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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 543 Documents
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Risk Management of Information Security in Inaportnet Using ISO/IEC 27005:2018 Bintang Rahmat Riadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/pq4jhh89

Abstract

This study aims to analyse information security risks in the Inaportnet system at the Port Authority Class II Tanjung Buton using the ISO/IEC 27005:2018 standard. The system is a digital innovation designed to expedite port services but faces significant challenges in information security. The first step involved identifying assets within the Inaportnet system, followed by recognizing potential threats and vulnerabilities associated with these assets. This process is crucial as it lays the groundwork for understanding where risks may arise. The research employs the Failure Mode and Effects Analysis (FMEA) method to identify, assess, and prioritise risks based on assets, threats, vulnerabilities, and existing controls. A total of 17 risks were identified, categorized from "very low" to "low" priority levels. The highest risk involves operational disruption due to sudden power outages, with an RPN score of 72. This study proposes risk mitigation recommendations, including Systems connected to the internet that are vulnerable to cyberattacks, such as hacking or malware, which can result in data theft or service disruptions. Therefore, it is essential to implement firewalls and intrusion detection systems to safeguard the network against external threats. The findings provide practical guidance for improving the information security and operational reliability of the Inaportnet system. By implementing these mitigations, the Port Authority is expected to enhance the reliability of port services and protect critical information.
Sentiment Analysis of Gojek, Grab, Maxim Applications Using Support Vector Machine Algorithm Muhammad Iqrom; M. Afdal; Rice Novita; Medyantiwi Rahmawita; Tengku Khairil Ahsyar
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/52fycr56

Abstract

This research analyzes user sentiment towards three major online transportation applications in Indonesia—Gojek, Grab, and Maxim using the \SVM algorithm. The analysis results indicate that Maxim has the highest positive sentiment rate (42.45%) compared to Grab (32.83%) and Gojek (20.21%). Maxim's advantages lie in its competitive pricing and driver professionalism. However, Gojek recorded the best performance in sentiment classification with an accuracy of 94%, followed by Maxim (90%) and Grab (87%). The evaluation based on five main variables (general sentiment, drivers, services, applications, and pricing/costs) reveals the strengths of each application in different categories. Maxim excels in general sentiment and driver satisfaction, Grab dominates in pricing/cost, and Gojek stands out in the application category. Wordcloud visualization reveals frequently mentioned words such as "driver," "application," and "order," reflecting users' primary concerns and experiences. This research provides valuable insights for online transportation service providers to improve service quality, although it has limitations in exploring external factors such as user demographics and marketing strategies, as well as relying on a single algorithm without comparison. The choice of the SVM algorithm is based on its ability to handle well-structured data and provide high accuracy in classification. SVM is effective in finding the optimal hyperplane that clearly separates data classes, making it suitable for sentiment analysis involving multiple variables.
Comparison of SVM, Naïve Bayes, and Logistic Regression Algorithms for Sentiment Analysis of Fraud and Bots in Purcashing Concert Ticket Vania Agresia; Ryan Randy Suryono
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/npyfdh47

Abstract

Music concerts are highly anticipated entertainment events, but they are often subject to fraud and the use of bots in online ticket purchases, to the detriment of fans and organisers. Fans may lose confidence in the ticket system and reduce interest in the event. For organizers, it can reduce the event's reputation and finances. This research aims to analyse public sentiment regarding this issue by comparing three classification algorithms: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. Data taken from Twitter which contains comments related to fraud and bots. The methods used include data crawling, preprocessing, sentiment labelling, and model evaluation. Preprocessing includes data cleaning, case folding, tokenising, stopwords, and stemming. Sentiment labelling is done manually or by human annotators. The results showed that SVM had the best accuracy of 91.27%, followed by Logistic Regression (90.03%) and Naïve Bayes (77.70%). Applying SMOTE to overcome class imbalance and improve the performance of negative sentiment models. This research emphasizes the importance of choosing the right algorithm and using SMOTE to improve the accuracy of sentiment analysis regarding fraud and bots in concert ticket purchases. The research results can be applied to improve bot usage detection systems and provide insight for organizers.
Comparison of Naïve Bayes, Random Forest, and Logistic Regression Algorithms for Sentiment Analysis Online Gambling Dwi Nanda Agustia; Ryan Randy Suryono
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
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.

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