Google Maps Research Articles

Exploring digital voices to evaluate user perception, satisfaction and service quality in libraries

PurposeThis study has aimed to thoroughly assess user sentiments and perceptions regarding the National Library of India (NLI). It has attempted to provide significant insights into user satisfaction by examining its strengths and shortcomings across key categories including collection, environment, facilities, location, management and staff. The study has contributed to the understanding of the factors influencing the attributes of libraries, facilitating improvements in services and enhancing the overall user experience.Design/methodology/approachThis study employed a mixed-methods approach combining quantitative and qualitative analysis to assess user sentiments towards the NLI. Using Google Maps reviews, the study utilized web scraping, content analysis and sentiment analysis to categorize reviews as positive, negative or neutral, providing insights into user experiences and an in-depth analysis of the views and opinions of the NLI.FindingsThe study involved sentiment analysis and content analysis of 818 Google Maps reviews to assess user satisfaction with the NLI. The results demonstrate 624 reviews as positive sentiments, 70 instances pinpointed specific negative concerns, primarily related to staff behavior and certain facilities, and 124 neutral reviews suggested mixed viewpoints among users. This analysis highlights the critical role of attributes such as collection quality, environment and facilities in shaping user perceptions, emphasizing the need for focused improvements based on user feedback. The study revealed six attributes, namely collection, environment, facilities, location, management and staff that influence the user perception.Research limitations/implicationsThis study is focused solely on Google Maps reviews. Hence, the results cannot be generalized to all online platforms. Reliance on online reviews may not fully capture the views of all the library users. Additionally, the scope of the study is limited to English-language comments, potentially overlooking valuable insights from non-English-language reviews.Practical implicationsThis study provides valuable insights for the NLI to enhance user satisfaction by addressing the specific concerns raised in online reviews. The findings offer actionable guidance for library management to refine services and maintain favorable public perceptions.Originality/valueThis study contributes to the field by providing a comprehensive analysis of user sentiment through sentiment and content analysis of online reviews, offering unique insights into the NLI’s public perception. The identification of key strengths and weaknesses adds practical value to library management for refining services. The originality of this study lies in its unique approach to evaluating user experiences, which guides future research and improvement efforts in library services.

Abstract 4137823: Influence of Hospital and Cardiac Rehabilitation Facility Proximity on Enrollment Rates Among Medicare Beneficiaries

Background: Participation in cardiac rehabilitation (CR) is enhanced by proximity to CR facilities and communication between inpatient and outpatient settings but may become fragmented when patients are distant from their discharging hospital and nearest CR facility. Research Question: This study investigates how patient distance to both hospitals and CR facilities impacts the rates of CR participation. Methods: This study included 100% Medicare fee-for-service claims for beneficiaries with 12 months of continuous Part A/B coverage who had coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or surgical or transcatheter aortic valve replacements (SAVR or TAVR) between 07/2016-12/2018, excluding patients discharged to hospice or died within 30 days. Google Maps defined travel distances from the patient to hospital and CR facility locations using geographic centroids of ZIP codes, which were then categorized into quartiles. CR enrollment was defined as attending at least one session within one year of discharge. Logistic regression models estimated marginal CR enrollment rates across both hospital and CR facility distance categories adjusting for patient, hospital, and regional factors. Results: The study included a total sample of 501,049 beneficiaries undergoing PCI (62%), CABG (19%), SAVR (7%), and/or TAVR (15%). The median travel distance to the hospital was 18.6 miles (IQR 8.2-43.3 miles) and to the CR facility was 7.7 miles (IQR 2-15.3 miles). A total of 188,963 (37.7%) beneficiaries attended at least one CR session, which varied across categories of distance to hospital (Q1: 36.8% to Q4: 37.5%) and CR facility (Q1: 44.7% to Q4: 30.0%, p<0.01). Adjusted CR enrollment rates improved when beneficiaries lived farther from their admitting hospitals but closer to CR facilities (Figure 1), with the highest enrollment for those located farthest from their admitting hospital but closest to the CR facility (47.5%) and lowest enrollment among those closest to the hospital but farthest from the CR facility (20.6%). Conclusions: Greater distance to the admitting hospital was associated with higher CR enrollment, while greater distance to the CR facility was associated with lower enrollment among eligible Medicare beneficiaries. Addressing geographic barriers to CR participation may require tailored solutions based on patient proximity to hospitals and CR facilities.

