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INOVTEK Polbeng - Seri Informatika
Published by Politeknik Negeri Bengkalis
ISSN : 25279866     EISSN : -     DOI : https://doi.org/10.35314
Core Subject : Science,
Computer Science & IT
The Journal of Innovation and Technology (INOVTEK Polbeng—Seri Informatika) is a distinguished publication hosted by the State Polytechnic of Bengkalis. Dedicated to advancing the field of informatics, this scientific research journal serves as a vital platform for academics, researchers, and practitioners to disseminate their insightful findings and theoretical developments. Scope and Focus: INOVTEK Polbeng - Seri Informatika focuses on a broad spectrum of topics within informatics, including but not limited to Web and Mobile Computing, Image Processing, Machine Learning, Artificial Intelligence (AI), Intelligent Systems, Information Systems, Databases, Decision Support Systems (DSS), IT Project Management, Geographic Information Systems, Information Technology, Computer Networks and Security, and Wireless Sensor Networks. By covering such a wide range of subjects, the journal ensures its relevance to a diverse readership interested in both the practical and theoretical aspects of informatics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 189 Documents
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Comparison of Naïve Bayes, Random Forest, and Logistic Regression Algorithms for Sentiment Analysis Online Gambling Dwi Nanda Agustia; Ryan Randy Suryono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/prk93630

Abstract

This study aims to compare the performance of Naïve Bayes, Random Forest, and Logistic Regression algorithms for sentiment analysis on the topic of online gambling. The dataset consisted of 4592 entries after preprocessing and applying the SMOTE technique to address class imbalance. The evaluation results show that Random Forest achieved the best performance with an accuracy of 78%, followed by Naïve Bayes and Logistic Regression, both achieving 77%. Random Forest excelled in classifying positive and negative sentiments, while Naïve Bayes demonstrated a significant improvement in recall for neutral sentiment, increasing from 0.45 to 0.82 after the SMOTE application. Logistic Regression showed less optimal performance, particularly for neutral sentiment. This study provides essential guidance for selecting the best algorithms for sentiment analysis in specific domains such as online gambling and highlights the importance of SMOTE in handling imbalanced datasets. The findings of this study can be used by practitioners and policymakers to make more informed decisions in regulating online gambling.
Sentiment Analysis of the Influence of the Korean Wave in Indonesia using the Naive Bayes Method and Support Vector Machine Natasha; Ryan Randy Suryono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/85x4wd90

Abstract

This study analyzes public sentiment towards the influence of the Korean wave in Indonesia using the Naive Bayes and Support Vector Machine (SVM) methods. The Korean wave, as a popular cultural phenomenon from South Korea, has had a significant influence on various aspects of Indonesian society. The dataset consists of 6,237 tweets obtained through a crawling process on social media X, with 80% data divided for training and 20% for testing. The pre-processing process includes cleaning, case folding, tokenizing, stopwords, and stemming. Data imbalance in sentiment distribution is overcome by the SMOTE technique. The test results show that the SVM model has the highest accuracy of 88%, outperforming the Naive Bayes model with an accuracy of 81%. Performance evaluation using precision, recall, and F1-score shows that SVM is more consistent in classifying positive and negative sentiments. Data visualization is done using bar charts and word clouds to illustrate the main patterns and themes in discussions related to the Korean wave in Indonesia. However, this study has limitations, such as data is only taken from one social media platform, so the results are less representative of public opinion as a whole. Nevertheless, this study provides new insights into how Indonesian society responds to popular culture phenomena online. These findings can also be utilized by policy makers to support the development of creative industries based on popular culture.
a Sentiment Analysis of Free Meal Plans on Social Media using Naïve Bayes Algorithms Yoga Zaen Vebrian; Kustiyono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/3m2fcz69

Abstract

This study analyses public sentiment towards the "Free Meal Plan" initiative introduced by the political pair Prabowo-Gibran. This policy aims to assist underprivileged communities in Indonesia and is a significant issue in the social and political context. Data was collected from the social media platform X (formerly Twitter), gathering 501 relevant comments based on their connection to the topic and high levels of engagement (such as retweets and likes). The comments were then processed using Text Preprocessing and TF-IDF techniques and applied to a Naïve Bayes model. The model achieved an accuracy of 69.3%, a precision of 72%, a recall of 57.05%, and an F1 score of 54.5%. These results indicate that the model is capable of classifying public sentiment, though it has challenges in accurately detecting negative sentiment. These findings provide valuable insights for policymakers to design more effective communication and policy strategies, particularly in addressing criticism or public dissatisfaction. The study highlights the importance of using text processing and machine learning techniques to analyze social media data in a structured way.
Consumer Satisfaction Analysis at Inos Coffee & Kitchen Using the C4.5 Algorithm Dedy Alan Wirawan; Abdul Rohman
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/nbpz0h21

