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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 256 Documents
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K-Medoids Clustering Method Iin Transaction Data Reports of UIN IB Padang With Bank Nagari Saputra, Muhammad Jihad; Bustami; Maryana
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/tq2tkw36

Abstract

Manual management of student financial transaction data remains a major challenge in many higher education institutions, including in the collaboration between Universitas Islam Negeri Imam Bonjol (UIN IB) Padang and Bank Nagari. Until now, no automated system has been developed to cluster student transaction data using the K-Medoids algorithm within higher education institutions in West Sumatra. This study aims to design a transaction clustering system that can identify student transaction patterns more efficiently. The K-Medoids algorithm is applied to transaction data that has been preprocessed through categorical transformation and normalization to address accuracy issues in distance-based analysis. The results show the formation of three main clusters: low (59 data points), medium (185 data points), and high (106 data points). This distribution reflects the variations in student transaction behavior and can be utilized by both the university and the bank to design more targeted service strategies, such as resource allocation and payment policy evaluation. This research provides an initial contribution to the application of K-Medoids-based data mining for optimizing transaction management in regional higher education institutions
Sentiment Analysis of BPD DIY Mobile Banking Application Using SVM and KNN Methods Nabil Fauzan; Putry Wahyu Setyaningsih
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/qyebc428

Abstract

This study aims to conduct sentiment analysis on user reviews of the BPD DIY Mobile Banking application available on the Google Play Store. The analysis is crucial due to the increasing number of user complaints regarding technical performance and user experience that have not been systematically addressed. Two machine learning algorithms, the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN), were used to classify reviews into positive and negative sentiments.  The dataset comprises 1,211 user reviews collected through web scraping and processed with comprehensive preprocessing stages, including cleaning, tokenizing, case folding, stopword removal, normalization, and stemming. The novelty of this research lies in the integration of Indonesian-specific preprocessing techniques and a comparative evaluation of two classification models, which are rarely applied in similar studies focused on regional banking applications.  The results indicate that SVM outperforms KNN, achieving 81.48% accuracy, 82.30% precision, and 88.50% recall, while KNN only reaches 55.56% accuracy, 63.00% precision, and 65.50% recall. With this level of accuracy, the SVM-based model can be effectively utilized for real-time sentiment monitoring and to identify critical issues in user experience. These findings offer strategic insights for BPD DIY to enhance application quality, particularly in addressing technical problems frequently highlighted by users.
Development of a Website-Based Facilities and Infrastructure Rental System using the Rapid Application Development Method Valentino Aldo; L. Budi Handoko
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/wcqyg231

Abstract

To improve the efficiency and transparency of the management of facilities and infrastructure at the Semarang City Youth and Sports Office, a web-based rental system was developed with the RAD approach. Evaluation using the time measurement technique showed that the booking process time was reduced from 10 minutes to 3 minutes, and payment validation, which previously took up to 1 hour, now takes place automatically in seconds. The system was built using Express.js based on Node.js for an efficient and structured backend, React.js for an interactive and responsive frontend, and MySQL as the main database. The system design uses visual aids such as use case diagrams, activity diagrams, and entity relationship diagrams. Testing was carried out using black box testing using the equivalence partitioning technique. As a result, the system meets all functional requirements and increases operational efficiency by up to 70% through payment gateway integration. Further development, it is recommended to add reporting and analysis features to support decision making.
Analysis of WAN Network Reliability Based on Response Time and Downtime at the Faculty of Information Technology UKSW Kevin; Indrastanti R. Widiasari
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: 10.35314/7bdz0a54

Abstract

Wide Area Network reliability is crucial in supporting academic and administrative activities in higher education institutions. This study aims to evaluate the reliability of the WAN network at the Faculty of Information Technology, UKSW, using response time and Downtime as the main indicators. The research employed a quantitative descriptive approach by utilizing PRTG Network Monitor, Ping, and Zabbix to measure network performance. The results showed that the average response time was 104.31 ms, with a maximum response time of 614.0 ms. The total Downtime recorded was 22 hours and 42 minutes, with a network uptime percentage of 80.16%. These findings indicate that while the network remains operational, optimization is needed to reduce latency fluctuations and minimize Downtime. Recommendations include enhancing network infrastructure and implementing proactive monitoring strategies.
Android Application Prototype for Detecting Mould on Bread using Machine Learning Siregar, Frissy; Barus, Daniel Haganta; Piay, Clara Stephanie Bernadeth; Indra, Evta
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/bptwhn82

Abstract

Mould contamination in bread poses a serious health risk if not detected early, especially in the food industry, which still relies heavily on manual visual inspection. This study aims to develop a prototype Android application capable of automatically detecting mould on bread using a machine learning approach based on the MobileNetV2 architecture. The classification model was trained on a dataset of 666 bread images, consisting of 533 training and 133 validation samples. Training was carried out over 37 epochs using data augmentation techniques and a learning rate of 0.0001. The results demonstrated consistent accuracy improvements and loss reductions without signs of overfitting. The model achieved 94% testing accuracy, with a precision, recall, and F1-score of 0.94 for both "Mouldy" and "Non-Mouldy" classes. The confusion matrix showed 125 correct predictions out of 133 test images. This research addresses the gap in lightweight and practical solutions for mobile-based mould detection. Unlike previous studies that used heavier models such as VGG16 or ResNet, this study shows that MobileNetV2 can achieve high performance with lower computational demands, making it suitable for real-world Android applications. The trained model was integrated into a simple Android interface, allowing users to upload images and instantly receive classification results. For future improvement, this prototype can be enhanced by incorporating object detection or image segmentation techniques such as YOLOv5 or U-Net to enable not only classification but also the localisation of mould areas in real-time.
Analysis of Differences Between AI and Human Texts Using the Natural Language Processing Method Cahyana, Dinda; Sijabat, VitoReyLukito; Irfan Fahmi, Mohammad
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: 10.35314/3wqgd409

