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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 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

Author-ID : 2436782

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 Top 10 Traveler Ranked Hotel di Kota Makassar Menggunakan Algoritma Decision Tree dan Support Vector Machine Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1153

Travel planning popularized by influencers sparks audiences to review destinations based on image and attractiveness before designing a trip alone, with family, with friends, or as a couple. This research offers ideas to analyze the sentiment of travelers who use hotel accommodation services in Makassar based on Tripadvisor website review data. The method used is the Cross-Industry Standard Procedure for Data Mining (CRISP-DM) with the following stages: business understanding stage, data understanding stage, data preparation stage, modeling stage, evaluation stage, and deployment stage. The results of this study show that the business understanding stage shows the importance of identifying and analyzing tourist behavior related to the assessment of location, cleanliness, service, and hotel value as a strategic step to develop a hotel imaging program to attract tourists with guest characteristics (solo, couple, business, family). The data understanding stage refers to the features processed according to the CRISP-DM method and supporting data for analysis and discussion of the context of Tripadvisor's Top 10 recommended hotels in Makassar. At the data preparation stage, the amount of text data that has been processed using the Decision Tree (DT) algorithm and Support Vector Machine (SVM) is 1,138 through data pre-processing (tokenize, transform cases, filter tokens by length, stopwords, stemming). At the modeling stage, the algorithm that shows the best performance is SVM, with an accuracy value of 98.98%, a precision value of 100%, a recall value of 97.96%, an f-measure discount of 98.97%, and an AUC value of 100%. At the evaluation stage, it can be seen that the classification of reviews is dominant on positive sentiment compared to negative sentiment. Thus, the recommendation for the deployment stage is optimizing products and services related to room amenities, room features, room type, cleanliness, service, value, and location.

Analisis Model Pendukung Keputusan Simple Additive Weighting (SAW) terhadap Top 10 Traveler Ranked Hotel Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1159

The decision support model is essential in helping passengers choose which tourist attractions and lodging facilities to use. However, creating systems for prioritizing institutional programs remains the sole focus of research into decision-support models.Considering this, this study offers ideas to analyze the Simple Additive Weighting (SAW) decision support model as a support for hotel stay decisions based on data from the top 10 traveler-ranked hotels in Makassar City as follows: Claro Makassar; The Rinra Makassar, Swiss-Belhotel Makassar; Novotel Makassar Grand Shayla; Almadera Hotel; Dalton Hotel Makassar; MaxOneHotels @Resort Makassar; Ibis Makassar City Center; Harper Perintis-Makassar; Aryaduta Makassar. The criteria set out in this study are divided into two parts: standards based on the availability of room rooms (property amenities, room features, and room types) and hotel guest ratings (location, cleanliness, services, value). The categories and weights of values based on room availability are as follows: property amenities are categorized as the cost with a weight of 0.50; Room feature is ranked as the cost with a weight of 0.25; room type is classified as the cost with a weight of 0.25. In addition, the categories and weights of values based on guest ratings are as follows: location is categorized as benefit with a weight of 0.20; cleanliness is categorized benefit with a weight of 0.25; Service is categorized benefit with a weight of 0.30, and value is classified as cost with a weight of 0.25. The results of this study show that the ranking can be categorized into two, namely, based on room availability and hotel guest ratings. Based on room availability criteria (property amenities, room features, and room type), Novotel Makassar Grand Shayla ranks first with a total value of 0.85, and Ibis Makassar City Center Hotel ranks second with a total value of 0.75. Based on the assessment of hotel guest rating criteria (location, cleanliness, services, value), Arya Duta Hotel ranks first with a total value of 0.94, and Almadera Hotel ranks second with a total value of 0.88. Thus, accommodation services can be recommended to guests based on room availability (property amenities, room features, and room type) or guest ratings (location, cleanliness, services, value) in the top 10 traveler-ranked hotels in Makassar City.

Implementation of Spatio-Temporal Analysis for Land Use Management and Urban Planning in North Halmahera Regency Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1256

This study aims to examine the attributes of spatio-temporal dynamics and the progression of land-use alterations, which is imperative for comprehending and appraising the condition and transformation of ecosystems. When employed in the context of North Halmahera Regency, this analysis can furnish fundamental insights to facilitate informed decision-making in urban planning. By understanding the spatio-temporal dynamics of land use change in the region, decision-makers can make more effective choices regarding infrastructure development, resource allocation, and sustainable urban growth, ultimately leading to more resilient and well-planned urban environments. In the methodology section, this study employed Landsat 8 OLI satellite imagery and Geographic Information System (GIS) technology to investigate the spatio-temporal dynamics and land use changes in North Halmahera Regency from 2013 to 2023. These Research Findings show that land use management for urban planning objectives should assess several dimensions, such as preserving Hibualamo cultural values, settlement patterns derived from household livelihood assets, and Tobelo City's economic activities.