Abstract

Consumer satisfaction is one of the important elements for the success of a business, especially in the culinary sector like Inos Coffee & Kitchen. This research is essential to explore the factors affecting consumer satisfaction, identify dominant factors such as service quality and comfort of the place, and generate decision rules that can assist management in formulating strategies to improve services and products. Several of these factors can be studied using the C4.5 algorithm, which is one of the decision tree methods in data mining. The data used in this study was obtained through a consumer satisfaction survey covering several variables, including food quality, service, price, atmosphere, and comfort of the place. The C4.5 algorithm is applied to build a model that can identify the most influential variables on consumer satisfaction. Furthermore, the results of this study support more accurate data-driven decision-making. The findings indicate that service quality and comfort of the place are dominant factors determining customer satisfaction at Inos Coffee & Kitchen. Additionally, the application of the C4.5 algorithm successfully generated rules that can serve as guidelines for management in making better decisions to enhance consumer satisfaction. This research is expected to assist Inos Coffee & Kitchen management in formulating more effective strategies to increase customer loyalty and contribute to the application of data mining technology in the culinary industry. This study expands the application of the C4.5 algorithm, which is typically used in data classification, into the context of the culinary industry to predict and understand factors influencing customer satisfaction. It adds relevant real case studies demonstrating how this algorithm can produce practical decision rules that are easy for businesses to implement.
Design of a Web-Based Regional Food Ordering Information System at Seribu Rasa Restaurant Ahmad Fadhil Kurniahadi Al Jufri; Sagala Alex Paskalis; Novi Rukhviyanti
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/mb5xe359

Abstract

The rapid development of information technology has presented significant opportunities for the culinary industry to improve operational efficiency and customer satisfaction. Seribu Rasa Restaurant, offering regional Indonesian specialities, faces challenges with its manual order management system, leading to long queues, extended waiting times, and limited access to menu information. This highlights the importance of adopting a web-based information system to enhance customer experience and business efficiency. This study aims to design a web-based food ordering information system for Seribu Rasa Restaurant to simplify the ordering process, expedite transactions, and improve data management. Using the Waterfall methodology, the research followed a systematic approach comprising requirements analysis, system design, implementation, testing, and maintenance. Data collection was conducted through interviews with restaurant management, observation of business processes, and a literature review. The system was developed with modern web technologies such as HTML, CSS, and JavaScript, with MySQL for database management. The results show that the developed system enables customers to easily browse menus, place orders, and make integrated payments online. System testing indicates that key features, including menu browsing, order placement, and online payment, function effectively and meet user needs.
Application Of K-Means Algorithm to Cluster Students' Reading Patterns in the Digital Age Yongky Permana Putra; Reflan Nuari
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/j8gz8h32

Abstract

This study aims to group students' reading patterns in the digital era using the K-Means algorithm. This algorithm divides data into clusters, such as reading duration, type of reading, reading frequency, and devices used. Data were obtained through questionnaires distributed to 224 students of SMK Negeri 4 Bandar Lampung, with 214 valid data analysed after the preprocessing stage. The selection of vocational high school students as this study was based on previous journal references that examined reading patterns in PAUD to SMA students, so special attention is paid to vocational high school students, understanding reading patterns that have different needs compared to references with other levels of education. The clustering process produced four clusters with unique characteristics, reflecting differences in reading patterns based on the type of media used, intensity, and digital devices. The results of the study showed that clusters with high digital reading intensity can be directed to utilise e-books and online learning platforms optimally, while clusters with a preference for printed books require strengthening physical reading habits through literacy activities. With a Davies-Bouldin index value of -2.224, the quality produced is proven to be very good. These findings provide guidance for educators to develop technology-based education policies and personal approaches to improving student literacy. Designing learning programs with methods and student reading patterns to support the quality of education in the digital era.
Implementation of the PSO-SMOTE Method on the Naive Bayes Algorithm to Address Class Imbalance in Landslide Disaster Data Azwar Damari; Taghfirul Azhima Yoga Siswa; Wawan Joko Pranoto
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7wcvrb72

