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According to data from the Maternal Perinatal Death Notification (MPDN), the maternal death recording system of the Ministry of Health, the number of maternal deaths increased from 4,005 cases in 2022 to 4,129 cases in 2023. The objectives of this research are:Identify Variable Relationships: To assess how staff training, technology usage, and data integration affect the accuracy and efficiency of recording and reporting.Evaluate Direct and Indirect Effects: To analyze the direct and indirect impacts of these variables on the outcomes of the recording and reporting system. Provide Improvement Recommendations: To develop recommendations for enhancing the recording and reporting system based on the analysis results. Research Methodology Path analysis is required to evaluate the relationships between various factors that influence the recording and reporting system.Steps in Path Analysis: Data Collection, Path Analysis Model, Statistical Analysis, Interpretation of Results. Staff Training (X1): Data Accuracy (Y1): β = 0.45 (p < 0.01).Reporting Efficiency (Y2): β = 0.40 (p < 0.05), Technology Usage (X2):Data Accuracy (Y1): β = 0.35 (p < 0.05)Reporting Efficiency (Y2): β = 0.55 (p < 0.01), Data Integration (X3):Data Accuracy (Y1): β = 0.50 (p < 0.01), Reporting Efficiency (Y2): β = 0.45 (p < 0.01). Path analysis reveals that staff training, technology usage, and data integration have a significant impact on the recording and reporting system at Ardimulyo Health Center. Improvements in these three variables can enhance the accuracy and efficiency of reporting. Recommendations for improvement include investing in staff training, implementing better technology, and enhancing data integration.

According to data from the Maternal Perinatal Death Notification (MPDN), the maternal death recording system of the Ministry of Health, the number of maternal deaths increased from 4,005 cases in 2022 to 4,129 cases in 2023. The objectives of this research are:Identify Variable Relationships: To assess how staff training, technology usage, and data integration affect the accuracy and efficiency of recording and reporting.Evaluate Direct and Indirect Effects: To analyze the direct and indirect impacts of these variables on the outcomes of the recording and reporting system. Provide Improvement Recommendations: To develop recommendations for enhancing the recording and reporting system based on the analysis results. Research Methodology Path analysis is required to evaluate the relationships between various factors that influence the recording and reporting system.Steps in Path Analysis: Data Collection, Path Analysis Model, Statistical Analysis, Interpretation of Results. Staff Training (X1): Data Accuracy (Y1): β = 0.45 (p < 0.01).Reporting Efficiency (Y2): β = 0.40 (p < 0.05), Technology Usage (X2):Data Accuracy (Y1): β = 0.35 (p < 0.05)Reporting Efficiency (Y2): β = 0.55 (p < 0.01), Data Integration (X3):Data Accuracy (Y1): β = 0.50 (p < 0.01), Reporting Efficiency (Y2): β = 0.45 (p < 0.01). Path analysis reveals that staff training, technology usage, and data integration have a significant impact on the recording and reporting system at Ardimulyo Health Center. Improvements in these three variables can enhance the accuracy and efficiency of reporting. Recommendations for improvement include investing in staff training, implementing better technology, and enhancing data integration.

The newest renewable energy source in the world and one of the most reasonably priced is solar energy. Because solar energy has so many benefits all year round, it can be a cost-effective energy source when used, especially since it is so abundant globally. to produce electricity by converting sun energy. The equator-based nation of Indonesia boasts an abundance of solar energy resources, with an average daily solar radiation intensity of about 4.8 kwh/m2. However, there is an abundance of solar-based energy sources that can be utilized. Especially in Lolo Wano Village, where the intensity of solar radiation is quite high, it is an option to develop a Solar Power Plant (PLTS) as a solution to electrical energy needs. In order to specifically identify the class of unknown object labels, classification techniques are employed since they are able to identify models that distinguish between different data classes or data ideas. In the meantime, the Naïve Bayes algorithm takes into account multiple factors that will influence a decision's final result in order to forecast future opportunities based on data that has already been collected. The information utilized comes from observations made by the LOLO WANO VILLAGE PLTS Community (PLTS). The data gathered from the satisfaction survey will be divided into two categories: training data and testing data. The testing data's accuracy will be evaluated using the output of the training data model. The classification findings demonstrate that, with the maximum level of accuracy at 87.50%, the Naïve Bayes algorithm is appropriate for gauging student satisfaction with online learning.