EMPOWERMENT OF THE IMPORTANCE OF DIGITALIZATION ADAPTATION OF UMKM 'MANISAN MANGGO' IN CEMPAKA VILLAGE, TALUN DISTRICT, CIREBON REGENCY, WEST JAVA, INDONESIA

Cempaka Village is one of the villages located in Talun District, Cirebon Regency. The livelihood of the people of Cempaka Village is mostly to become laborers and farmers. However, there are also businesses such as grocery stores, street vendors, and home-based businesses. UMKM in Cempaka Village, Talun Regency itself show significant potential for the local economy. Candied mango is one of the products made by the people of Cempaka Village which is fairly capable of producing in large quantities and already has a fairly wide market share. However, in this case, mango candied UMKM still use conventional marketing which makes less widespread information and has not maximized the development of the digital era. One of the efforts made is to empower the importance of digitalization in mango candied UMKM in Cempaka village, namely by expanding access to information and marketing through making points on Google Maps and E-Commerce with the aim of increasing the marketing competitiveness of mango candied products.

Android and IoT Integrated Smart Wildlife Surveillance using Night Vision

Animal are important part of our ecosystem. Animal are very critical to our ecosystem. Due to increase in animal trafficking many wild animal are becoming endanger and also there population is decreasing. Because of this humans have created zoo, national park and sanctuaries which can work as safe heavens for these animal to avoid extinction. There can be scenario where animal are ill or poor health condition which can be a also cause of the death. Many people visit national park and sanctuaries where they are not able to see all animal. This project will be implement in python and will implement YOLO V7 algorithm to detect animals. Once animal is detected it will send name and GPS co-ordinates of the animal. These GPS co-ordinates will display on mobile and farmer can find animal at given location. This project can also be placed in farm land where human and animal conflict are common. If animal is detected it can send alert or sound can be generated to distract it. This project will help detection on wild animal using camera and deep learning technology. Android app will be used to locate the animal on google map..

AI-Based Object Detection, Distance Measurement and Speaking System for Blind Stick

The sense of sight is fundamental to how humans perceive and navigate their surroundings. For visually impaired individuals, this lack of vision creates significant challenges in detecting obstacles and measuring distances, making daily navigation difficult and potentially dangerous. In response to this problem, we propose an AI-based object detection, distance measurement, and speaking system integrated with a blind stick to enhance mobility and independence for the visually impaired. This system leverages cutting-edge artificial intelligence and image processing techniques to provide real-time object detection and distance measurement. By combining ultrasonic sensors with an AI-powered object recognition module, the system accurately identifies obstacles and calculates their distance, offering directional audio feedback to the user. Designed to be compact and user-friendly, the blind stick system provides seamless assistance in both indoor and outdoor environments. The object detection process utilizes advanced algorithms like YOLO (You Only Look Once) to identify obstacles. Distance measurement is performed using ultrasonic sensors to ensure accurate proximity readings. When an obstacle is detected, the system provides immediate auditory feedback, alerting users to the direction and distance of the object. The Google Maps API enhances usability by offering location awareness and route planning, empowering users to navigate with confidence. This AI-based blind stick system aims to improve the mobility and safety of visually impaired individuals, offering a more autonomous experience in their daily lives.

