each device that receives it such as assisted villages by utilizing available apbd funds in maximizing work programs designed so that they can be implemented optimally and effectively. by getting the best from all aspects of the work program implementation, of course there are important points in designing an annual work program without exception. data mining itself can help the department of population, family planning, women's empowerment and child protection in analyzing each work program design from before it is implemented onwards to look at various aspects of past data whose grouping is in the form of classification. The purpose of this study is to build a classification model with the addition of a sigmoid activation function that uses svm and perceptron to obtain a comparison value for the accuracy of the algorithm used to obtain the best working program design. The classification results are used to get the best value for classifying the best P2KBP3A work program dataset where it can be seen that the average accuracy value is 87.5%, the f1 value is 82.2%, the precision value is 80.2%, and the recall value is 87.5% so that the final result of the research results obtained a good accuracy value.
Downloads
Download data is not yet available.
References
[1] S. Tamaela, Y. Andry Lesnussa, V. Y.I. Ilwaru, and A. M. Balami, “Analysis of Support Vector Machine (SVM) Method and Simple Additive Weighting (SAW) Method in Making Decisions to Choose Specialization,” J. Mat. MANTIK, vol. 6, no. 2, pp. 104–113, 2020.
[2] S. Hartati, A. Harjoko, R. Rosnelly, I. Chandradewi, and Faizah, Performance of SVM and ANFIS for classification of malaria parasite and its life-cycle-stages in blood smear, vol. 937. Springer Singapore, 2019.
[3] N. A. Sinaga, Ramadani, K. Dalimunthe, M. S. A. A. Lubis, and R. Rosnelly, “Komparasi Metode Decision Tree, KNN, dan SVM Untuk Menentukan Jurusan Di SMK,” J. Sist. Komput. dan Inform., vol. 3, no. 2, pp. 94–100, 2021.
[4] I. Permadi and A. K. Nugroho, “Klasifikasi Citra Menggunakan Kombinasi Jaringan Syaraf Tiruan Model Perceptron dan Algoritma One vs Rest,” INOVTEK Polbeng - Seri Inform., vol. 4, no. 2, p. 193, 2019.
[5] A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 1, pp. 75–82, 2020.
[6] J. P. Pinto, S. Kelur, and J. Shetty, “Iris Flower Species Identification Using Machine Learning Approach,” 2018 4th Int. Conf. Converg. Technol. I2CT 2018, pp. 1–4, 2018.
[7] Asmita Shukla, Ankita Agarwal, Hemlata Pant, and Priyanka Mishra, “Flower Classification using Supervised Learning,” Int. J. Eng. Res., vol. V9, no. 05, pp. 757–762, 2020.
[8] C. Savas and F. Dovis, “The impact of different kernel functions on the performance of scintillation detection based on support vector machines,” Sensors (Switzerland), vol. 19, no. 23, pp. 1–16, 2019.
[9] M. W. Huang, C. W. Chen, W. C. Lin, S. W. Ke, and C. F. Tsai, “SVM and SVM ensembles in breast cancer prediction,” PLoS One, vol. 12, no. 1, pp. 1–14, 2017.
[10] K. N and R. V, “Ensemble of Multiple Kernel SVM Classifiers for Detection of Online Spam Reviews,” SSRN Electron. J., no. Icicnis, pp. 479–488, 2021.
[11] A. Bekkanti, V. P. Gunde, S. Itnal, G. Parasa, and C. Z. Basha, “Computer based classification of diseased fruit using K-means and support vector machine,” Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 1227–1232, 2020.
[12] I. M. D. Maysanjaya, “Comparative study of classification method on diagnosis of plasmodium phase,” J. Phys. Conf. Ser., vol. 1516, no. 1, 2020.
[13] M. W. Huang et al., “Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach. A Case Study on Automatic Classification of Global Terrorist Attacks,” J. Phys. Conf. Ser., vol. 16, no. 1, pp. 1–6, 2019.
[14] R. Sigit, E. Triyana, and M. Rochmad, “Cataract Detection Using Single Layer Perceptron Based on Smartphone,” ICICOS 2019 - 3rd Int. Conf. Informatics Comput. Sci. Accel. Informatics Comput. Res. Smarter Soc. Era Ind. 4.0, Proc., 2019.
[15] K. Hulliyah, “Predicting Airline Passenger Satisfaction with Classification Algorithms,” IJIIS Int. J. Informatics Inf. Syst., vol. 4, pp. 82–94, Mar. 2021.
