Yerik Afrianto Singgalen

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• Doctor of Philosophy
• Lecture at Atma Jaya Catholic University of Indonesia

This research investigates the combination of the Support Vector Machine algorithm with the Synthetic Minority Over-sampling Technique to improve classification performance in sentiment analysis, especially in handling imbalanced datasets. Employing a dataset comprising 1,928 text entries, the research highlights SVM's challenges in managing imbala...

Monitoring land use, buildings, and vegetation index of ecotourism areas in North Halmahera can support planning space utilization in urban areas for tourist areas as the concept of land use management and urban planning. This study offers ideas for analyzing the distribution of buildings, vegetation index, and land use in the mangrove ecotourism a...

This study analyzes the vegetation index and mangrove forest utilization through ecotourism development in Guraping and Dodola Island of North Maluku Province. This research uses a remote sensing approach through Landsat 8 Operational Land Imagery (OLI) from 2013 and 2021, calculated based on the normalized difference vegetation index (NDVI) algori...

This article aims to describe the mangrove forest utilization for sustainable livelihood through a community-based ecotourism approach. This research conduct in Kao Village, North Halmahera District of Indonesia. This study was done in a qualitative method using a life-history approach. The data was collected through in-depth interviews, observatio...

Sustainable livelihood approach has been a strategic approach that can improve the economy of rural communities and create harmonization of socio-cultural, economic, environmental and political development through policies. However, it is holistic and contextual, enabling the existence of different capital characteristics formed based on the commun...

This study examines guest experiences at Nihi Resort in Sumba, Indonesia, utilizing an adapted SERVQUAL framework to analyze service quality dimensions in a remote luxury hospitality context. Through systematic analysis of guest reviews collected from TripAdvisor, the research employs a qualitative methodology incorporating open, axial, and selecti...

This research examines the integration of coffeeshop management into higher education curricula through Business Model Canvas methodologies, utilizing Le Café at Atma Jaya Catholic University of Indonesia as a case study. The investigation employs qualitative methodology, incorporating document analysis and participant observation, to evaluate the...

This study investigates predictive analytics applications in the hospitality sector, specifically employing the XGBoost algorithm to predict room selection patterns based on guest data. Analysis of 900 booking records revealed that three variables—"Length of Stay," "Rating," and "Guest Type"—exhibited the strongest predictive power for room prefere...

This study presents a strategic planning framework for sustainable development through comprehensive spatio-temporal analysis, using Nusa Lembongan's mangrove ecosystem as a case study. The research implements an innovative methodological approach combining temporal satellite imagery analysis from 2014 to 2024 with systematic stakeholder feedback a...

This study investigates implementing a Hybrid Deep Learning Convolutional Neural Network (CNN) model for classifying multispectral satellite imagery to detect land cover changes in Untung Jawa Island, Indonesia. The research addresses critical limitations in conventional image classification methods that struggle with capturing subtle terrain modif...

Educational institutions, particularly tourism study programs, face significant challenges in managing fragmented and inefficient social media promotion strategies that hinder student recruitment and weaken institutional visibility. These problems arise from inconsistent content delivery, lack of stakeholder coordination, and limited performance mo...

This research investigates the influence of digital media narratives on investment decision frameworks within halal tourism ecosystems through digital ethnographic methodology. The study employs a comprehensive methodological approach incorporating systematic data collection across multiple digital platforms, including specialized forums, social me...

Sentiment analysis in hotel guest reviews has become essential for evaluating customer satisfaction and service quality. This study improves sentiment classification accuracy by utilizing the Multilingual BERT model with an improved performance evaluation framework. Using the Knowledge Discovery in Databases (KDD) methodology, this research involve...

The rapid growth of the hospitality industry and the increasing reliance on online reviews emphasize the need for advanced sentiment analysis tools to understand customer preferences effectively. This study explores the application of IndoBERT, a pre-trained language model tailored for the Indonesian language, in classifying sentiments from hotel g...

This research examines the economic dynamics of halal tourism development in Setanggor Village, a traditional craft village in Lombok, Indonesia, focusing on the interrelationship between cultural preservation and sustainable economic growth. Digital ethnographic methodology facilitates comprehensive analysis by systematically observing online inte...

Buku ini menyajikan pembahasan komprehensif tentang konsep dan implementasi pariwisata berbasis komunitas di Indonesia yang terdiri dari 6 bab utama. Karya ini mengeksplorasi bagaimana model pariwisata yang menempatkan masyarakat lokal sebagai aktor utama dalam perencanaan, pengelolaan, dan pemanfaatan potensi wisata dapat mendorong pembangunan ber...

