Statistical Analysis Techniques Research Articles

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.

Seismic Performance of Reinforced Concrete Frames on Sloped Ground Using Non-Linear Static Pushover Analysis

Nepal, renowned for its majestic Himalayan Ranges, is also highly vulnerable to seismic activity. Buildings on hill slopes often endure more significant damage during earthquakes than those on flat terrain. Traditional seismic analyses of buildings typically employ linear-static and dynamic methods, which might not fully represent the building's behavior under actual seismic conditions. This research addresses this gap by assessing the seismic performance of buildings on hill slopes using non-linear static analysis techniques. Specifically, the study evaluates the seismic response of 5-story buildings with stepback and stepback-setback configurations on slopes of 15°, 30°, and 45°. This evaluation is conducted through non-linear static analysis, also known as Pushover Analysis, using ETABSv20 software. Key performance metrics analyzed include the building's period, topstorey displacement, and base shear. The findings reveal that buildings with stepbacksetback configurations demonstrate superior seismic performance on the sloping ground compared to those with stepback configurations. This study underscores the importance of employing non-linear analysis methods to better understand and improve seismic resilience in hilly terrains.

Multiple Role Conflicts In The Employment Field In Indonesian Government Agency Institutions with Work Life Balance (WLB) and Job Stress As Mediator Variables on Employee Performance

Work-life balance issues are an important aspect of the psychology of happiness, which is determined by a strong sense of self-worth, satisfaction, and a variety of life-affirming relationships. This study aims to explore how dual role conflicts affect employee performance in the Manpower Office (Disnaker) in Indonesia, by considering the role of work-life balance (WLB) and work stress as mediator variables. The research method used involved collecting data from 154 employees in the disnaker through questionnaires and interviews. The data was then analyzed using statistical analysis techniques to identify the relationship between multiple role conflicts, WLB, work stress, and employee performance. The results showed that dual role conflict had a significant effect on WLB and work stress, but did not affect employee performance levels. Meanwhile, work stress and WLB cannot mediate the dual role with performance. This research provides important insights for human resource management in government agencies to develop policies and programs that support the balance between work and personal life of employees. An emphasis on managing multiple role conflicts and work stress in the work environment can improve employee well-being as well as the overall effectiveness of the organization. The practical implications of this study are the importance of appropriate interventions to improve the working conditions of employees in the public sector, while improving quality public services.

THE INFLUENCE OF MARKET RATIO ON CUMULATIVE ABNORMAL RETURNS IN COMPANIES LISTED ON THE JAKARTA ISLAMIC INDEX (JII)

This research was conducted to test whether there is an influence of earnings per share and price earnings ratio on the Cumulative Abnormal Return of 14 companies listed on the Jakarta Islamic index during 2020-2022. This research uses field research, this research uses quantitative research methods. The approach used in this research is an associative approach which aims to determine the relationship between two or more variables. With data collection techniques in the form of documentation using primary data sourced from secondary data, and processing using E-Eviws 12 (X64). There is a statistical analysis technique with 14 companies selected using the purposive sampling method registered in the Jakarta Islamic Index during 2020-2022. The research results obtained show 1) Earning Per Share has no positive influence and is not significant on Cumulative Abnormal Return. This is proven by the t-test carried out, where a value of 0.9314 > 0.05 was obtained, so it can be concluded that H1 is rejected. 2) Price Earning Ratio has no positive influence and is not significant on the Cumulative Abnormal Return value. This is proven by the t-test carried out, where a value of 0.9287 > 0.05 was obtained, so it can be concluded that H2 is rejected. 3) Simultaneously Earning Per Share and Price Earning Ratio do not have a positive and insignificant influence on Cumulative Abnormal Return which has been proven from the F-Test results where a value of 0.986304 > 0.05 is obtained, so it can be concluded that H4 is rejected.

