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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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A Web-Based Decision Support System for Determining High-Achieving Students Using The Simple Additive Weighting Method at SMK Kanisius Ungaran Cesillia Ayu Kumala Sari; Yoannes Romando Sipayung
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/n202pv96

Abstract

This study develops a web-based Decision Support System (DSS) to assist in determining academically high-achieving students at SMK Kanisius Ungaran. The current evaluation process in the school relies largely on manual assessment, which can make the management of multiple evaluation criteria time-consuming and difficult to organize systematically. To support a more structured evaluation process, this research applies the Simple Additive Weighting (SAW) method as a multi-criteria decision-making approach. Four assessment criteria were used in the system: report card average scores, school examination results, non-academic achievements, and attendance. Each criterion was assigned a weight based on institutional priorities. The system was implemented as a web application using Next.js and React.js for the front-end interface, while Supabase with PostgreSQL was used for data storage and management. The SAW procedure integrated into the system includes score normalization, weighted aggregation, and the generation of ranking results for students. A sample dataset consisting of five student alternatives was used to demonstrate the calculation process and system functionality. The results show that the system can process student evaluation data and generate ranking outputs based on the predefined criteria and weights. In the calculation example, the highest-ranked student obtained a final score of 0.9902. The developed system demonstrates how the SAW method can be operationalized within a web-based platform to support the organization and processing of multi-criteria student evaluation data. The study primarily contributes a practical implementation of a DSS for academic assessment in vocational secondary education contexts.
Development of Worker Network Information System at the Batu City Manpower Using the Prototyping Method Okta; Wildan Suharso
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.
Comparative Analysis of Machine Learning Models for BUMN Bank Stock Sentiment Classification During Danantara Formation Period Hafizha Nurul Qolby; 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: 10.35314/91z79392

Abstract

Discussions about state-owned bank stocks (BBRI, BBNI, and BMRI) on platform X intensified during the formation of Danantara. However, the correlation between social media sentiment and stock movements remains weak due to high noise levels and potential buzzer activity. This study combines sentiment and text similarity analyses (cosine similarity) to identify repeated communication patterns in discussions related to state-owned bank stocks. A total of 1,086 tweets were manually labeled and verified by two independent validators Text features were represented using TF–IDF and evaluated through four classical machine learning algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, and XGBoost. The model was validated using a hold-out scheme (80:20) and assessed with a confusion matrix. The sentiment distribution of the dataset shows 53% negative and 47% positive tweets Logistic Regression achieved the highest accuracy of 66%. The cosine similarity analysis identified 1.8% of tweets with similarity ≥0.90, indicating limited recurring communication patterns. These findings suggest that integrating sentiment and text similarity analyses can serve as an initial approach to detect indications of coordinated activity and to understand public opinion dynamics toward state-owned bank stocks.
Comparison of the Effectiveness of NocoDB and Nocobase Performance in the Development of Electronic-Based Government System Applications Kurnia Ulisyah, Shelly; Wiyono, Briansyah Setio
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: 10.35314/6yxtrs98

Abstract

The implementation of government digitalization through the Electronic-Based Government System (SPBE) requires a fast and reliable application development platform. The low-code platform is an efficient solution, as it allows for application development with minimal coding. This research compares two open-source low-code platforms, NocoDB and NocoBase, focusing on performance effectiveness in supporting SPBE application development. Testing was conducted using K6 load testing with a configuration of 100 virtual users over the same duration. The results show that NocoDB has a higher throughput of 92.1 requests per second with a total of 2,763 requests, though accompanied by 86 failed checks, indicating response fluctuations under high load. In contrast, NocoBase recorded 33.3 requests per second with 100 successful requests and no failures, demonstrating more consistent response stability despite lower throughput. Thus, NocoDB is more effective for high-load scenarios, while NocoBase excels in service stability. These results are expected to serve as a technical reference in selecting the optimal low-code platform for the implementation of digital government systems based on SPBE.
Comparative Analysis of No-Code and Conventional Development Efficiency in Beauty Product Application Redesign Permatasari, Hanum Zaqiah; Wiyono, Briansyah Setio
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/g56mcv29

Abstract

Advances in information technology are driving the business sector to seek faster and more resource-efficient software development methods. Conventional methods such as PHP–MySQL often require longer development times and high technical expertise, while no-code approaches offer a simpler alternative for small- to medium-scale applications. This study aims to analyze the efficiency of the no-code approach (MIT App Inventor) compared to conventional methods through a case study of the Innerlight application, applying the requirements engineering method that includes requirements formulation, analysis, specification, and validation of development results. Evaluation was conducted by measuring the processing time and effort estimation (person-days), as well as functional testing using black-box testing. The results show that no-code development requires an average of 6.5 working days (13 person-days), while the conventional method requires 8.5 working days (17 person-days), or approximately 24% more in terms of time and effort. Efficiency was measured based on project observation data without financial estimation or analysis of variation between teams. This study is a single case study, so the results cannot be generalized to other projects of different scales and complexities. The no-code approach is considered suitable for simple applications, while conventional methods are superior for systems that require flexibility and advanced logic control.
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.
Optimising Financial Transparency in the Oregon Cluster through the Development of a Web-Based System and Intelligent AI Chatbot Tyoffadhil Haidar; Permata Sari, Dian
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/r3k83309

