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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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Rainfall Prediction using the SARIMAX and LSTM Methods in Semarang City Rudi setyo P; Zuliarso, Eri
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6sg7m889

Abstract

The purpose of this study is to predict the decade rainfall in Semarang City using two main methods, namely Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Long Short Term Memory (LSTM). The methodology of this study begins with data preprocessing, which includes data deletion analysis using dropna and data normalization using Min-Max Scaling to reduce the scale to between 0 and 1. The dataset is then divided into 80% training data and 20% test data. The validity of the data (X_test, Y_test) using the best 56-epoch data validation (val_loss) is better than the validity of the training data (loss). On the other hand, SARIMAX uses the (2,1,2), (2,1,2,36) model, and its validation techniques include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Specifically, the RMSE of the LSTM model is 19.6, and the RMSE of the SARIMAX is 31.05. The MAE of LSTM is 15.0, SARIMAX is 24.5, the R2 of LSTM is 0.814, and SARIMAX is 0.52. Lower RMSE and MAE values indicate lower prediction errors, but a higher R2 value of 1 indicates that LSTM can explain 81% of the actual data variation, which is better than SARIMAX, which is only about 52%. The main finding of this study is that the LSTM model performs better when recommending rainfall datasets.
Design of a Website-Based Employee Absence Information System Using Laravel at PT Hesed Indonesia Wimar Ardana Gulo; Agung Wibowo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ynjh9g19

Abstract

This study aims to design and implement a web-based employee attendance information system using the Laravel framework at PT Hesed Indonesia, with the main contribution being the integration of real-time attendance data management features, automatic reports, and centralized access rights settings that were not available in the previous system. This system was developed to replace the manual attendance method, which was prone to recording errors, data manipulation, and slowed down the reporting process. Development was carried out using the Waterfall method, which includes the stages of requirement definition, design, coding & testing, integration & system testing, and operation & maintenance. A trial was conducted on 25 employees over one month, resulting in an attendance recording accuracy rate of 98% and a 70% reduction in recapitulation time compared to the previous method. The implementation results show that the system can improve efficiency and ease of data access, although it still has limitations such as the lack of integration with fingerprint devices or payroll systems. In the future, this system can be developed with attendance notification features, IoT integration for automatic attendance, and attendance data analysis to support managerial decisions.
Improving Butterfly Fish Image Classification Accuracy using HSV Feature Extraction and SMOTE-Based Data Balancing Putra, I Putu Arya; Wirawan, I Made Agus; Gunadi, I Gede Aris
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/vw52nn48

Abstract

Class imbalance in image data can reduce the accuracy of classification models, especially when the minority class data is much smaller than the majority class. This research focuses on enhancing the classification accuracy of butterflyfish images through the application of the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, combined with the K-Nearest Neighbor (KNN) algorithm utilizing HSV-based feature extraction. The datasets were collected in two conditions, namely conditioned (controlled background and lighting) and unconditioned (varied background and natural lighting). The research stages include preprocessing, HSV feature extraction, data balancing with SMOTE, and classification using KNN with various k values (3, 5, 7, 9) and cross-validation (k-fold 5 and 10). The experimental results show that SMOTE consistently improves accuracy on both types of datasets, with the best performance at k = 3 and k-fold = 10, namely 85.32% (conditioned) and 87.59% (unconditioned). This improvement occurs because a more balanced data distribution allows the model to optimally recognize features between classes. This study proves that the integration of SMOTE and KNN is effective in overcoming class imbalance in image classification, with potential applications in the fields of digital image technology, ecosystem management, and species identification.  
Application of Machine Learning for Classifying and Identifying Security Threats Using a Supervised Learning Algorithm Approach Arta, Yudhi; Mohamad Samuri, Suzani; Syafitri, Nesi; Hanafiah, Anggi; Oktaria, Wina; Maripati, Maripati; Pandu Cynthia, Eka
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/aqjdbj22

