OPTIMALISASI PENGGUNAAN SOFTWARE PALISADE DECISIONTOOLS SUITE VERSI 8.1.1 UNTUK ANALISIS PERAMALAN HARGA SAHAM YANG DIPERDAGANGKAN DI BURSA EFEK INDONESIA

GRACE KEZIA R T, Ir. Hari Agung Yuniarto, ST., M.Sc., Ph.D., IPU, ASEAN Eng.

2022 | Skripsi | S1 TEKNIK INDUSTRI

Berdasarkan data dari Bursa Efek Indonesia tahun 2021, volume transaksi Indeks Harga Saham Gabungan (IHSG) mengalami penurunan, mencakup industri properti yang menurun 33% dan industri barang konsumsi hingga 9.96%. Kontradiktif dengan ini, jumlah investor baru yang muncul sejak tahun 2020 mengalami peningkatan sebesar 92.99% (Kustodian Sentral Efek Indonesia, 2021). Oleh karena itu, perlu dilakukan penelitian terkait proses peramalan harga saham yang dapat membantu investor muda melakukan prediksi harga saham dengan lebih efisien. Pada penelitian ini dilakukan optimalisasi penggunaan software Palisade DecisionTools Suite versi 8.1.1 dengan tujuan melihat kemampuan software dalam peramalan dan membandingkan akurasi metode yang tersedia pada software terhadap standar akurasi yang dipaparkan oleh Lewis (1982). Lewis (1982) menyampaikan standar akurasi metode peralaman berdasarkan metrik MAPE dapat dikatakan sangat akurat jika bernilai minimal 10%. Peramalan dilakukan menggunakan metode distribution fitting, moving average, exponential smoothing, dan neural network terhadap harga penutupan saham mingguan emiten indeks LQ45 selama Januari 2015 - Desember 2019. Hasil peramalan menunjukkan bahwa semua metode tersedia pada software Palisade DecisionTools Suite versi 8.1.1 sudah sangat akurat dengan nilai rata-rata MAPE metode distribution fitting, moving average, exponential smoothing, dan neural network lebih kecil dari 10% dan metode neural network memiliki akurasi terbaik dengan nilai MAPE terkecil, yaitu 6,41%.

Based on data from the Indonesia Stock Exchange in 2021, the transaction volume of the Composite Stock Price Index has decreased, including property industry which decreased by 33% and consumer goods industry by 9.96%. Contrary to this, the number of new investors emerged since 2020 increased by 92.99% (Indonesian Central Securities Depository, 2021). Therefore, it is necessary to conduct research related to the stock price forecasting process that can help young investors make stock price predictions efficiently. In this study, optimization of the use of Palisade DecisionTools Suite software version 8.1.1 was carried out with the aim of seeing the ability of the software in forecasting and comparing the accuracy of the methods available in the software to the accuracy standard described by Lewis (1982). He conveyed that the standard accuracy of forecasting methods based on the MAPE can be said very accurate if it has minimum value of 10%. Forecasting is done using distribution fitting, moving average, exponential smoothing, and neural network methods on the weekly closing price of LQ45 index issuers during January 2015 - December 2019. Forecasting results show that all methods available in Palisade DecisionTools Suite software version 8.1.1 are very accurate with the average MAPE value less than 10% and the neural network method having the best accuracy. with the smallest MAPE value, which is 6.41%.

Kata Kunci : Peramalan Harga Saham, Palisade DecisionTools Suite versi 8.1.1, Distribution Fitting, Moving Average, Exponential Smoothing, Neural network