Adaptations in Vocational Guidance: Experience from the Colombian Amazon

As we move into late postmodernity, the instruments and processes proposed require research and development processes that allow them to remain in force within current technological mediations and trends. In this context, the instruments that facilitate vocational guidance processes are included. Therefore, this innovation experience arises as a response to the needs present in the Colombian context, based on two objectives: To reduce the number of items in the CHASIDE vocational orientation test, 2) To validate the resulting test. A longitudinal study with a quantitative approach was carried out, with a convenience sampling in three Colombian municipalities during 2019-2023. Quantitative statistical analysis techniques, data were analyzed including statistics of mean, variance, Pearson's correlation coefficient, hypothetical difference of means, degrees of freedom, t-statistic, P(t) and critical value of t (one tail and two tails), and others. Additionally, internal consistency was checked using Cronbach's Alpha. Managing to reduce the test from 98 items to 35 items, preserving its two dimensions (Aptitudes and Interests), with a simpler structure, which responds to a Multifactorial Profile (PM), its validation confirmed the new structure, that established 7 factors for the dimension of aptitudes, constituted by 27 items and 3 factors for the Interests dimension with 8 items. In conclusion, social changes are reflected in factor structures and their groupings within professions, leaving the unifactorial proposed by Holland in the RIASEC, to give way to interactions within aptitudes and interests. Aptitudes being more stable than interests over time.

Pengaruh Penggunaan Media Sosial Instagram Terhadap Pemahaman Akhlak pada Mahasiswa PAI Angkatan 2021 di Universitas Ibn Khaldun Bogor

Understanding morals is one of the important components that students must have in an effort to deepen their knowledge and values ​​of Islamic teachings as the basis of a Muslim's knowledge. There are two factors that can influence each individual's understanding of morals, namely internal factors and external factors. Internal factors come from within students such as thought patterns, attitudes and self-motivation. Meanwhile, external factors include family, school, social environment and social media. Support for achieving understanding of student morals through the social media Instagram can be obtained if students have the ability to act and behave critically, carefully, wisely, think objectively and appreciate the diversity of Islamic information obtained. This research aims to find out whether there is an influence of the use of Instagram media on students' understanding of Islam at Ibn Khaldun University, Bogor. The research method used is a quantitative method. The data collection techniques used were questionnaires and documentation. The analysis technique used by researchers is an inferential statistical analysis technique with an analysis stage in the form of an analysis prerequisite test stage consisting of a normality test and linearity test, hypothesis testing consisting of a simple linear regression analysis test and a t test with the help of the SPSS version 27 application. Results from Research shows that there is an influence of the use of social media on the understanding of morals of PAI students class of 2021 at Ibn Khaldun University, Bogor. The magnitude of the influence that social media has on Islamic understanding shows a figure of 17.9%. Social media has an influence in shaping students' understanding and behavior regarding morals. Students who have good personalities and are not easily influenced by anything can determine their understanding of the information received via Instagram social media.

Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

Water Treatment: Evaluation of Maleic Acid-Acrylamide Copolymer Inhibitor Efficiency on Calcite Scale by Response Surface Methodology

Mineral scales of calcite are common in the oil field and pose a serious integrity problem in the wellbore, flow lines, and equipment. It is also a challenge faced by industries such as refineries and power plants. Scale deposition is a complex process depending on various factors such as concentration of scaling species, temperature, pH, and flow rates. Deterministic models are used to predict the scale formation from the level of supersaturation of the scaling species in the water at the operating conditions. However, due to the complexity of the interaction of variables affecting the scaling and inhibition by chemicals, it is suitable to be represented by statistical models. This work focused on applying statistical analysis techniques such as response surface methodology to understand the effect of different operating parameters on the inhibition efficiency of maleic acid-acrylamide copolymer on CaCO3 scales. The copolymer was synthesized, and its inhibition efficiency on the calcite scale was tested using static jar tests at different pH, temperature, and inhibitor concentrations. The effect of the critical parameters on the inhibition efficiency was analyzed using the statistical technique of Response Surface Methodology (RSM). The design of experiments (DoE) was created using a Box–Behnken design with three levels for each factor. The linear and the quadratic effects of the factors were studied and the interaction effects were analyzed using analyses of variance (ANOVA) and RSM. A desirability function was used to optimize the performance for the combination of the variables. The analysis showed that the linear effect of the parameters had the highest impact on the inhibition efficiency. Significant interaction effects were also identified between the operating variables. A transfer function was used to model the experimental data of inhibitor performance.