[16] B. Yanto, J. Jufri, A. Lubis, B. H. Hayadi, and E. Armita, NST, “Klarifikasi Kematangan Buah Nanas Dengan Ruang Warna Hue Saturation Intensity (Hsi),” INOVTEK Polbeng - Seri Inform., vol. 6, no. 1, p. 135, 2021.
[17] A. Wanto et al., “Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN,” J. Phys. Conf. Ser., vol. 1255, no. 1, 2019.
[18] B. H. Hayadi, I. G. I. Sudipa, and A. P. Windarto, “Model Peramalan Artificial Neural Network pada Peserta KB Aktif Jalur Pemerintahan menggunakan Artificial Neural Network Back-Propagation,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 11–20, 2021.
[19] H. A. G. Al-kaf, K. S. Chia, and N. A. M. Alduais, “A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum,” Pet. Sci. Technol., vol. 36, no. 6, pp. 411–418, 2018.
[20] B. Yanto, B. -, J. -, and B. H. Hayadi, “Indentifikasi Pola Aksara Arab Melayu Dengan Jaringan Syaraf Tiruan Convolutional Neural Network (Cnn),” JSAI (Journal Sci. Appl. Informatics), vol. 3, no. 3, pp. 106–114, 2020.
[21] I. C. R. Drajana, “Metode Support Vector Machine Dan Forward Selection Prediksi Pembayaran Pembelian Bahan Baku Kopra,” Ilk. J. Ilm., vol. 9, no. 2, pp. 116–123, 2017.
[22] Y. U. Budiman et al., “Identifikasi Kata Benda Dan Bukan Kata Benda Menggunakan,” vol. 01, no. 5, pp. 759–768, 2022.
[23] E. Yuliani, A. N. Aini, and C. U. Khasanah, “Perbandingan Jumlah Epoch Dan Steps Per Epoch Pada Convolutional Neural Network Untuk Meningkatkan Akurasi Dalam Klasifikasi Gambar,” J. Inf. Politek. Indonusa Surakarta, vol. 5, no. 2442–7942, pp. 2–6, 2019.
[24] J. T. Samudra and P. S. Ramadhan, “Sistem Pendukung Keputusan Mencari Pelaksana Program Kerja Terbaik Menggunakan Metode MOORA,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 21, no. 1, p. 10, 2022.
[25] I. Romli and B. M. Putra, “Evaluasi Penilaian Kinerja Dalam Klasifikasi Data Mining Dengan metode Naïve Bayes,” vol. 1, no. 1, pp. 36–45, 2020.
[26] N. Ketut, M. Astuti, N. W. Utami, I. G. Putu, and K. Juliharta, “Classification of Blood Donor Data Using C4 . 5 and K-Nearest Neighbor Method ( Case Study : Utd Pmi Bali Province ),” vol. 18, no. 1, 2022.
[27] H. A. G. Al-kaf et al., “Predicting Airline Passenger Satisfaction with Classification Algorithms,” IJIIS Int. J. Informatics Inf. Syst., vol. 4, no. 1, pp. 82–94, Mar. 2021.
[28] A. A. A. Ali and S. Mallaiah, “Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3294–3300, 2022.
[29] N. Munasatya and S. Novianto, “Natural Language Processing untuk Sentimen Analisis Presiden Jokowi Menggunakan Multi Layer Perceptron,” Techno.Com, vol. 19, no. 3, pp. 237–244, 2020.
[30] A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, R. Sun, and I. Garciá-Magarinõ, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mob. Inf. Syst., vol. 2018, 2018.
[31] A. W. Putri, “Implementasi Artificial Neural Network (ANN) Backpropagation Untuk Klasifikasi Jenis Penyakit Pada Daun Tanaman Tomat,” MATHunesa J. Ilm. Mat., vol. 9, no. 2, pp. 344–350, 2021.
[32] G. Gunawan and Y. Reswan, “Desain Aplikasi Pengenalan Pola Tanda Tangan Menggunakan Metode Support Vector Machine (Svm),” J. Media Infotama, vol. 17, no. 1, pp. 8–12, 2021.
[33] U. Pujianto, I. A. E. Zaeni, and N. O. Irawan, “SVM Method for Classification of Primary School Teacher Education Journal Articles,” ICEEIE 2019 - Int. Conf. Electr. Electron. Inf. Eng. Emerg. Innov. Technol. Sustain. Futur., no. 1, pp. 324–329, 2019.
