Analysis of Customer Reviews of Fren.co Coffee & Eatery on Google Maps Using Logistic Regression and Random Forest Methods

Online review platforms provide valuable data for evaluating customer perceptions and service quality in food and beverage businesses; however, such data are typically unstructured and frequently exhibit naturally imbalanced sentiment distributions that may influence classification outcomes. This study analyzes customer reviews of Fren.co Coffee & Eatery on Google Maps using Logistic Regression and Random Forest within a controlled comparative framework. A total of 225 valid textual reviews were collected and labeled into positive, neutral, and negative categories based on rating scores. The data were preprocessed through case normalization, cleansing, tokenization, stop word removal, and stemming, and subsequently transformed into numerical feature vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting scheme. To preserve the original sentiment distribution, an 80:20 stratified sampling strategy was implemented during model evaluation. Experimental results indicate that Logistic Regression achieved higher overall accuracy of 0.89 (89%) and demonstrated more balanced precision and recall across sentiment classes compared to Random Forest, which achieved an accuracy of 0.87 (87%) and showed stronger bias toward the majority class. These findings suggest that, in small-scale and naturally imbalanced Google Maps review datasets, linear classification models may provide more stable and consistent predictive performance than ensemble-based approaches. The study contributes empirical evidence on model behavior under realistic imbalance conditions and strengthens methodological understanding of classical machine learning applications for sentiment analysis in regional hospitality businesses

Sentiment Classification of Google Maps Reviews for Tepian Pandan Restaurant Using Support Vector Machine

The rapid development of information technology has driven the increasing use of online review platforms as a means of sharing consumer experiences. Customer reviews now serve not only as a medium for expressing opinions but also as a valuable source of data in measuring the level of public satisfaction with a business, particularly in the culinary field. One of the most widely used platforms is Google Maps, which allows customers to provide ratings and comments regarding food quality, service, price, and the atmosphere of the place. The information presented in text form can be further analyzed to obtain a general overview of consumer perceptions. This study aims to analyze public satisfaction sentiment towards Tepian Pandan Restaurant based on reviews found on Google Maps by applying the Support Vector Machine (SVM). The method used refers to the text approach. mining which includes several stages, namely collecting review data, text preprocessing (such as case folding, tokenizing, and data cleaning), feature extraction using the Term Frequency – Inverse method Document Frequency (TF-IDF), and sentiment classification using the SVM model. The processed reviews were then grouped into two main categories: positive sentiment and negative sentiment. To assess model performance, this study used evaluation metrics such as accuracy, precision, recall, and F1-score. The test results showed that the Support Vector Machine (SVM) model was able to classify review sentiment with good and consistent performance. Therefore, this approach is considered effective in identifying customer satisfaction levels based on online review data. The findings of this study are expected to inform restaurant management's efforts to improve service and product quality based on customer feedback.

Analysis of Scholarship Website Users Using the End-User Computing Satisfaction Model and Importance Performance Analysis Model

The East Kalimantan Scholarship Website is a facility provided by the government of the East Kalimantan Provincial Education and Culture Office. This study aims to assess the user satisfaction of individuals using a scholarship website by applying two well-established models: End-User Computing Satisfaction (EUCS) and Importance-Performance Analysis (IPA). The EUCS model evaluates users’ satisfaction with key aspects of the website. The IPA model is employed to assess the relative importance and performance of these factors from the user’s perspective, enabling the identification of areas for improvement. The combined insights from these models can guide the enhancement of scholarship website services and user experience. Data was collected through questionnaires to respondents, who registered on the BKT site with the Complete category from various universities. The East Kalimantan Scholarship website evaluation system calculates the results of questionnaires from students with various study programs. This system uses EUCS statements in the categories of Content, Accuracy, Format, Ease of Use, Timelines, and User Statistics. The results of this study indicate that the hypotheses designed are all accepted and have a significant influence. Users are satisfied with the website's ease-of-use aspect, which is the strongest aspect in supporting user satisfaction. Conversely, the accuracy aspect shows the weakest relationship among other variables

Public Sentiment Analysis on the Free Nutritious Meal Program Using Logistic Regression and Support Vector Machine Algorithms