Penerapan Metode Spatio-Temporal Analysis dalam Analisis Dinamika Tutupan dan Penggunaan Lahan Berbasis NDVI dan NDWI Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1313

Landscape changes brought on by converting land from community plantations to industrial areas must be continuously monitored for spatial data documentation. This study proposes documenting changes in the environment of coconut farms in Indonesia's North Maluku Province's Gulo Village, North Kao District, and North Halmahera Regency. The spatio-temporal analysis method was employed in this investigation. The results of this study show that the spatio-temporal analysis method is very relevant to be used in identifying and analyzing the dynamics of land cover and use in buffer zones and industrial zones through the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) models. According to Landsat 8 OLI satellite image data based on 2018, 2019, and 2021, the land cover and use in the buffer zone, particularly in Gulo Village and Dowongimaiti Village, suggest agricultural activity and deep coconut plantations. The Landsat 8 OLI satellite image data calculation in August 2018 was 0.39 based on vegetation index data in the Gulo Village buffer zone. This value declined to 0.35 in September 2019 and increased to 0.43 in September 2021. In addition, changes in the vegetation index value of 0.39 in August 2018 were discovered based on data from the Dowongimaiti Village buffer zone, which declined to 0.38 in September 2019 and increased to 0.44 in September 2021. The NDWI model's calculation outcomes in industrial zones reveal water features and puddles with an average index value of -0.33. As a result, as a proactive and strategic measure to reduce the risk of disaster brought on by industrial activities, the output of the spatio-temporal analysis method can be used to optimize the function of supervision or control of environmental conditions in the form of appropriate program recommendations.

Performance Evaluation of SVM Algorithm in Sentiment Classification: A Visual Journey of Wonderful Indonesia Content Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1709

This study addresses the research problem of understanding public sentiment towards tourism-themed content on YouTube, with a specific focus on "A Visual Journey of Wonderful Indonesia." The primary aim is to explore how viewers perceive and depict Indonesia as a tourism destination through their comments on YouTube videos. Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, sentence analysis is conducted using the Support Vector Machine (SVM) algorithm with the Synthetic Minority Over-sampling Technique (SMOTE) to classify sentiments within a dataset of YouTube comments as positive, negative, or neutral. The analysis of frequently used words in the comments provides valuable insights into Indonesia's perception, revealing positive sentiments reflected in terms such as "beautiful," "wonderful," and "amazing," emphasizing the country's aesthetic appeal. Notably, terms like "orang" and "Indonesian" indicate appreciation for Indonesia's rich cultural heritage and its people. These findings highlight the pivotal role of destination branding efforts in shaping positive perceptions and emotions toward Indonesia. The results indicate the efficacy of the SVM-SMOTE model, achieving high accuracy (84.26%), precision (100.00%), recall (68.51%), f-measure (81.25%), and AUC (0.996) in accurately classifying sentiment patterns within analyzed YouTube content. This offers practical implications for destination managers and marketers. Conversely, the SVM algorithm without SMOTE demonstrates impressive accuracy, precision, and recall scores of 97.08%, but its AUC value of 0.607 suggests potential challenges in discriminating between positive and negative sentiment instances. These findings provide valuable insights into the role of digital media platforms in shaping destination perceptions and offer practical implications for destination marketers and managers

Social Network Analysis and Sentiment Classification of Robotic Restaurant Content using Naïve Bayes Classifier Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1710

Sentiment analysis is crucial in understanding public opinion, particularly in emerging technologies such as automation AI and robotic restaurant services. However, achieving accurate sentiment classification in sentiment analysis tasks poses challenges, especially when dealing with imbalanced data. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) through the Naive Bayes Classifier (NBC) algorithm and Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced data challenges in sentiment analysis. Social network analysis (SNA) collects and analyzes user-generated content related to automation AI and robotic restaurant services, providing insights into public sentiment. Additionally, the occurrence of frequently used words such as "people" (182), "food" (158), "jobs" (135), "robots" (137), "wage" (102), "work" (78), "robot" (79), "minimum" (78), "fast" (70), and "workers" (65) is examined. The performance of the NBC algorithm with and without SMOTE integration is compared. With SMOTE, the algorithm exhibits an accuracy of 70.11% +/- 3.52%, precision of 88.82% +/- 5.06%, recall of 46.06% +/- 6.13%, AUC of 0.967 +/- 0.016, and F-measure of 60.46% +/- 6.02%. Without SMOTE, the algorithm yields an accuracy of 48.90% +/- 4.36%, precision of 72.15% +/- 5.25%, recall of 44.32% +/- 7.15%, AUC of 0.777 +/- 0.051, and F-measure of 54.57% +/- 5.78%. Recommendations to further enhance the algorithm's performance include exploring additional optimization techniques, such as feature engineering and ensemble methods, and continuing data collection and augmentation efforts to improve dataset representativeness. Regular monitoring and evaluation and iterative refinement based on evolving data patterns are also recommended to ensure sustained effectiveness in sentiment analysis tasks.