This study investigates the relationship between cultural dynamics and tourist preferences in hotel and resort settings through comprehensive review data analysis across multiple countries of origin. Using thematic analysis methodology implemented through Atlas.Ti software, the research examines patterns in accommodation preferences, service expect...

This study employs a thematic analysis methodology to examine service quality dimensions by systematically investigating 1,284 verified guest reviews at Katamaran Hotel & Resort Lombok, Indonesia. The research utilizes Atlas.ti software for rigorous coding and theme development, implementing a five-phase analytical framework encompassing data colle...

This study examines the effectiveness of Coffee Vlogs as a promotional tool to build communities and strengthen brand image among urban audiences. The content analyzed includes Latte Art and Beverage Manufacturing content published by Latte Papa Channel. The study results indicate that creative and educational content successfully attracts attentio...

Mengupas transformasi manajemen kualitas di era industri 4.0. Dengan memanfaatkan teknologi digital seperti IoT, big data dan AI, penulis menjelaskan bagaimana pendekatan proaktif berbasis data dapat meningkatkan efisiensi dan efektivitas sistem manajemen kualitas. Masing-masing bab buku ini memberikan wawasan mendalam mengenai adopsi Kualitas 4.0...

This research offers a robust framework for integrating predictive analytics into hospitality operations, contributing to sustainable growth and competitive advantage in the industry. This research investigates the application of the Random Forest Regression model to predict the Length of Stay (LoS) of hotel guests, leveraging key features such as...

Buku ini mengupas tuntas peran transformasi digital dalam mengubah lanskap industri pariwisata. Dengan pendekatan komprehensif, buku ini menjelaskan dampak teknologi modern seperti big data, Internet of Things (IoT), dan destinasi cerdas terhadap daya saing destinasi di tengah dinamika global. Buku ini juga menyoroti pentingnya teknologi dalam mend...

This study explores applying Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for sentiment analysis and trend mapping of hotel reviews, specifically focusing on customer feedback from Hotel Vila Ombak in Lombok, Indonesia. The primary objective was to leverage these advanced deep learning models to capture nuanced sentiment patt...

This study investigates the performance of a sentiment classification model leveraging IndoBERT to analyze Indonesian hotel review data. Sentiment analysis is crucial for extracting actionable insights from customer reviews, yet challenges such as linguistic diversity and imbalanced datasets complicate accurate classification. The dataset comprises...

This study presents an optimized approach to sentiment classification of hotel reviews using a hybrid deep learning architecture. The model proposed combines Bidirectional Long Short-Term Memory (BiLSTM) with LSTM networks, enhanced by pre-trained GloVe word embeddings and SMOTE-ENN for handling data imbalance. The architecture incorporates a BiLST...

This study integrates knowledge discovery in databases (KDD) with sentiment analysis techniques to evaluate customer feedback and improve service quality in the hotel industry. The research emerges from the growing demand for data-driven strategies in the highly competitive hospitality sector, where understanding customer sentiment is crucial for e...

The growing reliance on online reviews as a critical decision-making tool in the hospitality industry underscores the need for robust sentiment analysis methodologies. Understanding customer feedback is essential for hotels to enhance service quality and maintain a competitive edge in an increasingly digital marketplace. However, traditional sentim...

The increasing importance of user-generated content in the hospitality industry necessitates advanced sentiment analysis tools to derive actionable insights from customer reviews. Traditional methods often struggle with the complexities of natural language, such as contextual dependencies and nuanced emotional expressions. This research leverages t...

This study explores the implementation of Rapid Application Development (RAD) frameworks in the design and optimization of websites for academic institutions, particularly focusing on enhancing the functionality of digital platforms used by study centers. By leveraging agile methodologies, this research aims to streamline the development process, e...

This study explores the application of data mining techniques to analyze customer feedback for improving service quality at Tanjung Lesung Beach Hotel. Utilizing the Knowledge Discovery in Databases (KDD) framework, the research systematically collected, cleaned, and analyzed 1,239 customer reviews from the Agoda platform. Through a thorough data c...

This research examines visitor perceptions of Shariah-compliant hotels within the context of contemporary hospitality, focusing on how these establishments meet guest expectations and foster satisfaction in a competitive global market. The study analyzes 445 customer reviews using a descriptive-analytical methodology to explore integrating Islamic...

