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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Sodality: Jurnal Sosiologi Pedesaan MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Sosial dan Humaniora Jurnal Kawistara : Jurnal Ilmiah Sosial dan Humaniora Journal of Indonesian Tourism and Development Studies JURNAL ELEKTRO Jurnal Perkotaan Jurnal Kebijakan dan Administrasi Publik AdBispreneur PAX HUMANA ARISTO JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komunikasi Kritis Humaniora MUWAZAH: Jurnal Kajian Gender Cakrawala Jurnal Penelitian Sosial Building of Informatics, Technology and Science Jurnal Mantik Journal of Information Systems and Informatics Jurnal Studi Sosial dan Politik Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Jurnal Sistem Komputer dan Informatika (JSON) JOURNAL OF BUSINESS AND ECONOMICS RESEARCH (JBE) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Cita Ekonomika: Jurnal Ilmu Ekonomi ARBITRASE: JOURNAL OF ECONOMICS AND ACCOUNTING International Journal on Social Science, Economics and Art KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Basic and Applied Science Indonesian Journal of Tourism and Leisure Jurnal InterAct Jurnal Sosiologi Engagement: Jurnal Pengabdian Kepada Masyarakat JKAP (Jurnal Kebijakan dan Administrasi Publik) Jurnal Kawistara
Yerik Afrianto Singgalen
Universitas Katolik Indonesia Atma Jaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Public Health Social Sciences Other

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Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3231

Abstract

Culinary is one of the economic activities that support national economic growth and represents the gastronomy of the archipelago in Indonesia. Culinary tourism activities have become famous for domestic and foreign tourists to experience the taste of a food based on the culture of each region. Makassar is one of the regions with diverse types of food and beverages and has a relationship with local socio-cultural values. Considering this, this study aims to analyze consumer sentiment toward food and services in ten restaurants in Makassar based on the recommendations of the Tripadvisor website using the Cross-Industry Standard Process for Data Mining (CRISPD-DM) and Service Quality (SERVQUAL) methods. The stages of CRISP-DM are as follows: the stage of understanding business processes; the stage of understanding data; the stage of preparing data; the modeling stage; the evaluation stage; and the deployment stage. The algorithms used as models are k-Nearest Neighbor (kNN), Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM). The results of this study show that the DT algorithm when using the SMOTE operator where the resulting accuracy value is 93.25%, precision is 88.74%, recall is 99.10%, and f-measure is 93.62%. In addition, the k-NN algorithm without using the SMOTE operator showed an accuracy value of 98.72%, a precision of 98.72%, a recall of 100%, and an f-measure of 99.36%. However, the resulting AUC value is 0.905 (90.5%). Meanwhile, when using the SMOTE operator, the SVM algorithm produces an accuracy value of 99.42%, a precision of 100%, a recall of 98.84%, and an f-measure of 99.42%. Meanwhile, the resulting AUC value is 1,000 (100%). Based on the ROC value, three algorithms can be used as models in the CRISPP-DM and SERVQUAL frameworks: the k-NN algorithm without SMOTE and the DT and SVM algorithms using the SMOTE operator
Sentiment Classification of S.E.A Aquarium Singapore Reviews through CRISP-DM using DT and SVM with SMOTE Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 3 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.4703

Abstract

In recent years, sentiment analysis has emerged as a critical area of research due to its wide-ranging applications in understanding public opinion, customer feedback, and social media sentiment. However, one of the significant challenges faced in sentiment analysis is the handling of imbalanced datasets, where the distribution of sentiment classes is uneven, leading to biased model performance. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to investigate sentiment analysis algorithms, mainly focusing on the Support Vector Machine (SVM) algorithm and the integration of the Synthetic Minority Over-sampling Technique (SMOTE). Through systematic experimentation and evaluation, the research demonstrates the superior performance of the SVM-SMOTE model in handling imbalanced datasets, achieving an accuracy of 98.46%, an AUC of 1.000, precision of 100.00%, recall of 96.91%, and an impressive F-measure of 98.42%. Additionally, the evaluation unveils specific toxicity scores across various categories, with Toxicity scoring at 0.11036 and 0.93915, Severe Toxicity at 0.00905 and 0.45895, Identity Attack at 0.02415 and 0.66373, Insult at 0.05149 and 0.85793, Profanity at 0.06392 and 0.93426, and Threat at 0.01562 and 0.51957. These numerical indicators provide quantitative insights into potential harm within analyzed content, emphasizing the efficacy of the SVM-SMOTE model in real-world applications and contributing to the advancement of sentiment analysis within the CRISP-DM framework.
Implementation of MOORA in Decision Support System Optimization for Hotel Accommodation Services Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 3 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i3.4730