Analysis the Impact of Credit Risk, Liquidity Risk, Management Efficiency on Profitability through Capital in KBMI 4 Banking

This research aims to analyze the influence or impact of credit risk (NPL), liquidity risk (LDR), management efficiency (BOPO) on profitability (ROA) through capital (CAR) as mediating variables in KBMI 4 banking, namely BRI, BCA, BNI and Mandiri Bank. The data collection method is bank annual report data from 2019 - 2023. The analysis method in this research is quantitative analysis, to determine the correlation between variables X1, X2, and X3 to Y and Z as mediating variables. using statistical path analysis techniques and to test the level of significance using the inner model test by looking at the p-value processed with the Smart PLS program. The research results concluded that LDR and BOPO had a negative and significant impact on CAR, BOPO had a negative and significant impact on ROA, NPL had a negative and insignificant impact on ROA. However, NPL has a positive and significant impact on CAR, LDR and CAR have a positive and significant impact on ROA. Apart from that, in testing the indirect effect, the results showed that CAR was able to mediate the influence of the BOPO variable on ROA which had a negative and significant impact. However, the CAR variable is unable to mediate the effect of NPL and LDR on ROA.

The Burden of Chronic Diseases with the Status of Family Medical History Among Older Adults in India.

This study aims to assess and compare the prevalence of chronic diseases by the first-degree Family Medical History (FMH) and also explores the relationship between FMH and selected Non-communicable diseases (NCDs) among older adults in India. The present study collated secondary data from the Longitudinal Ageing Study in India (LASI, 2017-18). The eligible respondents for the analysis of this study were aged 45years and above, where the final study sample consistedof 65,562 older adults across all Indian states and union territories. The LASI dataset collected responses onself-reported diseases: Hypertension, Stroke, Heart disease, Cancer, and Diabetes. These diseases have a high prevalence among the population and are considered in the present study. Along with disease status, respondents' first-degree relatives FMH were used to fulfil the objective. Descriptive statistical analysis and multiple logistic regression techniques were used to accomplish the objectives analysis. This approach was chosen due to the binary nature of our primary dependent variables. The study found that the prevalence of selected NCDs was considerably higher among older adults with FMH than those without FMH. It revealed that NCDs and the status of FMH of parents and siblings were significantly associated. Based on the multivariate-adjusted model, we foundsignificantly higher odds for developing the NCDs when the respondents have FMH among at least one of the first-degree relative. The likelihood among those with FMHof having hypertension (AOR: 2.058), diabetes (AOR: 2.94), heart diseases (AOR: 2.39), stroke (AOR: 1.62) and cancer (AOR: 2.32) was higher compared to no FMH of respective diseases. Similarly, significant associations were observed according to the different stratification of the number offirst-degree relatives FMH. The present study demonstrated that first-degree relatives FMH is indeed a dominant associated risk factor for chronic disease among the older adults of India. This study supports the promotion of a disease history tool for chronic disease prevention and early detection approaches as a valuable measure of NCD risk. Public health practitioners can take several steps to access FMH and incorporate FMH into public health programs for the screening of the risk population.

Optimized Feature Selection for DDoS Attack Recognition and Mitigation in SD-VANETs

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures.

