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Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Published by CV. Raskha Media Group
ISSN : -     EISSN : 31232507     DOI : https://doi.org/10.64803/cessmuds
Core Subject : Religion, Science, Education, Engineering,
Religion Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering
The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies (CESSMUDS) with ISSN No. 3123-2507 (online) is one of the activities organized by Raskha Media Group Publisher. The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies (CESSMUDS) is held to encourage Lecturers, Students, and Researchers to publish in the National Scientific Forum, hoping that society can widely feel its benefits. The lecturers, students, and researchers will be published as Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies (CESSMUDS). The proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies (CESSMUDS) are published by CV. Raskha Media Group is a Publisher managed by an editorial team experienced in their respective fields. Raskha Media Group Publisher accordance with the data in the Registration form stored in the Business Entity Administration System based on Deed Number 196 dated October 10, 2023, made by Notary ALWINE ROSDIANA PAKPAHAN, SH, located in the City of Medan, along with supporting documents received on October 11, 2023, regarding the registration of CV RASKHA MEDIA GROUP, abbreviated as RKMG, situated in the City of Medan, has been received and registered in the Business Entity Administration System.
Arjuna Subject : Umum - Umum
Articles 113 Documents
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The Effect of Product Quality and Promotion on the Purchase Decision of Proris Syrup at PT. Pharos Indonesia Kurnia Utami Pasi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.22

Abstract

This study investigates the impact of product quality and promotion, both individually and simultaneously, on consumer purchasing decisions for Proris Syrup. The research involved 71 consumers who had previously purchased the product. Data collection was carried out using documentation, interviews, and questionnaires as primary instruments. The analysis techniques applied include multiple linear regression, t-test, F-test, and the coefficient of determination (R²). The results of the t-test indicate that product quality and promotion each have a significant influence on consumer purchasing decisions. Furthermore, the F-test results confirm that when combined, both variables jointly have a significant effect on purchasing behavior. The R² value obtained is 0.784, meaning that 55.4% of the variation in consumer purchasing decisions can be explained by product quality and promotion. Meanwhile, the remaining 44.6% is influenced by other factors not examined in this study, such as price, personal preferences, cultural background, brand image, and service quality. Overall, the findings highlight the crucial role of product quality and promotional strategies in shaping consumer purchasing behavior. Companies are therefore encouraged to maintain high product standards and implement effective promotional activities to strengthen customer interest and improve sales performance.
The Effect of Product Quality and Service Quality on Customer Satisfaction in Restaurants at Medan City Lidya Kesuma Sari
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.23

Abstract

This study aims to examine the effect of product quality and service quality on customer satisfaction in restaurants. Product quality refers to the attributes of food such as taste, freshness, portion, and presentation, while service quality covers aspects like responsiveness, reliability, assurance, and empathy in service delivery. The research employs a quantitative method using a structured questionnaire distributed to 120 restaurant customers in a major urban area. The data were analyzed using multiple linear regression with SPSS software to determine the influence of the independent variables on customer satisfaction. The results reveal that both product quality and service quality have a positive and significant effect on customer satisfaction, with product quality showing a slightly stronger impact. This indicates that although the quality of service contributes to the overall dining experience, customers still prioritize the quality of the food itself. The findings support prior research suggesting that food excellence and service interactions are key determinants of satisfaction in the food service sector. The study contributes to marketing and service management literature by reinforcing the importance of maintaining high product and service standards to enhance satisfaction and customer loyalty. It also provides managerial implications for restaurant operators to focus not only on food preparation consistency but also on staff training to deliver excellent service. Future research may incorporate additional variables such as restaurant ambiance, perceived price fairness, and brand image as potential mediating or moderating factors influencing customer satisfaction.
The Principle of Presumption of Innocence and Overcriminalization: A Critical Study of The Influence of Social Media in The Digital Era Siregar, Fitria Ramadhani
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.24

Abstract

This study aims to examine the influence of social media on the presumption of innocence and the phenomenon of overcriminalization in the digital era. The presumption of innocence is a fundamental legal principle, which states that every individual is not considered guilty until proven otherwise. However, with the rapid development of social media, information is often disseminated without adequate verification, resulting in stigma and negative judgments against individuals who are considered to have committed crimes. The phenomenon of overcriminalization refers to the tendency to consider certain actions as crimes, which is often triggered by public opinion formed through narratives on social media. Social media, with its ability to spread information quickly and widely, often creates virality that can change public perception in an instant. News or accusations that were initially obtained from non-credible sources can quickly become trends, triggering emotional reactions and excessive responses from the public. This study uses a qualitative approach by analyzing various case studies and related literature to explore the relationship between social media dynamics, public perception, and its impact on legal principles. The results show that social media can undermine the presumption of innocence by creating public pressure that encourages repressive actions before the legal process takes place. In addition, excessive criminalization often occurs as a response to inaccurate information and sensationalism that develops on social media platforms. The conclusion of this study emphasizes the need for stricter regulation of the dissemination of information on social media and the importance of educating the public about respecting the principle of the presumption of innocence. This aims to maintain justice and human rights in an increasingly complex digital era.
The Influence of People, Process, and Physical Evidence on the Decision to Choose Telecommunication Services Amri, Fairuza Rikha
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.25

