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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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Digital marketing of smartphone manufacturing product: toxicity, social network, and sentiment classification Yerik Afrianto Singgalen
International Journal on Social Science, Economics and Art Vol. 14 No. 1 (2024): May: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijosea.v14i1.442

Abstract

This research explores digital interactions, analyzing toxicity, sentiment, and network dynamics using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Understanding and managing these elements are crucial for effective digital strategies with the rise of user-generated content. Leveraging machine learning, including Support Vector Machines (SVM) and Synthetic Minority Over-sampling Technique (SMOTE), toxicity analysis and sentiment classification are conducted. Data preprocessing involves text cleaning and feature engineering, aligning with the CRISP-DM data preparation phase. Toxicity levels are measured using various toxicity metrics, including Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, and Threat. Sentiment analysis employs SVM to classify sentiment polarity, while SMOTE addresses class imbalance as part of the CRISP-DM modeling phase. Social Network Analysis (SNA) techniques are also applied to study network structures following the CRISP-DM modeling phase. Network data are processed to compute key SNA metrics such as Diameter, Density, Reciprocity, Centralization, and Modularity. Findings reveal a toxicity level of 0.06194 and severe toxicity at 0.00730. Identity Attack stands at 0.01107, while insults and profanity are at 0.03803 and 0.04905, respectively. The threat is observed at 0.01359. The sentiment analysis indicates an accuracy of 97.94%, with a precision and recall of 98.07% and 99.86%, respectively, for the positive class. The f-measure for the positive class is 98.96%. The SNA metrics show a diameter of 4, a density of 0.000266, and a reciprocity of 0.000000. Centralization is calculated at 0.001468, while modularity stands at 0.999400.
Comprehensive analysis of tempel hamlet digital content reviews: Toxicity, sentiment, and social network Yerik Afrianto Singgalen
International Journal on Social Science, Economics and Art Vol. 14 No. 1 (2024): May: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijosea.v14i1.468

Abstract

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 unraveling viewer interactions within social networks. Comprehensive toxicity analysis was conducted, revealing specific scores for toxicity attributes and a prevalence of positive sentiment (72.5%). Sentiment classification utilizing the k-NN algorithm achieved exceptional accuracy (98.06%), showcasing its efficacy in sentiment discernment. Social network dynamics were examined, uncovering key metrics such as Diameter (3), Density (0.002140), Reciprocity (0.000000), Centralization (0.393200), and Modularity (0.552200), shedding light on network structures and interactions. Findings underscore the need for nuanced content moderation strategies and highlight the importance of fostering positive interactions in digital spaces. Recommendations include implementing targeted moderation policies, leveraging sentiment analysis for audience engagement, and fostering community-building initiatives to promote healthier online environments.
Land Use, Built-Up, and Vegetation Index in North Halmahera Regency through Spatio-Temporal Analysis Singgalen, Yerik Afrianto
Jurnal Manajemen Hutan Tropika Vol. 30 No. 1 (2024)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.30.1.70

Abstract

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 area of North Halmahera Regency using the spatio-temporal analysis method. The spatio-temporal analysis method comprises several stages: data selection, preprocessing, data integration, spatial analysis, temporal analysis, spatio-temporal analysis, data visualization, interpretation and understanding, and data visualization. The results of this study show that changes in the livelihood strategy of local people, from farmers and fishermen to traders, also affect land use patterns, from agricultural activities to economic activities, which triggers an increase in the number of buildings for production activities to product distribution. The implications of these findings on ecotourism development programs and policies and infrastructure development in the North Halmahera Regency are to consider community livelihoods and space or land use behavior in ecotourism areas based on vegetation, soil, and building index values. Thus, the intensification of building distribution and changes in vegetation index values from 2013-2023 reflect changes in people's livelihood strategies from agrarian activities to trade and from fishermen's activities to tourism transportation service providers.
Prediksi Ekspor Migas Indonesia Dengan Double Exponential Smoothing Juli Christanto, Henoch; Aprius Sutresno, Stephen; Mavish, Steven; Afrianto Singgalen, Yerik; Dewi, Christine
Jurnal Elektro Vol 15 No 1 (2022): Vol.15 No.1 April 2022: Jurnal Elektro
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v15i1.5123

