Journal of Numerical Optimization and Technology Management
Journal of Numerical Optimization and Technology Management (JNOTM) is a peer-reviewed, open access publication that is published online by SHM Publisher every six months. The Journal aims to offer a digital platform for academics and specialists to submit novel concepts and critical reviews that consider past successes and upcoming difficulties in optimization and technology management.
A comparison study of patients attendance to klinik rawatan keluarga pergigian (krkg) hospital universiti sains malaysia (Hospital USM): A study from the year 2017 to the year 2020
The study aimed to compare the patient's attendance to Klinik Rawatan Keluarga Pergigian (KRKG), Hospital Universiti Sains Malaysia (Hospital USM) from the year 2017 to the year 2020. Data was collected starting from January 2017 till December 2020. Data was plot according to the trendline for each year, and the mean of patient's attendance is being calculated, recorded, and present using a simple bar means. The equation for the particular year is being estimated and compared to the reference category using multinomial regression. From the analysis, it was found that the year 2020 having a decreasing [F-Stat (df)=6.786(1.759,19.349); p < 0.05] in the trendline of patients who attended KRKG, this is due to the global coronavirus pandemic. The finding had found that the year 2020 having has a significant decrease as compared to the previous year [F-Stat (df)=6.786(1.759,19); p < 0.05]. In the multinomial regression analysis, the estimates for the parameter can be identified compared to a baseline category. The finding had shown that the patient's attendance to KRKG is less due to the Covid-19 pandemic.
Copyright (c) 2023 Journal of Numerical Optimization and Technology Management
Warehouse Operation Breakthrough Transformation Towards Industry Revolution 4.0
Warehousing operation is facing huge challenges in meeting tremendous growth of global e-commerce. Conventional ways of handling goods inventory and logistics at warehouse operation are no longer able to cope with phenomenal growth in volume and speed of execution requirement demanded by customers. This research focus on breakthrough transformations of warehouse operation towards Industry Revolution 4.0. The relationship between workforce competency, perceived usefulness and technology innovation factors and warehouse transformations were evaluated. The outcome of this research identified the critical factors and area of improvements which must be considered as the logistic companies embark on transforming warehouse operation towards Industry Revolution 4.0 technologies.
Copyright (c) 2024 Journal of Numerical Optimization and Technology Management
Improvement accuracy of gradient boosting in app rating prediction on google playstore
Google Playstore is a platform that provides various useful applications for smartphone users, especially Android users. But in reality, users are often faced with many choices of applications with various features and functions in the Play Store itself. Rating applications on the Google Play store help users evaluate and choose applications that suit their needs. The purpose of this research is to optimize accuracy in predicting app ratings on the Google Play store using the Gradient Boosting algorithm. This research uses publicly accessible data on the Kaggle platform. The research process includes data collection, pre-processing, Data Spliting, algorithm modeling, and model evaluation. Apart from using the Gradient Boosting algorithm, this research also applies and optimizes other algorithms such as XGBoost, KNN, Logistic Regression, Decision Tree, Random Forest, LightGBM, AdaBoost, and SVM to predict app ratings on Google Playstore. By implementing and optimizing these algorithms, this study succeeded in achieving an accuracy of 92.62%, with MAE 0.311, RMSE 0.467, and R-square 0.144 using the Gradient Boosting algorithm. This research contributes to the development of better prediction methods in the mobile application industry and provides new insights regarding the factors that influence app ratings on the Google Play store.
Home credit default risk assessment using embedded feature selection and stacking ensemble technique
The objective of this study is to evaluate and compare the accuracy of typical credit assessment techniques, particularly Logistic Regression, Gradient Boosting, Random Forest (RF), Extra Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Cat Boost. Furthermore, the study involves stacking ensemble learning with feature selection based on embedded techniques. This research utilized a data set sourced from Kaggle, namely the Home Credit Default Risk gathering data. The results of the study indicate that the reached accuracies were as follows: Logistic regression - 92.02%, XGB - 92.01%, LGBM - 92.09%, RF - 92.07%, and CB - 92.06%. Additionally, while stacking with XGB, RF, and LGBM models, and utilizing the final logistic regression estimator 92.01%, the accuracy does not show any improvement when compared to the usual algorithm. It is even lower than the LGBM accuracy results. However, the findings of this study demonstrate better rates of accuracy in comparison to other previous research conducted by researchers, regardless of that used the same dataset. However, study Mahmudi et al. in 2022 performs better than it in terms of accuracy using oversampling approaches. This finding provides evidence that the accuracy of the model is affected by the quantity of features that are examined. The level of accuracy will be better the more optimally chosen features are for examination.
Estimating technical efficiency of crude palm oil in malaysia
The main purpose of this study is to apply parametric techniques in evaluating the technical efficiency (TE) of crude palm oil (CPO) production by the states in Malaysia. To achieve this, the parametric stochastic frontier analysis (SFA) approach was applied. This study involves a panel data consisting of 12 CPO producing states in Malaysia, over a 18 year time period from year 1999 to 2016. The output variable chosen was the annual CPO production and the input variables considered were plantation area, fruit mill capacity, labour and time variable. We found fruit mill capacity, labour and time as input variables that significantly affect the level of CPO output. Plantation area was proven to be statistically insignificant. Technical efficiency was found to be increasing over time. It was also found that the inefficiencies in the industry were mainly caused by ‘pure’ technical inefficiency rather than scale inefficiency. The overall mean TE of SFA is 0.79. Selangor is the top efficient state according to SFA. We concluded that the state of Malacca is overall the least efficient state due to their low ranking.