Abstract

This study aims to analyze the influence of People, Process, and Physical Evidence on consumers’ decisions to choose telecommunication services. In the era of digital communication, the competition among telecommunication companies is increasingly intense, with similar products and pricing structures offered by various providers. Therefore, service quality and the extended marketing mix elements play a crucial role in determining consumer preferences. The research employs a quantitative approach with data collected through questionnaires distributed to users of major telecommunication providers such as Telkomsel, XL, and Indosat. The variables include People (employee competence, friendliness, responsiveness), Process (service procedures, responsiveness, and complaint handling), and Physical Evidence (store design, website appearance, and supporting facilities). The data were analyzed using multiple linear regression to determine both partial and simultaneous effects. The results indicate that the three variables—People, Process, and Physical Evidence—have a positive and significant influence on the decision to choose telecommunication services, either partially or simultaneously. Among them, the Process variable has the most dominant effect, suggesting that efficient and transparent service procedures are key determinants in consumer choice. This study highlights the importance of managing service quality holistically, not only through product and price but also through human interaction, operational efficiency, and tangible service attributes, to enhance competitiveness and customer loyalty in the telecommunication industry.
Implementation of the Naive Bayes Algorithm in Spam Detection in SMS Messages Fadil, Ulfi Muzayyanah; Siregar, Kalfida Eka Wati; Ramadani, Wily Supi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.26

Abstract

This study discusses the application of the Naive Bayes algorithm to detect spam messages in Short Message Service (SMS) services. The background of this study is the increasing spread of spam messages containing advertisements, fraud, and malicious content, which necessitates an automated system to distinguish spam from non-spam. The methods used in this study include collecting labeled SMS data, preprocessing (text cleaning, tokenization, stopword removal, and stemming), and feature extraction using the Term Frequency-Inverse Document Frequency (TF-IDF) technique. The Naive Bayes model was trained on a Kaggle dataset and tested in Google Colab to evaluate classification performance using accuracy, precision, and recall metrics. The results showed that the Multinomial Naive Bayes model achieved an accuracy of 96.86%, with a strong ability to recognize ham (non-spam) messages and exemplary performance in detecting spam messages. These findings demonstrate that the Naive Bayes algorithm is effective and efficient at classifying Indonesian-language text messages, making it a suitable basis for developing a more innovative, faster automatic SMS spam detection system.
Designing a Cloud-Based Web Server Infrastructure at the Pematang Serai Village office Kurniawan, Fahmi; Putra, Randi Rian
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.27

Abstract

The cloud is a metaphor for computer networks/the internet, where the cloud represents them, abstracted from complex, hidden infrastructure. Using cloud technology, we can combine several computer devices into a single unit (cluster) and create multiple servers on a single computer device through virtualization. The Pematang Serai Village Office is one of the villages without a server network infrastructure. Currently, the village office has several computers located in each room, and they are not connected. This decentralizes the village office's data storage, sometimes making it difficult for employees to obtain the data they need for their work. For this reason, the village office needs a means to support centralized storage on a server. Therefore, the purpose of this study is to build a web server based on a cloud infrastructure. This study uses an experimental method, with experiments conducted on the Ubuntu operating system across the following research stages: literature review, network topology design, system design analysis, server network configuration, and testing. The results of this study show that a cloud-based web server can be used for centralized data storage and that the data stored on it can be accessed properly. The conclusion from this study is that it can overcome problems with centralized data storage, ensuring that data management systems in the Pematang Serai village office are better organized and that every village office employee who needs data can access it correctly, specifically for shared data.
Analysis of Indonesian Netizen Sentiment Towards the Government's Campaign on the Use of Artificial Intelligence Using the Naive Bayes Algorithm Nasution, Salsabila; Berutu, Asro Hayati; Aulia, Fatwa
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.28