Abstract

The rapid growth of harmful web content has intensified the demand for intelligent systems capable of accurately classifying cyber threats based on URL patterns. This study evaluates two widely used supervised learning algorithms, Random Forest and Naïve Bayes, for probabilistic classification of multi-class URL datasets. A synthetic dataset comprising 547,775 URLs was designed to reflect realistic threat distribution: benign (65.74%), phishing (14.46%), defacement (14.81%), and malware (4.99%). Each sample included simple structural features such as URL length, number of dots, HTTPS usage, and keyword indicators. Both models were tested using identical stratified train-test splits with varying sample sizes, including focused experiments on 15,000 and 100,000 entries. Results revealed that both models achieved high recall and precision only for the benign class, while failing to detect minority classes. For Random Forest, precision and recall for benign URLs reached 1.00 but dropped to 0.00 for phishing, defacement, and malware in all test scenarios. Naïve Bayes exhibited similar shortcomings, highlighting the impact of class imbalance and limited feature expressiveness. This research concludes that while Random Forest and Naïve Bayes are computationally efficient, they are inadequate for detecting cyber threats without preprocessing techniques such as SMOTE, cost-sensitive learning, or feature enrichment. Future work will explore adaptive hybrid models with contextual features and deep learning frameworks to enhance multi-class detection in real-world cybersecurity scenarios.
Implementation of Blockchain ​​in Property Sales Transactions Bunga, Auxylium; Iman Heri Ujianto, Erik
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/766t2511

Abstract

In the digital era, property sales face significant challenges such as lengthy processes, involvement of multiple intermediaries, and lack of ownership transparency. This study aims to create a prototype property transaction system that utilizes blockchain technology, integrated with smart contracts, a Node.js backend, and a MySQL database. The system is simulated with Ganache as a local network to test the decentralized and secure data storage of transactions. The research methods include simulation, direct observation, as well as functional testing and performance evaluation using a black box testing approach to ensure the reliability and efficiency of each system component. Test results show a functional success rate of 100% from 14 test scenarios. Based on these findings, the system has been proven to validly record transactions, avoid double transactions, and maintain data integrity. Smart contract technology can partly replace notary functions digitally, while blockchain serves as a means of secure and immutable transaction storage. Therefore, this system can enhance efficiency, transparency, and accountability in property transaction processes and make a significant contribution to the digitalization of the real estate sector.
BPMN Modeling and Information System Development at PT Maha Tour Medan Using a Design Thinking Approach Chirilda, Adinda; Suendri
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/a1hdj134

Abstract

In the digital era, property sales face significant challenges such as lengthy processes, involvement of multiple intermediaries, and lack of ownership transparency. This study aims to create a prototype property transaction system that utilizes blockchain technology, integrated with smart contracts, a Node.js backend, and a MySQL database. The system is simulated with Ganache as a local network to test the decentralized and secure data storage of transactions. The research methods include simulation and direct observation, as well as functional testing and performance evaluation using a black box testing approach to ensure the reliability and efficiency of each system component. Test results show a functional success rate of 100% from 14 test scenarios. Based on these findings, the system has been proven to validly record transactions, avoid double transactions, and maintain data integrity. Smart contract technology can partly replace notary functions digitally, while blockchain serves as a means of secure and immutable transaction storage. Therefore, this system can enhance efficiency, transparency, and accountability in property transaction processes and make a significant contribution to the digitalization of the real estate sector.
Application of ADASYN Technique in Classification of Stroke Disease using Backpropagation Neural Network zikrillah aulia, said rizki; okfalisa, okfalisa; haerani, elin; oktavia, lola
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/jdhv9s39

Abstract

The high prevalence of stroke in Indonesia and the challenge of imbalanced medical record data are major obstacles to the development of an accurate early detection system. This research aims to build a reliable stroke classification model by applying the ADASYN (Adaptive Synthetic Sampling) oversampling technique to address class imbalance before the data is processed using the Backpropagation Neural Network (BPNN) algorithm. The ADASYN technique is applied with the goal of reducing the bias that arises from the imbalanced data distribution between the majority and minority classes. Testing was conducted through various data splitting scenarios (70:30, 80:20, 90:10) and hyperparameter variations to find the optimal configuration. The best results were obtained with the 90:10 data split scheme, using an architecture of 29 neurons and a learning rate of 0.01, which successfully achieved peak performance with an accuracy of 90.46% and an F1-score of 91.03%. This study demonstrates that the combination of ADASYN and BPNN is a highly effective approach for producing a stroke prediction model that is not only accurate but also sensitive to the minority class, thus having great potential as an early detection support tool in the healthcare sector.
Sentiment Analysis of LinkAja Digital Wallet Application Reviews on Google Play Store using Transfer Learning IndoBERT Sandy Sanjaya; Rangga Gelar Guntara; Syti Sarah Maesaroh
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/afjx7b16