A New Approach to Assess Sustainable Corporate Reputation with Citizen Comments Using Machine Learning and Natural Language Processing

This study investigates the assessment of sustainable corporate reputation through citizen comments and how it can be measured by sentiment analysis methods based on machine learning and text mining. The research analyses citizen feedback on municipalities in the field of public services and examines their impact on the social reputation of the services provided by municipalities. Support vector machines, one of the machine learning methods, was used for sentiment analysis. In the study, Google Maps comments of the citizens receiving services from the municipality were used. The results of the sentiment analysis reveal that sustainable corporate reputation is directly related to citizen satisfaction and feedback. In this context, municipalities should continuously receive feedback and make strategic improvements based on citizens’ comments to ensure sustainable service quality. Municipalities are especially appreciated by citizens for their fast, effective, and high-quality services. However, some negative comments focus on issues such as the slowness of services, cleaning problems, and staff attitudes, indicating that certain improvements are needed. This feedback emphasises the need for continuous improvement in service quality.

PENGEMBANGAN USAHA PADA UMKM YU MIN BATIK & BUTIK DESA SIDOREJO KECAMATAN COMAL KABUPATEN PEMALANG

<table width="576" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td width="402"><p>The Micro, Small and Medium Enterprises that are the object are the UMKM Yu Min Batik & Boutique owned by Mrs. Siti Aminah which is located in Sidorejo Village, Comal District, Pemalang Regency. This MSME operates in the field of clothing and modified batik cloth in Pemalang. The aim of implementing this activity is to develop business, good financial management and production performance. The business assistance provided includes marketing aspects. The efforts made by the accompanying team aim to promote Yu Min Batik & Boutique products through MSME activities such as bazaars and catalog content on online social media. This is due to increasingly sophisticated technological developments and seeing many entrepreneurs, especially MSMEs, promoting their businesses through online social media. The aim is to introduce and attract the interest of potential customers by understanding the individual characteristics of consumers so that the products sold will match their wishes.</p><p>The assistance method used is carrying out several programs such as making banners, making simple bookkeeping, Google maps, participating in MSME bazaar activities, making direction boards, making organizational structures, business profiles, and creating social media (Instagram). After analyzing and applying the program to Yu Min Batik products, it produced a positive impact on MSME owners. Such as introducing products through promotions on social media and placing banners, organizational structures, understanding simple bookkeeping, business profiles, and installing direction boards to fulfill product requirements to support promotions so that people have more confidence in the products they want to buy.<strong></strong></p></td></tr></tbody></table>

The Digital Voice of Students: Analysing Emerging Themes in a Corpus of University Reviews

For higher educational institutions, reviews on Google Maps offer a free and easily accessible platform for students to express their needs and voice their concerns voluntarily. Such a platform can be valuable for capturing a wide range of academic and non-academic aspects that may not be manifested via traditional feedback instruments such as surveys. Accordingly, this study explores the themes that emerge in a corpus of online reviews collected from Google Maps of 29 Saudi public universities while also highlighting how such reviews form a public discourse shaped by the medium itself. To this end, this research employed an NLP advanced technique, namely BERT, to categorise the reviews into themes based on their semantic similarity. Then, a thematic analysis was conducted to reveal six main recurrent themes in the corpus: location and accessibility, facilities and infrastructure, academic quality and teaching, student support services, religious sentiment and gratitude, and community and social environment. The findings indicate that while Google Maps reviews capture a number of cultural and social aspects of university life, they inherently foreground physical (e.g., amenities) and logistical (e.g., location) dimensions. The paper also demonstrates how online reviews provide students with a platform to raise issues related to both micro-level (e.g., specific courses, amenities) and macro-level concerns (e.g., inclusivity), hence their potential value in contributing to the evaluative discourse on higher education institutions. These results suggest the need for a systematic engagement with online feedback platforms to promote continuous institutional improvement and cultivate an inclusive, student-centred educational environment.

