Linear Regression Approach Research Articles

Pengaruh Motivasi Belajar Menggunakan Zoom dan Kecemasan Belajar Terhadap Prestasi Belajar Matematika Siswa Kelas X

The purpose of this study is that there are still many students who do not fully understand the use of zoom so that it affects learning motivation, some students still feel high learning anxiety so that it affects mathematics learning achievement, students who have low learning motivation affect learning achievement through learning anxiety. Furthermore, after the research implementation team conducted observations and interviews with several teachers at SMK N 2 Wonogiri, it was found that some students at the SMK still had difficulties in operating zoom and other similar media and the limited quota even though they received quota assistance but sometimes it was not sufficient. From this background, the research team wanted to investigate further about the effect of learning motivation using zoom and learning anxiety on mathematics learning achievement at SMK Negeri 2 Wonogiri. The method used is a quantitative approach, namely solving path analysis problems with a linear regression approach. The result of this research is that there is an effect of learning motivation using zoom on mathematics learning achievement. There is an influence of learning anxiety on mathematics learning achievement. There is an effect of learning motivation using zoom on mathematics learning achievement through learning anxiety. Keywords: learning motivation, zoom, learning anxiety, mathematics learning achievement

Digital quantification of lymphocytic infiltration on routine H&E images and immunotherapy response in non–small cell lung cancer.

9066 Background: Current biomarker(s) for immuno-oncology (IO) therapy response prediction in lung cancer are limited. Additional predictive biomarkers are useful to help refine patient selection and guide precision therapy. Methods: Biopsy and surgical specimens stained with hematoxylin-eosin (H&E) were subjected to whole-slide scanning for 446 advanced stage non-small cell lung cancer (NSCLC) treated with single agent immune check point inhibitors (ICI). A machine learning model was trained on H&E images for classification of tumor infiltrating lymphocytes (TILs), tumor cells, and stromal cells in specific tissue types. Results: TIL levels were found to be highly variable, with a range of 12 to 4270 cells/mm2, and median of 319 (Q1 = 159, Q3 = 681). TIL levels were assessed on tissue samples from multiple organs which had shown primary or metastatic NSCLC, and were similar across all specimen sites except the liver, for which median TIL levels were significantly lower, at 90 cells/mm2. There was no correlation between tumor mutational burden (TMB) and TIL levels, while high TIL levels were correlated with high PD-L1 (≥ 50%) expression. Patients who experienced a partial/complete response to ICI therapy had a trend to higher median TILs compared to those who had progressive/stable disease (350 versus 310 cells/mm2, P = 0.09). In a multivariable analysis after controlling for covariates (incl. sex, age, cigarette smoking, ECOG, PD-L1, TMB & treatment line), a higher TIL level (≥ 250 cells/mm2) was an independent predictor of IO response for both progression-free survival (PFS; HRadj 0.70; 95% CI, 0.55 - 0.89; P = 0.003) and overall survival (HRadj 0.73; 95% CI, 0.56 - 0.95; P = 0.02). In a ROC analysis considering single biomarkers, PD-L1 had the highest AUC (0.68, P < 0.001), while TIL (AUC = 0.53, P = 0.08) and TMB (AUC = 0.55, P = 0.05) had similar AUC values for classifying responders from non-responders based on objective response rate. Using weighted linear regression approach to combine the biomarkers, paired PD-L1/TMB had the greatest AUC (0.70, P < 0.001) compared to PD-L1 single assay. In the PD-L1 negative (< 1%, N = 50) subgroup, TIL levels had superior predictive performance for classification of IO responders (AUC = 0.77, P = 0.02) compared to TMB (AUC = 0.57, P = 0.3), such that patients with a high TIL level (≥ 250 cells/mm2) had an improved PFS (median PFS: 2.7 vs 2.2 months; HR = 0.48; 95% CI, 0.26 - 0.87; P = 0.02). Conclusions: Digital TIL quantification with use of machine learning is feasible. TIL levels appear to be a robust and independent biomarker of likelihood of response to IO treatment in NSCLC, especially in the PD-L1 negative subgroup. The findings of this study are under validation in additional lung cancer cohorts.

Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines

Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 °C (root mean squared error). Considering the temperature range from approximately 76 °C to 112 °C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines.

Nitrous oxide emissions from wastewater treatment - Revisiting the IPCC 2019 refinement guidelines

Many entities (such as water utilities and government agencies) that operate or manage wastewater treatment plants (WWTPs) are seeking to estimate and reduce greenhouse gas (GHG) emissions. Nitrous oxide (N2O) is a major GHG emitted directly from WWTPs with nitrification/ nitrogen removal processes. These N2O emissions are fugitive, highly variable and relatively difficult to measure. In the absence of direct emissions measurements, using typical GHG accounting guidelines (e.g., those published by the Intergovernmental Panel on Climate Change, IPCC), the calculation methods for estimating emissions apply one or more emission factor(s) (EF). The EFs reference parameters in wastewater treatment that can be measured or estimated reasonably reliably (e.g., influent total nitrogen (TN) load). For GHG estimation and reporting purposes, most operating entities are reliant on default EF values for wastewater N2O listed in GHG accounting guidelines (e.g., IPCC). Such default factors have historically been derived from expert opinion, using limited literature references, and based on actual measurements from wastewater treatment plants. When compared to the older IPCC (2006) Guidelines, the latest default EF for wastewater treatment N2O in the IPCC 2019 Refinement represents an increase of approximately 32-fold from 0.05% to 1.6% of influent TN load. This increase was derived from a literature review, as described in Volume 5 (Chapter 6, Annex 6A) of the IPCC (2019) Refinement to the 2006 Guidelines. In this study, the literature data cited by IPCC (2019), were checked and the same linear regression approach used to reproduce the derivation of an ‘average’ (default) EF for N2O from full-scale WWTPs. Several apparent literature citation errors were identified in the relevant annex that appear to have resulted in an over-estimation of the ‘average’ (default) EF in the IPCC (2019) Guidelines. After correction of these errors to align with the source literature data, the outcome of this study is a suggested revised default EF of 1.1% (±0.16%, α = 0.05) of WWTP influent TN load emitted as N2O. This revised default value is supported by inclusion of datapoints from newer literature references (2018-2021), in addition to those cited by IPCC (2019). It also aligns more closely with the weighted average N2O emission factors published by the Danish Environmental Protection Agency in 2020.

Organisational and staff-related effects on cultural competence in the hospital setting: a cross-sectional online survey of nursing and medical staff

BackgroundCultural competence is considered a core qualification for dealing with socio-cultural diversity and balancing disparities in health care.ObjectivesTo explore features supporting and inhibiting cultural competence in the hospital at both organisational and staff levels.DesignCross-sectional online survey in the form of a full census from May to November 2018.SettingTwo organisations that run a total of 22 hospitals in Germany.ParticipantsEight hundred nursing and medical professionals [nurses: n = 557; doctors: n = 243].MethodsUsing the Short Form Cultural Intelligence SCALE (SFCQ), cultural competence was measured and its relation to potential influencing factors at staff level and organisational level examined, using bivariate (t-Test, one-way ANOVA, Pearson and Spearman correlations) and multivariate (multiple linear regression) approaches. Model 1 examined features at organisational level, Model 2 at individual level and Model 3 included organisational and individual features.ResultsThe mean cultural competence measured was 3.49 [min.: 1.3; max.: 5.0]. In the bivariate and isolated multivariate models [Models 1 and 2], factors on both organisational and individual levels were significantly related to the hospital staff’s cultural competence. The multivariate overview [Model 3], however, revealed that individual features at staff level were the statistically relevant predictors. Positive influencing features included staff’s assessment of the importance of cultural competence in their professional context [B: 0.368, 95% confidence interval 0.307; 0.429], participation in competence training [B: 0.193; 95% confidence interval 0.112; 0.276] and having a migration background [B: 0.175; 95% confidence interval 0.074; 0.278], while negative features included length of medical service [B: -0.004; 95% confidence interval -0.007; -0.001].ConclusionsThe development and practice of cultural competence appear to be determined less by organisational features and more on the level of individual actors. In addition to staff development, adequate organisational structures and an economic incentive system are required to promote sociocultural diversity in hospitals.