Abstract

Managing finances within neighborhood associations frequently encounters persistent hurdles regarding transparency and the speed of information delivery. This study develops a web-based financial platform for the Oregon Cluster, uniquely integrated with a Gemini-powered virtual assistant to enable real-time data inquiries. The novelty of this research lies in the integrated service model that pairs an automated payment gateway with conversational AI, effectively removing traditional bottlenecks in community reporting. Performance evaluation showed significant improvements compared to manual processes payment verification latency dropped by over 99.9% (from 24–48 hours to less than 10 seconds), and the time required for residents to access specific balance information was reduced by approximately 95% (from 5–10 minutes to near-instantaneous retrieval). Functional validation via Black Box Testing achieved a 100% success rate across 15 core modules. Furthermore, a System Usability Scale (SUS) evaluation with 21 respondents yielded an average score of 85.36, placing the system in the "Good" to "Excellent" category. While highly effective, feedback indicates a need to further simplify the administrative interface to reduce the treasurer's cognitive load. Overall, this integrated system markedly strengthens local financial accountability and empowers residents to monitor cash reports independently and efficiently
Development of an Android-Based Educational Game for Early Childhood Mathematics Learning Using Fisher–Yates Shuffle Egi Bahari Dwi Fraska; Umi Chotijah; Putri Aisyiyah Rakhma Devi
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: 10.35314/1en46441

Abstract

This study aims to develop an Android-based educational game to support early childhood mathematics learning through an interactive approach. The game implements the Fisher–Yates shuffle algorithm to randomize question sequences and object positions, with the goal of reducing repetition and increasing gameplay variation. The development process follows the Software Development Life Cycle (SDLC), including requirements analysis, planning, design, development, and testing. System evaluation is conducted using black-box and white-box testing to assess functionality and algorithm implementation. The results show that all system features operate as expected, achieving a 100% success rate with no identified errors. In addition, the Fisher–Yates shuffle algorithm produces unique random sequences across all trials, indicating consistent randomization performance. These findings demonstrate that the developed application functions reliably and has potential as a supporting tool for early childhood mathematics learning.
An Integrated AHP–TOPSIS Model to Enhance the Effectiveness of Disaster Logistics Distribution (Case Study: Regional Disaster Management Agency of Minahasa) Brandon Natanael Gerungan; Audy Aldrin Kenap; 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/g19x8g95

Abstract

This study is motivated by the challenges faced by the Regional Disaster Management Agency (BPBD) of Minahasa Regency in determining the priority of disaster logistics distribution in a rapid, accurate, and objective manner under constraints of limited resources and time, where decisions often rely on manual processes that are susceptible to subjective bias. The objective of this study is to design and implement an integrated model of the Analytical Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution as a Disaster Logistics Management Information System to support structured and measurable decision-making. The research method involves the development of an AHP–TOPSIS integration model, in which AHP is utilized to determine the weights of priority criteria, while TOPSIS is applied to generate a ranking of affected areas based on priority levels. The findings indicate that the integrated AHP–TOPSIS-based system enhances the objectivity and accuracy of logistics distribution decisions, making them data-driven and grounded in accountable mathematical calculations. The implementation results demonstrate that this approach effectively addresses the limitations of manual decision-making, producing consistent and accountable aid allocation decisions. In conclusion, the AHP–TOPSIS integration model serves as a significant strategic solution in improving the effectiveness, targeting accuracy, and efficiency of aid distribution processes at BPBD Minahasa. It is recommended that the application of this method be further developed by adapting it to the specific needs and geographical conditions of affected areas, as well as ensuring data accuracy to maintain the validity of priority analysis results..
Analysis of Cryptocurrency Investment Patterns Using Machine Learning Farrel Amri Naufal Sandio; Renny Sari Dewi
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: 10.35314/7ny29y07

Abstract

The rapid growth of cryptocurrency, particularly Bitcoin, has introduced high-return investment opportunities accompanied by extreme price volatility, posing challenges for accurate forecasting. Previous studies have applied various machine learning models for Bitcoin price prediction; however, limited attention has been given to how different training data horizons affect model performance and generalisation. This study addresses this gap by comparing three machine learning algorithms: Linear Regression (LR), XGBoost, and Long Short-Term Memory (LSTM). The analysis examines different training periods, with a primary focus on a 3-year training scenario. Historical Bitcoin data (1-minute intervals) from Kaggle was aggregated into daily observations and processed using strict chronological splitting (80:20) without data leakage. Feature engineering was applied using lag-based variables, moving averages, and volatility indicators, whilst LSTM utilised sequence windowing with 30–60 time steps. Empirical results from the 3-year training scenario show that LR and XGBoost achieve strong predictive performance (R² = 0.9757 and 0.9667), whilst LSTM performs moderately (R² = 0.72) with higher prediction errors. Additional exploratory experiments on shorter training horizons (e.g., 6 months) indicate a decline in performance across models, reflected in unstable generalisation and negative R² values on test data, suggesting overfitting. However, directional accuracy remains above 55% in the primary scenario. These findings suggest that model performance is sensitive to the length and stability of historical data. Whilst simpler models such as linear regression and tree-based methods demonstrate consistent performance in the evaluated setting, conclusions regarding model superiority should be interpreted within the scope of the experiment.

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