Detection and Tracing of Wild Animals using Machine Learning

Animals are important part of our ecosystem. Animals are very critical to our ecosystem. Due to increase in animal trafficking many wild animals are becoming endanger and also their population is decreasing. Because of this human have created zoo, national park and sanctuaries which can work as safe heaven for this animal to avoid extinction. There can be scenario where animals are ill or poor health condition which can be an also cause of the death. Many people visit national park and sanctuaries where they are not able to see all animal. This project will be implemented in python and will implement RCNN algorithm to detect animals. Once animal is detected it will send name and GPS co- ordinates of the animal. These GPS co- ordinates will display on mobile and tourist can find animal at given location. This project can also be placed in farm land where human and animal conflict are common. If animal is detected it can send alert or sound can be generated to distract it. This project will help detection on wild animal using camera and deep learning technology. Android app will be used to locate the animal on google map.

Case–control study of fatal bicycle crashes in peri-urban areas of Delhi

ObjectivesThere is a lack of evidence on interventions to improve the safety of cycling use in low- and middle-income countries. We investigated the impact of road design and traffic characteristics...

Analyzing Road Users' Behavior: A Data Mining Approach Using Google Maps Popular Time and Web Scraping for Rest Area Visitation Patterns on Highways and Toll Roads

Analyzing Road Users' Behavior: A Data Mining Approach Using Google Maps Popular Time and Web Scraping for Rest Area Visitation Patterns on Highways and Toll Roads

Economic and environmental pollutant impact of Maternal-Fetal Telemedicine.

The global issue of greenhouse gas emissions has significant implications for the environment and human health. Telemedicine provides a valuable tool for delivering healthcare while reducing gas emissions by limiting the need for patient travel. However, the environmental effects of telemedicine in high-risk pregnancy populations remain unassessed. The aim of this study was to estimate the economic and environmental impact of an outpatient teleMFM program. This retrospective cohort study examined all visits at 3 teleMFM clinics more than 90 miles away from the nearest in-person MFM office between 10/1/2021 and 6/1/2022. Travel distances and times were calculated for each appointment between the patient's home, telemedicine clinic, and nearest in-person clinics, using zip code data and Google Maps web-based map calculator tools. Travel cost savings and environmental impact were calculated by determining differences in mileage reimbursement rate and emissions between those incurred in attending telemedicine appointments and those that would have been incurred if in-person using inflation-adjusted Internal Revenue Service annual standard mileage reimbursement rate ($0.58 per mile), and the U.S. Environmental Protection Agency Office of Transportation and Air Quality's average annual emissions and fuel consumption for gasoline-fueled passenger vehicles. During the study period a total number of 2,712 appointments were scheduled, of which 2,454 were kept (cancellations removed) and analyzed. Visiting a teleMFM clinic resulted in 204 miles, 200 minutes and 118.32 dollars saved per patient visit compared to visiting the nearest in-person clinic. Over a 7 month period a total of 96.6 metric tons of emissions were saved. This study demonstrates the positive economic and environmental impact of teleMFM utilization in communities remote from in-person care. Given the contribution of greenhouse gas emissions to climate change, such findings may provide strategies for our specialty make informed policy, advocacy, and business decisions.

Derived cannabinoid product availability among online vape shops

ObjectivesTo determine the proportion of online vape shops that offer derived cannabinoid products in a large metropolitan area (San Diego, California), measure their compliance with state hemp regulations, and estimate whether these shops receive more website traffic compared to those that offered nicotine or tobacco. MethodsWe obtained vape shops (n = 109) using browser-based (i.e., Google Search) and map-based (i.e., Google Maps and Yelp) searches in San Diego from March to August 2023 and conducted a content analysis of their websites to identify derived cannabinoid products offered for mail-order purchasing. Using website traffic data, we examined the association between derived cannabinoid product availability and monthly website visits. ResultsAmong the 109 online vape shops in the study sample, 35.8 % offered derived cannabinoid products for mail-order purchase and 26.6 % sold flavored (excluding terpenes) and inhalable cannabinoid products. Compared to vape shops that did not offer derived cannabinoid products (Mean: 23,619; 95 % CI: 23,605–23,634), those offering flavored and inhalable cannabinoid products received 2.5 times more monthly website visits (Mean: 57,950; 95 % CI: 57,913–57,986) and those offering any derived cannabinoid products received 5.5 times more monthly website visits (Mean: 130,694; 95 % CI: 130,607–130,782). ConclusionThe prevalence of flavored and inhalable derived cannabinoid products, despite being prohibited under California’s hemp regulations, highlights the need for additional enforcement measures to ensure retailer compliance. The higher website traffic observed among vape shops offering cannabinoid products may be indicative of greater popularity of retailers selling derived cannabinoid products compared to those selling nicotine or tobacco products alone.