STUDY TO EXAMINE THE IMPACTS OF SPIRITUAL LEADERSHIP STYLE ON ORGANIZATIONAL PERFORMANCE BASED ON INDIVIDUAL-LEVEL PERFORMANCE: A META-ANALYSIS

Background: We undertook meta-analyses to see the impacts of spiritual leadership (SL) style on organizational performance based on the individual level performance indicators like job satisfaction, organization commitment, personal fulfillment, turnover intention, and employees' well-being. Method: A Random-Effects Model was carried out for all meta-analyses. Q statics and I-Square provide evidence of homogeneity in the effect sizes of the studies. This leads us to give more emphasis on the reporting bias analysis. We covered graphics as well statistical methods for reporting bias analysis. We applied Rosenthal’s failsafe N, for estimating the number of unpublished studies for sampling bias, Begg & Mazumdar rank correlation test for selection process bias, Egger's linear regression approach for funnel plot asymmetry, and association with reporting bias, and Funnel plot with trim-and-fill methods for detecting availability bias. We also conducted an outlier check-in effect size with a Galbraith plot. Results: Our results show that the SL styles have a positive impact on organizational performance based on the individual level performance measurement such as job satisfaction and organizational commitment.

Statistical shape modelling of the human mandible: 3D shape predictions based on external morphometric features

One of the main limitations in subject-centred design approach is represented by getting 3D models of the region of interest. Indeed, 3D reconstruction from imaging data (i.e., computed tomography scans) is expensive and exposes the subject to high radiation doses. Statistical Shape Models (SSMs) are mathematical models able to describe the variability associated to a population and allow predicting new shapes tuning model parameters. These parameters almost never have a physical meaning and so they cannot be directly related to morphometric features. In this study a gender-combined SSM model of the human mandible was setup, using Generalised Procrustes Analysis and Principal Component Analysis on a dataset of fifty mandibles. Twelve morphometric features, able to characterise the mandibular bone and readily collectable during external examinations, were recorded and correlated to SSM parameters by a multiple linear regression approach. Then a cross-validation procedure was performed on a control set to determine the combination of features able to minimise the average deviation between real and predicted shapes. Compactness of the SSM and main modes of deformations have been investigated and results consistent with previous works involving a higher number of shapes were found. A combination of five features was proved to characterise predicted shapes minimising the average error. As completion of the work, a male SSM was developed and performances compared with those of the combined SSM. The features-based model here proposed could represent a useful and easy-to-use tool for the generation of 3D customised models within a virtual interactive design environment.

A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components – selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation – with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD5, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer; R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer; R2 = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.

Groundwater Level Forecasting Using Multiple Linear Regression and Artificial Neural Network Approaches

Accurate and reliable groundwater level prediction is a critical component in water resources management. This paper developed two methods to predict forty-six months of groundwater level fluctuation. The approaches of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) were compared for predicting groundwater levels. MLR and ANN approaches were performed at two monitoring wells, Ubung and Ngurah Rai, in the Denpasar region of Bali, Indonesia, considering all significant inputs of hydrometeorological time series data: barometric pressure, evaporation, temperature, wind, bright sunshine, rainfall, and groundwater level. The model's performance was assessed statistically and graphically. The ANN-predicted groundwater levels agreed better with the observed groundwater levels than the MLR-predicted groundwater levels at all sites. The results show the ANN performs better than MLR in terms of statistical errors, notably mean square error (MSE) value of 0.6325; root mean square error (RMSE) value of 0.7953; mean absolute error (MAE) value of 0.6122 based on the MLR in the Ubung monitoring well, while ANN models got an MSE value of 0.143; RMSE value of 0.379, and MAE value of 0.311. For the Ngurah Rai monitoring well, the MSE value is of 1.3406, RMSE value of 1.1579, and MAE value of 0.9152 for MLR, while ANN models obtained MSE value of 0.0483, RMSE value of 0.2198, and MAE value of 0.1266.