Abstract

In Indonesia, there are several forms of exports, one of which is oil and gas exports. The Double Exponential Smoothing method is a forecasting method that can be used to forecast oil and gas export data. This method uses two smoothing parameters, namely the average smoothing parameter alpha (α), the trend smoothing parameter beta (β). In this study, the population used is data on oil and gas exports in Indonesia. And the sample used is Indonesia's oil and gas export data from January 2020 to October 2021, we get 22 data sourced from the Central Statistics Agency (BPS). The Double Exponential Smoothing method is used because the data to be processed has a trend, both an up and a down trend and gas exports in Indonesia have an up and down pattern. The Double Exponential Smoothing method can also predict the prediction of oil and gas exports in Indonesia in the next months, November 2021 to February 2022. To process the data, the researcher used the Holt Double Exponential Smoothing method .
Strategi Persaingan Pemasaran Indomaret dan Alfamart di Salatiga Menggunakan Game Theory Juli Christanto, Henoch; Aprius Sutresno, Stephen; Mavish, Steven; Afrianto Singgalen, Yerik; Dewi, Christine
Jurnal Elektro Vol 16 No 1 (2023): Vol.16 No.1 April 2023: Jurnal Elektro
Publisher : Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/jurnalelektro.v16i1.5126

Abstract

Indomaret and Alfamart are minimarket businesses that are currently experiencing significant growth, especially in the city of Salatiga. Each company strives to achieve maximum profit, and thus, it is essential to determine the most optimal competitive strategy to attract customers. This research was conducted using a questionnaire to understand what influences consumers in choosing a minimarket for shopping, providing valuable insights for determining minimarket strategies. Game Theory is employed to represent the respondents. The results of the study indicate that Alfamart will employ a location-based strategy in their stores, while Indomaret will utilize discount and promotion strategies for their easily accessible minimarkets.
Hotel Customer Segmentation for Marketing Strategy Optimization Using CRAF Framework Singgalen, Yerik Afrianto
Journal of Business and Economics Research (JBE) Vol 5 No 2 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jbe.v5i2.5132

Abstract

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 targeted marketing campaigns. A comparative study of SVM algorithms with and without SMOTE demonstrated the significance of data balancing techniques, with accuracies of 85.19% and 93.86%, respectively. Additionally, integrating Oracle Apex for data visualization and decision support enhances strategic planning and operational efficiency. The findings highlight that the combined use of advanced data analytics and sophisticated digital tools leads to improved customer satisfaction, refined marketing strategies, and sustained competitive advantage, contributing to the overall growth and success of the hospitality sector.
Customer Experience Analysis for Marketing Strategy Optimization Using CRAF Framework Singgalen, Yerik Afrianto
Journal of Business and Economics Research (JBE) Vol 5 No 2 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jbe.v5i2.5314

Abstract

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 overall high satisfaction levels. The effectiveness of the Support Vector Machine (SVM) model for sentiment classification is demonstrated, achieving an accuracy of 94.99% without SMOTE and 89.22% with SMOTE. Furthermore, deploying the analysis results using Oracle APEX enables creating an interactive information system that provides real-time insights into customer feedback, facilitating dynamic data management and strategy adjustments. The findings underscore the importance of comprehensive feedback analysis for identifying improvement areas, enhancing service quality, and elevating guest satisfaction. The study concludes that adopting systematic frameworks like CRAF, advanced analytical models like SVM, and interactive deployment platforms like Oracle APEX is essential for achieving long-term success and maintaining high standards in service delivery within the hospitality sector.
Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2486

Abstract

Sentiment analysis of visitors to the tourist destinations of Borobudur Temple in Indonesia needs to be done to determine the expected product and service preferences. In addition, sentiment analysis is also helpful for managers to adjust the needs of tourists to the infrastructure provided in the tourist destination area. The classification method used in the sentiment analysis is the Naïve Bayes Classifier (NBC) against 3850 visitor reviews at Borobudur Temple. Review data is pulled from Tripadvisor web pages filtered by language, review time, and travel characteristics to analyze foreign traveler preferences comprehensively. This research stage is divided into three parts: data preparation, data processing, sentiment analysis, and algorithm performance evaluation. In addition, SMOTE Upsampling is used to balance data. The results of implementing the Naïve Bayes Classifier (NBC) classification method obtained an accuracy value of 96.36%, a precision value of 93.23%, and a recall value of 100% with an Area Under Curve (AUC) value of 0.714. In addition, the results of ranking five famous words from the review data show that there are highlights of the physical condition of the temple, scenery, and tourist visit activities at Borobudur Temple, where the four most famous words in visitor reviews are the “temple,” “visit,” “Borobudur,” “sunrise” and “place.”
Analisis Performa Algoritma NBC, DT, SVM dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2766

