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All Journal Jurnal Sains Matematika dan Statistika Jambura Journal of Mathematics
Yogi Anggara
UIN Sunan Kalijaga Yogyakarta
Mathematics

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Analisis Klaster dalam Pembentukan Portofolio Robust Mean-Variance Epha Diana Supandi; Yogi Anggara
Jurnal Sains Matematika dan Statistika Vol 9, No 1 (2023): JSMS Januari 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v9i1.19003

Abstract

Pembentukan portofolio adalah proses menggabungkan beberapa aset dengan tujuan menghasilkan return tertinggi pada tingkat risiko terendah. Portofolio optimal model Mean-Variance (MV) sangat sensitif terhadap keberadaan outlier. Salah satu cara untuk mengatasi kelemahan portofolio MV adalah dengan menggunakan estimasi robust. Data penelitian menggunakan saham-saham yang terdaftar di Jakarta Islamic Index (JII) dimana pada tahap awal digunakan teknik clustering dengan metode K-Means. Hasil analisis kelompok terbentuk dua klaster, dimana klaster pertama terdiri dari saham ITMG, ADRO, PTBA, dan MDKA sedangkan klaster kedua terdiri dari saham INDF, TLKM, KLBF, dan UNTR. Hasil analisis kinerja saham menunjukkan bahwa klaster pertama model portofolio klasik Obj-10 paling baik karena memiliki sharpe ratio tertinggi. Sedangkan pada klaster kedua portofolio robust model Obj-100 paling baik
Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index Yogi Anggara; Arif Munandar
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20407

Abstract

RNN is a type of artificial neural network used to handle problems that require sequential data processing. ANFIS is a method that combines the advantages of fuzzy logic and artificial neural networks to create a system, so can adapt the parameters it uses according to the obtained data so that it can build an automated inference system. In this research, we make combination of RNN in ANFIS, which makes ANFIS able to accept input in the form of time series data so that ANFIS can recognize patterns contained in the time series data and its suitable for forecasting cases in the Jakarta Islamic Index. The membership functions used are three Gaussian functions. The results of the RNN-ANFIS Hybrid model training provide an interpretation that the first membership function represents the trend change indicator value, the second membership function represents the closing price change value in the last eight days, and the third membership function represents the pattern change value in the trend. The model for the Jakarta Islamic Index provides very good predictions with an MSE value of 0.001 and an MAE of 0.246.
Co-Authors Arif Munandar Epha Diana Supandi