Abstract

Service marketing challenges increase brand awareness related to accommodation services related to services, facilities, room comfort and quality, cleanliness, value for money, and location. Consumers who use Agoda's platform exhibit purchase behavior that makes ratings a benchmark before making a stay decision. This research aims to optimize the decision support system for selecting hotel accommodation services using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) decision support model. The MOORA method consists of the following stages: first, determining criteria, weights, and alternatives; second, determining the value of criteria, weights, and alternatives; third, the stage of normalization and optimization of attributes; Fourth, the stage of reducing the maximax and minimax values and ranking. Meanwhile, based on the classification of criteria, only the value of money is categorized as min, while other criteria include the max category. In addition, the weight of the criteria is as follows: services (0.15), facilities (0.15), room comfort and quality (0.20), cleanliness (0.20), value for money (0.20), and location (0.10). The results of this study show that The Trans Luxury Hotel ranks first with a total Yi value of 0.200649351. F, Pullman Bandung Grand Central ranked second with a total Yi value of 0.198075614. Meanwhile, Hilton Bandung ranks third with a total Yi value of 0.19758031. This shows that each hotel needs to increase its rating to attract the attention of potential customers in the decision-making process of staying.
Extract Sentiment and Support Vector Machine (SVM) Performance of Hotel Guest Review Classification Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 3 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i3.4737

Abstract

The hotel accommodation business highly depends on consumer preferences regarding products and services. The intensity of hotel guest visits and the level of guest satisfaction with the services provided by hotel management can be seen from various guest reviews on websites used as reservation media. Therefore, this research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) method to implement the data mining process using the webharvy application and the machine learning process using the Rapidminer application. Meanwhile, the operators used are Synthetic Minority Over-sampling Technique SMOTE in overcoming data imbalances and sentiment extract operators to obtain a total string score before sentiment labels are determined and processed using the Support Vector Machine (SVM) algorithm. The results of this study showed that SVM without using SMOTE operators resulted in an accuracy value of 95.82%, a precision value of 95.80%, a recall value of 100%, and an Area Under Curve (AUC) value of 0.798 (79.8%). Otherwise, SVM performance using SMOTE operators produces an accuracy value of 92.05%, a precision value of 100%, a recall value of 84.08%, and an Area Under Curve (AUC) value of 99.99 (99.9%). Furthermore, based on ten popular words, hotel guests are concerned about breakfast, staff, pool, room, and hotel. Thus, the guests' highlights are the menu served by the hotel, the service provided by employees, room conditions, and hotel brands. Therefore, hotel management needs to improve the quality of products and services to increase satisfaction and intention to stay again.
Implementation of Toxicity, Social Network, and Sentiment Classification: Alffy Rev Live in World E-sport Championship 2022 Rahadi, Abigail Rosandrine Kayla Putri; Setiawan, Ruben William; Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.5032

Abstract

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 during the event. Following the CRISP-DM methodology, the study systematically employs sentiment classification using Rapidminer, SVM with SMOTE for toxicity analysis, and Social Network Analysis (SNA). The findings reveal significant insights, including a sentiment classification accuracy of 98.73% using SVM with SMOTE, toxicity metrics such as Toxicity (0.04690) and Severe Toxicity (0.01203), alongside crucial SNA metrics like Diameter (2) and Density (0.001009). Additionally, frequently used words in the dataset include "keren" (94 occurrences), "Indonesia" (88 occurrences), "karya" (84 occurrences), and "Alffy" (59 occurrences). These findings offer valuable contributions to the esports community, informing community management strategies, event organization, and online engagement approaches. As a recommendation, deploying these analytical approaches could enhance community engagement and mitigate toxic interactions
Implementation of the GloVe in Topic Analysis based on Vader and TextBlob Sentiment Classification Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.5033