Social Network Analysis and Sentiment Classification of Extended Reality Product Content Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1712

This study explores Extended Reality (XR) products, specifically focusing on the Apple Vision Pro, to elucidate consumer perceptions and the underlying social dynamics of these innovative technologies. This research delves into Extended Reality (XR) products, specifically focusing on the Apple Vision Pro, aiming to understand consumer perceptions and social dynamics surrounding these innovative technologies. By leveraging sentiment analysis and Social Network Analysis (SNA) alongside CRISP-DM and SVM algorithms, this study provides a comprehensive insight into sentiment patterns, network structures, and influential factors within the XR community. A multi-faceted approach is adopted to achieve the research objectives. Sentiment analysis and SNA dissect sentiment patterns and uncover network structures within the XR community. The CRISP-DM framework guides the research process, ensuring systematic data analysis and interpretation. SVM algorithms classify sentiments, providing a robust analytical framework for understanding consumer sentiments towards XR products. The analysis yields significant insights into XR consumer perceptions and social dynamics. The calculated network metrics, including a density of 0.000124, absence of reciprocity, centralization value of 0.001331, and modularity value of 0.999000, shed light on crucial network dynamics within the XR community. Examining frequently used words reveals prevalent topics within the XR discourse, providing valuable context for understanding consumer sentiments. Furthermore, the evaluation of SVM algorithms demonstrates commendable performance metrics, with the SVM without SMOTE achieving an accuracy rate of 84.33%, precision of 84.67%, recall of 99.28%, and f_measure of 91.39%. In comparison, the SVM with SMOTE exhibits an accuracy of 81.82% and a precision of 97.58%. This research contributes valuable insights into the consumer landscape of XR products, mainly focusing on the Apple Vision Pro. By combining sentiment analysis, SNA, and established methodologies, the study offers a nuanced understanding of consumer perceptions and social dynamics within the XR community. These findings inform strategic decisions and contribute to advancements in XR technologies, offering valuable insights into the efficacy of sentiment analysis techniques in understanding consumer sentiments

Implementation of SVM and DT for Sentiment Classification: Tempel Hamlet Content Reviews Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1826

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 research involves several stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The process begins with understanding the business objectives of sentiment analysis and proceeds to explore and prepare the dataset for analysis. SVM and DT algorithms, enhanced with SMOTE, are then implemented for sentiment classification. The study reveals promising results in sentiment analysis tasks. When integrated with SMOTE, SVM achieves an accuracy of 99.21%, while DT attains an accuracy of 98.33%. The Area Under the Curve (AUC) metrics indicate high confidence in classifying positive instances, with SVM and DT demonstrating AUC scores of 1.000 and 0.996, respectively. These findings underscore the efficacy of SVM and DT algorithms, enhanced with SMOTE, in accurately classifying sentiment within text data, thereby addressing class imbalance issues effectively

Performance Evaluation of Sentiment Classification Models: A Comparative Study of NBC, SVM, and DT with SMOTE Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1827

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 metrics. The NBC model achieved an accuracy of 86.98% +/- 0.96%, precision of 100.00% +/- 0.00%, recall of 78.82% +/- 1.55%, and f-measure of 88.15% +/- 0.97%, with an AUC of 0.657 +/- 0.203. Similarly, the DT model exhibited an accuracy of 93.20% +/- 0.42%, precision of 90.87% +/- 0.64%, recall of 98.88% +/- 0.31%, and f-measure of 94.70% +/- 0.31%, with an AUC of 0.918 +/- 0.006. Furthermore, the SVM model demonstrated an accuracy of 96.80% +/- 0.65%, precision of 98.99% +/- 0.28%, recall of 95.77% +/- 1.03%, and f-measure of 97.35% +/- 0.55%, with an AUC of 0.994. These findings highlight the efficacy of these models in accurately classifying sentiments within digital content, suggesting their suitability for various data mining applications. Recommendations for future research include exploring ensemble methods, continuous model updating, alternative sampling techniques, feature engineering approaches, and collaboration with domain experts to enhance real-world applicability

Comparative Analysis of DT and SVM Model Performance with SMOTE in Sentiment Classification Yerik Afrianto Singgalen
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1828

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) across various performance metrics, including accuracy, precision, recall, f-measure, and Area Under the Curve (AUC). Using CRISP-DM, the research ensures a systematic data preprocessing, modeling, and evaluation approach. The findings reveal that both DT and SVM models with SMOTE achieve high accuracy rates, with DT yielding an accuracy of 98.37% +/- 0.48% and SVM achieving 98.91% +/- 0.59%. These models effectively distinguish between positive and negative sentiments, as precision, recall, and f-measure scores indicate. Additionally, the AUC scores underscore the robustness of the models in sentiment analysis tasks. These results highlight the potential of CRISP-DM as a structured methodology for sentiment classification research, providing insights into the performance of different machine learning algorithms in handling imbalanced datasets. Based on these findings, it is recommended that future studies further explore the application of CRISP-DM in sentiment analysis tasks and investigate the scalability of DT and SVM models with SMOTE in larger datasets.

Co-Authors A.Y. Agung Nugroho Abigail Rosandrine Kayla Putri Rahadi Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Aprius Sutresno, Stephen 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 Eugenius Kau Suni 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 Henoch Juli Christanto Heru Prasadja 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 Ruben William Setiawan Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno Suharsono SUHARSONO Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani

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