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All Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Buletin Ilmiah Litbang Perdagangan
Dwi Intan Sulistiana
Politeknik Statistika STIS
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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MEMANFAATKAN MODEL SARIMA DAN REGRESI VEKTOR UNTUK PRAKIRAAN CURAH HUJAN BULANAN DI KOTA BANDUNG Astri Nur Innayah; Dwi Intan Sulistiana; M. Yandre Febrian; Fitri Kartiasih
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 10 No. 2 (2024)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol10.iss2.2024.1663

Abstract

As one of the largest cities in Indonesia, Bandung has varying monthly rainfall intensity. High rainfall is very dangerous for people's lives and will have an impact on various sectors such as agriculture, fisheries, tourism, and transportation. For this reason, rainfall prediction is needed as an effort for the government to make policies and the community can anticipate the possibility of high rainfall that occurs. This study compares the effectiveness of SARIMA and Support Vector Regression (SVR) models in predicting monthly rainfall objectively, with the aim of improving decision making for stakeholders. Forecasting rainfall data is carried out based on the best method of the two methods that have been compared. The results showed that the SARIMA method outperformed the SVR method in forecasting precision, as seen from the lower RMSE value of 93.2045. The results provide valuable insights into weather prediction methodologies, benefiting authorities and the public.
The Impact of Indonesian Textile Imports on Employment: Predictive Analysis with Google Trends and News Sentiment: Politeknik Statistika STIS Dwi Intan Sulistiana; Erna Nurmawati
Buletin Ilmiah Litbang Perdagangan Vol. 19 No. 1 (2025): Buletin Ilmiah Litbang Perdagangan
Publisher : IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/bilp.19.1.47-67

Abstract

The textile and textile products (TTP) industry in Indonesia is one of the import-dependent sectors. The increase in imports of the textile industry has the potential to reduce the number of workers. This study aims to identify Harmonized System (HS) codes of TTP import that correlate with the number of workers and to predict imports for those HS codes.  This research employs conventional statistical methods, including Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA, ARIMA with Exogenous (ARIMAX), SARIMAX, and Holt-Winters, as well as machine learning methods such as Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost), and ARIMA-LSTM hybrid models. The best model is the ARIMAX model, which has the lowest Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). This model utilizes the most influential variables: the rupiah exchange rate, textile production index, percentage of news articles with positive sentiment, and Google Trends Index. This study also reveals that the volume of textile imports, as classified under HS codes 56, 60, and 63, is negatively correlated with the number of workers in the textile sector. Therefore, the government should consider import control policies for this product group. This step needs to be accompanied by an increase in the production capacity and competitiveness of the domestic textile industry. Additionally, the use of Google Trends data and news sentiment can serve as an early warning system to predict import surges more quickly and accurately.
Co-Authors Astri Nur Innayah Erna Nurmawati Fitri Kartiasih M. Yandre Febrian