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All Journal dCartesian: Jurnal Matematika dan Aplikasi Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Kubik Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Ekonomi dan Studi Pembangunan (Journal of Economics and Development Studies) Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Abdi Insani Indonesian Journal of Data and Science Jurnal Sains dan Edukasi Sains SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Jurnal Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS ADPEBI International Journal of Business and Social Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Akuntansi dan Keuangan Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Pendidikan Indonesia (Japendi) Jurnal Kedokteran STM (Sains dan Teknologi Medik) Eduvest - Journal of Universal Studies Multifinance KISA INSTITUE : Journal of Economics, Accounting, Business, Management, Engineering and Society Adpebi International Journal of Multidisciplinary Sciences d'Cartesian: Jurnal Matematika dan Aplikasi SJME (Supremum Journal of Mathematics Education)
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Journal : JAMBURA JOURNAL OF PROBABILITY AND STATISTICS

Prediksi Laju Inflasi dengan Metode Long Short-Term Memory (LSTM) Berdasarkan Data Laju Inflasi dan Pengeluaran Kota Ternate masipupu, Frangky Aristiadi; setiawan, Adi; Susanto, Bambang
Jambura Journal of Probability and Statistics Vol 6, No 1 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i1.30627

Abstract

Inflation is one of the main indicators that reflect the economic stability of a region. Ternate City, as one of the cities in North Maluku Province, exhibits fluctuating inflation dynamics from year to year. This study aims to forecast the inflation rate in Ternate using the Long Short-Term Memory (LSTM) method, which is a neural network architecture well-suited for processing time series data. The data used consists of monthly Consumer Price Index (CPI) figures for Ternate from 2016 to 2023, obtained from the Central Bureau of Statistics (BPS). The LSTM model was trained using monthly CPI changes as the basis for calculating inflation. The model evaluation results show a Root Mean Square Error (RMSE) of 0.9275, Mean Absolute Error (MAE) of 0.8369, and Mean Absolute Percentage Error (MAPE) of 20.13%. These results indicate that the LSTM model performs well in forecasting inflation in Ternate City and can be utilized as a decision-support tool in regional economic planning and policymaking.   
PERBANDINGAN HASIL PERAMALAN JUMLAH WISATAWAN MANCANEGARA DENGAN METODE BOX-JENKINS DAN EXPONENTIAL SMOOTHING SARI, EMMA NOVITA; SUSANTO, BAMBANG; SETIAWAN, ADI
Jambura Journal of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i1.9181

Abstract

Forecasting the number of tourist visits is needed by tourism businesses to provide an overview of the number of tourists in the future so that problems that might occur can be overcome properly. This study aims to compare the results of forecasting the number of foreign tourists using the Box-Jenkins and Exponential Smoothing methods. There are two data used, namely data on the number of foreign visitors visiting Indonesia from January 2008 to December 2017 (Data I) and Bali according to the entrance of Ngurah Rai Airport from January 2009 to March 2020 (Data II). The best forecast results are obtained by comparing the Root of Mean Square Error (RMSE) values. The comparison of forecasting results in Data I shows that the Holt-Winters Exponential Smoothing method is more appropriate to predict the number of foreign tourists visiting Indonesia because it has a smaller RMSE value. While, the results of forecasting periods 2 and 3 in Data II show results that are far different from the original data. After tracking, it turns out this is caused by an unexpected factor, the Covid-19 pandemic which caused the number of tourists to drop significantly during this period.