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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI STATISTIKA
Nur’eni Nur’eni
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Decision Sciences, Operations Research & Management Mathematics

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Metode Ensemble K-Nearest Neighbor untuk Prediksi Indeks Harga Saham Gabungan (IHSG) di Indonesia Moh. Jusman; Nur’eni Nur’eni; Lilies Handayani
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 3 (2022): MAY, 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i3.19641

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
Peramalan Curah Hujan di Kota Makassar dengan Menggunakan Metode SARIMAX Nur Hazimah Latief; Nur’eni Nur’eni; Iman Setiawan
Statistika Vol. 22 No. 1 (2022): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v22i1.990

Abstract

ABSTRAK Peramalan adalah memprediksi kejadian yang akan datang dengan melihat data dari masa lalu. Salah satu metode peramalan yaitu ARIMA yang dibedakan menjadi 2 yaitu ARIMA non-musiman dan ARIMA musiman. Penelitian ini menggunakan metode ARIMA musiman yang dikembangkan untuk mengatasi keterbatasan pada metode tersebut yang dikenal dengan SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogeneous Input) dengan menganalisis curah hujan di Kota Makassar dengan variabel eksogen yaitu suhu udara. Hasil yang dipakai dari penelitian ini adalah mendapatkan model SARIMAX yaitu SARIMAX (2,0,2)(1,0,0)12 dengan persamaan Zt = 0,5552Zt-12 - 0,2097Zt-1 + (0,2097)(0,5552)Zt-13 + 0,6135Zt-2 - (0,6135)(0,5552)Zt-14 + et - 0,614et-2 – 0,3859et-2 – 194,883X1t dengan hasil peramalan curah hujan di Kota Makassar Januari sampai Desember 2021 yaitu 868 mm3, 985 mm3, 848 mm3, 848 mm3, 731 mm3, 829 mm3, 868 mm3, 829 mm3, 712 mm3, 614 mm3, 790 mm3 dan 926 mm3 dimana terjadi kenaikan curah hujan tahun sebelumnya dengan curah hujan terendah terjadi pada bulan Oktober 2021 sebesar 614 mm3 dan terbanyak terjadi pada bulan Februari 2021 sebesar 985 mm3 dengan nilai MAPE sebesar 17,75%. ABSTRACT Forecasting is predicting data events from the future by looking at data from the past. One of the forecasting methods is ARIMA which is divided into 2, namely non-seasonal ARIMA and seasonal ARIMA. This study uses the seasonal ARIMA method which was developed to overcome the limitations of the method known as SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogeneous Input) by analyzing rainfall in Makassar City with an exogenous variable, namely air temperature. The purpose of this study is to obtain the results of forecasting rainfall in 2021. The results obtained are the SARIMAX model (2.0,2)(1,0,0)12 with the lowest rainfall forecasting results in Makassar City occurring in October 2021 at 614 mm3 and the most occurred in February 2021 at985 mm3 with a MAPE value of 17.75%.
Co-Authors Iman Setiawan Lilies Handayani Moh. Jusman Nur Hazimah Latief