This research explores the analysis of 388 hotel customer reviews to understand guest experiences, employing advanced analytical methodologies to uncover valuable insights for service quality enhancement. Utilizing the Knowledge Discovery in Databases (KDD) framework, the study applies Latent Dirichlet Allocation (LDA) for topic clustering and k-ne...

This research explores the application of Knowledge Discovery in Databases (KDD) to analyze hotel guest feedback and improve service quality at Bintang Flores Hotel in Labuan Bajo. Utilizing KDD methodologies, the study processed 589 guest reviews to identify key factors influencing customer satisfaction, including cleanliness (1.00), location (0.8...

This research investigates customer satisfaction at Meruorah Komodo Labuan Bajo through a comprehensive analysis of review data extracted from the Agoda platform. By examining 1,340 reviews, including 527 verified accounts, the study identifies key factors influencing guest experiences, such as service quality, room features, and location. The meth...

This research investigates the key determinants of customer satisfaction in the hospitality industry, focusing on cleanliness, service quality, location, and value. Analyzing guest reviews, the study reveals that 85% of guests consider cleanliness a primary factor influencing their overall experience, while 78% highlight service quality, particular...

This research explores the integration of social network analysis and topic clustering techniques to provide novel insights into digital interactions and thematic trends within the context of backpacker tourism. Utilizing a structured framework, 3,575 records across three content IDs (c2ZMFDS_3rU, Sv_yxz7T8rU, and i9t9pbdo-bk) were processed and cl...

This research explores the dynamics of backpacker tourism in Indonesia by analyzing online content from various regions, including Bandung, Dieng, Borobudur, Ijen, Bromo, Tumpak Sewu, Malang, Banyuwangi, and Bali. Using the Digital Content Reviews and Analysis Framework, the study systematically processed user-generated content to assess sentiment...

This research uses the Digital Content Reviews and Analysis Framework to explore the dynamic interplay between digital content, sentiment, and toxicity within the context of heritage tourism at the Sangiran site. The study is driven by the urgency to understand how digital narratives impact public engagement and perception, particularly for heritag...

This research urgently addresses the need to understand and manage viewer interactions with culturally significant video content, particularly the Rambu Solo ritual. By integrating the Digital Content Reviews and Analysis Framework with sentiment classification performance, toxicity score evaluation, and content analysis, the study systematically a...

This research investigates the role of digital narratives in promoting emerging destinations, with a focus on Indonesia's new capital (IKN). Utilizing the Digital Content Reviews and Analysis Framework, this study analyzed 248 digital posts, including social media posts and videos, to evaluate the effectiveness of tourism strategies that emphasize...

This study investigates sentiment analysis methodologies in the tourism domain, addressing the challenge of extracting meaningful insights from user-generated content, specifically TripAdvisor reviews of S.E.A Aquarium and Gardens by the Bay in Singapore. The research applies the CRISP-DM framework to develop and evaluate sentiment classification m...

This research investigates the complexities of online discourse by conducting a detailed toxicity and topic analysis of travel vlog content on user-generated platforms. By analyzing 1,503 posts using the Perspective API, the study finds generally low levels of toxicity, with an average toxicity score of 0.06995 and a peak of 0.78207, and similarly...

This research investigates the integration of Latent Dirichlet Allocation (LDA) for topic modeling with the performance evaluation of various classification algorithms—specifically, k-nearest Neighbors (k-NN), Support Vector Machines (SVM), Naive Bayes Classifier (NBC), and Decision Trees (DT)—within the Digital Content Reviews and Analysis Framewo...

This research presents a comprehensive approach to analyzing digital content by integrating toxicity analysis, clustering techniques, and Social Network Analysis (SNA) to understand online interactions better. The study finds that, while the average toxicity levels are relatively low, with scores such as 0.06355 for toxicity and 0.00468 for severe...

This study explores the dual role of media in preserving and potentially distorting cultural heritage, focusing on the portrayal of Sabu Island in KOMPASTV's expedition documentary. Utilizing the Digital Content Reviews and Analysis Framework, the research comprehensively dissection the documentary's content, uncovering critical insights into the i...

This study investigates user engagement within digital environments, explicitly focusing on creative content like music videos, and examines how sentiment and toxicity levels in user interactions influence engagement dynamics. Employing the Digital Content Reviews and Analysis Framework, the study reveals that 95.8% of user interactions exhibit pos...

This research explores the intersection of wildlife tourism and digital narratives, focusing on Sulawesi's endemic species. Utilizing the Digital Content Reviews and Analysis framework, the study combines content analysis, sentiment classification, and toxicity assessment to uncover critical insights. The findings highlight digital narratives' sign...