[34] S. N. Kapita, S. Mahdi, and F. Tempola, “Penilaian Pengetahuan Siswa Dengan Jaringan Syaraf Tiruan Algoritma Perceptron,” Techno J. Penelit., vol. 9, no. 1, p. 372, 2020.
[2] S. Hartati, A. Harjoko, R. Rosnelly, I. Chandradewi, and Faizah, Performance of SVM and ANFIS for classification of malaria parasite and its life-cycle-stages in blood smear, vol. 937. Springer Singapore, 2019.
[3] N. A. Sinaga, Ramadani, K. Dalimunthe, M. S. A. A. Lubis, and R. Rosnelly, “Komparasi Metode Decision Tree, KNN, dan SVM Untuk Menentukan Jurusan Di SMK,” J. Sist. Komput. dan Inform., vol. 3, no. 2, pp. 94–100, 2021.
[4] I. Permadi and A. K. Nugroho, “Klasifikasi Citra Menggunakan Kombinasi Jaringan Syaraf Tiruan Model Perceptron dan Algoritma One vs Rest,” INOVTEK Polbeng - Seri Inform., vol. 4, no. 2, p. 193, 2019.
[5] A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 1, pp. 75–82, 2020.
[6] J. P. Pinto, S. Kelur, and J. Shetty, “Iris Flower Species Identification Using Machine Learning Approach,” 2018 4th Int. Conf. Converg. Technol. I2CT 2018, pp. 1–4, 2018.
[7] Asmita Shukla, Ankita Agarwal, Hemlata Pant, and Priyanka Mishra, “Flower Classification using Supervised Learning,” Int. J. Eng. Res., vol. V9, no. 05, pp. 757–762, 2020.
[8] C. Savas and F. Dovis, “The impact of different kernel functions on the performance of scintillation detection based on support vector machines,” Sensors (Switzerland), vol. 19, no. 23, pp. 1–16, 2019.
[9] M. W. Huang, C. W. Chen, W. C. Lin, S. W. Ke, and C. F. Tsai, “SVM and SVM ensembles in breast cancer prediction,” PLoS One, vol. 12, no. 1, pp. 1–14, 2017.
[10] K. N and R. V, “Ensemble of Multiple Kernel SVM Classifiers for Detection of Online Spam Reviews,” SSRN Electron. J., no. Icicnis, pp. 479–488, 2021.
[11] A. Bekkanti, V. P. Gunde, S. Itnal, G. Parasa, and C. Z. Basha, “Computer based classification of diseased fruit using K-means and support vector machine,” Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 1227–1232, 2020.
[12] I. M. D. Maysanjaya, “Comparative study of classification method on diagnosis of plasmodium phase,” J. Phys. Conf. Ser., vol. 1516, no. 1, 2020.
[13] M. W. Huang et al., “Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach. A Case Study on Automatic Classification of Global Terrorist Attacks,” J. Phys. Conf. Ser., vol. 16, no. 1, pp. 1–6, 2019.
[14] R. Sigit, E. Triyana, and M. Rochmad, “Cataract Detection Using Single Layer Perceptron Based on Smartphone,” ICICOS 2019 - 3rd Int. Conf. Informatics Comput. Sci. Accel. Informatics Comput. Res. Smarter Soc. Era Ind. 4.0, Proc., 2019.
[15] K. Hulliyah, “Predicting Airline Passenger Satisfaction with Classification Algorithms,” IJIIS Int. J. Informatics Inf. Syst., vol. 4, pp. 82–94, Mar. 2021.
[16] B. Yanto, J. Jufri, A. Lubis, B. H. Hayadi, and E. Armita, NST, “Klarifikasi Kematangan Buah Nanas Dengan Ruang Warna Hue Saturation Intensity (Hsi),” INOVTEK Polbeng - Seri Inform., vol. 6, no. 1, p. 135, 2021.
[17] A. Wanto et al., “Forecasting the Export and Import Volume of Crude Oil, Oil Products and Gas Using ANN,” J. Phys. Conf. Ser., vol. 1255, no. 1, 2019.
[18] B. H. Hayadi, I. G. I. Sudipa, and A. P. Windarto, “Model Peramalan Artificial Neural Network pada Peserta KB Aktif Jalur Pemerintahan menggunakan Artificial Neural Network Back-Propagation,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 11–20, 2021.