Abstract

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. Leveraging sentiment analysis techniques, including Vader and TextBlob, the study analyzes a dataset of textual content related to tourism and gastronomy to discern prevailing sentiment distributions. The findings reveal a predominant prevalence of positive sentiments (72.19%), followed by neutral (23.33%) and negative sentiments (4.48%). These results shed light on the overall sentiment dynamics surrounding tourism and gastronomy content, indicating a predominantly positive reception among users. The study contributes to the body of knowledge in sentiment analysis research, particularly within tourism and gastronomy studies, offering valuable insights into user perceptions and attitudes. Such findings have implications for content creators, marketers, and policymakers seeking to enhance tourism and gastronomy experiences. Future research could delve deeper into the factors influencing sentiment expressions and explore strategies to leverage positive sentiments for promoting and advancing tourism and gastronomy endeavors within the CRISP-DM framework.
Implementation of Sentiment Classification using k-NN, SVM, and DT for the MukaRakat Official Music Video (IDR and Toki Sloki) Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.5044

Abstract

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 Neighbors (k-NN), Decision Tree (DT), and Support Vector Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE). The analysis incorporates performance metrics, including accuracy, precision, recall, f-measure, and the area under the curve (AUC) values. The dataset comprises user-generated comments and feedback from two distinct hip-hop music videos. Results indicate that all three algorithms exhibit notable accuracy in classifying sentiments, with SVM with SMOTE achieving the highest accuracy of 83.68%. DT demonstrates balanced performance metrics, particularly in precision and recall, with an accuracy of 79.12%. Meanwhile, k-NN exhibits a lower accuracy of 64.71% but showcases balanced precision and recall rates. These findings suggest the suitability of SVM with SMOTE for sentiment classification tasks in the entertainment industry, offering valuable insights for content creators, marketers, and platform administrators to enhance audience engagement and user experience. Additionally, the study underscores the importance of algorithmic evaluation and selection in content analysis, providing guidance for future research and practical applications in the entertainment domain within the framework of CRISP-DM.
Implementation of Toxicity, Sentiment, and Social Network Analysis (Epic Rap Battles of Presidency 2024) Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.5046

Abstract

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, elucidating the coexistence of harmful content, diverse sentiments, and intricate network structures. Leveraging VADER and TextBlob algorithms, toxicity and sentiment distribution patterns are meticulously identified, with metrics such as Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, and Threat presenting distinct numerical values. For instance, Toxicity measures at 0.09275 with a severe threshold of 0.98622, while sentiment analysis reveals varying proportions of negative, neutral, and positive sentiments across English, French, and German content. Specifically, VADER sentiment analysis for English content shows 25.38% classified as unfavorable, 41.13% as neutral, and 33.49% as positive sentiments, while TextBlob sentiment analysis for English content displays 8.59% negative, 64.12% neutral, and 27.29% positive sentiments. Similarly, TextBlob sentiment analysis for French content indicates 1.75% negative, 96.49% neutral, and 1.75% positive sentiments, and for German content, it illustrates 2.00% negative, 96.52% neutral, and 1.48% positive sentiments. These findings provide crucial insights into public sentiment, information dissemination, and community formation within online political discourse. The implications of this research extend to policymakers, electoral candidates, and digital platform developers, offering evidence-based strategies to cultivate healthier online environments and promote informed civic engagement. Further investigation is warranted to explore emerging trends and adapt analytical frameworks to the evolving landscape of digital communication. Ultimately, this study advances our understanding of digital political communication and underscores the necessity of interdisciplinary approaches in addressing contemporary socio-political challenges in the digital era.
Tourism and Travel Content Analysis for Market Segmentation using Toxicity and Sentiment Classification in Communalytic Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5294