Abstract

Landslides in Samarinda, which often occur after floods, pose a threat to settlements, infrastructure, and the agricultural sector. This study proposes a combination of Naïve Bayes, SMOTE (Synthetic Minority Oversampling Technique), and PSO (Particle Swarm Optimization) to address class imbalance in landslide prediction. The results show that while PSO successfully improves the accuracy of the Naïve Bayes model, the application of SMOTE led to a decrease in accuracy for some method combinations. This decrease is due to changes in data distribution caused by synthetic data, which can introduce noise and affect feature selection and model optimisation. However, the combination of Naïve Bayes with PSO optimisation resulted in a modest accuracy improvement (+0.48%). These findings suggest that SMOTE should be used cautiously, while PSO is more effective in enhancing the accuracy of the landslide prediction model. The implications for practical application are that although SMOTE and PSO can improve accuracy, the impact of synthetic data on data distribution must be considered, and further testing is needed to ensure its effectiveness in real-world conditions.
Development of Android-Based Management Information System Application for Student Final Project Services Mukhammad Rivaldi Ghani; Dwi Sukma Donoriyanto
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7mxq6g03

Abstract

The final project service system in the Industrial Engineering Study Program at Universitas Pembangunan Nasional "Veteran" East Java is designed to support the administrative operations performed by both lecturers and students. However, the current system still relies on conventional methods such as Google Forms, email, and WhatsApp, which complicate the processes of submitting pre-proposals, proposal seminars, and result seminars. Therefore, a management information system is needed to integrate all final project service processes into a single application. The objective of this study is to develop a design for a final project management application for students of the Industrial Engineering Study Program at UPN "Veteran" East Java, based on Android. The application is designed using the Rapid Application Development (RAD) method, which consists of three stages: Requirements Planning, Design Workshop, and Implementation. The system design includes the use of context diagrams, Entity Relationship Diagrams (ERD), and Data Flow Diagrams (DFD) to illustrate the data flow. Verification and validation tests show that all features of the application function as intended. This application is expected to facilitate and streamline the management of final project submissions, making the service more effective and efficient.
Analysis of Student Satisfaction in Axioo Class Program Towards LMS UPMYSKILL using Technology Acceptance Model Atiek Zahratul 'Ulum; Suprih Widodo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2040yg17

Abstract

In the context of e-learning development, this study attempts to determine the factors that influence student satisfaction with UpMySkill Learning Management System (LMS). Furthermore, this study explores the problems faced by students in the Axioo Class Program (ACP) and builds a hypothesis that perceived usefulness (PU) and ease of use (PEOU) have an impact on behavioural intention (BU), attitude towards use (ATU), and actual use (AU). Slovin and purposive sampling methods were used to select 313 students in total, and data collection took place between December 20 and 27, 2024. Cronbach's Alpha was used to assess the reliability and validity of the instruments. PLS-SEM analysis revealed that PEOU had a positive effect on PU, while PU had a substantial effect on ATU (β = 0.700; p < 0.001) and AU. 55% of the variation in BU and 47.3% of the variation in student satisfaction can be explained by this model. Improving interactive features, providing real-time technical assistance, and streamlining the LMS design are some suggestions to improve the quality of UpMySkill
Optimization of Intrusion Detection System with Machine Learning for Detecting Distributed Attacks on Server Yuliswar, Teddy; Elfitri, Ikhwana; W purbo, Onno
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): Maret
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/vem9da98

Abstract

This study develops an intrusion detection system optimized with machine learning techniques for efficient and effective detection of Distributed Denial-of-Service (DDoS) attacks. Using the Decision Tree algorithm, the system is designed to maximise accuracy in the identification and classification of DDoS attacks. The CIC-DDoS2019 dataset, which consists of various comprehensive simulated attack scenarios, is used as the basis for training and validation, providing the model with robust capabilities in recognizing DDoS attacks with high accuracy. This IDS successfully achieved a 100% detection rate, which is a significant result in the network security environment. The system is integrated into the existing network infrastructure, monitoring data flows in real-time and performing predictive analysis to detect early indications of attacks. Each attack detection immediately triggers a notification sent via a Telegram bot, ensuring that the security team can react quickly to isolate and address the attack. These notifications include details such as the source, type of attack, detection time, and involved protocol information, enabling more informed and strategic response actions. The use of Telegram bots for real-time communication not only enhances the speed of response to threats but also supports system scalability by facilitating adjustments and integration across various operational scenarios. The system's quick detection and response is a big step forward for machine learning-based intrusion detection systems (IDS). It provides opportunities for further research and practical applications that can adapt to various digital security scenarios.

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