Jaringan Syaraf Tiruan dengan Metode Learning Vector Quantization (LVQ) dalam Menentukan Klasifikasi Jenis Tilang Berdasarkan Kendaraan

Winda Usman, Irfan Sudahri Damanik, Jaya Tata Hardinata

Abstract


Learning Vector Quantization (LVQ) is a method of classifying patterns in which each output represents a particular category or class. The author uses the LVQ method to classify the type of ticket based on the vehicle that his research had previously conducted at the Simalungun District Prosecutor's Office. In this study, the author provides a solution to facilitate the filing of ticket data in the Simalungu District Prosecutor's Office, so that the data that has existing tickets can be classified according to their respective types. The results of this study indicate that LVQ is able to classify with an accuracy rate of 76.0%.

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References


A. Wanto and A. P. Windarto, “Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation,” Jurnal & Penelitian Teknik Informatika Sinkron, vol. 2, no. 2, pp. 37–44, 2017.

A. Wanto, A. P. Windarto, D. Hartama, and I. Parlina, “Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density,” International Journal Of Information System & Technology, vol. 1, no. 1, pp. 43–54, 2017.

A. Wanto, M. Zarlis, Sawaluddin, and D. Hartama, “Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves in the Predicting Process,” Journal of Physics: Conference Series, vol. 930, no. 1, pp. 1–7, 2017.

S. P. Siregar and A. Wanto, “Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting),” International Journal Of Information System & Technology, vol. 1, no. 1, pp. 34–42, 2017.

J. R. Saragih, M. Billy, S. Saragih, and A. Wanto, “Analisis Algoritma Backpropagation Dalam Prediksi Nilai Ekspor (Juta USD),” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 15, no. 2, pp. 254–264, 2018.

E. Hartato, D. Sitorus, and A. Wanto, “Analisis Jaringan Saraf Tiruan Untuk Prediksi Luas Panen Biofarmaka di Indonesia,” Jurnal semanTIK, vol. 4, no. 1, pp. 49–56, 2018.

S. Setti and A. Wanto, “Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World,” JOIN (Jurnal Online Informatika), vol. 3, no. 2, pp. 110–115, 2018.

R. E. Pranata, S. P. Sinaga, and A. Wanto, “Estimasi Wisatawan Mancanegara Yang Datang ke Sumatera Utara Menggunakan Jaringan Saraf,” Jurnal semanTIK, vol. 4, no. 1, pp. 97–102, 2018.

A. A. Fardhani, D. Insani, N. Simanjuntak, and A. Wanto, “Prediksi Harga Eceran Beras Di Pasar Tradisional Di 33 Kota Di Indonesia Menggunakan Algoritma Backpropagation,” Jurnal Infomedia, vol. 3, no. 1, pp. 25–30, 2018.

J. Wahyuni, Y. W. Paranthy, and A. Wanto, “Analisis Jaringan Saraf Dalam Estimasi Tingkat Pengangguran Terbuka Penduduk Sumatera Utara,” Jurnal Infomedia, vol. 3, no. 1, pp. 18–24, 2018.

A. Wanto et al., “Levenberg-Marquardt Algorithm Combined with Bipolar Sigmoid Function to Measure Open Unemployment Rate in Indonesia,” in Conference Paper, 2018, pp. 1–7.

I. A. R. Simbolon, F. Yatussa’ada, and A. Wanto, “Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia,” Jurnal Informatika Upgris, vol. 4, no. 2, pp. 163–169, 2018.

S. P. Siregar, A. Wanto, and Z. M. Nasution, “Analisis Akurasi Arsitektur JST Berdasarkan Jumlah Penduduk Pada Kabupaten / Kota di Sumatera Utara,” in Seminar Nasional Sains & Teknologi Informasi (SENSASI), 2018, pp. 526–536.

A. Wanto, “Optimasi Prediksi Dengan Algoritma Backpropagation Dan Conjugate Gradient Beale-Powell Restarts,” Jurnal Teknologi dan Sistem Informasi, vol. 3, no. 3, pp. 370–380, Jan. 2018.

B. K. Sihotang and A. Wanto, “Analisis Jaringan Syaraf Tiruan Dalam Memprediksi Jumlah Tamu Pada Hotel Non Bintang,” Jurnal Teknologi Informasi Techno, vol. 17, no. 4, pp. 333–346, 2018.

M. A. P. Hutabarat, M. Julham, and A. Wanto, “Penerapan Algoritma Backpropagation Dalam Memprediksi Produksi Tanaman Padi Sawah Menurut Kabupaten/Kota di Sumatera Utara,” Jurnal semanTIK, vol. 4, no. 1, pp. 77–86, 2018.

Y. Andriani, H. Silitonga, and A. Wanto, “Analisis Jaringan Syaraf Tiruan untuk prediksi volume ekspor dan impor migas di Indonesia,” Register - Jurnal Ilmiah Teknologi Sistem Informasi, vol. 4, no. 1, pp. 30–40, 2018.

A. Wanto, “Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan Pada Kabupaten/Kota Di Provinsi Riau,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 5, no. 1, pp. 61–74, 2018.

I. S. Purba and A. Wanto, “Prediksi Jumlah Nilai Impor Sumatera Utara Menurut Negara Asal Menggunakan Algoritma Backpropagation,” Jurnal Teknologi Informasi Techno, vol. 17, no. 3, pp. 302–311, 2018.

A. Wanto, “Prediksi Angka Partisipasi Sekolah dengan Fungsi Pelatihan Gradient Descent With Momentum & Adaptive LR,” Jurnal Ilmu Komputer dan Informatika (ALGORITMA), vol. 3, no. 1, pp. 9–20, 2019.

N. Nasution, A. Zamsuri, L. Lisnawita, and A. Wanto, “Polak-Ribiere updates analysis with binary and linear function in determining coffee exports in Indonesia,” IOP Conference Series: Materials Science and Engineering, vol. 420, no. 12089, pp. 1–9, 2018.

A. Wanto, “Prediksi Produktivitas Jagung Indonesia Tahun 2019-2020 Sebagai Upaya Antisipasi Impor Menggunakan Jaringan Saraf Tiruan Backpropagation,” SINTECH (Science and Information Technology), vol. 1, no. 1, pp. 53–62, 2019.

B. Febriadi, Z. Zamzami, Y. Yunefri, and A. Wanto, “Bipolar function in backpropagation algorithm in predicting Indonesia’s coal exports by major destination countries,” IOP Conference Series: Materials Science and Engineering, vol. 420, no. 12089, pp. 1–9, 2018.

A. Wanto et al., “Analysis of Standard Gradient Descent with GD Momentum And Adaptive LR for SPR Prediction,” 2018, pp. 1–9.

M. Magister et al., “Efektivitas Penerapan Sanksi Denda E-Tilang Bagi Pelanggar Lalu Lintas Berdasarkan Undang-Undang Nomor 22 Tahun 2009 Tentang Lalu Lintas Dan Angkutan Jalan (Studi Di Polres Rembang),” Hukum Khaira Ummah, vol. 12, no. 4, pp. 754–766, 2017.

Y. A. Lesnussa, S. Latuconsina, and E. R. Persulessy, “Aplikasi Jaringan Saraf Tiruan Backpropagation untuk Memprediksi Prestasi Siswa SMA ( Studi kasus : Prediksi Prestasi Siswa SMAN 4 Ambon ),” Jurnal Matematika Integratif, vol. 11, no. 2, pp. 149–160, 2015.




DOI: http://dx.doi.org/10.30645/senaris.v1i0.84

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