This study explores the dynamics of guest preferences and satisfaction within the context of Sharia-compliant hospitality, using data from 445 verified reviews at The Sahira Hotel. Employing a descriptive-analytic methodology, the research utilizes visitor data extracted from Agoda's review platform, focusing on room preferences, stay duration, and...

This study delves into viewer engagement and sentiment dynamics surrounding the Muara Enggelam documentary video, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology across six stages. Rooted in the imperative of comprehending audience perceptions and interactions within digital media contexts, particularly in explo...

The research delves into the livelihood and coping strategies of indigenous communities in the digital era, focusing on the analysis of digital content. Utilizing the CRISP-DM framework, the study investigates toxicity scores, topics, and social networks within digital content, particularly examining video documentaries portraying indigenous commun...

This research explores the development of a prototype for the Sangiran Information System, utilizing the Rapid Application Development (RAD) framework to meet the specific needs of researchers, destination managers, and tourists. The study emphasizes the importance of user-centric design, facilitated by iterative refinement, which ensures the syste...

This research examines the engagement with tourism digital content for Sumba Island through sentiment and toxicity analysis. The study uses advanced models such as Perspective, Vader, and TextBlob to reveal an average toxicity score of 0.04066, indicating minimal harmful language. Sentiment classification shows a predominantly positive reception, w...

This research investigates the impact of digital content on specialized tourism activities, focusing on birdwatching, using tools such as Communalytic and RapidMiner. By analyzing 1,021 posts, the study reveals an average toxicity score of 0.13839, with VADER identifying 32.78% negative sentiment and TextBlob identifying 17.07% negative sentiment....

The research on the comparative spatio-temporal analysis of NDVI, NDBI, and SAVI values for Kumo Island, Kakara Island, and Tagalaya Island from 2013, 2018, and 2024 offers insights into environmental dynamics influenced by human activities, particularly tourism. Key findings indicate that NDVI values, reflecting vegetation health, improved across...

This research underscores the pivotal role of integrating spatial data and remote sensing technologies within a spatio-temporal analysis framework for regional development planning. Analyzing NDVI, NDBI, and SAVI values from 2013, 2018, and 2024 provided significant insights into vegetation health, urbanization, and soil conditions on Kumo Island....

This study evaluates the ecological trends on Tagalaya Island by analyzing the NDBI, NDVI, and SAVI indices from 2013 to 2024. The NDBI data reveals a notable improvement in vegetation conditions over this period. In 2013, NDBI values ranged from-0.8818104 to-0.3152868, indicating poor vegetation health. Although there was a slight deterioration by...

This research underscores the significant role of remote sensing and spatio-temporal analysis in promoting sustainable tourism development on Kakara Island, North Halmahera. Applying NDVI, NDBI, and SAVI models provided valuable insights into vegetation health, urban expansion, and soil-adjusted indices from 2013 to 2024. NDBI values in 2013, 2018,...

This study analyzes digital narratives surrounding Wamena's cultural heritage using the Digital Content Reviews and Analysis Framework, focusing on sentiment, toxicity, and thematic content. The research explores the complex interplay between community perspectives, cultural preservation, modernization, and external influences such as tourism. Toxi...

Penelitian ini mengeksplorasi dampak tren staycation dan workcation terhadap perkembangan industri perhotelan dan pariwisata di wilayah perkotaan. Peningkatan permintaan wisatawan lokal yang memilih staycation dan profesional yang memiliki gaya hidup workcation mendorong industri perhotelan untuk berinovasi dan meningkatkan kualitas layanan. Dengan...

This study investigates the implementation of Rapid and Participatory Application Development (RPAD) in educational settings, emphasizing its urgent necessity in enhancing project-based learning environments. Given the rapid pace of technological advancements and the evolving demands of modern education, there is an urgent need for innovative metho...

This research employs sentiment analysis techniques to examine audience perceptions across three videos featuring tourist vlog content. Utilizing the CRISP-DM framework, the study compares the performance of VADER and TextBlob in sentiment classification, analyzing the distribution of polarity values and agreement levels between the two models. The...

This research investigates the sentiment and toxicity of viewer responses to digital content on food and tourism using the Digital Content Reviews and Analysis Framework. Employing advanced text processing and sentiment analysis models such as Perspective, TextBlob, and Vader, the study analyzed 4,166 comments. The findings reveal a predominantly p...