The influence of socio-economic factors on tobacco farmers' income (case study in Suralaga District, East Lombok Regency)

Tobacco is a commercial commodity with high economic value. The tobacco producing industry has a considerable contribution in state revenues, employment and the source of farmer income. The purpose of this study was for; 1) explain the characteristics of tobacco farming; 2) Analyzing the influence of socio-economic factors on farmer income. The research was conducted in Suralaga District, East Lombok Regency. The sampling technique used was purposive sampling as many as 30 farmer respondents who carry out tobacco farming. The data analysis method used in this study was quantitative analysis with multiple linear regression approaches. The results of the analysis show the socio-economic factors that influence the income of tobacco farmers is the factor of the amount of production (X1), the value of land lease (X2), the value of fertilizer use (X4), the value of the use of agricultural medicine (X5), labor costs (X6 ), and the selling price of tobacco (X7).

People versus machines: The impact of being in an automatable job on Australian worker’s mental health and life satisfaction

This study explores the effect on mental health and life satisfaction of working in an automatable job. We utilise an Australian panel dataset (HILDA), and take a fixed effects linear regression approach, to relate a person being in automatable work to proxies of their wellbeing. Overall, we find evidence that automatable work has a small, detrimental impact on the mental health and life satisfaction of workers within some industries, particularly those with higher levels of job automation risk, such as manufacturing. Furthermore, we find no strong trends to suggest that any particular demographic group is disproportionately impacted across industries. These findings are robust to a variety of specifications. We also find evidence of adaptation to these effects after one-year tenure on the job, indicating a limited role for firm policy.

Improving the Accuracy of Rainfall Prediction Using Bias-Corrected NMME Outputs: A Case Study of Surabaya City, Indonesia.

Generating an accurate rainfall prediction is a challenging work due to the complexity of the climate system. Numerous efforts have been conducted to generate reliable prediction such as through ensemble forecasts, the North Multi-Model Ensemble (NMME). The performance of NMME globally has been investigated in many studies. However, its performance in a specific location has not been much validated. This paper investigates the performance of NMME to forecast rainfall in Surabaya, Indonesia. Our study showed that the rainfall prediction from NMME tends to be underdispersive, which thus requires a bias correction. We proposed a new bias correction method based on gamma regression to model the asymmetric pattern of rainfall distribution and further compared the results with the average ratio method and linear regression. This study showed that the NMME performance can be improved significantly after bias correction using the gamma regression method. This can be seen from the smaller RMSE and MAE values, as well as higher R2 values compared with the results from linear regression and average ratio methods. Gamma regression improved the R2 value by about 30% higher than raw data, and it is about 20% higher than the linear regression approach. This research showed that NMME can be used to improve the accuracy of rainfall forecast in Surabaya.

Chemical composition and sources of amines in PM2.5 in an urban site of PRD, China

Atmospheric amines have attracted increasing attention due to their significant impact on new particle formation, particle hygroscopicity and particle optical properties. In this study, four low-molecule-weight amines were detected from PM2.5 filter samples collected at an urban site of Pearl River Delta (PRD) region of China in 2018 autumn. During the campaign, the mass concentrations of ambient particulate methylamine (MA, CH3NH2), dimethylamine (DMA, (CH3)2NH), trimethylamine (TMA, (CH3)3N), and diethylamine (DEA, (C2H5)2NH) were quantified at daily or 12-h resolution using an optimized Ion Chromatograph (IC) method. The total measured amine concentration was 297 ± 209 ng/m3, which can account for 0.76 ± 0.33% of PM2.5 mass concentrations. The particulate amines in PRD urban area were dominated by MA (243 ± 179 ng/m3), accounting for over 80% of total amines, then followed by DMA (49 ± 30 ng/m3, 16.5%), TMA (4 ± 2 ng/m3) and DEA (1 ± 1 ng/m3). Based on the correlation analysis, MA and DMA mainly presented as nitrate and sulfate salts. We speculate the amines tend to react with gas-phase HNO3 or particle-phase nitrate to form particulate amine salts via local process in Guangzhou. As the relative humidity (RH) increased, enhanced partitioning of amine towards the particle phase was observed. Using approach of multiple linear regression, 71% of the particulate amines in PRD urban site could be explained by acid-base process and the rest by primary emissions from combustion sources (29%).

