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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 298 Documents
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Characteristic Analysis of Trojan-Spy Malware on the Android Operating System through a Reverse Engineering Approach Nur Muhamad Abdul Mutholib Fimbay; Diah Risqiwati
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/24bxyj81

Abstract

The rapid advancement of communication technology has led to the widespread use of Android devices, accompanied by an increasing number of security threats, including Trojan-Spy malware. This type of malware often disguises itself as a legitimate application while covertly collecting and transmitting sensitive data. This study analyzes the characteristics of Trojan-Spy malware on the Android OS using a reverse engineering approach. The analysis focuses on a real-case sample, UndanganPernikahan.apk, which was distributed through WhatsApp using social engineering. The research was conducted through several stages, including initialization, decompilation, static analysis, code reversing, behavioral analysis, and quantitative runtime evaluation. The main contribution of this study lies in the detailed characterization of a Trojan-Spy sample as an integrated threat, combining SMS interception, notification harvesting, remote command execution, and data exfiltration through a Telegram-based command-and-control channel. The findings also demonstrate how the malware conceals its activity through WebView-based camouflage and control-flow manipulation. In addition, runtime analysis confirms that these malicious functions are actively executed and significantly impact system performance. These results show that reverse engineering is not only effective for identifying malware structure but also for reconstructing its operational behavior in real-world attack scenarios, particularly those involving socially engineered distribution through messaging platforms.
Sentiment Analysis of Tourists’ Perceptions of Ubud as a World Gastronomy Destination Using the Lexicon-SVM Method Ni Wayan Sumartini Saraswati; I Wayan Dharma Suryawan; I Kadek Agus Bisena; I Dewa Made Krishna Muku; Dewa Ayu Kadek Pramita
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zxfzmr57

Abstract

The development of Ubud as a sustainable gastronomic tourism destination requires understanding tourist perceptions expressed on digital platforms. This study analyzes tourist sentiment toward Ubud’s gastronomy using English-language reviews from TripAdvisor and X (formerly Twitter) through a hybrid Lexicon–Support Vector Machine (SVM) approach. A total of 28,550 pieces of textual data were analyzed, consisting of 23,647 TripAdvisor reviews and 4,903 X posts. The methodology includes data collection, text preprocessing, sentiment labeling using the VADER lexicon, TF-IDF feature extraction, and SVM classification. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that positive sentiment dominates on both platforms, with accuracies of 89.7% for X and 92.31% for TripAdvisor. Word cloud analysis further indicates that tourist perceptions are influenced by food quality, service, atmosphere, and pricing. These findings demonstrate the potential of the hybrid Lexicon–SVM approach for supporting sustainable gastronomic tourism development in Ubud. The study also contributes comparative insights into sentiment characteristics between structured reviews and real-time social media platforms.
Analysis of the Effect of RetinexNet-Based Image Preprocessing on Object Detection Performance Using YOLOv8 Under Low-Light Conditions Bihandoyo Masdhi; Giat Karyono; Azhari Shouni Barkah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6tnkh649

Abstract

The decline in performance in object detection systems based on deep learning is skewed to be meaningfully inclined when the system is used in low-lighting conditions, especially due to the decrease in visual quality of the image. In this study, the focus is directed to evaluate the effect of the application of RetinexNet-based image preprocessing on object detection performance using YOLOv8 in low-light environments. The experimental process was carried out to compare the detection results between models that used preprocessing and those that did not use preprocessing, based on evaluation metrics such as precision, recall, and mean average precision (mAP). The results indicate that improving the visual quality of the sword image is always followed by an increase in detection accuracy, because these changes can cause a shift in the distribution of visual features that have an impact on the model's generalization ability. In addition, the phenomenon of domain shift resulting from image changes using RetinexNet was also found, which had an effect on the consistency of YOLOv8 performance. The main contribution of this study is to provide empirical evidence that preprocessing strategies for low-light conditions not only need to focus on improving visual quality but also need to be adapted to the characteristics of the detection model in order to obtain a more adaptive pipeline under extreme lighting conditions.
Development of a PKM Website Prototype for UMM Students Using User-Centered Design Naufal Ghifari Ramadhana; Wildan Suharso; Briansyah Setio Wiyono
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/z80tnx33