[19] H. A. G. Al-kaf, K. S. Chia, and N. A. M. Alduais, “A comparison between single layer and multilayer artificial neural networks in predicting diesel fuel properties using near infrared spectrum,” Pet. Sci. Technol., vol. 36, no. 6, pp. 411–418, 2018.
[20] B. Yanto, B. -, J. -, and B. H. Hayadi, “Indentifikasi Pola Aksara Arab Melayu Dengan Jaringan Syaraf Tiruan Convolutional Neural Network (Cnn),” JSAI (Journal Sci. Appl. Informatics), vol. 3, no. 3, pp. 106–114, 2020.
[21] I. C. R. Drajana, “Metode Support Vector Machine Dan Forward Selection Prediksi Pembayaran Pembelian Bahan Baku Kopra,” Ilk. J. Ilm., vol. 9, no. 2, pp. 116–123, 2017.
[22] Y. U. Budiman et al., “Identifikasi Kata Benda Dan Bukan Kata Benda Menggunakan,” vol. 01, no. 5, pp. 759–768, 2022.
[23] E. Yuliani, A. N. Aini, and C. U. Khasanah, “Perbandingan Jumlah Epoch Dan Steps Per Epoch Pada Convolutional Neural Network Untuk Meningkatkan Akurasi Dalam Klasifikasi Gambar,” J. Inf. Politek. Indonusa Surakarta, vol. 5, no. 2442–7942, pp. 2–6, 2019.
[24] J. T. Samudra and P. S. Ramadhan, “Sistem Pendukung Keputusan Mencari Pelaksana Program Kerja Terbaik Menggunakan Metode MOORA,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 21, no. 1, p. 10, 2022.
[25] I. Romli and B. M. Putra, “Evaluasi Penilaian Kinerja Dalam Klasifikasi Data Mining Dengan metode Naïve Bayes,” vol. 1, no. 1, pp. 36–45, 2020.
[26] N. Ketut, M. Astuti, N. W. Utami, I. G. Putu, and K. Juliharta, “Classification of Blood Donor Data Using C4 . 5 and K-Nearest Neighbor Method ( Case Study : Utd Pmi Bali Province ),” vol. 18, no. 1, 2022.
[27] H. A. G. Al-kaf et al., “Predicting Airline Passenger Satisfaction with Classification Algorithms,” IJIIS Int. J. Informatics Inf. Syst., vol. 4, no. 1, pp. 82–94, Mar. 2021.
[28] A. A. A. Ali and S. Mallaiah, “Intelligent handwritten recognition using hybrid CNN architectures based-SVM classifier with dropout,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 3294–3300, 2022.
[29] N. Munasatya and S. Novianto, “Natural Language Processing untuk Sentimen Analisis Presiden Jokowi Menggunakan Multi Layer Perceptron,” Techno.Com, vol. 19, no. 3, pp. 237–244, 2020.
[30] A. U. Haq, J. P. Li, M. H. Memon, S. Nazir, R. Sun, and I. Garciá-Magarinõ, “A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms,” Mob. Inf. Syst., vol. 2018, 2018.
[31] A. W. Putri, “Implementasi Artificial Neural Network (ANN) Backpropagation Untuk Klasifikasi Jenis Penyakit Pada Daun Tanaman Tomat,” MATHunesa J. Ilm. Mat., vol. 9, no. 2, pp. 344–350, 2021.
[32] G. Gunawan and Y. Reswan, “Desain Aplikasi Pengenalan Pola Tanda Tangan Menggunakan Metode Support Vector Machine (Svm),” J. Media Infotama, vol. 17, no. 1, pp. 8–12, 2021.
[33] U. Pujianto, I. A. E. Zaeni, and N. O. Irawan, “SVM Method for Classification of Primary School Teacher Education Journal Articles,” ICEEIE 2019 - Int. Conf. Electr. Electron. Inf. Eng. Emerg. Innov. Technol. Sustain. Futur., no. 1, pp. 324–329, 2019.
[34] S. N. Kapita, S. Mahdi, and F. Tempola, “Penilaian Pengetahuan Siswa Dengan Jaringan Syaraf Tiruan Algoritma Perceptron,” Techno J. Penelit., vol. 9, no. 1, p. 372, 2020.
Published
2023-03-31
How to Cite
Samudra, J., Rosnelly, R., & Situmorang, Z. (2023). Comparative Analysis of SVM and Perceptron Algorithms in Classification of Work Programs. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(2), 285-298. https://doi.org/https://doi.org/10.30812/matrik.v22i2.2479
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
