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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 5 No 3 (2023): December 2023" : 5 Documents clear
Decision Support System for Determining the Best Internship Students Using the Combined Compromise Solution Method Pasaribu, A. Ferico Octaviansyah; Aldino, Ahmad Ari; Surahman, Ade; Setiawansyah, Setiawansyah
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.v5i3.4231

Abstract

Interns are individuals who are undergoing a period of practical learning in an organization or company as part of their educational curriculum. During the internship, students have the opportunity to apply the knowledge they learn in class to real-world situations, as well as gain valuable work experience. The selection of the best intern can involve several problems or challenges. One of them is the difficulty in evaluating students' practical skills based solely on their academic performance. The Decision Support System (DSS) to determine the best internship students using the Combined Compromise Solution Method provides a holistic approach in the selection process. This method combines elements of the Compromise Solution Method that consider compromise solutions between alternatives. With this comprehensive approach, DSS can assist institutions or companies in selecting internship students that best suit their needs and expectations, as well as ensure the success of internships that are beneficial to both parties. The results of the ranking of the best internship student alternatives showed that rank 1st with a value of 5.7847 was obtained by Jonathan, rank 2nd with a value of 5.2625 was obtained by Handoko R, and rank 3rd with a value of 4.6117 was obtained by M. Ali Fikri. The results of this ranking help companies determine the best internship students by applying the combine compromise solution method
Decision Support System for Tourist Attraction Recommendations Using Reciprocal Rank and Multi-Objective Optimization on the basis of Ratio Analysis Ariany, Fenty; Suryono, Ryan Randy; Setiawansyah, Setiawansyah
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.v5i3.4663

Abstract

A tourist attraction is a destination or place visited by tourists to enjoy a variety of attractions, natural beauty, culture, history, or recreation. Attractions can be beaches, mountains, lakes, national parks, historical buildings, museums, amusement parks, and much more. One common problem is confusion in choosing the right attraction, where the information available is incomplete or inaccurate, causing tourists difficulty in making the right decision. Therefore, there needs to be a holistic and integrated approach in choosing tourist attractions, taking into account these aspects so that the tourist experience becomes more meaningful and meaningful for all parties involved. The research objective of the Attraction Recommendation Decision Support System Using Reciprocal Rank and MOORA is to develop a system that can provide optimal attraction recommendations to users based on their preferences against diverse criteria, such as distance, cost, travel time, and cleanliness level. By using the Reciprocal Rank approach to take into account the user's subjective preferences towards each criterion. Meanwhile, by applying MOORA, this study aims to optimize the relative performance of alternative attractions based on the relationship between criteria. Thus, this research is to provide useful tools for users to make better and more informed decisions. The ranking results provide recommendations for alternative krui beach with a final value of 0.3752 to rank 1, alternative tanjung setia beach with a final value of 0.3558 to rank 2, alternative klara beach with a final value of 0.3512 to rank 3
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.

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