MODEL PERAMALAN NOVEL GREY UNTUK DATA MUSIMAN TERHADAP INDEKS HARGA PRODUSEN SEKTOR JASA PENDIDIKAN - Evita Putri Apriliani, - (2022)
Model Grey pertama kali dikemukakan oleh Professor Julong dari China pada tahun 1983. Sejak saat itu, berbagai macam model dikembangkan berdasarkan model Grey oleh beberapa peneliti, masing-masing memiliki kelemahan dan kelebihannya tersendiri. Salah satu model Grey dikembangkan dengan mempertimbangkan fluktuasi tren serta pola musiman dinamakan model Novel Grey untuk data musiman. Penelitian ini menggunakan model Novel Grey untuk data musiman terhadap data Indeks Harga Produsen (IHP) sektor Jasa Pendidikan di Indonesia. Penelitian dilakukan untuk mengetahui tingkat akurasi model Novel Grey data musiman. Model digunakan untuk nilai peramalannya data Indeks Harga Produsen (IHP) sektor Jasa Pendidikan Triwulan 1 tahun 2018 hingga Triwulan 2 tahun 2022. Metode Mean Absolute Percentage Error (MAPE) beserta kategorinya digunakan untuk menentukan tingkat akurasinya. Penelitian ini memperoleh hasil peramalan yang sangat baik dengan mendapatkan nilai MAPE 0,4661% untuk data training dan 2,1404% untuk data testing, yang berada dalam kategori < 10%. The Grey model was first proposed by Professor Julong from China in 1983. Since then, several researchers have developed various models based on the Grey model, each of which has its own strengths and weaknesses. One of the Grey models developed by considering trend fluctuations and seasonal patterns is called the A Novel Grey Prediction Model for Seasonal Time Series. This study uses the A Novel Grey Prediction Model for Seasonal Time Series on the Producer Price Index (PPI) data for the Education Services Sector in Indonesia. The study was conducted to determine the accuracy of the A Novel Grey Prediction Model for Seasonal Time Series. The model is used for the forecast value of Producer Price Index (PPI) data for the Education Services sector from the 1st Quarter of 2018 to the 2nd Quarter of 2022. The Mean Absolute Percentage Error (MAPE) method and its categories are used to determine the level of accuracy. This study obtained very good forecasting results by obtaining MAPE values of 0.4661% for training data and 2.1404% for testing data, which were in the <10% category.