SPATIOTEMPORAL OF NITROGEN DIOXIDE (NO2) CONCENTRATION IN THE URBAN ENVIRONMENT OF KLANG VALLEY, MALAYSIA

The high concentration of nitrogen dioxide (NO2) directly results in Klang Valley’s air quality deterioration, causing a public health risk. This study was conducted to analyse the daily-averaged and annual concentration of nitrogen dioxide (NO2) on a spatial-temporal scale at five continuous monitoring stations under the Department of Environment (DOE) in Klang Valley, namely, Klang, Shah Alam, Petaling Jaya, Kajang, and Cheras from 2000 to 2009 using Man-Kendall statistical analysis and interpolation technique in Geographic Information System (GIS). The result clearly showed that the Petaling Jaya station was identified as the most polluted compared to other stations, with an average concentration of more than 0.050 ppm every year and reaching the maximum concentration of 0.069 ppm where the mean was 0.030 in 2001. Based on the p-value derived from the Mann-Kendall statistical analysis, the Klang, Petaling Jaya, Shah Alam, and Cheras stations recorded a significant trend with p-values < 0.05 at 0.0001 and 0.020, respectively. The annual concentration of NO2 in all the stations was in the range of 0.015 to 0.04 ppm from 2004 to 2009, compared to 0.005 to 0.01 ppm from 2000 to 2003. The highest annual-averaged NO2 concentration was reported at the Petaling Jaya station between 0.035 and 0.004 ppm for all years except 2007 and 2009 when concentrations were in the 0.03 to 0.035 ppm. Notably, the Petaling Jaya station had the highest annual NO2 concentration, which ranged from 0.025 to 0.04 ppm due to emissions from motor vehicles. The major pressure on road infrastructure was recognised, mainly a lack of space to accommodate the effect of the maximum density of motor vehicles and traffic, resulting in traffic congestion in the city centre.

Combining statistical analyses and GIS-based approach for modeling the sanitary boundary of drinking water wells in Yaoundé, Cameroon

In slums and urban areas with unplanned housing such as in the city of Yaounde, Cameroon, poor water quality and inadequate sanitation pose significant health risks. The absence of locally-defined sanitary boundaries, tailored to hydrogeological conditions, hinders effective zoning and land use planning, exacerbating environmental degradation and health hazards. In this study, the sanitary boundary between drinking water wells and sources of pollution in the city of Yaoundé was defined using statistical analysis techniques and Geographic Information Systems (GIS). The Groundwater Quality Index (GWQI) and certain significant parameters affecting water quality notably the transmissivity of the aquifer and well depth were used to establish the sanitary boundary equation which was then interpolated in a GIS environment to obtain the sanitary boundary map of the study area. The linear equation deduced from the significant factors was defined to map the health boundaries between wells and pollution sources for a nominal value of the GWQI (GWQI = 25). Of the 112 wells analysed, 37 % had an excellent GWQI, 16 % were good while the remaining 47 % were poor. Statistical analysis showed a strong correlation between the GWQI and significant factors of groundwater pollution, such as the distance between well and pit latrines (r = −0.753), the aquifer transmissivity of the formation (r = 0.671) and the depth of the wells (r = - 0.855) but no correlation with elevation (r = 0.017) and well age (r = 0.090). Linear regression analysis confirmed the association of the GWQI with the main factors of pollution (p ≤ 0.05). A coefficient of determination of R2 = 0.85 was obtained when validating the linear regression plot based on independent data between measured and predicted GWQI. The sanitary boundary map shows that the wells in our study area should be located between 39 m and 370 m, with an average value of 215 m. New regulations on the distance between well and pit latrines are essential to prevent groundwater pollution.

Pengaruh Motivasi Belajar Mahasiswa PAI UNP melalui Pergaulan

Sociability is a continuation of the process of social interaction that exists between individuals and their social environment, then motivation is one of the things that affects the success of learning activities, without the learning process will not run well. This study aims to determine the effect of association on the learning motivation of PAI students class of 2020 UNP. This research includes quantitative research with the type of correlation and descriptive statistical analysis techniques. The data collection method uses primary data. In this study using validity test and reliability test. There were 60 PAI 2020 UNP students who were sampled in this study. Data data were collected through instrument questionnaires from socialization and learning motivation variables. The results of the data data are processed through statistical calculations and average correlations obtained through the use of SPSS 16.0. The data shows that there is a positive and significant influence between socialization on learning motivation by 59.3%. The results showed that association partially had a positive and significant effect on learning motivation, which means that learning motivation is influenced by association. Researchers recommend that be smart in choosing associations.