A new look at asymmetric effect of oil price changes on inflation: Evidence from Malaysia

The existing literature that examined the effect of oil price on domestic price inflation only focused on consumer price at an aggregate level. The studies that focused on producer and production levels, and based on disaggregated data are very lack. Besides, previous studies also mainly applied the linear regression approach in studying the effect of oil price. This study seeks to explore the mentioned issues by focusing on sectoral consumer (CPI), industrial (IPI), and producer (PPI) prices of Malaysia. The Markov-switching (MS) regression technique is applied. The models are innovated by incorporating the asymmetric effects of oil price changes. The results reveal different reactions of sectoral domestic price inflation to oil price changes. The oil price has asymmetric effects on domestic price inflation with higher impacts on industrial and producer prices than the consumer price. The effect is larger in sectors that have higher linkages with oil/ energy resources. These sectors are oil-intensive and are sensitive to oil price changes. Among these sectors are the CPI transportation sector, IPI manufacturing, and electrical sectors as well as PPI fuel, chemicals, and manufacturing sectors. However, oil is not the main factor causes to domestic inflation. The main determinants of inflation are real exchange rate, aggregate supply, and demand. Besides, the policy decisions are also influential on price stability. The sectors of CPI transportation, PPI animals & vegetable oils, and PPI fuel have a high tendency to increase prices and should be well-monitored.

Simple linear regression approach for evaluating models to estimate stature based on upper limb dimensions of adult Bangladeshi males

BackgroundThe stature of a living human reflects the nutritional, genetic, and disease patterns of individual experiences. This study adopted a simple linear regression method and R2 values to identify the preferred model for stature estimation based on the lengths of the arm, radius, ulna, and hand; breadth of the hand; and circumference of the wrist of the adult Bangladeshi male population. This cross-sectional study was performed in the Anatomy Department of Sir Salimullah Medical College, Dhaka, from January 2009 to June 2011. One hundred right-handed adult Muslim Bangladeshi males aged 25 to 45 years participated in the study.ResultsThe regression model using right and left ulnar length explained 63% of the measured stature with the least standard error of the estimate (0.435 and 0.436), the model using left and right arm length explained 60%, the model using left and right radius length explained 51%, and the model using left- and right-hand length explained 44% of the measured stature. However, the models using left and right handbreadth and wrist circumference explained only 11 to 13% of the measured stature with a higher standard error of the estimate (6.66 to 6.73). For 25- to 45-year-old Bangladeshi Muslim males, the ulnar length of both sides was the best predictor of stature.ConclusionsLinear regression equations in estimating stature effectively may encourage its application in future studies addressing different age groups, sexes, nutritional statuses, religions, and ethnicities of Bangladesh.

Internal determinants of export performance of small and medium enterprises in Vietnam

The aim of this study is to investigate the internal determinants of the export performance of small and medium enterprises (SMEs) in Vietnam. Building upon the Resource-Based Theory, hypotheses on the internal determinants of export performance of SMEs in Vietnam are proposed. A sample of 569 SMEs with export activities in Vietnam during the year 2018 is collected to carry out this research. To ensure the reliability of data, this study excludes joint venture companies due to ambiguous participation between the government and the foreign element. A linear regression approach is applied along with the Robust standard errors method to test the proposed hypotheses of the research model. In addition, this study also examines several control variables in the model, including cultural distance, geographic distance, economic distance, and institutional distance. Estimation results reveal that firm size, firm industry, and research and development (R&D) have a positive and statistically significant impact on the export performance of SMEs in Vietnam, while the manager’s gender has a negative and statistically significant influence on the export performance of these firms. Based on the empirical findings, several governance implications are provided for managers to improve the export performance of their SMEs in Vietnam.