The development of culinary MSMEs in Banjarsari District, Surakarta City has developed rapidly, but the increase in the number of culinary MSMEs in Banjarsari District, Surakarta City is not necessarily followed by an increase in the financial performance of these MSMEs  This study aims to find out and analyze the influence of financial literacy, financial technology, entrepreneurial orientation, and innovation towards the financial performance of culinary MSMEs in Banjarsari District, Surakarta City. Data collection in this study uses a questionnaire distributed to respondents. The sample in this study amounted to 95 culinary MSMEs in Banjarsari District, Surakarta City with the type of sampling, namely purposive sampling with the consideration that the MSMEs have been running for at least 2 (two) years. The analysis methods used in this study are descriptive analysis, multiple linear regression analysis, t-test, F test (model accuracy test), and determination coefficient test (R2).  The results showed that the determination coefficient (adjusted R Square) is 0.548. Means This means that the amount of contribution of the influence of the independent variable X1 (financial literacy), X2 (financial technology), X3 (entrepreneurial orientation) and X4 (product innovation) towards Y (performance finance) by 54.8%. The rest (100% - 54.8%) = 45.2% is influenced by other variables outside the model such as the work environment, company size, market competitiveness, operational costs, working capital, etc.

This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.

PT Mass Rapid Transit Jakarta operates a mass transportation system from Lebak Bulus Station to Bundaran HI. One of the traction substations is located in Cipete Raya, with a voltage rating of 20kV/1.2kV. A critical piece of equipment in this substation is the traction transformer, with a capacity of 4850 kVA/2x2500 kVA. The purpose of this study is to predict the service life of the Cipete Raya traction transformer based on temperature and load using the linear regression method. This study employs direct observation, analyzing load data from traction transformers 1 and 2 at Cipete Raya from January 2022 to June 2024, along with transformer temperature measurements. Secondary data include the technical specifications of the Cipete Raya traction transformer. The linear regression analysis for transformer 1 yields the equation y = 687.42 + 11.97x, indicating a 5.75% annual increase over the next 5 years, with a very strong correlation coefficient of R = 0.919. For transformer 2, the equation is y = 815.4543 + 6.488x, showing a 3% annual increase, with a strong correlation coefficient of R = 0.814. Based on the transformer aging calculations for June 2024, Transformer 1 has a per unit aging value (V) of 0.0014 and an estimated service life (n) of 407.689 years, while Transformer 2 has a V of 0.0012 and an estimated service life of 496.77 years. The aging model evaluation using MAPE shows that the prediction accuracy for transformers 1 and 2 is 6% and 3%, respectively, indicating excellent modeling performance.    

Tropical diseases remain a serious public health challenge in Southeast Asia, particularly malaria, which has high morbidity and mortality rates. The complexity of their spread is influenced by various factors, including climate, environment, and population, requiring a spatially-based analytical approach to understand their distribution patterns. This study aims to develop a regression-based spatial model to predict the spread of tropical diseases and identify hotspots in high-risk areas. The data used include tropical disease case reports from national health agencies, climate data (temperature, rainfall, humidity) from BMKG and WorldClim, and population data (density and mobility) from  BPS and other official sources. The analysis was conducted using a Geographic Information System GIS for spatial mapping, as well as the application of spatial regression models, namely the Spatial Lag Model SLM and Spatial Error Model SEM. The results show that the developed model is able to predict disease distribution with a high level of accuracy, demonstrated by statistical validation through AIC, and Morans I. One of the main findings is the identification of malaria hotspots with a confidence level of 93, as well as the mapping of tropical disease risk predictions covering the Southeast Asian region. These results have significant implications for public health policy, particularly in resource allocation, prevention program planning, and priority area-based interventions. Furthermore, this study recommends the integration of big data and machine learning technologies to enrich predictive models and develop more adaptive early warning systems. Thus, this research contributes to strengthening tropical disease control strategies in Southeast Asia with a comprehensive spatial data-driven approach.

The current administration system for apartment buildings in Pontianak city still relies on manual data recording methods, which tend to cause various recording errors such as delays in data processing, incorrect data entry, and difficulties in accessing information. This condition has an impact on suboptimal service to apartment residents. As an alternative to address this problem, it is proposed to develop a web-based information system that can digitize data from various business processes in apartment management. This system is designed using the Laravel framework, a popular framework for web application development. The stages in this community service include in-depth analysis of system requirements that are suitable for the conditions of apartment buildings in Pontianak city, designing a user-friendly and efficient system, building the system based on the design that has been created from manual form recording, and conducting training for apartment managers to be able to operate the system well. This technology transfer has a positive impact on the change in the administration model and it is expected to create more effective and efficient apartment management and is easily accessible because it is online. Some other benefits that occur include increased accuracy of resident data, ease of accessing information, data transparency, and improved service quality for residents

This research aims to find out how to create a scanning machine tool and a training model using a scanning machine tool for passing accuracy in football. The research method used in this research is the ADDIE model research and development (R & D) method. The stages carried out in the research were 5 stages, namely 1.) Analysis, 2.) Design, 3.) Development, 4.) Implementation, 5.) Evaluation. The research subjects for the implementation of the scanning machine tool and the training model with the tool were 20 players from the Football and Futsal Club (KSBF) of Jakarta State University. In this research, researchers collaborated with 3 experts, namely technical director, coach of the Estrelass Del Futbol (EDF) Academy and electronics expert. The validation test used in this research was a justification test consisting of 3 experts. After the expert validation test is carried out, the next step is the implementation of the scan machine tool and the training model with the scan machine tool. The result of this research is the creation of a product and guidebook of a scanning machine.

There are three issues studied in this thesis, namely: The role of Micro Business Productive Fund Assistance (BPUM) on the sustainability of MSMEs, procedures for distributing Micro Business Productive Fund Assistance (BPUM) to MSMEs, and maintaining business continuity during the Covid-19 pandemic. The aim of this research is to determine the role of Micro Business Productive Fund Assistance (BPUM) on the sustainability of MSMEs, to find out the procedures for distributing Micro Business Productive Fund Assistance (BPUM) on the sustainability of MSMEs and to find out how to maintain business continuity during the Covid-19 pandemic in the sub-district. Tungkal Ilir in 2020 and 2021. To reveal this problem in depth and comprehensively, researchers used a qualitative approach by collecting data using observation, interviews and documentation. The data analysis technique used is field analysis using the Miles and Huberman interactive model including data reduction, data display, data verification. From the research results, it can be concluded that the role and potential of BPUM are: The BPUM program will be a temporary buffer for micro businesses to reduce the negative impact of social restrictions/pandemic, encouragement to increase MSME data, accuracy of targeting of aid distribution and encouragement to BPUM beneficiaries related to licensing, certification and taxation systems. The procedures for distributing BPUM are listed in Chapter IV, Article 7 of the Regulation of the Minister of Cooperatives and Small and Medium Enterprises of the Republic of Indonesia Number 6 of 2020 concerning General Guidelines for National Support for Economic Recovery in the context of threats that endanger the nation's economy and saving the national economy during the COVID-19 virus. And there are three factors that play a role in maintaining business continuity during the COVID-19 pandemic, namely the ability to innovate, manage customers and return on investment.

The North Sumatra Aceh National Sports Week (PON) which will be held in September 2024 in Papu, North Sumatra has drawn many pros and cons from the public. This topic allows the public to provide criticism, suggestions and opinions regarding the 2024 North Sumatra Aceh PON. Instagram is a popular social media for conveying public opinion. The sentiment analysis process can find and resolve problems based on public opinion on social media such as Instagram. The classification method used in this research is the Naïve Bayes Classifier. Datasets can be obtained from the data crawling process using the Google Chrome extension: IGCommentExport. The data is labeled positive, neutral, or negative. The results of the labeling process showed 770 negative data, 256 neutral data and 920 positive data. Then pre-processing is carried out on the data that has been previously labeled, and a word weighting process is also carried out using TF-IDF. After that, modeling was carried out using the Naïve Bayes Classifier and the final process was evaluation-testing. The high accuracy results from the fourth experiment which compared 90% of the training data with 10% of the testing data resulted in an accuracy of 75%. Meanwhile, the sentiment test results show that positive sentiment is more numerous than negative sentiment and neutral sentiment.

In the era of data revolution and artificial intelligence, machine learning model optimization has become one of the most dynamic and crucial research areas. This article reviews the latest techniques in machine learning model optimization with a focus on pursuing maximum performance. We discuss various methods applied to improve model accuracy and efficiency, ranging from hyperparameter tuning techniques, advanced optimization algorithms such as Bayesian optimization, to innovative approaches such as meta-learning and transfer learning. These optimization techniques not only aim to improve model performance but also to overcome challenges related to big data, model complexity, and computational limitations. We investigate how these methods can be integrated in machine learning pipelines to achieve better results with more efficient resources. Through a review of recent literature and case studies of applications in various domains, this article provides in-depth insights into the trends and developments in model optimization, as well as practical recommendations for researchers and practitioners in pursuing maximum performance from their machine learning systems. A better understanding of these cutting-edge techniques is expected to facilitate the achievement of better and more innovative results in future machine learning applications.

This research aims to determine the influence of stock prices, stock returns, and capital market training on interest in investing in shares in the capital market among students at the Faculty of Economics, Slamet Riyadi Surakarta University. The population of this research were students from the Faculty of Economics, Slamet Riyadi Surakarta University, from whom a sample of 95 respondents was taken using a purposive sampling method with the criteria being that students had/are currently taking capital markets courses. The analytical methods for this research are descriptive analysis, multiple linear regression analysis, t test, F test (model accuracy test), and coefficient of determination test. The results of the research prove that stock prices, stock returns and capital market training partially have a positive and significant effect on interest in investing in shares in the capital market among students at the Faculty of Economics, Slamet Riyadi Surakarta University.

Electronic Medical Records (RME) is an information system used to manage patient medical data electronically, which aims to improve the efficiency and quality of health services in hospitals. This study aims to evaluate the performance and effectiveness of RME in hospitals using the PIECES model. The main focus of the research is on the dimensions of Performance, Information, Economics, Efficiency, and Control to identify the strengths and weaknesses of RME and provide recommendations for improvement. This research uses the Study Literature Review (SLR) method which involves collecting and critically analyzing relevant literature from scientific journals, articles and books. The collected data is evaluated based on the dimensions of the PIECES model to provide a comprehensive picture of RME implementation. The research results show that RME can improve data access speed, system response time, and accuracy of medical information, which supports better clinical decision making. Economically, RME helps reduce operational costs and improve work efficiency. However, challenges such as network reliability, data security, and interoperability still need to be overcome to achieve the full benefits of RME. Dimension Control highlights the importance of strict security policies and user training to ensure safe and effective use.

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

Diabetes Mellitus (DM) is a metabolic disorder characterized by high blood sugar levels due to insulin deficiency. Factors causing Diabetes Mellitus (DM) are lifestyle which includes diet, lack of exercise, monitoring blood sugar, and medication. Most people do not realize that they have DM and only find out when they experience severe symptoms. To avoid this, the k-Nearest Neighbor (KNN) method can be used to predict the possibility of developing diabetes. The aim of this research is to classify diabetes mellitus using the K-Nearest Neighbor (KNN) method and make people more aware of the risk of disease through healthy lifestyle changes. Data received from the Dharma Husada Clinic is categorized based on researchers' needs, including age, BMI, insulin, skin thickness, glucose, diabetes, genetics, and insulin. This research was carried out in three main steps: dataset input, preprocessing, and evaluation. The first stage is data analysis which begins by entering a dataset to train and test the model, where each data element has certain characteristics (attributes) and classes. Preprocessing steps include training data generation and data cleaning, which includes sanitization, lowercase, normalization, stopwords, stemming, and tokenizing. The final step is evaluating. Evaluation includes building an evaluation model and measuring the level of accuracy, building a predictive model, and saving the model. This research shows that the K-Nearest Neighbor (KNN) method can be used to classify diabetes mellitus (DM), but especially in a small dataset consisting of 245 dates and 8 attributes it is not accurate for patients aged 30 years. . A k value that is too small can cause overfitting, and a k value that is too large can cause underfitting. However, if the amount of data is small, the choice of k can have a large impact.    