Abstract

The development of artificial intelligence (AI) has encouraged the Indonesian government to adopt this technology in various public service sectors. However, the use of AI has received mixed responses from the public, particularly on social media. This study aims to analyze Indonesian netizen sentiment towards the government's AI campaign using the Naive Bayes algorithm. Data was collected from the Twitter platform and analyzed through preprocessing, sentiment classification, and model evaluation. The results show that the majority of netizen sentiment is negative, with concerns related to unfairness for creative workers, a lack of regulation, and the use of AI for political gain. This research is expected to provide input for the government in designing more ethical and inclusive AI adoption policies.
Integrated Water and Waste Management: A Systematic Literature Review on the Role of Human Resources and Sustainable Practices Pangeran; Tomy Sun Siagian; Rizky Saputra; Dhea Agusty Ningrum; MHD. Andi Rasyid
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.29

Abstract

The growing pressure on water resources from urbanization, industrialization, and climate change demands an integrated and sustainable approach to water and waste management. This study explores the relationship between human dimensions, technology, and institutional governance in improving the effectiveness of sustainable water and waste management systems. The research addresses the limited studies that combine Green Human Resource Management (GHRM) practices, smart technology adoption, and adaptive institutional models such as Integrated Water Resources Management (IWRM) within one conceptual framework. This study develops an integrated conceptual model that explains the triadic relationship between green human resources as an enabler, digital technology as a driver, and collaborative governance as a bridge to effective water and waste systems. A Systematic Literature Review (SLR) guided by PRISMA 2020 was conducted using thematic synthesis and bibliometric analysis to identify trends, dominant theories, and relationships among key variables. The findings show that effective water and waste management depends not only on technological innovations such as AI, IoT, smart sensors, big data, and Life Cycle Assessment (LCA), but also on human readiness and institutional capacity. GHRM practices green recruitment, environmental training, and sustainability-based performance appraisals enhance technological adoption and promote an eco-conscious culture. Moreover, the expansion of IWRM into “IWRM extended to waste” emphasizes cross-sector collaboration and community engagement. Sustainable water and waste management can thus be achieved through synergy among humans, technology, and governance within an adaptive socio-technical system.
Analysis of User Interaction Association Patterns in E-Learning Systems Using the Apriori Algorithm Rizka; Berutu, Asro Hayati; Nabawy, Putri; Pratama, Haris; Supiyandi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.30

Abstract

The development of e-learning systems has generated a vast volume of user interaction data. Every activity—such as logging in, viewing materials, taking quizzes, and downloading assignments—contains valuable information that can be leveraged to enhance the effectiveness of online learning systems. This study aims to analyze user interaction association patterns in an e-learning system using the Apriori algorithm. A data mining approach was employed to identify relationships among features frequently accessed together, with a minimum support threshold of 0.4, minimum confidence of 0.6, and lift > 1.0. The dataset used consists of simulated (dummy) data representing seven user transactions and five main e-learning features. The analysis produced eight significant association rules with lift values above 1.0, indicating non-random relationships among features. Feature combinations such as {login} → {view_material} and {take_quiz} → {view_score} exhibited strong relationships, with confidence values reaching 0.75. These findings suggest the existence of dominant user interaction patterns that can be utilized to optimize navigation design, recommendation features, and overall user experience in e-learning systems. This research contributes to the application of the Apriori algorithm for exploring user access patterns in online education contexts, providing an analytical foundation for developing more adaptive and behavior-driven systems.
Sentiment Analysis of Reviews from Google Play: Azur Lane, Genshin Impact, Arknights Siregar, Kardandi Alfarizi; Cahyadi, Bhagaskara; Samosir, Legiman; Azhard, Alfani; Supiyandi
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.33

Abstract

Sentiment analysis of user reviews for mobile games is essential for understanding player perceptions of a game. This study focuses on sentiment analysis of user reviews for three popular mobile games: Azur Lane, Genshin Impact, and Arknights, using the Support Vector Machine (SVM) algorithm. The objective of this research is to classify reviews into three sentiment categories: Positive, Negative, and Neutral. Data was collected through web scraping from the Google Play Store, with a total of 6,000 reviews analyzed. The data preprocessing steps included cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF feature extraction. The results show that the SVM model achieved an accuracy of 79.50%, with the best performance for Positive and Negative sentiments, but struggled with Neutral sentiment classification. The sentiment distribution revealed that Azur Lane had a higher proportion of Negative reviews compared to Genshin Impact and Arknights, which received predominantly Positive feedback. This study provides insights into the potential of using SVM for sentiment analysis in mobile games, and highlights areas for improvement, such as better handling of Neutral sentiment through more advanced models or balanced datasets.

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