Parametrizing the mesoscale enhancement of oceanic surface turbulent fluxes: A physical–statistical approach

AbstractThe mesoscale enhancement of surface turbulent fluxes at the air–sea interface is driven by the mesoscale surface wind‐speed variability, especially the gustiness velocity and the mesoscale wind‐speed standard variation. This study proposes a parametrization of these two variables. A large dataset based on the operational 2.5‐km AROME convection‐permitting model is used in a coarse‐graining framework, to quantify various quantities that are subgrid at the scale of a 100‐km resolution global circulation model grid cell. This provides a learning dataset to help build the parametrization. The analysis of two case studies of intense wind‐speed mesoscale variability, combined with a literature review, provides a physically based set of 12 potential predictors, accounting for the convection activity and the large‐scale dynamics. The least absolute shrinkage and selection operator then frames a penalized multivariate linear regression approach to identify the most relevant predictors objectively. Five predictors are selected for predicting the gustiness velocity: the updraft mass flux at the lifting condensation level, the density‐current spreading velocity, the large‐scale horizontal shear and divergence, and the large‐scale wind speed. The parametrization of the mesoscale wind‐speed standard deviation requires an additional predictor, namely the cold‐pool object aggregation index. The proposed parametrization performs significantly better than the previously published parametrizations and is able to capture 80, 99, and 93 of the mesoscale enhancement of the momentum, sensible heat, and latent heat fluxes, respectively. From the perspective of a global circulation model implementation, in which some predictors may be unavailable, simpler versions of the parametrization, that is, involving fewer predictors, are also discussed.

Integrated empowerment model of traditional fisherman’ wives in Sungsang Coast, Banyuasin-Indonesia

The present study aims to propose a comprehensive model arranged by the role of female fisherman during the high fishing season in Sungsang coastal area of Banyuasin regency, Indonesia. The quantitative methods using a questionnaire, field observation, and deep interview with 116 respondents consisting of fisherman’ wives and children were used as the input for the model. The study was conducted from June 2019 to March 2020 and was in 5 villages in Sungsang coastal area. The statistical analysis using the linear regression approach and path analysis was used to obtain a suitable model for the studied cases. The result showed that the fisherman’s wives played the important role related to handle and manage the assets and network development. The formulation of empowerment model indicated that the improvement should be made such as the specific program for woman empowerment to educate the fisherman’ wives about handing the assets and taking them into the land activities during the high fishing season.

The Influence of Work Motivation, Job Satisfaction, and Organizational Commitment on Employee Performance at PT. Gasing Group in Makassar City

This study aims to analyze the effects of motivation, job satisfaction, and organizational commitment on employee performance at PT. Gasing Group in Makassar City. Primary data were gathered through interviews, observations, and questionnaire responses from employees at the PT. Gasing Group in Makassar City and secondary data were gathered from a variety of sources, including documentation and writing (books, reports, scientific works, and research results) and information from parties involved in the study (job descriptions, work procedures, and other references). sample in this study amounted to 55 respondents according to the number of employees of PT. Gasing Group Makassar City. This type of quantitative descriptive research uses the approach of Simple Correlation Analysis and Multiple Linear Regression to measure the relationship between variables using the SPSS 22.0 program. The results showed that work motivation, job satisfaction, and organizational commitment had a positive and significant effect on the performance of PT. Gasing Group Makassar City. The most dominant variable, job satisfaction, has the greatest effect on employee performance compared to the other 3 variables.

Forecasting the Number of Students in Multiple Linear Regressions

The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predicting the number of prospective students in higher education was done by utilizing data mining and multiple linear regression approaches. By using 2 independent variables, namely administration costs (X1), accreditation score (X2), and the number of students who was registered each year as dependent variable (Y). For the test data, it used database for the last 13 years. By using multiple linear regression, the intercept value was sought and the coefficient of determination until the regression coefficient was obtained with the equation Y = 45.28 + -0.02.X1 + 121.58.X2, noted that if X2 was constant, the increasing of one unit was in X1 would have the effect of increasing -0.02 units on Y. Secondly, if X1 was constant, the increasing of one unit was in X2, would have the effect of increasing 121.58 units in Y. Thirdly, if X1 and X2 were equal to zero, the magnitude of Y was 45.28 units. Therefore, the proposed approach could be provided the acceptable predictive results.