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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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Website Utilization to Optimize Kurnia Laundry's UMKM Business Operations in Ogan Ilir Apriansyah; Haryanto, Dedi; Dafa Haqiki, Arka; Reno Saputra, Zulhipni
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k4ybzz13

Abstract

The ever-evolving information technology has a significant impact on many industries, one of which is micro, small, and medium enterprises (MSMEs). Kurnia Laundry is an MSME in Ogan Ilir, still using a manual system in recording transactions, stock management, and customer service, which causes inefficiencies and operational delays. The main purpose of the research focuses on the development of a website-based Point of Sale (POS) system that can optimize Kurnia Laundry's business operations. This research adopts the Research and Development (R&D) method with a waterfall model approach, which includes the phases of analysis, design, implementation, system testing, and maintenance. The results of the study show that the POS system is able to increase the efficiency of transaction recording, speed up the service process, and provide transparent service information to customers. The implementation of this system also helps business owners monitor business performance in real time, manage customer data, and optimize stock management. Thus, the use of the website as a POS system has a significant impact on increasing the efficiency and competitiveness of laundry MSMEs.  
Reengineering the Digital Attendance System Using Business Process Reengineering Approach at PT. Esa Solusi Mandiri (ESACO) Wijdaniah, Jauza; Suharso, Wildan
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/77h3e848

Abstract

This study aims to analyze and redesign the employee attendance process at PT Esa Solusi Mandiri, which is still conducted manually using paper-based forms and Microsoft Excel recapitulation. The Business Process Reengineering (BPR) approach is applied to identify value-added and non-value-added activities within the attendance process. Data were collected through semi-structured online interviews with one key informant responsible for attendance management, as well as documentation in the form of monthly attendance reports. The duration of each process stage was estimated based on interview results and administrative document analysis, resulting in a total cycle time of 190 minutes for the manual attendance process. Process efficiency was evaluated using Throughput Efficiency (TE) as an indicator of the proportion of value-added time. Based on this analysis, a conceptual design of a digital attendance process was developed, incorporating automated recording and real-time data access for HR and Finance departments. The results indicate that the proposed digital process has the potential to reduce the cycle time to 11 minutes and increase the TE value from 23.68% to 81.82%. These findings represent the potential improvement in administrative efficiency, given that the proposed digital process has not yet been implemented or tested in real operational conditions.
Analysis and Development of a School Platform with a User Experience Approach at Samirejo 3 Elementary School Jannah, Zuliana Nurul; Riadi, Aditya Akbar; Meimaharani, Rizkysari
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wnxj8m38

Abstract

The digitization of educational services has become an important need for schools to improve the effectiveness of data management and information delivery. Samirejo 3 Elementary School still applies manual processes in attendance recording, information management, and communication with parents, resulting in frequent delays and inaccuracies in data. This study aims to analyze and develop a web-based school platform using a user experience (UX) approach to make the system easier to use and better suited to user needs. The research methods include observation, interviews, and literature study, while system development uses the Waterfall model. System evaluation is conducted using User Acceptance Testing (UAT) and the System Usability Scale (SUS). UAT involved 10 respondents consisting of admins, teachers/class guardians, and parents, with an average result of 95.6%, indicating that the system was very well accepted. UX testing using the SUS method involved the same respondents and obtained an average score of 79, which falls into the "Good" category, indicating that the system is easy to understand, efficient, and comfortable to use. The research results show that this platform can enhance data management efficiency, accelerate information dissemination, and provide a more optimal user experience. Thus, the developed platform supports the school's digital transformation and improves the quality of information services at Samirejo 3 Elementary School.
LoRA-Enhanced Sentiment-Aware Topic Modeling for Indonesian Generative AI Perception Wisnu Ginanjar Saputra; Noor Latifah; Fajar Nugraha
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/4m8w7t49