Abstract

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 sentiment. Toxicity score calculations indicated prevalent negative interactions, with scores ranging from 0.05542 to 0.86967 for Toxicity, 0.00536 to 0.50704 for Severe Toxicity, 0.01921 to 0.59834 for Identity Attack, 0.03305 to 0.76573 for Insult, 0.03737 to 0.78492 for Profanity, and 0.01075 to 0.48617 for Threat, underscoring the need for compelling content moderation. Sentiment analysis using VADER and TextBlob demonstrated a generally positive reception of travel vlogs, with VADER classifying 3.73% of posts as unfavorable, 19.83% as neutral, and 76.44% as positive. In comparison, TextBlob classified 2.71% of posts as unfavorable, 35.59% as neutral, and 61.69% as positive for English posts. Notably, VADER and TextBlob agreed on sentiment classification for 446 out of 587 posts (75.98%), with a Cohen’s kappa statistic of 0.471, indicating moderate agreement. These findings suggest that well-regulated and thoughtfully designed digital content significantly enhances user engagement and optimizes destination marketing strategies. Future research should incorporate advanced analytical tools and comprehensive data sets to refine these insights further, supporting the development of more targeted and effective marketing efforts in the tourism sector. This study thus contributes to a deeper understanding of digital media's impact on tourism marketing, offering practical recommendations for leveraging content to foster positive and engaging tourist experiences
Travel Content Evaluation through Sentiment and Toxicity Analysis using CRISP-DM Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5397

Abstract

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 25,361 posts, with 23,292 processed for sentiment and 24,171 for toxicity. Various algorithms, including k-NN, DT, NBC, and SVM, were applied with SMOTE to address data imbalance. The SVM algorithm achieved the highest performance with an accuracy of 54.80% and an F-measure of 66.01%, while others showed lower efficacy. The deployment phase integrated these models for real-time analysis, providing actionable insights into user engagement. Findings emphasize the significant impact of sentiments on brand perception and the necessity of managing toxic behavior for a healthier online environment. Despite limitations such as dataset imbalance and model dependency, the study offers valuable recommendations for content creators, advocating for robust moderation and sentiment-based strategies to enhance user interaction. Future research should include diverse datasets and advanced tools to improve the findings' robustness and applicability. This research contributes to understanding digital content dynamics and provides strategic insights for optimizing content creation and user engagement.
Co-Authors A.Y. Agung Nugroho Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Astuti Kusumawicitra Astuti Kusumawicitra Laturiuw Astuti Kusumawicitra Laturiuw Bernardus Alvin Rig Bernardus Alvin Rig Biafra Daffa Farabi Biafra Daffa Farabi Billy Macarius Sidhunata Brito, Manuel Charitas Fibriani Christanto, Henoch Juli Christine Dewi Danny Manongga Dasra, Muhamad Nur Agus Eko Sediyono Eko Widodo Elfin Saputra Elfin Saputra Elly Esra Kudubun Fang, Liem Shiao Faskalis Halomoan Lichkman Manurung Gatot Sasongko Gilberto Dennis G E Sidabutar Gintu, Agung Rimayanto Gudiato, Candra Henoch Juli Christanto Henoch Juli Christanto Heru Prasadja Heru Prasadja, Heru Hindriyanto Dwi Purnomo Hironimus Cornelius Royke Irene Sonbay Irwan Sembiring Jesslyn Alvina Seah Jonathan Tristan Santoso Juli Christanto, Henoch Kartikawangi, Dorien Kusumawicitra, Astuti Manuel Brito Marthen Timisela Mavish, Steven Michael Kenang Gabbatha Nantingkaseh, Alfonso Harrison Nicolas Arya Nanda Susilo Nugroho, A. Y. Agung Octa Hutapea Octa Hutapea Pamerdi Giri Wiloso Pamerdi Giri Wiloso Pamerdi Giri Wiloso, Pamerdi Giri Pedro Manuel Lamberto Buu Sada Pinia, Nyoman Agus Perdanaputra Pontolawokang, Theresya Ellen Pristiana Widyastuti Pristiana Widyastuti Purwoko, Agus Puspitarini, Titis Radyan Rahmananta Radyan Rahmananta Rafael Christian Rahadi, Abigail Rosandrine Kayla Putri Rahmadini, Asyifa Catur Richard Emmanuel Adrian Sinaga Rosdiana Sijabat Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno, Stephen Aprius Suharsono SUHARSONO Suni, Eugenius Kau Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani