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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.
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Articles 7 Documents
 1 
Search results for , issue "Vol 5 No 4 (2024): March 2024" : 7 Documents clear
Algorithmic Advancements in Heuristic Search for Enhanced Sudoku Puzzle Solving Across Difficulty Levels Pratama, Moch Deny; Abdillah, Rifqi; Herumurti, Darlis; Hidayati, Shintami Chusnul
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.v5i4.4622

Abstract

Computer technology, particularly artificial intelligence, has found diverse applications in the rapidly evolving era of the industrial revolution, notably in gaming, delving into artificial intelligence and explicitly applying game-solving techniques to Sudoku puzzles. Sudoku, a popular game requiring logical precision, serves as an ideal platform for exploring algorithms such as depth-first search, breadth-first search, and heuristic search. This research identifies memory-intensive demands in breadth-first search and the potential issue of infinite traversal in depth-first search. To address these challenges, the study proposes implementing the heuristic search algorithm, which prioritizes promising paths based on estimations of proximity to the goal state made by a heuristic function. The primary objective is to enhance Sudoku puzzle-solving by comparing the performance of the heuristic search algorithm with traditional breadth-first and depth-first search methods, with a particular focus on improving efficiency and reducing memory usage, including time and steps. The results indicate that the heuristic search algorithm outperforms traditional methods, demonstrating faster completion times and reduced memory requirements, thereby contributing to the advancement of Sudoku-solving algorithms. The study evaluates their performance across different difficulty levels, utilizing data from sudoku.com and extremesudoku.info. Notably, the heuristic search algorithm emerges as a superior method, outperforming other algorithms in terms of completion steps and time efficiency. The implementation and analysis involved three types of Sudoku puzzle-solving methods, revealing that the heuristic search algorithm significantly outperforms other algorithms, optimizing its performance in solving Sudoku puzzles. The average time required to complete Sudoku puzzles from data sourced from Sudoku.com was 0.02, 0.05, and 0.61 seconds for each level, respectively. In contrast, according to extremesudoku.info, it took 0.31 seconds for the highest difficulty level. Furthermore, the average total steps needed on sudoku.com ranged from 43 to 1201 steps for each level, spanning from easy to hard. On extremesudoku.info, 509 steps were required for the highest difficulty level. These results affirm the reliability of heuristic search, consistently demonstrating encouraging outcomes and outperforming other algorithms across diverse conditions. This strategic selection facilitates a comprehensive analysis of Sudoku problem-solving algorithms, allowing for the exploration of algorithmic performance and providing a comprehensive range of Sudoku puzzles, thereby ensuring the study's robustness and validity
Prediksi Keterlambatan Pembayaran SPP Siswa dengan Pendekatan Metode Naive Bayes dan K-Nearest Neighbors Alfiansyah, Deden Moh; Soetanto, Hari
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.v5i4.4643

Abstract

Education in Indonesia faces cost challenges which have an impact on the provision of education, especially Education Development Contributions (SPP) in private schools. This research aims to improve the administrative efficiency of student tuition payments at Wirasaba Karawang Vocational School through a classification approach. 725 payment data for one semester in 2023 are used to predict payment delays. About 22% of students experience delays. By understanding delay patterns, this research proposes solutions to improve administrative efficiency with appropriate preventive measures. The hope is that the results of this research can provide benefits to Vocational School Wirasaba Karawang and contribute to the development of a more efficient education administration system, thereby improving education services in Indonesia as a whole. This research describes the problem of late SPP payments, applies a classification method to predict these delays, aims to increase the efficiency of payment administration, and has the potential to provide preventive solutions that can reduce late payments. The contribution of this research is the development of a more efficient education administration system through an information technology approach, with interim results in the form of analysis of late payment patterns based on 2023 data from Wirasaba Karawang Vocational School
Reduksi False Positive Pada Klasifikasi Job Placement dengan Hybrid Random Forest dan Auto Encoder Pahlevi, M. Riza; Rasywir, Errissya; Pratama, Yovi; Istoningtyas, Marrylinteri; Fachruddin, Fachruddin; Yaasin, Muhammad
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.v5i4.4864

Abstract

The False Positive (FP) interpretation shows a negative prediction result and is a type 1 error answer with an incorrect positive prediction result. Based on this, we try to reduce type 1 errors to increase the accuracy value of the classification results. A low FP rate is critical for the use of Computer Aided Detection (CAD) systems. In this research proposal, to reduce FP, we use a Random Forest (RF) evaluation result design which will be reinterpreted by the Auto Encoder (AE) algorithm. The RF algorithm was chosen because it is a type of ensemble learning that can optimize accuracy in parallel. RF was chosen because it performs bagging on all Decision Tree (DT) outputs used. To suppress TP reduction more strongly, we use the Auto Encoder (AE) algorithm to reprocess the class bagging results from RF into input in the AE layer. AE uses reconstruction errors, which in this case is Job Placement classification. From the test results, it was found that combining the use of a random forest using C4.5 as a decision tree with an Autoencoder can increase accuracy in the Job Placement Classification task by a difference of 0.004652 better than without combining it with an autoencoder. Apart from that, in testing using a combination of RF and AE, fewer False Positive (FP) values ​​were produced, namely 11 items in the Cross Validation-5 (CV-5) Test, then 13 items in the Cross Validation-10 (CV-10) test and in testing split training data of 60%, the FP was only 12. This value is less than the false positives produced by testing without Autoencoder, namely 12 items on CV-5, 15 items on CV-10, and 13 on split training data
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.

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