Performances of academic departments using data envelopment analysis (DEA) approach
Universities are the backbone of a country's development. Public universities are subsidised by the government, with funds coming from taxpayers’ contributions. It is therefore important that public universities utilise their resources efficiently. This study evaluates the performance of five academic departments for the period 2016-2018. The tool used to evaluate efficiency is Data Envelopment Analysis (DEA) with CCR output-oriented model using a software DEA-Solver-LV version 8. The secondary data of the three input variables are the number of academic staff, the number of non-academic staff and capital grants. The three output variables used in this study are the number of publications, the number of PhD students and the number of undergraduate graduates. The results show that the departments that are really efficient in the years 2016 to 2018 are the academic departments B and E. The average efficiency score from 2016 to 2018 is 0.972436, 1.0000 and 0.990989, respectively, which shows that the performance of departments in general has been somewhat inconsistent over the three years.
Logistics 4.0 operation transformation determinant factors
The phenomenal growth of e-commerce and online purchase have greatly impacted global logistics industry. As a results, integration of Logistics and Industry Revolution 4.0 is inevitable due to increasing complexities and strong needs to fulfill demanding customer responsiveness. The term Logistics4.0 is coined to reflect the integration of smart systems in industrial logistics which transformed the entire logistics operation and it is getting high focus from researchers and industry practitioners. This article focused on the influential factors which contributed towards Logistics4.0 operation transformation. The business feasibility, workforce competency and technology readiness factors were evaluated thoroughly which provided essential and critical insights to the global logistics industry as it embarked towards smart Logistics4.0.
Profitability performance of full-fledged islamic banks and economic growth in malaysia: a panel data approach
This research aims to investigate the influence of return on assets, return on equity and net profit margin on economic growth in Malaysia. The secondary data for this study were collected from annual reports of the five full-fledged Islamic banks in Malaysia for six years (2016-2021). This study employs panel data regression by indicating a random effect model as the best estimator. The findings of the panel regression analysis show return on assets (ROA) and net profit margin (NPM) of full-fledged Islamic banks in Malaysia have positive and significant effects on economic growth. Therefore, this proves solid evidence that Islamic banking institutions and their financial performances are one of Malaysia's economic growth channels. It will motivate many people to go for Islamic banking rather than conventional ones, given its contribution to the country's economic growth. From another perspective, Islamic banks too will inspire and be taken into consideration by the conventional banks towards opening Islamic windows to meet Malaysian's growing demand.
M/M/c/K state-dependent models for controlling the pilgrims and the design of hajj facilities
In Hajj overflow of pilgrims causes queuing delay and in turn, is controlled by the capacity of facilities. The concept of flow control can be used to avoid the building of extreme queues. Thus, in this paper, a new analytical tool for measuring the performances of pilgrim movements in Hajj has been developed. The application of M/M/c/K queuing models for controlling the pilgrims and the design of facilities in Hajj is discussed. The paper also used some data on the flow of pilgrims for implementing the use of M/M/c/K queuing models for the above mentioned controlling and design. The result show that, the average number of pilgrims waiting in queue almost zero, because maybe most of the pilgrims only spend least then 12 second in the system.
Analysis of technology foresight for metaverse in tourism sector by integrating quantitative approaches
The name "metaverse" is a combination of the words "meta" and "universe." The metaverse refers to both present and future digital platforms that are interconnected and focuses on virtual and augmented reality. The purpose of this research to identify the drivers of the future of metaverse in tourism and study the future trend of metaverse in tourism. The target respondents are select and cover mainly by developers and organizational users of metaverse in tourism. In the conduct of this research, both quantitative and qualitative research methods have been taken, and both methods will be apply in the process of the data analysis and data interpretation. In this research, the STEEPV analysis is apply. The STEEPV technique will be utilized in order to determine or identify all the drivers of metaverse in tourism. Data from the questionnaire are analyze using "Social Science Statistics Package" (SPSS). It shows the result of drivers of metaverse in tourism portable devices in the second phase of the research with impact-uncertainty analysis. The top two drivers are “government policy in digitizing the nation” and “technology reliability”. Four different scenarios have been formed based on the top two drivers chosen from the impact-uncertainty analysis. These four alternate scenarios reflect the four potential outcomes between 2022 and 2032. Hence, this research can help future researchers and developers increase their awareness of adopting the metaverse in tourism sector in future. A further explanation of the findings has been given in the discussion.
Deep convolutional generative adversarial networks for data imbalance in convolutional neural networks for facial expression classification
Facial expression recognition technology is a critical direction of emotion computing research, and it is an essential part of human-computer interaction. The facial expression recognition method is a classification method. An excellent classification method and widely used today are the Convolutional Neural Network (CNN). However, there are still shortcomings in accuracy in the CNN method if the available dataset is minimal and imbalanced. There are two ways to overcome this, adding the training data or changing the architecture on CNN. In this research, the researcher uses the method to add to the training dataset using the Deep Convolutional Generative Adversarial Networks (DCGAN) method.