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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi
Arwin Halim
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Transportation Veterinary

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Stock Price Prediction Using XCEEMDAN-Bidirectional LSTM -Spline Kelvin Chen; Ronsen Purba; Arwin Halim
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.14424

Abstract

Bidirectional Long Short Term Memory (Bidirectional LSTM) is a machine learning technique with the ability to capture data context by traversing backward data to forward data and vice versa. However, the characteristics of stock data with large fluctuations, high dimensions and non-linearity become a challenge in obtaining high stock price prediction accuracy values. The purpose of this study is to provide a solution to the problem of stock data characteristics with large fluctuations, high dimensions and non-linearity by combining the Complete Ensemble Empirical Mode Decomposition With Adaptive Noise method for exogenous features (XCEEMDAN), Bidirectional Long Short Term Memory (LSTM), and Splines. The predicted data will go through normalization and preprocessing using XCEEMDAN then the XCEEMDAN decomposition results are divided into high and low frequency signals. The bidirectional LSTM handles high frequency signals and the Spline model handles low frequency signals. The test is carried out by comparing the proposed XCEEMDAN-Bidirectional LSTM-Spline model with the XCEEMDAN-LSTM-Spline model using the same parameters and changing the noise seed randomly 50 times. The test results show that the proposed model has the smallest RMSE average value of0.787213833 while model which is compared only has the smallest RMSE average value of 0.807393567.
Pelatihan Pengenalan Pemrograman Komputer pada SMA Dharma Bakti Lubuk Pakam Arwin Halim; Hernawati Gohzali; Irpan Adiputra Pardosi; Ng Poi Wong; Sunario Megawan
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 4 No. 2 (2025): Mei 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v4i2.4963

Abstract

The lack of ability of the students of SMA Dharma Bakti Lubuk Pakam in mastering computers, especially the programming field, is a significant challenge in preparing them for the digital era. In the industrial era 4.0, mastering programming skills is essential, especially the Python programming language which is widely used in data analysis, artificial intelligence, and application development. To overcome this problem, Mikroskil University organized an introduction to Python programming training through Community Service activities. The training covered both theory and hands-on practice to build a better understanding. To measure the success of the training, the pre-test and post-test were conducted as well as a quiz-based evaluation. The training results showed a significant increase in students' understanding level of 85.42%. The results of the evaluation of the level of mastery of the training material showed that 26.67% of students mastered the material well, and 16.66% of students moderately mastered the material. However, 56.67% of the students still lacked mastery of the material due to the limited duration of the training and limited experience in programming. Overall, this training succeeded in improving digital literacy for students while supporting technological transformation in the school environment. To improve the effectiveness of the training, a longer training duration and more interactive learning methods are recommended. It is also recommended that similar training be extended to other schools to improve the programming skills of the younger generation and their readiness to face the challenges of the digital industry.
Co-Authors Hernawati Gohzali Irpan Pardosi Kelvin Chen Ng Poi Wong Ronsen Purba Sunario Megawan