Abstract

Personal Knowledge Management (PKM) is very crucial for students to manage their own knowledge and activities in learning. However, previous PKM system development in academia lacks user involvement during development, resulting in the final system design not aligning with students' actual workflow and causing low usability. In this study, the prototype of the web-based PKM system will be designed with iterative user-centered design (UCD) that centers on the students for the design of the system solutions. The research adopts four phases of UCD: user and context identification, specification of user requirements, design of the solutions, and usability evaluation. User personas were created through literature review and contextual observations. The prototype was designed with Figma and tested with the System Usability Scale (SUS), initially involving 16 students from UMM in their fifth semester and above, with 15 valid responses analyzed after excluding one extreme outlier. A SUS score of 71.17 ± 14.59, with a 95% confidence interval of [63.09–79.25], places the system in the "Acceptable" (Grade C) category. This shows that the UCD approach can successfully develop a moderately usable PKM system, which still needs further improvement in learnability to better address students' problems in the document management process.
YOLOv8-Based Object Detection for Automated Cattle Sex Identification in Tomohon City Mario Trinto Risky Rettob; Kristofel Santa; Gladly Caren Rorimpandey
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/05dd8843

Abstract

Accurate identification of cattle sex is essential in livestock management to support proper data recording and strategic decision-making. Conventional identification methods are often time-consuming and dependent on human expertise, which may reduce efficiency and accuracy. Therefore, this study aims to develop an automated cattle sex identification system using an object detection approach based on the YOLOv8 algorithm. The dataset consisted of primary images collected directly in Tomohon City using a smartphone camera and secondary images obtained from the Roboflow platform. All images were annotated with bounding boxes and classified into two categories: male and female cattle. The dataset was divided into training, validation, and testing sets with a ratio of 70:20:10. Model training and evaluation were conducted using the Roboflow platform, and the final model was integrated into a web-based system to enable real-time detection. The experimental results show that the proposed model achieved 97.1% precision, 92.5% recall, and 98.7% mean average precision (mAP). These findings indicate that the system performs reliably and can serve as an effective tool to support livestock data management in Tomohon City.
Development of a SAW-Based Student Registration Website at SD Negeri 6 Tondano Lorentza Claudia Rianti Peres Tawaa; Kristofel Santa; Glenn David Paulus Maramis
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/xq90y471

Abstract

The development of a web-based information system for the new student registration process and school profile at SDN 6 Tondano was carried out using a research and development (R&D) approach. This system is designed to streamline administrative processes, improve efficiency, and enhance the transparency of school services. Key features include an informative homepage, a user-friendly registration page, and a student and teacher data management module integrated with the Simple Additive Weighting (SAW) algorithm to support an objective and fair student selection process. The use of the SAW method allows for the combination of various assessment indicators into a single comprehensive score that assists the school in making decisions regarding the selection of prospective students. The implementation of this system is expected to improve operational efficiency, strengthen communication between schools and parents, and support digital transformation in the elementary education sector. Research results indicate that the developed system effectively and sustainably meets schools’ needs and contributes to the delivery of modern and reliable educational services.
Bridging Theory and Prediction: A Hybrid Explainable SEM–Machine Learning Approach to Consumer Purchase Intention Sussy Susanti; Patah Herwanto; Henny Utarsih
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/d0jnct08

Abstract

The growing use of Instagram as a visual and interactive marketing platform has intensified scholarly interest in how social media content shapes consumer purchase intention. However, most prior studies have relied either on theory-driven Structural Equation Modeling (SEM) or data-driven machine learning, with limited integration between causal explanation, predictive evaluation, and model interpretability. This study addresses this methodological gap by proposing a hybrid explainable SEM–machine learning framework that combines PLS-SEM, XGBoost, and SHAP to examine the relationship between social media content, brand image, and purchase intention. Data were collected from 500 Indonesian Instagram users exposed to fashion and lifestyle brand-related content. The PLS-SEM results show that social media content significantly affects brand image (β = 0.581, p < 0.001), while brand image significantly influences purchase intention (β = 0.511, p < 0.001). Brand image also significantly mediates the relationship between social media content and purchase intention, with a significant indirect effect (β = 0.297; 95% BC-CI: 0.241–0.356). In the predictive stage, Linear Regression and tuned XGBoost demonstrated stable generalization, with test R² values of 0.288 and 0.277, respectively, while Random Forest showed overfitting with a negative test R². SHAP analysis revealed that brand image was the strongest predictive feature (mean |SHAP| = 0.302), followed by social media content (0.268), indicating that brand image plays a more prominent role in forecasting purchase intention. The findings contribute theoretically by reinforcing brand image as a key mediating mechanism, methodologically by integrating validated latent constructs into explainable machine learning, and practically by offering digital marketers a dual-lens approach that combines structural explanation with predictive importance.
An Integrated Smart Village Information System for Digital Public Services and BUMDes Marketplace in Temesi Village Made Junindra Maha Arta Sang; I Gede Aris Gunadi; Gede Rasben Dantes
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6kh1s185

