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All Journal Journal of Applied Data Sciences Jurnal Sistem Informasi
Sulistyo Puspitodjati
Gunadarma University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Integration of Webqual Method to Importance Performance Analysis and Kano Model to Analyze System Quality of E-Government: Case Study LAPOR! Fiena Rindani; Sulistyo Puspitodjati
Jurnal Sistem Informasi Vol. 16 No. 2 (2020): Jurnal Sistem Informasi (Journal of Information System)
Publisher : Faculty of Computer Science Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (643.26 KB) | DOI: 10.21609/jsi.v16i2.937

Abstract

The management of public complaints in many institutions in Indonesia has not been managed effectively and integrated. Each institution works partially and not coordinated well. This causes duplication of handling complaint, or worse is a complaint possibly not handled by any of institution, due to the reason that the complaint is not their authority. For this reason, the Government of the Republic of Indonesia established a S4N-LAPOR!. Meanwhile, there are still some deficiencies in LAPOR. This research purpose is to analyze/evaluate LAPOR using an integration of Webqual method to Importance Performance Analysis (IPA) and Kano model. This research is only carried out by analyzing web-based LAPOR services. The questionnaires are distributed to 159 respondents. The webqual method is used to determine the dimension and attributes, while the IPA and Kano model is conducted for data analysis. The integration from the results of both data analysis shows that there are 14-attributes out of 23 that need to be the priority for LAPOR improvements.
Enhancing Sharia Stock Price Forecasting using a Hybrid ARIMA-LSTM with Locally Weighted Scatterplot Smoothing Regression Approach Aris Gunaryati; Achmad Benny Mutiara; Sulistyo Puspitodjati
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.514

Abstract

Predicting Sharia stock prices is complex because it has high volatility and non-linear data patterns. To improve the accuracy of the forecast, the right technique is needed according to the existing data pattern. One of the techniques currently developing is integrating (hybrid) two forecasting models. This study proposes a hybrid autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) model with the locally weighted scatterplot smoothing (lowess) linear regression technique. This model is designed by creating a linear regression between the actual value and the predicted results of the ARIMA and LSTM models using the Lowess technique. The dataset used here is the closing stock prices of four Indonesian Islamic banking companies. The hybrid ARIMA-LSTM model with lowess linear regression significantly outperforms the individual ARIMA and LSTM models because it produces better performance metrics, namely mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), for training and testing datasets. The proposed hybrid model effectively reduces noise, and the model can capture complex patterns in the Sharia stock price dataset, and the prediction results are more accurate. The accuracy values for training data and data testing datasets were respectively 97.6% and 98.3% (BANK. JK), 98.3% and 98.2% (BRIS. JK), 99.4% and 99.5% (BTPN. JK), and 97.7% and 99.3% (PNBS. JK).
Co-Authors Achmad Benny Mutiara Aris Gunaryati Fiena Rindani