Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Educational Technology and Learning Creativity
Goh Khang Wen
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Mathematics

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Stock Price Prediction in Bursa Malaysia Nurfadhlina Binti Abdul Halim; Goh Khang Wen
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 7, No 1 (2007)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v7i1.949

Abstract

Investment in stock is a highly risky investment, it is because the existence of randomness in thestock price. In lecture, usually we used Binomial model to price the stock. But, in real world, how dowe price the stock? Because the stock price is random, the volatility and drift is a crucial items tobehold. The main questions is how to calculate this volatility and drift, and the answer to thequestion is the sample variance and the sample mean. At any time, the stock price will be either up ordown from the previous price. This is where we need a method or model to calculate parameters forup-state and down-state for the stock price. And it will cover the volatility and the drift in anembrace. The method we used in this paper is the Hull-White algorithm. Hull-White algorithm is tofind the parameters value of u and d for prediction to stock price. Using SPSS, we will run the data toget the sample variance and sample mean. Then, using Maple 10, we calculate the u and d beforeenter the value of u and d into programming C++.
A Co-joint Deterministic Search Direction Sampling Procedure with Probabilistic Soft Approach Ismail bin Mohd.; GOH KHANG Wen; TAN EE LING
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 9, No 2 (2009)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v9i2.999

Abstract

Since hard continuous optimization models contain more than one solution and even continuumsolution, it is impossible to seek all the solution by using the existent optimization methods.Therefore, in this paper we introduce a co-joint deterministic and probabilistic approach whichmodifies a soft approach for solving hard continuous optimization models. An algorithm of co-jointapproach and several numerical experiments have been presented in this paper. The specialnumerical test results have shown that the co-joint approach is more effective than soft approachalgorithm. Fortunately, we have found that the co-joint algorithm can be used to determine whetherthe optimization model is hard continuous optimization models or not.
Digital HR, Employee Empowerment, Internal Mobility, Career Development on ‎Knowledge Innovation, and Employee Engagement in Technology Firms in Indonesia Mintawati, Hesri; Maulana , Agung; Pratiwi, Intan Deanida; Sedera, Rakotoarisoa Maminiaina Heritiana; Wen, Goh Khang; Aridan, Muhammad
Journal of Educational Technology and Learning Creativity Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v3i2.2479

Abstract

Purpose of the study: This study aims to explore the relationships between Digital Human Resource Management, Employee Empowerment, Internal Mobility, Career Development, Knowledge Innovation, and Employee Engagement in Indonesian technology companies. Methodology: A quantitative approach was used, collecting data from 675 employees via a structured questionnaire with a five-point Likert scale. The data were analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS 3.0). Main Findings: Digital HR directly influences Employee Empowerment and Internal Mobility, which positively affect Career Development. Career Development impacts Knowledge Innovation and Employee Engagement, with Knowledge Innovation serving as a mediator between Career Development and Engagement. Novelty/Originality of this study: This study integrates Resource-Based View (RBV) and Social Exchange Theory (SET), demonstrating how Digital HR competencies and employee reciprocity drive innovation and engagement, providing insights for HR managers in technology firms to enhance talent development and competitiveness.
Improving Transparency and Efficiency of Administrative and Organizational Governance Through Smart Digital Technology and Cloning Systems Aspizain, Caniago; Syamsurizal, Syamsurizal; Masria, Masria; Rustanto, Agung Edi; Khabibjonovna , Kushieva Nodira; Wen, Goh Khang
Journal of Educational Technology and Learning Creativity Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v3i2.2481

Abstract

Purpose of the study: This study aims to improve the transparency and efficiency of administrative and organizational governance in mosque management through the development of Smart Mosque Digital Technology (SMDT) and a cloning system, enhancing financial administration and operational processes. Methodology: The study employs a mixed-methods approach, including literature review, interviews, surveys, and observational data collection. The SMDT application was developed using the Waterfall model of the System Development Life Cycle (SDLC). User satisfaction was measured through a questionnaire based on the End-User Computing Satisfaction (EUCS) model and analyzed using descriptive statistics with a sample of direct users from Nurul Jihad Mosque in Jakarta. Main Findings: The implementation of SMDT significantly improved transparency, accuracy, and efficiency in financial and administrative governance. User satisfaction levels were categorized as "very satisfied" across all measured dimensions, with scores ranging from 50% to 76.5% in the highest satisfaction category. The system enhanced data management, financial reporting, governance transparency, and operational efficiency in mosque operations. Novelty/Originality of this study: This study presents a novel approach to mosque administration by introducing a fully integrated digital system that streamlines operational processes, improves transparency, and provides a replicable model that can be cloned across other mosques. The integration of digital technology into mosque governance represents a significant advancement in the field of religious organizational management, addressing the gap between traditional administration methods and contemporary digital transformation requirements.
Hybrid Otsu Morphological Pre-processing for EfficientNetB4 Based Acute Lymphoblastic Leukemia Classification Audina, Maretta Mia; Surono, Sugiyarto; Thobirin, Aris; Wen, Goh Khang
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40730

Abstract

Image quality plays a crucial role in improving the performance of image-based classification models, particularly when raw images exhibit noise, uneven illumination, and unclear object boundaries. This study proposes a hybrid segmentation approach to enhance object separation by reducing background interference and refining object contours. The method combines Otsu thresholding for initial object–background separation with elliptical morphological operations to improve region consistency and boundary definition.The segmented grayscale images are replicated into three channels and resized to 224×224 pixels before being used as input to an EfficientNetB4-based classification model optimized with the AdamW optimizer and fine-tuning. Experimental results under identical data splits, training settings, and fine-tuning protocols show that the proposed segmentation-based method achieves a final test accuracy of 97%, outperforming the baseline model trained on raw images (95% test accuracy) using the same EfficientNetB4-AdamW configuration. These results demonstrate that incorporating segmentation in the preprocessing stage effectively enhances discriminative feature learning and improves overall classification performance.
Co-Authors Agung Edi Rustanto Aris Thobirin, Aris Aspizain, Caniago Audina, Maretta Mia Intan Deanida Pratiwi Ismail bin Mohd. Khabibjonovna , Kushieva Nodira Masria Masria Maulana , Agung Mintawati, Hesri Muhammad Aridan Nurfadhlina Binti Abdul Halim Sedera, Rakotoarisoa Maminiaina Heritiana Sugiyarto Surono, Sugiyarto Syamsurizal - - Tan Ee Ling