Abstract

The digital transformation of village governance requires information systems that not only provide administrative services but also support transparency, community participation, and local economic activities. Previous village information systems mostly focused on basic administration or information publication, while integrated public services, community reporting, budget transparency, and village-owned enterprise marketplace features remain limited. This study aims to develop an integrated web-based Village Information System based on the Smart Village concept in Temesi Village, Gianyar Regency. The system was developed using the software development life cycle with the waterfall model, including requirements analysis, system design, implementation, testing, deployment, and maintenance. Data were collected through interviews, focus group discussions, and observations involving village officials and community representatives. The system was implemented using Laravel, MySQL, and a model-view-controller architecture. The developed system provides population data management, administrative service requests, service status tracking, village information, community complaints, budget transparency, village maps, galleries, and a village-owned enterprise marketplace. Functional testing showed that the main features worked according to the expected scenarios. Usability testing involving 50 respondents, consisting of 6 information technology experts, 4 village officials, and 40 village residents, obtained an average System Usability Scale score of 86.15, categorized as Excellent. These results indicate that the system is feasible, usable, and supports Smart Village implementation in Temesi Village.
Development of a YOLOv8-Based Real-Time Vehicle Speed Estimation System for the Universitas Riau Campus Feri Candra; Dimas Hanafie Sugiono Putra
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/n50qad65

Abstract

This research proposes developing a real-time vehicle speed detection system using the You Only Look Once v8 architecture to monitor speed limit violations on the Universitas Riau campus. The system leverages YOLO's capabilities for real-time detection of fast-moving objects, particularly two- and four-wheeled vehicles, by processing camera video streams on a GPU-based laptop, offering a flexible and cost-effective solution. System testing was conducted at varying vehicle speeds across multiple road locations within the Universitas Riau campus area, demonstrating high detection accuracy with minimal errors and real-time identification of violations. The results indicate an average Mean Absolute Error of 1.2 km/h and Root Mean Square Error of 1.8 km/h for speeds of 30–50 km/h for two- and four-wheeled vehicles under diverse lighting conditions, with detection accuracy reaching 98% and a 100% violation identification success rate, positioning it as an effective solution for campus traffic management to mitigate speeding incidents.
Implementation of Data Mining for Predicting Formula 1 Team Performance Using the Trend Moment Method Based on Historical Data Tiffely Tannisa; Anita
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/393kjy70

Abstract

Formula 1 is an international racing competition that generates large-scale performance data of constructors in the form of time series, particularly total points accumulated each season. Such data can be utilized for predictive analysis using data mining techniques. This study aims to implement the Trend Moment method to predict the performance of five Formula 1 constructor teams for the 2026 season based on historical standings points data from 2019 to 2025. The data used in this study is secondary data obtained from the official FIA and Formula1.com websites. The research method applies time series forecasting using a simple linear regression model Y = a + bX. Model evaluation and validation are conducted using Mean Absolute Percentage Error (MAPE) to measure the level of prediction accuracy. The results show that McLaren Mastercard F1 Team is predicted to achieve 800 points in the 2026 season, followed by Oracle Red Bull Racing with 700 points, Scuderia Ferrari HP with 539 points, Mercedes-AMG Petronas Formula One Team with 366 points, and Atlassian Williams F1 Team with 94 points. The evaluation results indicate MAPE values ranging from 9.38% to 71.89%, suggesting that the model performs well on stable data patterns but is less effective on data with high volatility. The novelty of this study lies in the application of the Trend Moment method to Formula 1 constructor performance data based on official historical records, combined with MAPE evaluation to provide a simple, measurable, and easily interpretable predictive model that can be applied to broader professional sports analytics.

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