Prediksi Jumlah Perceraian Menggunakan Metode Multilayer Perceptron

Authors

  • Ikhsanul Hamdi Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Elvia Budianita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
  • Iis Afrianty Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru

DOI:

https://doi.org/10.30865/mib.v7i3.6291

Keywords:

Divorce, MLP, Model, MSE, Predictions

Abstract

Divorce is a situation when a married couple decides to end their relationship and separate legally. The increasing number of cases in divorce cases filed at the Bangkinang Religious Court every month has led to a gradual increase and decrease. This study uses the Multilayer Perceptron (MLP) method and evaluates using Mean Squared Error (MSE) to determine prediction accuracy. The data used is divorce data from the Bangkinang Religious Court from January 2014 to December 2022 collected and processed from the Religious Court office. A total of 102 data in the form of time series data. In this study using MLP which consists of three layers, namely the input layer, hidden layer, and output layer. And using architectural testing consisting of 6-7-1, 6-9-1, and 6-12-1 with learning rate parameters: 0.01, 0.03, 0.09 with a comparison of training and test data 70:30, 80:20, 90 :10. Based on the test results using MSE, the best architecture was obtained, namely by comparing data 90:10 with 6-9-1 architecture, learning rate: 0.03, Epoch: 300, Alpha fixed value: 0.1, MSE results were successfully obtained: 0.01144 and the pattern of the number of splits from January until May 2023 has decreased, thus, this MLP can provide predictive results that help in predicting the number of divorces.

References

N. Osela, “Komunikasi Persuasif Mediator dalam Perkara Perceraian di Pengadilan Agama Bangkinang,†Jom Fisip, vol. 6, no. 2, pp. 1–15, 2019.

F. L. Limbong, H. Ismi, and U. Hasanah, “Akibat Hukum Perceraian Terhadap Pembagian Harta Bersama Berdasarkan Kompilasi Hukum Islam Di (Kelurahan Tanah …,†J. Online Mhs. Bid. Ilmu Huk., vol. V, no. April 2018, pp. 1–23, 2018.

N. Y. A, “A ‘missing’ family of classical orthogonal polynomials,†Jom Fisip, vol. 44, no. 8, pp. 1–8, 2018, doi: 10.1088/1751-8113/44/8/085201.

H. Mukhtar, R. Muhammad, T. Reny Medikawati, and Yoze Rizki, “Peramalan Kedatangan Wisatawan Mancanegara Ke Indonesia Menurut Kebangsaan Perbulannya Menggunakan Metode Multilayer Perceptron,†J. CoSciTech (Computer Sci. Inf. Technol., vol. 2, no. 2, pp. 113–119, 2021, doi: 10.37859/coscitech.v2i2.3324.

K. Khoirudin, D. Nurdiyah, and N. Wakhidah, “Prediksi Penerimaan Mahasiswa Baru Dengan Multi Layer Perceptron,†J. Pengemb. Rekayasa dan Teknol., vol. 14, no. 1, p. 1, 2019, doi: 10.26623/jprt.v14i1.1212.

S. Sen, D. Sugiarto, and A. Rochman, “Prediksi Harga Beras Menggunakan Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM),†Ultim. J. Tek. Inform., vol. 12, no. 1, pp. 35–41, 2020, doi: 10.31937/ti.v12i1.1572.

M. Resha and S. Syamsu, “Prediksi Penyebaran Kasus Tuberkulosis dengan metode Artificial Neural Network dan Multi-Layer Perceptron di kota makassar,†JNSTA J. Nat. Sci. Technol. ADPERTISI, vol. 2, no. 1, 2021.

P. Githa Pratiwi, I. Ketut Gede Darma Putra, and D. Purnami Singgih Putri, “Peramalan Jumlah Tersangka Penyalahgunaan Narkoba Menggunakan Metode Multilayer Perceptron,†J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), vol. 7, no. 2, p. 143, 2019, doi: 10.24843/jim.2019.v07.i02.p06.

M. A. B. Ferdinand, A. P. Wibawa, I. A. E. Zaeni, and H. A. Rosyid, “Single Exponential Smoothing-Multilayer Perceptron Untuk Peramalan Pengunjung Unik Jurnal Elektronik,†Mob. Forensics, vol. 2, no. 2, pp. 62–70, 2020, doi: 10.12928/mf.v2i2.2034.

G. Asgaruning and A. Primajaya, “Jaringan Syaraf Tiruan Untuk Prediksi Daerah Rawan Banjir Studi Kasus Kabupaten Karawang,†JOINS (Journal Inf. Syst., vol. 6, no. 2, pp. 153–161, 2021, doi: 10.33633/joins.v6i2.4577.