This research explores the critical role of leveraging digital data and structured frameworks, specifically the Customer Review and Analysis Framework (CRAF), to optimize customer experience in the hospitality industry. Analyzing 1,028 guest reviews from Ayaka Suites Hotel reveals that 987 posts are positive, while only 40 are negative, indicating...

This research highlights the significant impact of digital content on shaping tourist perceptions and behaviors, particularly emphasizing the influence of travel vlogs. Utilizing the Tourism and Travel Content Analysis (TTCA) framework, the study analyzed 1,972 review posts out of 2,250, revealing critical insights into viewer engagement and sentim...

This research explores the implementation of the Customer Reviews and Analysis Framework (CRAF) as a crucial tool for optimizing marketing strategies in the hospitality industry. By conducting thorough data analysis and sentiment evaluation, CRAF provides valuable insights into guest preferences and behaviors, facilitating the creation of highly ta...

This research, framed by the CRISP-DM methodology, offers a comprehensive analysis of sentiment and toxicity in digital content, focusing on tourism-related videos. Utilizing advanced machine learning models like VADER and TextBlob for sentiment analysis, as well as APIs such as Detoxify and Perspective for toxicity assessment, the study analyzed 2...

This research investigates the effectiveness of music-video content as a tool for tourism destination marketing, employing the CRISP-DM framework to approach data collection, analysis, and interpretation systematically. Focusing on the music video "Welcome to Sumba Island" by Marapu Reggae Official, the study analyzes public sentiment and toxicity...

This study leverages the Tourism and Travel Content Analysis (TTCA) framework to explore user sentiment and behavior in response to digital travel content. Utilizing sentiment analysis models such as VADER and TextBlob, the research analyzed 13,162 posts, revealing that 13.92% were negative, 15.02% neutral, and 71.06% positive, according to VADER....

This research employs the CRISP-DM framework to analyze digital engagement through travel vlog content, explicitly focusing on vlogs about Gili Trawangan. The study systematically follows the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Utilizing the VADER sentiment analysis mo...

This research employs the CRISP-DM framework to analyze consumer sentiment and preferences regarding DJI Avata drone products, aiming to provide data-driven strategic recommendations for marketing and product development. By systematically exploring business objectives, preparing and cleaning data, and modeling sentiment, the study reveals high con...

Effective website management is crucial for organizations seeking to engage users and communicate effectively with stakeholders. This research explores the role of specialized expertise in typography, audio and visual design, copywriting, and the implementation of Rapid Application Development (RAD) frameworks in optimizing website management pract...

This study investigates the sentiment of viewers towards GPT-4o technology videos by analyzing 1538 English language posts using two sentiment analysis tools, VADER and TextBlob. The analysis reveals a fair level of agreement between the two tools, with 929 posts (60.40%) classified consistently, yielding a Cohen's kappa statistic of 0.388. The sen...

This study utilizes the CRISP-DM framework to conduct a comprehensive sentiment analysis of visitor reviews for Batu Cave, leveraging advanced tools such as VADER, TextBlob, and the SVM model. The analysis of 1201 TripAdvisor reviews reveal critical visitor perceptions, highlighting both positive aspects, such as the site's beauty and cultural sign...

This research addresses the complexities of digital content analysis, focusing on toxicity, sentiment, and social network dynamics, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) as the overarching framework. The research problem centers on understanding the prevalence of toxicity, discerning sentiment nuances, and unravel...

This research delves into the complex realm of digital political communication, employing a comprehensive approach that integrates toxicity analysis, sentiment classification, and social network analysis within the framework of the CRISP-DM methodology. The study illuminates the multifaceted nature of online discourse through meticulous examination...

This research investigates the efficacy of sentiment classification models, specifically k-NN and DT algorithms, in the context of destination branding, with a focus on Labuan Bajo tourism. Utilizing the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, the study systematically navigates through all six stages, including busines...

This study investigates the performance of the Support Vector Machine (SVM) algorithm in sentiment analysis tasks within the context of tourism destination branding, utilizing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Specifically, the research compares SVM performance with and without the Synthetic Minority Over-sam...

This study employs Social Network Analysis (SNA) to investigate a digital discourse ecosystem's structural characteristics, dynamics, and toxicity levels. Using a dataset comprising threaded discussions and chain networks, we analyze user interactions and quantify harmful language's presence. The SNA reveals a network comprising 453 nodes and 330 e...