Abstract

Public understanding of generative AI in low-resource language contexts remains underexplored, particularly in relation to how sentiment aligns with thematic discussions on social media. In Indonesia, empirical studies examining this interaction at scale are still limited. This study introduces a sentiment-aware topic modeling framework that integrates parameter-efficient fine-tuning of IndoBERT using Low-Rank Adaptation with topic discovery via BERTopic. The approach enables large-scale analysis of Indonesian social media data under constrained computational settings. Analysis of Indonesian Twitter discourse shows that general discussions of Generative AI are largely neutral and cautious, contrasting with more optimistic trends reported in Western contexts. In comparison, enthusiast communities exhibit predominantly positive sentiment, while ethics-related discussions display balanced polarization. These results highlight the contextual nature of public perception across different discussion domains. The findings demonstrate the applicability of parameter-efficient NLP methods for sentiment and topic analysis in under-resourced languages and provide insights relevant to technology development and policy formulation.
Sentiment Analysis E-Wallet Application Services Using the Support Vector Machine and Long Short-Term Memory Methods Arya Darmansyah, Mochammad Dzikri; Vitianingsih, Anik Vega; Lidya Maukar, Anastasia; Yuliani, SY.; Fitri Ana Wati, Seftin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/apedaz75

Abstract

The rapid growth of financial technology services in Indonesia has increased the volume of user reviews, yet their utilization for sentiment-based insights remains limited in the e-wallet sector. This study compares the effectiveness of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) in classifying the sentiment of 3,185 DANA e-wallet reviews collected from the Google Play Store and Instagram. The research process includes text preprocessing, lexicon-based labeling, and feature extraction using TF-IDF for SVM and word embeddings for LSTM. Model evaluation is conducted using a confusion matrix based on accuracy, precision, and recall, without inferential statistical testing. The results show that LSTM outperforms SVM, achieving an accuracy of 86.66%, a recall of 81.86%, and a precision of 82.09%, while the best SVM variant with an RBF kernel attains an accuracy of 84.93%. This study contributes by identifying key service-related factors influencing user satisfaction and dissatisfaction and by providing practical, sentiment-based insights to support service quality improvement. The novelty lies in the multi-platform analysis of Indonesian e-wallet reviews and the direct comparison of classical machine learning and deep learning approaches without statistical hypothesis testing. These findings confirm the effectiveness of deep learning for sentiment analysis of unstructured Indonesian text.
Plagiarism Detection in English Academic Documents using A Lexical-Semantic Hybrid and Support Vector Machine Virginia, Callista; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2zz12581

Abstract

Detecting plagiarism in academic writing has become increasingly challenging due to advanced text modification strategies that reduce surface-level similarity while preserving the original meaning. This study proposes a hybrid plagiarism detection system that integrates lexical and semantic similarity features to distinguish between plagiarism and altered documents in academic texts. As a key contribution, this study provides a systematic evaluation of a lexical–semantic hybrid plagiarism detection approach using Support Vector Machine (SVM) on English-language academic documents, where all plagiarism cases across different obfuscation levels are consolidated into a single plagiarism class. Lexical similarity is modeled using Term Frequency–Inverse Document Frequency (TF–IDF), while semantic similarity is captured through Sentence-BERT embeddings. These features are combined into a two-dimensional hybrid similarity representation and classified using SVM. The proposed approach is evaluated on the PAN 2025 dataset using stratified 5-fold cross-validation. Experimental results show that the hybrid SVM-based model achieves an average accuracy of 92.5% with the optimal kernel, along with competitive precision, recall, F1-score, and AUC values. Kernel-based evaluation and cross-validation analyses further demonstrate the robustness and generalization capability of the proposed framework, indicating that the hybrid lexical–semantic representation is effective for distinguishing plagiarism and altered content in English academic writing.  
Detection Of Coffee Bean Defects In Speciality Coffee Association Standards Using YOLOv12 Hocwin Hebert; Alamsyah, Derry
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/47yqwd13

Abstract

Coffee (Coffea spp.) is a high-value plantation commodity with a significant role in the global economy. Coffee consumption, reaching more than two billion cups per day, continues to increase global demand for coffee beans. To ensure quality and consumer acceptance, green coffee bean quality evaluation must follow consistent international standards. However, inspection is still carried out manually, making it time-consuming and subjective. This study proposes coffee bean defect detection based on the Specialty Coffee Association (SCA) standard using YOLOv12. YOLOv12 addresses limitations of previous YOLO versions by integrating R-ELAN to improve training efficiency and reduce gradient loss, as well as Flash Attention to enhance focus on important regions in complex images. A total of 225 images were obtained through augmentation from 45 original samples captured using a smartphone camera under controlled indoor conditions, with each image representing 300 grams of Mandheling coffee beans. The dataset was divided into training (80%), validation (10%), and testing (10%). Eight experimental configurations were evaluated using variations in initial learning rate (0.001 and 0.0005), batch size (8 and 16), and epochs (100 and 150). The optimal configuration of an initial learning rate of 0.0005, a batch size of 16, and 150 epochs achieved a precision of 87%, recall of 85%, and an F1 score of 84%. These results indicate that the effectiveness of YOLOv12 in detecting coffee bean defects depends on proper hyperparameter tuning. The model performs well on visually prominent defects such as cherry pods but shows reduced performance on subtle defects, including floaters, fungus damage, and slight insect damage.
Classification of Diabetic Retinopathy Using ShuffleNet V2 and Real-ESRGAN with CLAHE Image Enhancement Edison, Nicholas; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/g1xj7p28