N. Nafiiyah, “Rice Price Prediction System Based on Rice Quality and Milling Level using Multilayer Perceptron,†J. Inform. Univ. Pamulang, vol. 7, no. 1, pp. 39–43, 2022.

Y. M. Nimas R, “Penerapan Multilayer Perceptron Untuk Identifikasi Kanker Payudara,†JCI J. Cakrawala Ilm., vol. 2, no. 8, pp. 3261–3268, 2023, doi: 10.31862/9785426311961.

I. Mp et al., “Implementasi Multilayer Perceptron Untuk Memprediksi Harapan Hidup Pada Pasien Penyakit Kardiovaskular,†J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, pp. 57–68, 2022.

Y. Shara Lubis, A. Marwan Elhanafi, and H. Dafitri, “Implementasi Root Mean Square Error Untuk Melakukan PrediksiHarga Emas Dengan Menggunakan Algoritma Multilayer Perceptron,†Snastikom, vol. 8, pp. 2–6, 2021.

P. A. Nugroho, “KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Implementasi Jaringan Syaraf Tiruan Multi-Layer Perceptron Untuk Prediksi Penyinaran KOMPUTA : Jurnal Ilmiah Komputer dan Informatika,†vol. 12, no. 1, pp. 82–90, 2023.

J. R. Saragih, D. Hartama, and A. Wanto, "JIF : Jurnal Ilmiah Informatika Prediksi Produksi Susu Segar Di Indonesia Menggunakan Algoritma Backpropagation, JIF : Jurnal Ilmiah Informatika†vol. 8, no. 01 2020, doi:10.33884/jif.v8i01.1847.

R. Damanik, M. B. Sirait, S. Yolanda, E. Ketaren, I. P. Sinaga, and M. Harahap, “Diagnosa Penyakit Kulit Pada Anjing Dengan Algoritma Multilayer Perceptron,†J. Mahajana Inf., vol. 4, no. 2, pp. 50–56, 2019.

S. Anwar, D. A. Kurnia, A. Faqih, and S. R. Sari, “Prediksi Hasil Belajar Hybrid Menggunakan Artificial Neural Network Dengan Multilayer Perceptron,†JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 5, p. 1591, 2022, doi: 10.30865/jurikom.v9i5.5024.

I. H. Harahap, E. Budianita, and I. Afrianty, “Penerapan Algoritma Jaringan Syaraf Tiruan Backpropagation Untuk Prediksi Jumlah Jamaah Pendaftar Haji Provinsi Riau,†Semin. Nas. Teknol,vol 2021, SNTIKI 13, November, pp. 32–42, 2021.

O. S. Bachri, “Forecasting Jumlah Perkara Perceraian Menggunakan Single Moving Average di Pengadilan Agama Sumber,†J. Ilm. Intech Inf. Technol. J. UMUS, vol. 1, no. 02, pp. 23–32, 2019, doi: 10.46772/intech.v1i02.67.

U. Dwi Rahayu, N. L. Chusna, and M. Fachri, “Analisis Kasus Perceraian Pada Pengadilan Negeri Bekasi Menggunakan Algoritma K-Means Clustering,†Ikraith-Informatika, vol. 6, no. 1, pp. 165–172, 2022.

R. , L. Y. D. Ainun Nur, “Implementasi Model JST Dalam Menentukan Bantuan Langsung Tunai Menggunakan Algortima Multilayer Perceptron Pada Desa Karang Anyar Kec. Aek Kuo,†Snastikom, Vol. 1 No. 1, 2021.

I. E. Widodo, A. Handojo, and S. Halim, “Aplikasi Pemetaan Penyakit Demam Berdarah di Surabaya dengan Metode Neural Network Multilayer Perceptron,†J. Infra, vol. 8, no. 1, 2020.

Zulastri, I. Afrianty, E. Budianita, and F. Syafria, “Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian,†Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1188–1195, 2022.

A. Lasarudin and R. Maku, “Prediksi Pertumbuhan Jumlah Penduduk Menggunakan Algoritma Neural Network,†J. Ilmu Komput., vol. 2, no. 2, p. 37, 2022, doi: 10.31314/juik.v2i2.1715.

H. Putra and N. Ulfa Walmi, “Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation,†J. Nas. Teknol. dan Sist. Inf., vol. 6, no. 2, pp. 100–107, 2020, doi: 10.25077/teknosi.v6i2.2020.100-107.

Ni Kadek Ary Indah Suryani, Oka Sudana, and Ayu Wirdiani, “Forecasting Pneumonia Toddler Mortality Using Comparative Model ARIMA and Multilayer Perceptron,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 4, pp. 528–537, 2022, doi: 10.29207/resti.v6i4.4106.

Downloads

Published

2023-07-31

Issue

Vol. 7 No. 3 (2023): Juli 2023

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).