Influence Of Working Group Activities Of Teachers On The Effectiveness Of Mathematics Teaching At EBC Besilau Laulara And EBC Fahisoi Lequidoe Academic Year 2021

The data analysis technique used in this research is descriptive statistics analysis techniques and inference analysis techniques with the aim of studying the research variable itself. In relation to the variables and samples mentioned, the techniques used to collect data are observation, questionnaire and documentation and according to Setiono and Riwonoto (2015) states that the Likert scale is the psychometric scale that is generally used in the questionnaire. And the instrument to be used to measure alternative responses is the Likert scale, this scale can be measured by choosing the perfect alternative answers, with the value used being 1 to 5 variables, so the researcher concluded that the questionnaire distributed to teachers with variables (X) is the influence of the activity of the teachers (GTP) and variable (Y) is the effectiveness of teaching mathematics, from the dependent and independent variables used by the researcher to find out whether or not the influence of one variable on another variable. Thus the results of the inference statistics analysis on the influence of the activity of the Working Group teachers (GTP) with a sample of 27 shows that the table = 0, 9 with a count = 2. Because the largest count of the table (2 > 0,9) is that we can say that we revenue Ho and accept Hi with his conclusion that there is a significant influence between the Activity of the Working Group dose teachers on the effectiveness of mathematics teaching (Y) at EBC Fahisoi Lequidoe and EBC Besilau Laulara.

Pengaruh Perputaran Arus Kas, Current Ratio, Debt to Asset Ratio dan Debt to Equity Ratio Terhadap Return on Asset pada Perusahaan Manufaktur Sub Sektor Makanan dan Minuman yang terdaftar di Bursa Efek Indonesia Tahun 2015-2022

The purpose of this study is to ascertain how return on assets in food and beverage manufacturing companies listed on the Indonesia Stock Exchange is impacted by cash flow turnover, current ratios, debt to equity ratios, and debt to asset ratios for the years 2015–2022. This study employs a quantitative methodology that combines a verification and descriptive strategy. While verification statistical analysis uses traditional assumption tests, multiple linear regression tests, Pearson product moment correlation coefficient tests, determination coefficient tests, and hypothesis testing with partial and simultaneous tests (t and f tests), descriptive statistical data analysis techniques are used to find mean, maximum, minimum, and standard deviation data. 56 financial reports from businesses in the Food and Beverage Manufacturing Companies provide the sample data. sampling utilizing nonprobability sampling methods combined with purposive sampling. IBM SPSS version 26.0 was the analysis tool used in this investigation. The study's findings indicate that return on assets is not affected partially or simultaneously by cash flow turnover, current ratio, debt to equity, or debt to asset ratio

Association Analysis and Structured Equation Modelling in Groundnut (Arachis hypogaea L.)

India is one among the top countries producing groundnut in the world ranking. The breeding programmes of groundnut aim at developing early maturing and high yielding varieties. Correlation and Path analysis is a tool which provides information on magnitude and intensity of association among yield components and yield. The present study analyses effects of the yield components on yield in the segregating population of groundnut. The traits days to maturity, number of matured pods, kernel yield, hundred pod and kernel weight are positively correlated with pod yield while the vegetative traits height of main axis and number of branches are negatively correlated with pod yield. Path analysis revealed that days to accumulation of 25 flowers, days to maturity, number of matured pods, kernel yield, hundred pod and kernel weight exerted low to moderate direct effects on pod yield. Structural Equation Model (SEM) is a powerful statistical technique for Path analysis that allows for more complex models than multiple regressions with its single dependent variable. In groundnut segregating population, structural equation modelling splits the yield component into traits before harvest and after harvest. Though both the variables are correlated, the traits before harvest have higher influence on pod yield.

