HIGH ORDER PADA AUTOMATIC CLUSTERING FUZZY TIME SERIES UNTUK PERAMALAN JAKARTA STOCK EXCHANGE (JKSE)

RAMADHAN, BAYU CATUR and Rini, Dian Palupi and Buchari, Muhammad Ali (2021) HIGH ORDER PADA AUTOMATIC CLUSTERING FUZZY TIME SERIES UNTUK PERAMALAN JAKARTA STOCK EXCHANGE (JKSE). Undergraduate thesis, Sriwijaya University.

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Abstract

Saham adalah salah satu jenis investasi populer di era ini yang menawarkan keuntungan tinggi dengan resiko yang tinggi juga. Salah satu cara untuk meminimalkan kerugian saat investasi adalah melihat perkembangan indeks harga saham. Salah satu indeks harga saham yang tercatat di Bursa Efek Indonesia (BEI) adalah Jakarta Stock Exchange (JKSE). JKSE atau sering disebut indeks harga saham gabungan (IHSG) digunakan investor untuk mengambil keputusan dan juga sebagai indikator utama ekonomi, artinya nilai JKSE dapat digunakan untuk mengetahui kondisi perekonomian negara. Oleh karena itu dibutuhkan peramalan terhadap nilai JKSE. Salah satu metode peramalan adalah Fuzzy Time Series (FTS). Namun FTS memiliki 2 kekurangan yaitu interval yang statis dan relasi waktu yang singkat. Salah satu metode untuk mengatasi kekurangan tersebut adalah interval dinamis dan high order FTS. Penelitian ini menggunakan automatic clustering untuk membuat interval dinamis, FTS Chen sebagai metode peramalan, dan nilai Mean Absolute Percentage Error (MAPE) sebagai alat ukur error peramalan. Hasil penelitian didapatkan nilai error FTS 12.28%, FTS dan Automatic Clustering (ACFLR) 6.22%, dan High Order ACFLR 0.067%.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Automatic Clustering, Fuzzy Time Series Chen, High Order Fuzzy Time Series, Jakarta Stock Exchange, Mean Absolute Percentage Error.
Subjects: C Auxiliary Sciences of History > CB History of civilization > CB158-161 Forecasts of future progress
H Social Sciences > HF Commerce > HF5548.7-5548.85 Industrial psychology > HF5548.32.A266 Computer science. Economics. Electronic commerce.
H Social Sciences > HG Finance > HG4551-4598 Stock exchanges
T Technology > T Technology (General) > T173.2-174.5 Technological change > T174 Technological forecasting
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Bayu Catur Ramadhan
Date Deposited: 26 Apr 2023 04:04
Last Modified: 26 Apr 2023 04:04
URI: http://repository.unsri.ac.id/id/eprint/97062

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