The Free Nutritious Meal Program is a national policy initiated by the Indonesian government to improve the nutritional status of school-aged children and support long-term human resource development. The implementation of this policy has generated diverse public responses expressed through social media platforms, particularly YouTube. This study aims to analyze public sentiment toward the Free Nutritious Meal Program and to compare the performance of Logistic Regression and Support Vector Machine algorithms in multiclass sentiment classification. A total of 3,920 Indonesian-language YouTube comments were collected and processed through text preprocessing stages, including case folding, tokenization, stop word removal, and stemming. Sentiment labeling was conducted using a lexicon-based approach, and feature representation was generated using the Term Frequency–Inverse Document Frequency method. The dataset was divided into training and testing sets using an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results indicate that positive sentiment dominates public opinion. Although both algorithms achieved similar accuracy (0.79), Support Vector Machine demonstrated more balanced recall and F1-score across minority classes, indicating stronger robustness in handling imbalanced high-dimensional text data. These findings highlight the effectiveness of the Support Vector Machine algorithm in digital public policy evaluation through social media–based sentiment analysis

Impact of Ease of Use, Usefulness, Attitude, and Trust on AI Adoption Intentions in Higher Education

The integration of Artificial Intelligence (AI) in higher education has attracted increasing attention, particularly in computer education. However, students’ acceptance of AI is influenced by several factors that require further investigation. This study aims to examine the effect of perceived ease of use, perceived usefulness, attitude, and trust on the utilization of AI in learning activities among computer education students. A quantitative approach was employed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with data collected from undergraduate students. The findings reveal that ease of use and usefulness significantly influence students’ attitudes toward AI, while trust plays a crucial role in shaping both attitudes and actual utilization. Furthermore, attitude is confirmed as a mediating variable that strengthens the relationship between ease of use, usefulness, trust, and the adoption of AI tools in learning. These results provide empirical support for the Technology Acceptance Model (TAM) and extend it by incorporating trust as an additional construct, offering new insights into AI adoption in higher education. The study highlights both theoretical contributions and practical implications for educators, particularly in the Indonesian context, to integrate AI effectively into learning environments

Behavior-Driven Gamification Framework for Enhancing Health Insurance Engagement Using TOGAF-Based Business Architecture

Health insurance providers face significant challenges in engaging policyholders and encouraging preventive health behaviors. Traditional engagement methods often lead to low participation, reducing the effectiveness of wellness programs and increasing long-term healthcare costs. This paper presents a behavior-driven gamification framework designed to enhance policyholder engagement within the health insurance sector, utilizing a TOGAF-based business architecture approach. By integrating the principles of behavioral science with game mechanics, this model aims to motivate policyholders to actively participate in their health management through personalized, interactive experiences. The application of TOGAF’s Architecture Development Method (ADM) ensures that the gamification framework    is aligned with business objectives, operational processes, and technological infrastructure, providing a sustainable and scalable solution for health insurers. The proposed framework enhances customer engagement, improves health outcomes, and reduces operational costs by incentivizing healthy behaviors, fostering a more proactive and satisfied customer base. This research contributes to the growing field of digital health innovation and offers a strategic roadmap for integrating gamification within health insurance systems.

Advertising Business Processes through Data-Driven Enterprise Architecture: A Conceptual Model of PT Akuratman

The advertising industry is undergoing a profound transformation driven by rapid advancements in data analytics, digital infrastructure, and artificial intelligence. Traditional marketing methods, which once relied heavily on intuition and generalized audience segmentation, are now being replaced by hyper-targeted strategies that utilize real-time insights to deliver more effective and measurable outcomes. This paper presents a conceptual study aimed at designing and optimizing business processes within PT Akuratman, a fictional digital advertising agency that adopts a data-driven operational model. Using the ArchiMate enterprise architecture framework, the study structures and analyzes four core revenue streams: Campaign Management Fees, Leads-Based Pricing, Technology Licensing, and Performance-Based Advertising. Each stream is examined through a multi-layered integration of business functions, application systems, and supporting technological infrastructure. The proposed architecture leverages cloud platforms, AI-driven analytics, and scalable data pipelines to support real-time decision-making, campaign personalization, and strategic agility. The model not only enhances operational efficiency but also reinforces client engagement and marketing ROI in a competitive digital environment. Furthermore, it serves as a practical reference for industry practitioners and scholars aiming to align enterprise architecture with emerging technological innovations. The study also suggests potential areas for future research, including adaptive architecture evolution, automation strategies, and regulatory considerations in big data ecosystems