Abstract

Artificial Intelligence has become increasingly proficient in generating text that mimics human writing, yet existing detection tools remain limited in accuracy and adaptability. Previous studies indicate that systems like Turnitin and GPTZero often perform below 80% accuracy and struggle with paraphrased or advanced AI-generated content. This study addresses that gap by analyzing linguistic differences between AI-generated and human-written texts using Natural Language Processing. A dataset of 487,235 texts (305,797 human-written and 181,438 AI-generated) was processed using TF-IDF vectorization and classified with the Multinomial Naive Bayes algorithm. The model achieved 99.35% accuracy and an F1-score of 0.9948, with balanced performance in detecting both text types. Results show that while AI-generated texts are structurally consistent, they often lack the emotional depth and cultural nuance found in human writing. These findings suggest NLP methods are highly effective in distinguishing between the two, and have practical implications for developing more reliable detection systems to ensure textual authenticity in education, journalism, and digital media monitoring.
Design of Website-Based Waste Management System using Laravel Framework in RT 06 Kramat Jati Eka Saputra, Budi; Isa
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/ga9s2g90

Abstract

Waste management fees at the Neighbourhood Association level often experience obstacles such as recording errors, late payments, and a lack of information transparency. This has an impact on low service efficiency and citizen participation. The purpose of this research is to build a web-based information system that can support the waste management process, using the Laravel framework as the main tool to improve efficiency, transparency, and administrative accountability in RT 006 RW 010, Kramat Jati, East Jakarta. The study was conducted on 10 residents and RT administrators as respondents, selected based on their activity in every resident activity from February to May 2025. The system allows RT administrators to manage data digitally, while residents can monitor payment status in real-time. For residents who are not familiar with technology, admins can enter data manually. The test results show that all features in the system run smoothly and support the administration process effectively. This system has been proven to significantly reduce manual recording and recap time, as well as increase citizen involvement in the payment process. In the future, this system has the potential to be further developed with the integration of digital payments such as QRIS and mobile applications, to expand reach and improve service convenience
User Satisfaction Analysis of the Website Using the E-Servqual Method Zuleffa, Mazia; Hari Widi Utomo; Arif Riyandi
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/m9a6r812

Abstract

This study aims to specifically analyze the key service quality dimensions—Efficiency, Privacy, and Contact—that influence user satisfaction with the PMB website of Madyathika Polytechnic. A structured questionnaire based on the E-SERVQUAL model was distributed to respondents, and the collected data were analyzed using descriptive statistics, SPSS-based validity and reliability testing, and Importance Performance Analysis (IPA). The findings reveal that although several service dimensions meet user expectations, attributes such as cross-device accessibility, user data privacy, and clarity of contact information still show negative service quality gaps. These results provide a foundation for targeted recommendations to improve the overall digital service experience. This research contributes to the strategic enhancement of digital service quality in higher education admissions systems.
Implementation of Ibis Pain X Application in Fashion Design Learning Based on Students' Learning Interests Raudatul Jannah; Fitriati, Ita; Irawati, Ika; Fitrianingsih, Nur; Nurhairunnisah
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: 10.35314/661f3e36

Abstract

This study aims to examine the use of the Ibis Paint X application in fashion design learning to enhance the interest and learning outcomes of Grade X Fashion Design students at Vocational High School 1 Monta. This research is the first study to directly implement Ibis Paint X in the context of fashion design education at a vocational school. The study is motivated by the low student interest in manually drawing designs and the limited availability of digital learning media. The research employed an experimental method with a One-Group Pretest -Posttest design, involving 9 purposively selected students. Data were collected through observation, pretest, posttest, and a Likert scale questionnaire. The paired sample t-test results showed a significant improvement (p = 0.000 < 0.05), with the average pretest score of 51.1 and posttest score of 91.1. A Cohen’s d value of 4.00 indicates a very large effect. The average score of the learning interest questionnaire was 4.45, indicating a high category. These findings demonstrate that Ibis Paint X is effective in increasing student engagement, motivation, and learning outcomes. The results encourage vocational school teachers to integrate Ibis Paint X into the fashion design software syllabus as an innovative and contextual digital learning medium.
Enhancing Fraud Detection Performance in E-Commerce Platforms Using Gradient Boosting Algorithms Saputra, Ardi; Rafrastara, Fauzi Adi; Ghozi, Wildanil
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/s8q6t594

Abstract

The rapid growth of e-commerce has attracted many users. However, as transaction volumes increase, so do cases of fraud. This not only causes financial losses for sellers but also threatens the trust that is so important in the e-commerce industry. Previous studies have used the Naïve Bayes and Multilayer Perceptron algorithms to detect fraud in e-commerce with accuracy percentages of 95.00% and 94.00%, respectively, without other assessment measures, including precision, recall, and F1-score. This research seeks to create a predictive model for the likelihood of online sales fraud by comparing Gradient Boosting, Neural Network, Random Forest, and Naïve Bayes models through feature extraction and feature scaling pre-processing, with 10-fold cross-validation. The dataset used was taken from the Kaggle platform. The features included in the dataset include buyer characteristics, products sold, transaction volume, devices used, and other fraud indicators. The study's findings demonstrate that the Gradient Boosting algorithm excels in detecting fraud risk with an accuracy rate of 95.30%, precision of 94.10%, recall of 95.30%, and an F1-score of 93.80%.  These findings are anticipated to enhance the development of more efficient e-commerce security solutions.

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