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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Faktor Exacta STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology International Journal Multidisciplinary Science
Achmad Benny Mutiara
Gunadarma University
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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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).
A Hybrid Intelligent Framework for Stress Management: Integrating Machine Learning and Analytical Network Process for Engineering Education Systems Prameswari Rizcha Julianda; Achmad Benny Mutiara; Wahyu Kusuma Raharja; Rossi Septy Wahyuni
Journal of Applied Data Sciences Vol 7, No 4: December 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i4.1509

Abstract

Final-year engineering students frequently face severe psychological stress driven by demanding capstone projects and rigorous accreditation standards. This research proposes a novel decision-support framework to objectively evaluate, categorize, and systematically manage academic stress among engineering undergraduates. By integrating automated non-parametric machine learning with a multi-criteria network decision model, the research bridges diagnostic psychological profiling and strategic institutional intervention. Data were gathered from six hundred eleven final-year students across four engineering departments using a validated psychometric questionnaire. The automated classification engine utilizes a nearest-neighbor algorithm based on spatial Euclidean proximity to categorize individual distress severity into depression, anxiety, and stress levels without relying on rigid parametric assumptions. Subsequently, a strategic situational assessment and a multi-perspective personal performance framework were established to translate abstract psychological findings into measurable indicators. Clinical experts and academic domain specialists evaluated these indicators using an analytical network process, incorporating complex interdependencies and feedback loops to compute steady-state priority weights. Empirical results revealed that over ninety percent of respondents experienced at least moderate psychological distress. Crucially, the mathematical prioritization demonstrated that the financial perspective holds the paramount priority weight, followed by internal cognitive resilience, continuous knowledge acquisition, and external social relationships. This proves that financial stability serves as a foundational prerequisite for academic recovery and intellectual execution. The primary contribution of this research lies in converting static psychometric evaluations into a dynamic, clinically validated decision-support tool. Higher education institutions and academic counselors can implement this prioritized framework to allocate institutional resources effectively, design targeted financial relief initiatives, and optimize student well-being alongside academic excellence.
Co-Authors Adam Huda Nugraha Adang Suhendra Aris Gunaryati Aris Gunaryati Asep Juarna Dewi Agushinta R. Diana Ikasari Ernastuti Ernastuti Hustinawaty Hustinawaty, Hustinawaty Millati Izzatillah Prameswari Rizcha Julianda Prihandoko . Rifiana Arief Rina Refianti Rina Refianti Rossi Septy Wahyuni Ryan Arya Putra Sayidati Karima Septi Andryana Sulistyo Puspitodjati Teddy Mantoro Tubagus Maulana Kusuma Wahyu Kusuma Raharja Witta Listiya Ningrum Zekri Fitra Ramadhan