Deep Learning and Statistical Process Control Approach to Improve Quality in Immigration Services

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

Semantic Segmentation Uncertainty Assessment of Different U-net Architectures for Extracting Building Footprints

Abstract. Automatic extraction of building footprints from aerial and space imageries has been found ever increasing importance in urban planning, disaster management, and environmental monitoring. However, achieving accurate building footprint extraction poses significant challenges due to diverse building characteristics and their similarities to their background elements. While conventional methods in building footprint extraction have mainly relied on image processing techniques, recent advancements in deep learning, particularly semantic segmentation algorithms like U-Net, have shown promise in addressing these challenges through machine learning. This study explores different depths of the U-Net model for building footprint extraction, aiming to identify the optimum architecture while investigating the semantic uncertainty of the building footprint extraction. Utilizing aerial imagery from cities including Berlin, Paris, Chicago, and Zurich, collected from Google Maps and OpenStreetMap (OSM) data, five U-Net models have been compared with varying depths. In addition, the impact of dataset sizes and learning rates on model performance has been investigated. Results confirmed that the U-Net-32-1024 model achieves the highest intersection over union (IoU), Accuracy, and F1-score. Moreover, increasing the training dataset size leads to significant improvements in model performance with IoU, Accuracy and F1-score reaching their values of 73.73%, 88.65% and 88.53%. However, challenges remain in accurately delineating buildings in dense urban areas. Nonetheless, our findings demonstrated the effectiveness of U-Net models in building footprint extraction.

E-cigarette retail outlets in Mexico: a country that bans the sale.

Restrictive e-cigarette policies have not allowed for the characterization of the sale of these tobacco products in the countries where they are implemented. This is because it is believed that there are no stores. However, the use of e-cigarettes persists in these countries. A content analysis of electronic cigarette points of sale through the internet in Mexico was conducted. Cross-sectional study. Structured searches were performed in the Google Maps search engine with the terms "electronic cigarette store" and "vape store" in combination with the name of each state of Mexico, in the period from October 2021 to February 2022. A total of 695 e-cigarette stores were located, of which 481 (69.2%) were physical stores, 207 hybrid stores (29.8%), 4 online stores (0.6%) and 3 (0.4%) vending machines. A total of 316 e-cigarette brands and 578 vape liquid brands were identified. The sales attraction strategy they used the most was free shipping of products (85.8%). Stores used WhatsApp (92.5%), Facebook (86.3%) and Instagram (74.4%) to promote their products. Only 35.1% of stores checked the age of their customers, of which, 95.9% asked if the customer was of legal age and 4.1% asked for the date of birth. In addition, 25.6% of the stores warned about nicotine addiction and 24.2% specifically about the ingredients of the liquids. E-cigarettes are widely available and marketed in a variety of retail outlets in Mexico. The high number of stores, brands of liquids, and e-cigarettes indicates non-compliance with existing regulations. E-cigarette consumption has increased globally, even in countries with restrictive regulations. However, there is limited data on the density and characteristics of vape stores in low- and middle-income countries with restrictive regulations. This study found no significant differences in e-cigarette sales between countries with permissive and restrictive regulations. Therefore, it is suggested that, in addition to establishing restrictive policies on the sale of e-cigarettes, their enforcement should also be monitored. Countries with restrictive measures should regulate Internet sales and promotion of e-cigarettes with greater emphasis.