This research employs the CRISP-DM framework to analyze sentiment and toxicity dynamics in tourist vlog reviews thoroughly. The study delves into sentiment classification and toxicity identification nuances by leveraging machine learning algorithms such as k-NN, SVM, NBC, and DT with SMOTE. Utilizing a dataset comprising a substantial number of pos...

This research explores sentiment classification and toxicity assessment in cultural documentary videos through a systematic analysis framework based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The study evaluates the sentiment polarity of viewer comments by utilizing a diverse array of machine-learning algorithms, including k...

This study investigates sentiment analysis methodologies within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining), aiming to discern the efficacy of various algorithms in sentiment classification tasks. The research uses a structured approach to evaluate SVM, NBC, DT, and K-NN algorithms with the SMOTE oversampling techniq...

This research aims to improve sentiment analysis of reviews related to Garden by the Bay, a prominent tourist destination in Singapore, by leveraging the CRISP-DM methodology and Synthetic Minority Over-sampling Technique (SMOTE). The study employs a comprehensive approach, integrating CRISP-DM phases to systematically collect, clean, and analyze d...

This research investigates public sentiment towards tourism and gastronomy content through sentiment classification methodologies, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Gastronomy plays a pivotal role in promoting culinary tourism rooted in cultural heritage. Food influencers significantly introduce cul...

This study presents a comprehensive analysis of sentiment classification algorithms applied to content from the entertainment industry, specifically focusing on hip-hop music videos. Following the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, the research evaluates the performance of three prominent algorithms: k-nearest N...

This academic study investigates sentiment, toxicity, and social network dynamics within esports, focusing on the Esport World Championship 2022 featuring Alffy Rev's music performance. The research problem centers on discerning sentiment perceptions among esports enthusiasts and music fans while evaluating toxicity levels in online interactions du...

The study aimed to evaluate sentiment classification models using toxicity scores and to conduct Social Network Analysis (SNA) to understand network dynamics. The research used CRISP-DM methodology to comprehensively analyze sentiment classification models and toxicity scores. It utilized various machine learning algorithms, including Decision Tree...

This study delves into the response of viewers to video content focusing on Wasur National Park in Papua, Indonesia, with a particular emphasis on its implications for livelihood and ecology. The increasing popularity of online platforms such as YouTube has provided a medium for content creators to showcase natural landscapes and cultural heritage,...

This research addresses the research problem of sentiment classification and topic analysis in the context of the Aftermovie Piala Presiden Esports 2019 (Onic Esport), employing the CRISP-DM methodology. Leveraging a dataset comprising 1830 posts, sentiment analysis was conducted on 191 posts using Vader and TextBlob algorithms, revealing the distr...

This research investigates the implementation of the Rapid Application Development (RAD) methodology in the context of website management for personal branding. The background of the study elucidates the increasing importance of personal branding in the digital age and the role of websites as crucial platforms for individuals to showcase their expe...

This research investigates the efficacy of employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to analyze sentiment classification models. The study focuses on evaluating the performance of Decision Trees (DT) and Support Vector Machine (SVM) models integrated with the Synthetic Minority Over-sampling Technique (SMOTE...

This research explores the performance of sentiment classification models, namely Naive Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM), using the CRISP-DM methodology in the context of digital content analysis and data mining. The analysis was conducted on a SMOTE dataset in Rapidminer, yielding significant performance...

The study aims to investigate the effectiveness of sentiment analysis algorithms, specifically Support Vector Machine (SVM) and Decision Tree (DT), integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance issues. Guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, the researc...

This research focuses on designing and implementing a coffee shop management system using the Rapid Application Development (RAD) methodology. Through meticulous planning, user-centric design, and rigorous testing, the system addresses the specific operational needs of coffee shops, including supplier management, inventory tracking, sales analysis,...

This research identifies a research gap in understanding the impact of contextual factors on sentiment and toxicity within online discussions of sports events, focusing on the MotoGP event in Mandalika. By exploring how contextual nuances influence public sentiment and toxicity levels, this study aims to enhance the effectiveness of online discours...

The research problem revolves around the challenges in effectively marketing culinary tourism aligned with tourist preferences in Indonesia, necessitating a substantial exploration of consumer sentiments related to culinary diversity through the lens of food influencer content. Food influencers are crucial in stimulating tourists' interest in gastr...

The music industry's increasing reliance on digital platforms like YouTube for dissemination raises concerns about the potential impact of music videos on viewer sentiment and well-being. This study seeks to assess the toxicity and sentiment of the Wonderland Indonesia music video by Alffy Rev through Support Vector Machine analysis, contributing t...