Abstract

The approach of visitor sentiment analysis to Borobudur Temple tourist destinations in Indonesia can be classified using various algorithms to get optimal results. Good algorithm performance can be seen from the confusion matrix (accuracy, precision, recall) value, Area Under Curve (AUC) value, and Receiver Operating Characteristic (ROC). This study used the Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM) algorithms against 3850 text data obtained from the Tripadvisor website, especially reviews of Borobudur Temple visitors. The method refers to the Cross-Industry Standard Process for Data Mining (CRISP-DM) for optimizing tourist destination products and services by paying attention to six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results of this study show that the results of NBC's algorithm performance evaluation can be seen to have a change in the confusion matrix value at the accuracy value from 98.73% to 95.6%, the precision value changed from 98.72% to 98.97%, the recall value also changed from 100% to 96.54%. In addition, the Area Under Curve (AUC) of NBC also changed from 0.500 (50%) to 0.693 (69.35%). In addition, the results of the DT algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 97.55% to 94.40%, the precision value increased from 97.63% to 91.86%, the recall value also changed from 99.90% to 99.47%. The Area Under Curve (AUC) of DT value also changed from 0.591 (59.1%) to 0.932 (93.2%). The results of the SVM algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 98.73% to 99.41%; the precision value changed from 98.72% to 100%, and the recall value also changed from 100% to 99.01%. The Area Under Curve (AUC) of the SVM value also changed from 0.961 (96.1%) to 1.00 (100%). In addition, the T-test results show that the SVM algorithm is more dominant compared to other algorithms, where the SVM algorithm T-test value is 0.994 compared to the DT algorithm T-test value of 0.944 and the NBC algorithm T-test value of 0.98. Based on the Receiver Operating Characteristic (ROC) value, it can be seen that the DT algorithm also shows good performance in addition to SVM. It indicates that in analyzing the sentiment of visitors to Borobudur Temple, the best-recommended algorithm is the Support Vector Machine
Analisis Sentimen Wisatawan terhadap Kualitas Layanan Hotel dan Resort di Lombok Menggunakan SERVQUAL dan CRISP-DM 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.v4i4.3199

Abstract

The era of digital transformation has sparked innovations in product and service marketing strategies in various sectors, one of which is the tourism sector. In the hospitality industry context, product marketing using website-based digital media allows consumers as hotel guests to review the products and services received. The Tripadvisor website is a digital marketing platform that provides review features for app users, especially consumers, to give ratings and reviews. This study aims to analyze the quality of hotel services using the Service Quality (SERVQUAL) framework based on the results of the classification of hotel guest sentiment data using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithm by the stages of the Cross-Industry Standard Process for Data Mining (CRISP-DM). The CRISP-DM framework consists of six stages, namely: business understanding stage, data understanding stage, data preparation stage, modeling stage, evaluation stage, and deployment stage. The SERVQUAL framework consists of several dimensions: reliability dimension; responsiveness; assurance; empathy; tangibles. The review data that will be processed is the consumer review data of The Oberoi Beach Resort Lombok; Sheraton Senggigi Beach Resort; Sudamala Resort Sengiggi; Holiday Resort Lombok; Aston Sunset Beach Resort. The results of this study show that the SVM algorithm performs better than NBC, where the accuracy value is 98.57%, the precision value is 100%, the recall value is 97.14%, and the f-measure value is 98.54%. The AUC value is 100%, and the t-Test value is 98.6%. Unlike the case with the results of SVM's algorithm performance evaluation without using the SMOTE Upgrading Operator, where the accuracy value is 95.71%, the precision value is 95.71%, the recall value is 100%, and the f-measure value is 97.81%. In addition, the AUC value is 91.1%, and the t-Test value is 95.7%.
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