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This study focuses on analyzing the welfare index in the ASEAN region (covering six major countries) by comparing two perspectives: objective welfare (Human Development Index/HDI) and subjective welfare (World Happiness Index). Using a balanced panel dataset from 2015–2023, the research applies different econometric approaches for each model, namely the Random Effect Model (REM) for HDI analysis and the Common Effect Model (CEM) for happiness analysis. Empirical findings indicate a striking welfare paradox across the six sample countries. In the objective dimension (HDI), economic stability (GDP) and governance free from corruption (CPI) are proven to be the main positive and significant drivers, while government expenditure (GovExp) shows no meaningful impact, suggesting budget inefficiency. Conversely, in the subjective welfare model, the Easterlin Paradox emerges, as GDP and the corruption index have no significant effect on the happiness index. The happiness levels in these six countries tend to be more influenced by government expenditure. This study concludes that strong economic fundamentals and clean governance free from corruption are essential to building a high quality of human life, whereas citizens’ life satisfaction is more determined by the direct presence of the state through public spending.

This research aims to examine how prices and reviews influence consumers 'purchasing decisions on the Shopee platform in Jambi City. The rapid development of e-commerce has encouraged consumers to be more careful and discerning when selecting products, making price and product reviews important considerations in buying decisions. This study employed a quantitative approach via a survey method for data collection. The questionnaires were distributed online and successfully gathered responses from active Shopee users in Jambi City. Purposive sampling was employed to make sure that respondents met research criteria. The information gathered underwent analysis using IBM SPSS Statistics 27, which involved conducting assessments of validity, reliability, and classical assumptions, and multiple regression tests to see the impact of each variable on buying decisions. The study revealed that product price and reviews hold an important and relevant impact on consumer buying decisions. This research indicates that the more competitive the price and the better the quality, the bigger the possibility of customers buying products on Shopee.

Website Accessible Jambi City Population and Civil Registry Service https://disdukcapil.jambikota.go.id. The problem with the Jambi City Population and Civil Registry Service website is that not all information is available, especially on the Profile menu there is a Media Information sub-menu, Data menu and Facilities and Infrastructure menu. On the Information Media sub-menu, there is a Demographic Data sub-menu, where the contents of the sub-menu are still empty, preventing users from obtaining information. On the Public Facilities and Infrastructure menu from the sub menu, the data cannot be accessed so that it makes users unable to get information. And in the appearance of the Jambi City Population and Civil Registry Service, when accessed via Google Chrome, the appearance is disorganized, so users have to open the website using a laptop/PC to get a website display that is orderly and easy for users to understand. Quality measurement is carried out based on user satisfaction point of view in order to improve the quality of service to the community and make optimal use of the website. In analyzing user satisfactionwebsite DUKCAPIL Jambi using the webqual 4.0 method, there are 4 variables, namely usability (usability), information quality (information quality), interaction quality (interaction quality), and user satisfaction (user satisfaction) and using the software (software) SPSS. Of the 3 hypotheses proposed, all hypotheses were accepted in this study.

Diabetes Mellitus is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action or both. Hyperglycemia is a medical condition in the form of an increase in glucose levels beyond normal limits which is a characteristic of several diseases, especially Diabetes Mellitus, in addition to various other conditions. Diabetes Mellitus is currently a global health threat. Classification is one of the techniques of data mining that can be used to help predict the results of the classification of types of diabetes using the naïve Bayes algorithm. Testing was carried out using 5 evaluation models including rapid miner with 3 options, namely use training set, 5 Fold Cross-Validation, 10 Fold Cross-Validation, and 2 other evaluation models, namely Microsoft Excel and Python. Testing data regarding Diabetes Mellitus has high accuracy in the excel evaluation model, which is 89.00% compared to other evaluation models. Meanwhile, the lowest accuracy is the Python evaluation model which obtains an accuracy of 86.36%. The Naïve Bayes algorithm can be said to be one of the most effective algorithms, both in terms of calculations and the final results, where the test can be used as a basis for diabetes mellitus considering the accuracy results are above 85%.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

Toko Jahit SA’aminah is a business engaged in tailoring services and the sale of sewing supplies that still manages data manually using record books. This condition causes several problems, such as slow data recording, the risk of data loss or damage, difficulties in monitoring the status of tailoring work and inventory, as well as obstacles in preparing tailoring service and sales reports. This study aims to design and develop a web-based tailoring service and sales information system to optimize the effectiveness and efficiency of operational performance. The system development method used is the waterfall method, which includes the stages of requirements analysis, system design using UML (Use Case Diagram, Activity Diagram, and Class Diagram), implementation using the Laravel framework with the PHP programming language and MySQL database, as well as system testing using the Black Box Testing method. The results show that the developed system is able to facilitate the management of tailoring service and sales data, monitor the status of tailoring work, check the availability of sewing supplies, and accelerate the preparation of tailoring service and sales reports to be submitted to the owner of Toko Jahit SA’aminah.

Employee performance is a strategic aspect that plays an important role in achieving company goals, especially in facing increasingly fierce business competition. Employee performance is not formed directly, but is influenced by various internal organizational factors, such as work discipline, compensation systems, and work environment conditions. This study aims to analyze the influence of work discipline, compensation, and work environment on employee performance at PT Dunia Aneka Usaha. This study uses a descriptive quantitative approach. The sampling technique used is a saturated sample, so that the entire population of 70 employees was used as research respondents. Data were collected through questionnaires compiled based on indicators of each variable and measured using a Likert scale. Furthermore, the data were analyzed using validity and reliability tests to ensure the quality of the research instrument, as well as statistical analysis in the form of t-tests, F-tests, and multiple linear regression analysis to test the proposed hypotheses. The results of the study indicate that work discipline, compensation, and work environment have a positive and significant effect on employee performance, both partially and simultaneously. These findings indicate that improving work discipline, providing appropriate compensation, and creating a conducive work environment can encourage improved employee performance. Therefore, companies are advised to manage these three factors sustainably so that employee performance improves and organizational goals can be achieved optimally.

With the rapid growth of Jambi City's tourism sector, the demand for souvenir products as a representation of local culture has increased. However, this potential has resulted in fierce competition among businesses. To generate new customers, businesses require effective digital and physical marketing strategies. The purpose of this study was to examine how social media marketing, product quality, and location at Gerai Adila simultaneously and partially influence consumer purchasing decisions. A survey method was used to implement a quantitative approach; data from 387 respondents, collected through Google Forms, were selected using the Slovin formula from a population of 11,723 customers in 2024. In data analysis, instruments, classical assumptions, multiple linear regression, and hypothesis testing were conducted using SPSS 25. The results showed that social media marketing, product quality, and Gerai Adila's location partially had a positive and significant impact on consumer decisions to purchase products at Gerai Adila; social media marketing played a role in increasing consumer brand awareness and purchasing interest, while product quality influenced consumer satisfaction and trust. In addition, it was proven that all three variables had a positive and significant impact on purchasing decisions. The results show that to increase the competitiveness of souvenir shops in Jambi City, digital marketing strategies, consistent product quality, and the right location are important factors.

The implementation of the Electronic-Based Government System (SPBE) is essential for achieving efficient, transparent, and accountable village governance. Sido Rukun Village in Merangin Regency, Jambi Province, has begun using several government applications but lacks a structured enterprise architecture aligned with the national SPBE framework. This study aims to develop an enterprise architecture for SPBE in the business process domain at Sido Rukun Village. The research employs the TOGAF ADM (The Open Group Architecture Framework – Architecture Development Method) approach, involving stages such as identifying current business processes, designing a target architecture, and conducting a gap analysis between the as-is and to-be states. The findings include a business process architecture blueprint compliant with Presidential Regulation No. 95 of 2018 and Presidential Regulation No. 132 of 2022 on the National SPBE Architecture. This blueprint encompasses BPMN-based business process models and supporting artifacts that serve as a foundation for integrated information systems at the village level. The study’s implications are significant: it provides Sido Rukun Village with a practical and standardized technical blueprint for implementing a sustainable electronic-based government system, thereby supporting its transformation toward a Smart Village capable of adapting to evolving information and communication technology trends.

The massive adoption of Internet of Things (IoT) devices is expanding the cyberattacks surface, particularly by the Mirai botnet, which exploits the dynamic characteristics of data traffic. This research proposes a Mirai detection approach based on a Recurrent Neural Network (RNN) optimized using Bayesian Optimization to improve prediction accuracy on sequential data. Unlike previous studies, this research utilizes the latest CIC IoT-DIAD 2024 dataset and applies probabilistic optimization to the hyperparameter space, including RNN units, dropout, and learning rate. The experiment was conducted on 201,021 valid data points, with dimensionality reduction using PCA as the optimal point to represent essential features without redundancy. The results show a significant increase in accuracy from 97.95% to 99.69%, accompanied by an 84% decrease in False Negatives, an 86% decrease in False Positives, and an AUC value of 0.9999. These findings confirm that integrating RNN and Bayesian Optimization not only improves numerical performance but also strengthens the reliability of the intrusion detection system for modern IoT ecosystems with controlled computational loads.

The dynamic changes in weather patterns in Jambi City require an accurate temperature prediction system, thus this study aims to compare the performance of Random Forest and Support Vector Regression (SVR) algorithms in predicting daily maximum temperatures using weather data from 2020–2024 obtained from OpenMeteo with the application of Feature Engineering including lag and rolling window features. The test results indicate that the SVR model with a Radial Basis Function (RBF) kernel optimized using Grid Search (C=10, epsilon=0.2, gamma=0.01) significantly outperforms Random Forest based on a statistical Paired T-test (p-value < 0.05), yielding an R-squared (R²) value of 87.46%, Mean Absolute Error (MAE) of 0.3818 °C, and Root Mean Squared Error (RMSE) of 0.4964 °C compared to Random Forest's R² of 84.05%, where the previous day's temperature (lag) and three-day rolling average were identified as the most dominant predictors, leading to the recommendation of SVR as the more effective method for temperature prediction in the study area.

Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.

The use of the VSCO application continues to face technical issues, including errors during editing, limited feature access, and login problems that affect user satisfaction. This study analyzes user satisfaction with the VSCO application using the End User Computing Satisfaction (EUCS) method. The study involved 385 VSCO users as respondents, with data collected through questionnaires and analyzed using SmartPLS 3.0. In this research, Accuracy variable does not affect user satisfaction, whereas the Content, Format, Ease of Use, and Timeliness variables have a significant effect on user satisfaction. The study shows that content quality, interface design, ease of use, and system timeliness are the main factors influencing user satisfaction with the VSCO application.

This study aims to investigate the impact of social media advertising on clothing choices at Universitas Dinamika Bangsa Jambi students. In today's world, where many people, especially young people who frequently shop online, often struggle to accurately determine the quality of items. A quantitative approach was employed, with a survey as the primary method of data collection. A questionnaire was distributed online via Google Forms and successfully elicited responses from 102 active students who are also social media users. The sampling technique used was purposive sampling, with participants selected based on criteria that matched the focus of the study. The data were then processed using SmartPLS 4 software with the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to test the relationship between variables. The main findings indicate that social media promotions have a strong positive influence on students' clothing purchasing decisions. This underscores the crucial role of targeted advertising strategies in the digital world in shaping consumer preferences. This research is expected to serve as a guide for clothing entrepreneurs in developing online marketing plans that better suit the tastes and needs of students as their target market.

This study aims to design the User Interface (UI) and User Experience (UX) on the VINIX Showcase Website as a personal branding platform and digital Skill Passport for participants of the VINIX Seven Aurum Program using the Design Thinking method. The background of this research is the absence of an integrated digital platform that can systematically and easily document and display participants' skills, projects, certificates, and professional identity. The design process is carried out through five stages of Design Thinking, namely Empathize, Define, Ideate, Prototype, and Test, starting with exploring user needs, formulating problems, developing solution ideas, creating Prototypes, and Usability Testing. The results of the study consist of the UI/UX design of the VINIX Showcase Website, which includes registration and Login features, user Dashboard, Skill Passport, project upload, public Showcase, and automatic CV generation feature. Testing using the Usability Testing method showed that the resulting design has a good level of ease of use and comfort and is acceptable to users. This research is expected to be an effective digital solution in supporting personal branding, skills documentation, and improving the professionalism of VINIX Seven Aurum Program participants.

This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.

Diabetes Mellitus is a disease caused by the failure of the pancreas organ in producing the hormone insulin in excess causing increased blood sugar levels and resulting in a lack of insulin. This study discusses the application of the k-means clustering method to determine risk factors for diabetes mellitus. By using the clustering method, data will be grouped into several clusters or groups which in this study compare by applying several data mining tools such as RapidMiner, SPSS, WEKA, and Python. From the results of the comparison carried out resulted in 5 calculations, namely the manual calculation of cluster 1 with a ratio value of 73% being the first priority, calculations using RapidMiner resulting in cluster 3 with a ratio value of 58% being the first priority, calculations using SPSS cluster 2 with a ratio value of 34% being the first priority, and calculations using Python produce cluster 1 with a ratio value of 55% being the first priority.

The development of information technology has encouraged restaurants and cafés to function not only as dining places, but also as venues for hosting various events. However, the event reservation process at Rumah Makan Ny. Hartini and Café Rain is still carried out manually through logbooks, telephone calls, and WhatsApp, resulting in problems such as unorganized data, delayed confirmations, and miscommunication with customers. In addition, the manual system limits access to information regarding venue availability, reservation schedules, and additional facilities required by customers. This study aims to develop a web-based event reservation information system using the prototyping method. The system design was carried out using Unified Modeling Language (UML), including use case diagrams, activity diagrams, and class diagrams to model user interactions, process flows, and system structure. The results of the study show that the developed system is able to automate the reservation process, customer data recording, reservation confirmation, schedule management, and additional facilities management. This system improves operational efficiency, data accuracy, and service quality, while also making it easier for customers to make reservations independently and obtain information quickly and accurately.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.