Gambaran Self-Esteem Siswa SMA dan Implikasinya dalam Pelayanan Konseling

This study is motivated by students who cannot express their opinions, cannot accept their strengths and weaknesses and have difficulty interacting with friends and the irenvironment, feel inferior to their body shape, different skin color compared to their friends and during class hours students do not dare to express their opinions in front of the class because they are not confident. This study aims to describe the description of self-esteem at SMAN 1 2X11 Enam Lingkung. This type of research is quantitative with a descriptive approach. The population of this study was 694 students at SMAN 1 2X11 Enam Lingkung in the 2024/2025 school year and a sample of 328 students taken through proportional random sampling technique. Data collection in this study used a Likert scale, namely a self-esteem questionnaire that has been tested for validity with the Pearson correlation formula (0.355). The data in this study were processed with descriptive statistical analysis techniques. The results of this study indicate that the self-esteem of students at SMAN 1 2X11 Enam Lingkung as a wholeis in themoderate category (65.24). Based on the results of this study, counseling teachers can provide assistance to improve students' self-esteem in the form of information services, content mastery services and group guidance services.

APPLICATION OF THE LEARNING CYCLE LEARNING MODEL TO THE BIOLOGY LEARNING OUTCOMES OF CLASS

This type of research is experimental research with a research design in the form of a pretest-posttest control group design. The objectives of carrying out this research are (1) to find out the description of the biology learning outcomes of students who are taught using the Learning Cycle model, (2) to find out the description of the biology learning outcomes of students who are taught without the Learning Cycle model, (3) to find out the differences in the biology learning outcomes of students who are taught with the Learning Cycle model. Learning Cycle with students taught without the Learning Cycle model. The independent variable in this research is the Learning Cycle learning model and the dependent variable is student biology learning outcomes. The subjects in this research were 38 students in class X IPA1 and X IPA2 at SMA Negeri 1 Gowa. The data collection technique in this research is by using students' biology learning outcomes tests. Meanwhile, the data analysis technique is by using descriptive and inferential statistical analysis techniques. The results of this research show that there are differences between students taught using the Learning Cycle model and students taught without the Learning Cycle model. Keywords: Learning Cycle, Learning Outcomes

Development of A Man-Machine Interface for Managing Photovoltaic Energy: Applied Study

Objective: Develop and evaluate a human-machine interface (HMI) that integrates advanced monitoring, forecasting, and management functionalities for photovoltaic solar energy systems, aiming to optimize energy production and operational efficiency. Theoretical Framework: This study is based on concepts of modeling and simulation, solar energy management, and problem-solving methodologies such as Soft System Methodology (SSM). Method: An applied approach was adopted using modeling, simulation, and statistical analysis techniques. The research included a bibliographic review in scientific databases, a case study, and SSM to organize and solve complex problems. 121 digital solar energy platforms in Brazil were analyzed to define the interface requirements. The interface was developed with React JS, Axios, Bootstrap v5, Apache Echarts, HTML, CSS, JavaScript, and Python libraries for forecasting models. Results and Discussion: The interface, named "Solar Smart Manager," enables efficient monitoring and management of energy production using critical data such as temperature, time of day, and solar irradiation. Tests in a real operational environment demonstrated improvements in energy management, incident response, and preventive maintenance. The functionality of validating solar radiation incidence data represents a significant contribution to the energy sector, promoting sustainability and innovation. Research Implications: The practical and theoretical implications of this research provide insights into the efficient and optimized management of photovoltaic solar energy systems, contributing to a better understanding and optimization of available solar resources. Originality/Value: This study contributes to the literature by developing an innovative interface that improves operational efficiency and solar energy management. The relevance and value of this research are evidenced by its positive impact on the energy sector, promoting sustainability and innovation.