Decision Support System for Soil Suitability of Banana Cultivation in Banyuwangi using Decision Tree Algorithm

Banana plants (Musa paradisiaca) are among the leading agricultural commodities in Banyuwangi Regency and have long played an essential role in supporting the local economy. However, in recent years, the productivity of banana plantations has experienced a significant decline. This decrease is closely related to unsuitable soil conditions, including excessive moisture, unstable temperature fluctuations, and extreme soil acidity (pH). Such unfavorable conditions create challenges for farmers, who often find it difficult to evaluate soil characteristics accurately. As a result, cultivation strategies become less effective and crop yields fail to reach their optimal potential. To address this problem, this study developed a web-based Decision Support System (DSS) designed specifically for assessing soil suitability for banana cultivation. The DSS applies the Decision Tree algorithm to classify soil conditions based on three key parameters: moisture, temperature, and pH. The system development process followed the Rapid Application Development (RAD) methodology, which emphasizes iterative prototyping and active participation of farmers, ensuring that the solution is practical and aligned with real-world field needs. Validation of the system was carried out through Black Box Testing and model evaluation, which produced an accuracy rate of 70.9% in classifying soil suitability. The DSS not only passed all functional tests but also generated practical recommendations for soil management strategies aimed at improving crop conditions. Ultimately, this research contributes a reliable, user-friendly, and farmer-oriented tool to support sustainable banana cultivation in Banyuwangi, with the potential to enhance productivity and strengthen decision-making capacity

Sentiment Analysis of Public Satisfaction Toward Banjar Grilled Chicken Restaurant Using Random Forest

This study aims to explore the level of public satisfaction with the Banjar grilled chicken restaurant by utilizing customer reviews on the Google Maps platform. These reviews serve as a primary source of information that reflects public perceptions regarding the quality of food, service standards, pricing, and the overall atmosphere of the restaurant environment. In the digital era, online reviews have become an essential factor influencing consumer decisions, as many potential customers rely on shared experiences before visiting a restaurant. However, the large volume of reviews available on Google Maps makes manual analysis inefficient, impractical, and excessively time-consuming, especially when the data continues to grow over time. Therefore, this study adopts a text mining–based analytical approach combined with the Random Forest algorithm to automatically classify customer sentiment in a structured and systematic manner. The data used in this research consist of Indonesian-language comments collected from Google Maps, which are then categorized into two main sentiment classes: positive and negative. The research process involves several stages, including data collection, text preprocessing such as cleaning and normalization, word weighting using the TF-IDF method, and sentiment classification using the Random Forest algorithm, followed by model evaluation through a confusion matrix to measure performance accuracy. The final results are expected to provide a comprehensive overview of customer satisfaction levels and offer valuable insights that can assist restaurant management in improving service quality, enhancing customer experience, and developing more effective business strategies in the future

Implementation Augmented Reality for Campus Building Visualization at Politeknik Pertanian Negeri Samarinda

This study aims to develop and implement a marker-based Augmented Reality (AR) application for visualizing campus buildings at Politeknik Pertanian Negeri Samarinda. The application, built using Unity 3D and Vuforia SDK, enables users to scan physical markers on buildings to display interactive 3D models and access detailed information about each facility. The research follows the Multimedia Development Life Cycle method, encompassing stages of concept, design, material collection, assembly, testing, and distribution. Technical testing evaluated the application’s performance across three Android devices, assessing marker detection range (0.5–3 meters), lighting conditions (optimal at 500–100,000 lux), and functionality (100% success in black-box testing). User Acceptance Testing (UAT) involved 30 respondents (new students) and yielded an average score of 4.245 out of 5, indicating high satisfaction. The results demonstrate that the AR application effectively enhances campus navigation and engagement, with marker-based tracking proving reliable for precise object visualization. This project contributes to the adoption of AR technology in educational institutions for promotional and orientation purposes