Abstract

Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if not detected and treated early. Manual DR grading from fundus images is time-consuming and highly dependent on expert availability, motivating the need for automated and efficient decision-support systems. This study proposes a lightweight DR severity classification model using ShuffleNet V2 combined with a preprocessing pipeline consisting of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Real-ESRGAN-based super-resolution. Unlike prior works that mainly employ these enhancement techniques with deeper or computationally expensive networks, this study explicitly investigates their synergistic integration with ShuffleNet V2 to improve lesion visibility while preserving computational efficiency for resource-constrained environments. Experiments conducted on the APTOS 2019 dataset demonstrate that the proposed combination significantly improves classification performance, achieving a best accuracy of 90.70%, with balanced precision, recall, and F1-score when optimized using Adam. Comparative analysis with SGD optimizer further reveals a trade-off between accuracy and inference speed. The results confirm that combining CLAHE and Real-ESRGAN with ShuffleNet V2 offers an effective and efficient solution for automated diabetic retinopathy grading, highlighting its suitability for large-scale screening and low-resource clinical deployment
Classification Of Ulos Fabric Motifs Using MobileNetV3-Small Architecture Sihombing, Mecha Bella Permata; Devella, Siska
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/d2sfn245

Abstract

Ulos fabric is an important cultural heritage of the Batak people in North Sumatra, characterized by diverse motifs and philosophical meanings that support social and ritual life. Public knowledge of Ulos motifs is declining due to lifestyle changes and limited use of digital technology for cultural education, so accurate image-based motif classification is needed for preservation and wider utilization. This study evaluates the performance of the lightweight MobileNetV3-Small architecture with a transfer learning approach for classifying Ulos motif images, positioning it as one of the earliest uses of MobileNetV3-Small for Ulos motif classification compared to previous Ulos studies that relied on heavier CNN or earlier MobileNet variants. The dataset consists of 906 images split into 80% training, 10% validation, and 10% testing, and the model is trained using the Adam optimizer with a batch size of 32 and learning rates of 0.001 and 0.0001. On the test data, the model achieves accuracies of 98.96% and 97.92%, with consistently high precision, recall, and F1-scores, demonstrating the effectiveness of MobileNetV3-Small for Ulos motif classification as a digital educational medium to support Batak cultural heritage preservation.
Performance Analysis of MobileNetV2 and GhostNetV2 in Classifying Cervical Cancer Images in the SIPaKMeD Dataset Shela; Siska Devella
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/62samp73

Abstract

Cervical cancer remains a significant global health burden, largely due to limited screening coverage and the reliance on manual cytological interpretation. The intrinsic complexity of cervical cell morphology and constraints in clinical resources necessitate automated classification systems that are both accurate and computationally efficient. This study aims to evaluate and compare the performance of two lightweight CNN architectures, MobileNetV2 and GhostNetV2, for cervical cell image classification using the SIPaKMeD dataset. The dataset comprises 4,049 cell images, which were preprocessed through normalization, augmentation, and partitioning into training, validation, and testing sets. Both models were implemented using transfer learning and trained under comparable hyperparameter settings with basic data augmentation. Model performance was assessed using confusion matrices and standard evaluation metrics, including accuracy, precision, recall, and F1-score. Experimental results demonstrate that MobileNetV2 achieved superior performance with an accuracy of 98.50%, outperforming GhostNetV2, which attained a maximum accuracy of 97.60%. The consistent performance across metrics indicates robust and balanced classification capability. These findings suggest that MobileNetV2 offers an optimal trade-off between accuracy and computational efficiency, making it a promising candidate for deployment in resource-constrained and edge-based cervical cancer screening systems. Nevertheless, further external validation and clinical evaluation are required prior to real-world implementation.

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