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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
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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Journal : Building of Informatics, Technology and Science

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Implementasi Metode Simple Multi Attribute Rating Technique (SMART) dalam Pemilihan Zona Prioritas dan Alternatif Berbasis Data Klasifikasi Indeks Vegetasi Yerik Afrianto Singgalen
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4085

Abstract

Vegetation index analysis using the Normalized Difference Vegetation Index (NDVI) model needs to be processed using a decision support model to follow up on the Landsat 8/9 Operational Land Imaginer (OLI) satellite image data interpretation results. However, studies using the Simple Multi-Attribute Rating Technique (SMART) method to determine priority zones based on vegetation index classification data are still limited. This study uses the SMART decision support model to process NDVI classification data in mangrove areas. The stages in this study consist of four parts: the data collection stage, the data processing stage; the data analysis stage; and the data interpretation stage. At the data collection stage, the raster data used was sourced from the United States Geology Survey (USGS) platform, namely Landsat 8/9 OLI with coordinate raster data (Lat 01o43'18" N, Lon: 128o04'15" E) in 2013, 2018, and 2023. In addition, video and aerial photographs at the study site were taken using drones (Phantom 4 Version 2). At the data processing stage, the model used in calculating raster data is NDVI using the QGIS 3.30.1 application. This research data analysis and interpretation stage uses the SMART decision support model. The SMART decision support model is used to produce recommendations for priority zones for mangrove ecotourism development based on the results of the NDVI classification (minimum value, average value, maximum value) adjusted to the Decree of the State Minister of Environment Number 201 of 2004 concerning standard criteria and guidelines for mangrove forest destruction (rare, medium, and dense). Based on the calculation of the utility value of criterion C1 as a cost with a weight of 0.50 in the NDVI classification data for 2023, the second observation station is recommended as a priority zone with a total value of 0.50. Meanwhile, the calculation of the utility value of criterion C3 as a cost with a weight of 0.50 in the NDVI classification data in 2023 recommended the third observation station as a priority zone with a total value of 0.88. This means that the SMART method can be used to identify and analyze priority and alternative zones for the sustainable development of mangrove ecotourism areas.
Analisis Hasil Implementasi Multi-Attribute Utility Theory (MAUT) dalam Pengemasan Paket Wisata Tematik Yerik Afrianto Singgalen
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4144

Abstract

The travel industry plays a vital role in maximizing the marketing of tourist destinations, but the process of determining tour packages must consider consumer purchasing power concerning destination ticket prices, distance and travel time, availability of accommodations and amenities services, and regulations. This study seeks to use the Multi-Attribute Utility Theory (MAUT) decision support model to tourist case studies from Ternate City to determine superior tour packages. In the meantime, the context of destinations, accommodation services, and transportation services is incorporated into the use of the MAUT decision support model. The following criteria are established based on the category of the location: entrance fee; facilities and infrastructure; local tour guides; type of activity at the destination; Security, and Hygiene. The following criteria are established based on the category of lodging services: standard room rate, property amenities, room features, room type, and services. In addition, the criteria established based on the category of transportation services are as follows: rental pricing; car type; vehicle amenities; driver experience. The findings of this study indicate that A5 tourist destinations are recommended, with a total value of 0.90, based on destination category, criteria and criteria values related to ticket prices (10), facilities and infrastructure (20), availability of local tour guides (10), diversity of activities (20), safety (20), and cleanliness (20). In addition, based on the criteria and weights related to standard room rental costs (20), property amenities (20), room features (20), room type (10), and services (30), we propose A1 with a total value of 0.85 in the accommodation services category. In the field of transportation services, we offer A2 with a total score of 0.83 based on criteria and weights relating to rental price (25), vehicle type (25), car amenities (25), and driver experience (25). Using the MAUT decision support model, it is evident that the packaging of tour bundling becomes more effective and efficient
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%.
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
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