Ensemble K-Nearest Neighbors Method to Predict Composite Stock Price Index (CSPI) in Indonesia

Moh. Jusman; Nur’eni Nur’eni; Lilies  Handayani

doi:10.20956/j.v18i3.19641

Authors

Moh. Jusman Tadulako University
Nur’eni Nur’eni
Lilies Handayani

DOI:

https://doi.org/10.20956/j.v18i3.19641

Keywords:

Composite Stock Price Indeks, Ensemble K-NN, Prediction

Abstract

The Composite Stock Price Index (CSPI) is a guide for investors to see the movement of stock prices as a whole from time to time. These movements always change from time to time, so it is necessary to use analytical methods to make predictions. The method that can be used to examine this is the K-Nearest Neighbor method. The combination of the results of several K-NN predictions is an effective way to get one final prediction result, namely the method ensemble K-NN. The response variable used in this study is the Composite Stock Price Index (CSPI), while the predictor variables are the gold price, the rupiah exchange rate against the dollar, and the Dow Jones Industrial Average (DJIA) index. The data used are 52 periods. The data used for training are 39 periods and the data used for testing is 13 periods. The prediction results from the ensemble have better results than the K-NN. The prediction results from the ensemble have better results than the single K-NN. The prediction results from the method are ensemble K-NN average of 6078, 634 with a MAPE value of 7,16% including high accuracy

References

Caraka, R. E., Chen, R. C., Yasin, H., Suhartono, Lee, Y., & Pardamean, B., 2021. Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data. Indonesian Journal of Science & Technology, 243-266.

Gufron, Surarso, B., & Gernowo, R., 2019. Implementation of the K-Nearest Neighbor Method to determine the Classification of the Study Program Operational Budget in Higher Education. 1st International Conference of Health, Science & Technology (ICOHETECH) 2019, 201-204.

Gustriansyah, R., 2017. Analisis Metode Single Exponential dengan Brown Exponential Smoothing Pada Studi Kasus Memprediksi Kuantiti Penjualan Produk Farmasidi Apotek. Seminar Nasional Teknologi Informasi dan Multimedia, Vol. 5, No. 1, 7-12.

Hidayati, Y. P., 2019. Prediksi Indeks Pembangunan Manusia (IPM) dengan Menggunakan Metode Ensemble K-Nearest Neighbor (K-NN).

Jainuddin, M., 2016. Hubungan Antara Ketersediaan Buku Referensi Perpustakaan Dengan Peningkatan Minat Baca Siswa Pada Perpustakaan SMPN 17 Kendari. Jurnal Ilmu Komunikasi UHO, Vol. 1, No. 2.

Kang, S.,2021. k-Nearest Neighbor Learning with Graph Neural Networks. Mathematics, 9, 830.

Khaira, U., Utomo, P. E., Suratno, T., & Gulo, P. C., 2019. Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Autoregressive Integrated Moving Average (ARIMA). Jurnal Sains dan Sistem Informasi, Vol. 2, Vo. 2, 11-17.

Khoiri, H. A., & Arghawaty, E., 2020. Menganalisis Nilai IHSG Beserta Faktor-Faktor Yang Mempengaruhi Di Era Pandemik Covid-19. Jurnal Riset Akuntasnsi & Keuangan Dewantara, Vol. 3, No. 2, 110-121.

Kusumodestoni, R. H., & Sarwido., 2017. Komparasi Model Support Vector Machines (SVM) dan Neural Network Untuk Mengetahui Tingkat Akurasi Prediksi Tertinggi Harga Saham. JURNAL INFORMATIKA UPGRIS, Vol. 3, No. 1, 1-9.

Lusia, D. A., & Suhartono., 2013. Ensemble Method Based on Two Level ARIMAXFFNN for Rainfall Forecasting in Indonesia. International Journal of Science and Research (IJSR), Vol. 2, No. 2, 144-149.

Nurmahaludin., 2014. Analisis Perbandingan Metode Jaringan Syaraf Tiruan Dan Regresi Linear Berganda Pada Prakiraan Cuaca. Jurnal INTEKNA : Informasi Teknik dan Niaga, Vol. 14, No. 2, 102-109.

Puspitasari, I., Suparti, & Wilandari, Y., 2012. Analisis Indeks Harga Saham Gabungan (IHSG) Dengan Menggunakan Model Regresi Kernel. Jurnal Statistika Undip, Vol. 1, No. 1, 93-103.

Satriya, R. H., Santoso, E., & Sutrisno., 2018. Implementasi Metode Ensemble K-Nearest Neighbor untuk Prediksi Nilai Tukar Rupiah Terhadap Dollar Amerika. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 4, 1718-1725.

Saxena, R., Sharma, D. S., & Gupta, M., 2021. Role of K-nearest neighbour in detection of Diabetes Mellitus. Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 10, 373-376.

Sinta, D., 2014. Ensemble K-Nearest Neighbors Method to Predict Rice Price in Indonesia. Applied Mathematical Sciences, Vol. 8, No. 160, 7993-8005.

Permana, T., 2020. Perbandingan Hasil Prediksi Kredit Macet Pada Koperasi Menggunakan Algoritma KNN dan C5.0. Conference on Innovation and Application of Science and Technology (CIASTECH 2020) (pp. 737-746). Malang: Universitas Widyagama Malang.