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Penggunaan Hybrid K-Means dan General Regression Neural Network untuk Prediksi Harga Saham Indeks LQ45

Authors

  • Gilland Achyar Matematika, Universitas Islam Bandung
  • Onoy Rohaeni Matematika, Universitas Islam Bandung

DOI:

https://doi.org/10.29313/jrm.v2i2.1193

Keywords:

Saham Indeks LQ45, Prediksi, K-Means, General Regression Neural Network

Abstract

Abstract. General Regression Neural Network (GRNN) is a nonparametric method of developing the concept of an artificial neural network. The GRNN operation is based on the estimated expected output value determined by the input set. One of the characteristics of GRNN is that the number of neurons in the pattern layer will increase with the amount of training data. This problem can be solved with K-means. The K-means method in this study aims to obtain various groups of training data based on similar characteristics so that it is easier for GRNN to study data in a group and reduce the problem of network complexity and large computations. One of the implementations of hybrid K-means & GRNN is to predict the price of the LQ45 stock index. The LQ45 stock price index is a combination of 45 stock members with high liquidity. One of the efforts before market participants make a decision to invest is to predict the stock index in the future to understand the investment prospects of a company in the future so as to reduce the risk for investors in investing. The results of this study indicate that the K-Means & GRNN hybrid model has a MAPE value of 0.943%. The results of the LQ45 index stock price prediction for the next period show the LQ45 index price of Rp1,002,28.

Abstrak. General Regression Neural Network (GRNN) merupakan metode nonparametrik dari pengembangan konsep jaringan syaraf tiruan. Operasi GRNN didasarkan pada estimasi nilai harapan output ditentukan oleh himpunan input. Salah satu karakteristik dari GRNN adalah jumlah neuron pada pattern layer akan bertambah seiring meningkatnya jumlah data pelatihan. Permasalahan tersebut dapat diatasi dengan K-means. Metode K-means pada penelitian ini bertujuan untuk mendapatkan berbagai kelompok data pelatihan yang dikelompokkan berdasarkan karakteristik yang serupa sehingga GRNN lebih mudah mempelajari data dalam suatu kelompok serta mengurangi masalah kompleksitas jaringan dan jumlah komputasi yang besar. Salah satu implementasi hybrid K-means & GRNN adalah memprediksi harga saham indeks LQ45. Harga saham indeks LQ45 merupakan gabungan dari 45 anggota saham dengan likuiditas yang tinggi. Salah satu upaya sebelum pelaku pasar mengambil keputusan untuk berinvestasi adalah memprediksi nilai indeks saham pada waktu yang akan datang untuk memahami prospek investasi saham sebuah perusahaan pada masa yang akan datang sehingga mengurangi resiko bagi investor dalam berinvestasi. Hasil dari penelitian ini menunjukkan bahwa model hybrid K-Means & GRNN memiliki nilai MAPE sebesar 0.943%. Hasil prediksi harga saham Indeks LQ45 untuk periode selanjutnya menunjukkan harga indeks LQ45 sebesar Rp1.002,28.

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Published

2022-12-20