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All Journal Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Impresi Indonesia
Karijadi, Irene
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Religion Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Pelatihan Lean Hospital di Rumah Sakit Gotong Royong Surabaya: Lean Hospital Training at Gotong Royong Hospital Surabaya Dewi, Dian Retno Sari; Karijadi, Irene; Gunawan, Ivan; Wibowo, Wahyudi; Mulyana, Ig. Jaka; Hermanto, Yustinus Budi
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 1 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i1.8265

Abstract

Hospitals should improve services to patients. The concept of Lean Management can be used as an approach to improve hospital efficiency. The purpose of the training is to provide an understanding of Lean Management in hospitals (Lean Hospital) to hospital leaders and employees and be able to identify waste. The training was conducted with partners, namely Surabaya Gotong Royong Hospital. The training methods are the presentation of Lean Hospital and group discussions. Through this training, the knowledge of leaders and employees of Gotong Royong Surabaya Hospital about Lean Hospital can be improved. In group discussions, various types of waste and the order of priority for improvement were identified. This training has been able to increase the understanding of leaders and employees of Gotong Royong Surabaya Hospital about Lean Hospital. This can be seen from the ability of participants in the discussion to identify and assess the priority number of waste and provide suggestions for improvement. This activity can be followed up with research or community service on improving efficiency at Gotong Royong Surabaya Hospital with the Lean Management approach.
Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction Karijadi, Irene; Mulyana, Ig. Jaka
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 22 No. 1 (2020): June 2020
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (562.291 KB) | DOI: 10.9744/jti.22.1.11-16

Abstract

Improving accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is difficult due to randomness and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition is employed to decompose original data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power Numerical testing demonstrated that the proposed method can accurately predict the wind power in Belgian.
Studi Penerapan Deep Learning untuk Prediksi Pergerakan Indeks Harga Saham Gabungan Karijadi, Irene; Widjaja, Yoseph; Dewi, Dian Retno Sari; Asrini, Luh Juni
Jurnal Impresi Indonesia Vol. 4 No. 12 (2025): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i12.7215

Abstract

This study aims to develop and evaluate a prediction model for the movement of the Composite Stock Price Index (JCI) using the Long Short-Term Memory (LSTM) method as one of the effective deep learning approaches in capturing nonlinear time series patterns. This study tested three model approaches, namely the Univariate model which only uses historical JCI data, the Multivariate All Feature model which integrates all external variables, and the Multivariate Selected Feature model which uses selected external variables. External variables considered include world gold prices, world oil prices, rupiah exchange rates against the United States dollar, and international stock indices that affect the Indonesian capital market. The model performance evaluation was carried out using the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) indicators. The results showed that the Multivariate All Feature model provided the best performance with a MAPE value of 0.76, an RMSE of 66.72, and an MAE of 51.58, which was consistently lower than the other two models. Significance tests using ANOVA and Tukey HSD confirmed significant performance differences between models. These findings indicate that the integration of external variables is able to significantly increase the accuracy of JCI predictions. This research is expected to be a reference for investors, capital market analysts, and researchers in the development of artificial intelligence-based decision support systems in the Indonesian stock market.
Co-Authors Asrini, Luh Juni Dian Retno Sari Dewi Ig. Jaka Mulyana Ivan Gunawan Wahyudi Wibowo Widjaja, Yoseph Yustinus Budi Hermanto