This study explores the utilization of web technology for the attendance system at Kalisusu Village Office, Nabire, to address the inefficiencies and errors of the manual attendance system. Using the Waterfall method, this study includes needs analysis, system design, implementation, testing, and maintenance. The system was developed using PHP, MySQL, and the Bootstrap framework to create a responsive interface. The results indicate a significant improvement in the efficiency and accuracy of attendance records, with a 75% reduction in data processing time and a 95% increase in recording accuracy compared to the manual system. The main challenge in implementation was user adaptation to the new system, which was overcome through intensive training. This research resulted in a web-based attendance system that not only enhances attendance management at Kalisusu Village Office but also provides a model that can be adapted by other village offices. The broader implications of this research include the potential for improved administrative efficiency and transparency at the village government level.    

The Field Work Practice (PKL) conducted by students of STMIK Pesat Nabire at the Waharia Village Office, Nabire Regency, Central Papua Province, aims to develop and implement a web-based citizen data collection system. This activity is driven by the need for an efficient and integrated data collection system to facilitate administrative processes and public services in Waharia Village. The developed web-based data collection system is designed to record demographic information, residential status, and other essential data of village residents. The development process involves needs analysis, system design, software implementation, and training for village officials. The results of this PKL show that the web-based data collection system can improve the accuracy and speed of data processing, minimize errors in manual recording, and facilitate information access for relevant parties. It is hoped that with this system, work efficiency at the Waharia Village Office will increase, public services will improve, and citizen data will be managed more securely and structurally. This system is also expected to serve as a model for other villages in Nabire Regency in adopting information technology for village administration management.    

This review aims to investigate the role of forecast dispersion and accuracy in explaining cross-sectional return anomalies in financial markets. By synthesizing recent theoretical and empirical research, the study examines how differences in information precision among investors lead to heterogeneous beliefs, which in turn affect asset prices and returns. The methodology involves a comprehensive literature review to identify key findings and theoretical frameworks that link forecast dispersion to market dynamics. Results indicate that higher forecast dispersion, associated with greater uncertainty and risk, correlates with higher expected returns as compensation. Conversely, accurate forecasts enhance market efficiency by reducing information asymmetry, thereby mitigating anomalies. The study also highlights theoretical models that explain anomalies like returns to skewness and disagreement through the lens of forecast dispersion. Empirical evidence supports these models, demonstrating the significant impact of forecast dynamics on asset pricing anomalies. The review concludes by emphasizing the need for further research to refine models capturing forecast dynamics and exploring the behavioral biases influencing forecast accuracy and dispersion. Understanding these factors is crucial for improving investment strategies, market efficiency, and risk management practices.

This research explores the relationship between public information precision, borrower risk-taking behavior, and financial reporting regulations. It examines how varying levels of accounting disclosures influence creditor-borrower dynamics in financial markets. Enhanced precision in public information, such as accounting earnings, promotes market efficiency by reducing information asymmetry and improving creditors' ability to accurately assess borrower creditworthiness. While higher precision generally mitigates borrower risk-shifting tendencies, regulatory context and economic conditions modulate these effects. This literature review systematically identifies and analyzes peer-reviewed articles on forecast dispersion, accuracy, and their implications for cross-sectional return anomalies in financial markets. The findings reveal that higher forecast dispersion is linked to greater uncertainty and perceived risk, leading to higher expected returns, while accurate forecasts reduce information asymmetry and improve market efficiency. Differences in forecast precision significantly contribute to market anomalies. In conclusion, forecast dispersion and accuracy are critical in explaining cross-sectional return anomalies. Future research should refine models, explore behavioral biases, and evaluate technological advancements, emphasizing balanced financial reporting regulations to harness transparency benefits while mitigating potential costs during economic expansions.