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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 30 Documents
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Search results for , issue "Vol. 11 No. 1 (2026): February" : 30 Documents clear
A Development of Worker Network Information System at the Batu City Manpower Using the Prototyping Method Okta; 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/vxt13a80

Abstract

The unemployment rate in Indonesia continues to pose significant social and economic challenges, requiring government initiatives such as job training programs. However, administrative processes that still rely on Google Forms lead to several issues, including data duplication (19 duplicates out of 598 registrations, or 3.2%), slow verification procedures (24–32 working hours per period), and limited real-time monitoring. This study focuses on developing a digital job training module integrated with the Worker Network Information System (SiJoker) at the Batu City Manpower Office. The system was developed using the prototyping method through three iterative cycles with direct user involvement, allowing the solution to be refined according to actual operational needs. The module includes participant registration, training management, and document validation features. System evaluation was conducted using Black Box Testing with 19 functional scenarios covering account management, training management, document management, verification, and reporting. The test results confirmed valid outputs for all scenarios without any critical errors. User evaluation by three staff members also validated system feasibility, particularly the effectiveness of explicit document status indicators, simplified navigation, and enhanced system responsiveness through optimized database queries.
Application of NFT and ERC-1155 Algorithm in Digital Certificate Verification Process Hardi, Azkaa Rahiila; Jaya, Safitri
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/bezhpb82

Abstract

Digital certificate forgery remains a real problem in education and employment because traditional verification processes rely on centralized databases, are vulnerable to manipulation, and often take a long time. This study designs and implements a blockchain-based digital certificate verification system that models certificates as Non-Fungible Tokens (NFTs) using the ERC-1155 standard on the Manta Pacific Layer 2 network, and incorporates a Soulbound Token (SBT) mechanism to ensure that certificates cannot be transferred. The research adopts a prototyping method through eight stages, starting from architecture design and prototype development to the integration of ERC-1155 smart contracts with IPFS and wallets, as well as testing of minting functions, QR code-based verification, and rejection of asset transfers. The results demonstrate successful on-chain certificate issuance with significantly reduced transaction costs compared to ERC-72 based certificates on Layer 1 networks reported in previous studies, while maintaining a decentralized audit trail. The SBT implementation successfully rejects every attempt to transfer certificates to other wallets, thereby preventing the sale or illicit transfer of credential ownership. These findings indicate that the combination of ERC-1155, SBT, and IPFS on a Layer 2 network has strong potential as an efficient, secure, and practically adoptable digital certificate verification model for educational institutions.
Development of a Blockchain-Based LMS for Digital Learning Transparency Rahmat, Revo; Jaya, Safitri
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/nyjn9270

Abstract

Current blockchain-based Learning Management Systems (LMS) are predominantly limited to partial certificate validation using ERC-721 and face significant scalability constraints and high transaction costs on Layer-1 infrastructures, failing to address the complete educational lifecycle. This study proposes a novel end-to-end decentralized LMS architecture integrating the Manta Pacific Layer 2 network for cost efficiency, the ERC-1155 standard for bulk license management, and Livepeer/IPFS protocols for autonomous content distribution. Employing a prototyping method, system performance was evaluated on the Manta Pacific Sepolia Testnet through 97 transaction scenarios covering course creation, enrollment, and real-time progress tracking via the Goldsky Indexer. Testing parameters focused on gas efficiency, transaction latency, and data integrity. Test results demonstrate significant operational efficiency with an average gas cost of 260.899 wei per transaction and a stable average block confirmation time of 10.0 seconds. Forensic validation confirmed 100% data consistency between internal system logs and blockchain explorer trails, alongside the successful execution of an automatic, intermediary-free revenue split (90/10). The proposed architecture proves capable of overcoming cost and latency barriers in educational blockchain adoption, offering a transparent, accountable, and technically feasible infrastructure for institutional scale.
Comparative Evaluation of Preprocessing Techniques in Twitter Sentiment Analysis for Indonesia’s 2024 Regional Elections Asro; Solihin
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/tt65bb54

Abstract

The rapid expansion of social media has positioned Twitter as a critical platform for capturing public opinion during political events, including Indonesia’s 2024 Regional Elections. This study investigates the impact of preprocessing strategies and class balancing on the performance of sentiment analysis models applied to election-related tweets. An initial dataset of 9,096 tweets was collected and refined into 6,202 relevant entries from 2024–2025 through text cleaning, normalization, tokenization, and duplicate removal. Sentiment distribution analysis reveals a dominance of positive sentiment (58.4%), followed by negative (33.6%) and neutral (8.0%) expressions. Two classical machine learning classifiers—Naïve Bayes and Logistic Regression—were implemented using TF–IDF feature representation. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training data, and hyperparameter optimization was conducted using GridSearchCV. Model evaluation employed an 80/20 train–test split with accuracy, precision, recall, F1-score, and confusion matrices as performance metrics. Experimental results indicate that logistic regression combined with SMOTE and hyperparameter tuning achieved the highest accuracy of 93.08%, outperforming Naïve Bayes. The findings confirm that carefully designed preprocessing pipelines and class balancing significantly enhance the reliability of sentiment classification in political social media analysis.
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.  

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