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All Journal Indonesian Journal of Computer Science and Engineering "Journal of Data Science Journal Majelis Paspama
Uchendu Onwusoronye Onwurah
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Demand Forecasting of Domestic Gas Consumption: A Comparative Study of Trend Analysis, Moving Average, Single and Double Exponential Smoothing Methods Uchendu Onwusoronye Onwurah; Chukwuebuka Martinjoe U-Dominic; Christopher Chukwutoo Ihueze; Onyekachukwu Godspower Ekwueme; Obiora Jeremiah Obiafudo; Emmanuel Okechukwu Chukwumuanya
Indonesian Journal of Computer Science and Engineering Vol. 2 No. 01 (2025): IJCSE Volume 02 Nomor 01, Mei 2025
Publisher : CV. Cendekiawan Muda Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70656/ijcse.v2i01.429

Abstract

The increase in population and global economy has led to an increase in energy demand and consumption. Domestic gas consumption has continued to increase on a daily basis. Forecasting is essential to support decisions such as inventory management, production planning, and procurements in natural gas production and distribution. This study is aimed at forecasting natural gas demand in a selected area using trend analysis, moving average, single exponential smoothing, and double exponential smoothing techniques. 16 years (2009–2024) historical data were collected from a domestic gas distribution plant. The data were analyzed, and forecasts were made using trend analysis, moving average, single exponential smoothing, and double exponential methods. A comparative study revealed that trend analysis outperformed the other forecasting techniques, based on the lowest mean absolute percentage error (MAPE), mean absolute deviation (MAD), and mean squared deviation (MSD) as the decision criteria. The performance of double exponential smoothing is very close to that of the trend analysis. This study concludes that both trend analysis and double exponential smoothing, based on their lower MAPE and MAD, can be adopted by the gas plant in forecasting the domestic gas demand in the selected area.
Feature Engineering for Predictive Maintenance: Identifying Key Predictors of Machine Defects Using Machine Learning Chinedu Sebastian Ani; Godwin Harold Chukwuemeka; Uchendu Onwusoronye Onwurah
Journal Of Data Science Vol. 3 No. 02 (2025): Journal Of Data Science, September 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/jds.v3i2.7267

Abstract

In the modern industrial environments, the ability to predict equipment failure before it occurs is essential for minimizing downtime and maximizing operational efficiency. This research explores the use of feature engineering to identify key indicators of mechanical faults in a cement mill fan system. Vibration data were collected over 34 weeks from critical components of the fan and processed using several statistical techniques to extract relevant features. Various feature selection methods including Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), ReliefF, Chi-square, ANOVA, and Kruskal-Wallis were used to determine the most informative features. These features were then used to train and evaluate machine learning models, with neural networks demonstrating superior performance. Among all models, the neural network optimized with Chi-square-selected features achieved the highest classification accuracy, fastest prediction speed, and lowest misclassification cost. These results highlight the effectiveness of combining robust feature selection with deep learning methods for reliable fault detection and predictive maintenance in industrial systems.
The Integration of TPM in SS-DMAIC Framework to Reduce Production Losses and Enhance Process Performance: a Case Study Chukwuebuka Martinjoe U-Dominic; Ifeyinwa Juliet Orji; Uchendu Onwusoronye Onwurah; Nwufo Maduka Augustine
Journal Majelis Paspama Vol. 3 No. 01 (2025): Journal Majelis Paspama, January 2025
Publisher : Journal Majelis Paspama

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Abstract

The primary cause of production system losses is widely recognised to be unplanned stoppages and breakdowns, as idle machines waste time and lower output and throughput. Many firms have undergone a paradigm shift from traditional methods to more sophisticated process improvement strategies to address the dynamic and ever-changing production constraints. To lower production downtimes and other process-related issues, an integrated improvement approach of TPM-SS-DMAIC was used in this study. The new suggested approach was validated in the cable manufacturing industry. The goals of the study were achieved in terms of increased worker knowledge, better quality, and fewer unplanned stoppages and breakdowns. Standard operating procedures were updated in response to suggestions for ways to reduce needless stoppages in extrusion line operations. Furthermore, following the process change, the overall equipment effectiveness (OEE) increased from 58% to 63%, indicating a noteworthy level of improvement.
Ramifications of Artificial Intelligence on Organizational Performance in Nigeria Godspower Onyekachukwu Ekwueme; Uchendu Onwusoronye Onwurah; Ifenyinwa Faith Ogbodo
Journal Majelis Paspama Vol. 2 No. 2 (2024): Journal Majelis Paspama, July 2024
Publisher : Journal Majelis Paspama

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Abstract

Artificial intelligence (AI) has been identified to be very significant in the twenty-first century in almost every discipline, including engineering, science, education, medical, business, accounting, finance, marketing, economics, manufacturing, the stock market, and law. This study examined the impact of artificial intelligence on organizational performance in a microfinance bank. Relevant data were drawn from selected one hundred (100) staff of XYZ Microfinance bank based in Nigeria, using a well-structured questionnaire. The data collected were descriptively analyzed. The results showed that artificial intelligence has positive impact on the organizational performance. The results also revealed that high cost of implementation, anxiety among workers, role displacement, ethical issues, significant investment in technology and training, among others are the challenges affecting the adoption of artificial intelligence in the business organization in Nigeria. The study recommends that Businesses must take proactive measures to address the obstacles to AI adoption if they want to optimize the technology's beneficial effects on organizational performance.
Co-Authors Chinedu Sebastian Ani Christopher Chukwutoo Ihueze Chukwuebuka Martinjoe U-Dominic Emmanuel Okechukwu Chukwumuanya Godspower Onyekachukwu Ekwueme Godwin Harold Chukwuemeka Ifenyinwa Faith Ogbodo Ifeyinwa Juliet Orji Nwufo Maduka Augustine Obiora Jeremiah Obiafudo Onyekachukwu Godspower Ekwueme