Abstract

The LinkAja digital wallet receives an average rating of 3.5 on the Google Play Store despite having a higher number of user reviews than its competitors, indicating a strong need for data-driven evaluation of user satisfaction. This study performs sentiment classification on LinkAja user reviews using the IndoBERT model implemented within the CRISP-DM framework. A total of 1,483 reviews posted from January 1 to May 31, 2025, were analyzed through automatic labeling using a pretrained IndoBERT sentiment model and validated using an 80:20 hold-out scheme. Model performance was evaluated using accuracy, the F1 score, and the Matthews Correlation Coefficient (MCC) to address class imbalance. The results show high classification performance with 95% accuracy, a macro F1-score of 0.92, a weighted F1-score of 0.94, and an MCC of 0.90. Sentiment distribution reveals a dominance of negative sentiments at 59.5%, followed by positive (26.1%) and neutral (14.4%) sentiments. Theoretically, this study reinforces the superiority of IndoBERT over conventional machine learning methods for Indonesian sentiment analysis. Practically, the findings provide actionable insights into service improvements, particularly regarding transaction stability and system reliability.
UTAUT2 Approach to Byond BSI Adoption in Padang: Mediation and Moderation Effects Nurul Mu'tamim; Vidyarini Dwita
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/b0cv3709

Abstract

The use of Islamic mobile banking apps is still not optimal, even though people’s interest in digital finance is growing. This study examines how social influence, enjoyment (hedonic motivation), and user habits shape people’s intention to use these apps and how that intention affects actual usage. It also tests whether word of mouth (WOM) strengthens the link between intention and behavior. Using a quantitative approach, the study surveyed 105 active BYOND BSI users in Padang City through purposive sampling. Data were analyzed with the PLS-SEM method using SmartPLS 4.0 and bootstrapping with 5,000 samples to ensure reliable results. The findings show that enjoyment and habit strongly affect intention, and intention significantly impacts actual use. However, social influence has no significant effect on intention, and WOM does not moderate the relationship between intention and use. Moreover, intention mediates the effects of enjoyment and habit on use but not that of social influence. Overall, the results highlight that users’ positive experiences and consistent habits are key drivers behind their intention and actual use of Islamic mobile banking services.  
Legal Service Satisfaction Assessment Information System at the Tapaktuan District Attorney's Office Using the Rating Scale and MAUT Methods Melsya, Laura; Irawan, Muhammad Dedi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wd05bk49

Abstract

Public satisfaction assessment of legal services is an important indicator for improving the quality of public service. The Tapaktuan District Attorney’s Office requires a system that can measure public satisfaction objectively and systematically. This study aims to design and develop an information system for evaluating legal service satisfaction using the Rating Scale and Multi-Attribute Utility Theory (MAUT) methods. The rating scale is used to assign scores to five criteria: service speed, service friendliness, staff competency, report handling process, and facility comfort. The assessment results are then analyzed using MAUT to obtain overall satisfaction rankings. The system is developed as a web-based application for easy access by staff and the public. Respondent samples are selected proportionally and representatively, including the general public, complainants/defendants, witnesses, lawyers, and employees who have received legal services, using accidental sampling during the survey period, with 30 respondents to ensure data representativeness. From five criteria, three alternatives, and 30 respondent samples, the final score obtained is 4.51, indicating a “very satisfied” level for Case Reporting Services. This system is expected to assist the Tapaktuan District Attorney’s Office in evaluating and improving the quality of legal services consistently.

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