Optimising availability and geographical accessibility to emergency obstetric care within a sub-national social health insurance scheme in Nigeria.

Health insurance is a key instrument for a health system on its path to achieving universal health coverage (UHC) and protects individuals from catastrophic health expenditures, especially in health emergencies. However, there are other dimensions to care access beyond financial accessibility. In this study, we assess the geographical accessibility of comprehensive emergency obstetric care (CEmOC) within the Lagos State Health Insurance Scheme. We geocoded functional public and private CEmOC facilities, established facilities registered on the insurance panel as of December 2022, and assembled population distribution for women of childbearing age. We used Google Maps Platform's internal directions application programming interface to obtain driving times to facilities. State- and local government area (LGA)-level median travel time (MTT) and a number of CEmOC facilities reachable within 30 min were obtained for peak travel hours. Across Lagos State, MTT to the nearest public CEmOC was 25 min, reduced to 17 min with private facilities added to the insurance panel. MTT to the nearest public facility in LGAs ranged from 9 min (Lagos Island) to 51 min (Ojo) (median = 25 min). With private facilities added, MTT ranged from 5 min (Agege and Ajeromi-Ifelodun) to 36 min (Ibeju-Lekki) (median = 13 min). On average, no public CEmOC facility was reachable within 30 min of driving for women living in 6 of 20 LGAs. With private facilities included in the scheme, reachable facilities within 30 min remained zero in one LGA (Ibeju-Lekki). Our innovative approach offers policy-relevant evidence to optimise insurance coverage, support efforts in advancing UHC, ensure coverage for CEmOC, and improve health system performance.

Sentiment Analysis of Google Maps User Reviews on the Play Store Using Support Vector Machine and Latent Dirichlet Allocation Topic Modeling

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision.

Cartografía temática y recursos TIC en la enseñanza y aprendizaje de la Geografía regional de Europa

In this article, we present a classroom experience with university students in training to be secondary school geography teachers. It is based on a didactic proposal using thematic cartography (Eurostat, ESPON, ESPON-SIESTA and the Information Society Atlas in Galicia) and ICT resources (statistical sources, free picture banks, aerial photography, Google Earth and Google Maps). The proposal´s aim is to use contents from the regional geography of Europe as a base or starting point to show a series of didactic recommendations for the teaching and learning of geography and social sciences. What is more, everything is with a clear idea, from a descriptive and rote geography to an active and critical geography, in which students participate in the construction of knowledge and increase their ability to reflect on geographical issues. The results of the experience will highlight the value of these tools in secondary education. We had also previously assessed the presence of thematic cartography and ICT resources within the required official Galicia curriculum to teach geography in compulsory secondary education. The idea is to deepen the value of the curriculum in didactic code, to know what tools it offers, how it offers them and with what intentions. This solution, among other opportunities, will allow us to identify a curriculum offering a specific teaching model. On the other hand, it will also give us the opportunity to firmly rebuke all the proposals considered to be erroneous, repetitive, or of little didactic value.

Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine

Batam City, as a rapidly developing tourism destination in Indonesia, continues to strive to enhance the potential of its tourist attractions to attract more visitors. The assessment of reviews from tourists is crucial in identifying necessary development measures to improve the quality of tourist attractions. This research aims to analyze the sentiment of reviews for the Alun-Alun Kota Batam tourist destination by leveraging data from Google Maps. Two classification methods, Naive Bayes and Support Vector Machine, are employed for sentiment analysis, and their performances are compared. From 1140 collected reviews, the data is categorized into three labels: positive, negative, and neutral. The research results indicate that the Support Vector Machine method achieves higher accuracy (94%) compared to Naive Bayes (83%). This study contributes insights into visitor sentiments towards Alun-Alun Kota Batam, with implications for policy development and more effective actions in enhancing local tourism appeal.