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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 51 Documents
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Search results for , issue "Vol. 10 No. 1 (2025): March" : 51 Documents clear
Implementation of Web-Based Administrative Payment Information System Using Laravel 10 Framework Tatik Wulandari; Iwan Setiawan Wibisono
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/02mmk443

Abstract

The development of information technology drives the need for an efficient data processing system, especially in the education sector. Madrasah Tsanawiyah NU 14 Kaligading manages financial administration manually, which causes various obstacles such as data inaccuracy and slow processes. This study aims to develop a web-based information system using the Laravel 10 framework to manage tuition payments in a more structured manner. The waterfall software development method encompasses needs analysis, system design, coding, testing, and maintenance. This system is designed to facilitate the management of school financial data with main features including user data input, payments, and student arrears. The implementation results show that the system can improve the efficiency and accuracy of financial management, accelerate the administration process, and facilitate real-time data access. In addition, this system also supports long-term data archiving, improves the effectiveness of administrative staff, and minimises errors in recording. This implementation is expected to help schools utilize modern technology to improve the quality of management and administrative services.
Expert System for Assessing Anxiety Levels in Toxic Relationships Using the Case-Based Reasoning Method Based On The Web: Implementation of the Case-Based Reasoning Method Intan Putri Ariska; Filmada Ocky Saputra
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/th3pr838

Abstract

Experiences in toxic relationships often trigger significant emotional stress and impact mental health disorders. This study aims to develop a web-based expert system using the Case-Based Reasoning (CBR) method to evaluate anxiety levels caused by toxic relationships. The system is designed to provide more specific treatment by accurately analysing patterns of disorders resulting from toxic relationships. The system's development follows the waterfall model. System testing was conducted using the black-box testing method, demonstrating that the system performs as expected. The results of manual calculations were compared with the system outputs and showed consistency. Testing using 300 cases—80% as training data (240 cases) and 20% as testing data (60 cases)—achieved an accuracy of 91.67%. The recommendations provided include initial steps to manage anxiety. This indicates that the CBR method effectively distinguishes anxiety levels based on similar cases. These findings contribute to clinical psychology by providing a technological tool for quickly identifying anxiety levels. For practitioners, this system can serve as an initial reference before further treatment, while for users, it offers easy access to understanding their mental condition. This system is expected to be an innovative solution supporting accessible mental health care.
Application of Technology Artificial Intelligence in Claim Settlement at PT. Asuransi Allianz Life Syariah Medan Branch Marpaung, Fachrunisa Winda; Aslami, Nuri; B. Syarbaini, Ahmad muhaisin
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/7ygp5d67

Abstract

This research aims to analyse the effectiveness of implementing artificial intelligence (AI) technology in accelerating the settlement process of Sharia insurance claims at PT Asuransi Allianz Life Syariah Medan Branch. The main focus of this research is to measure the impact of AI technology on customer satisfaction and the company's performance in handling Sharia insurance claims. Using qualitative research methods, data were collected through in-depth interviews with company representatives and analysis of the company's internal documents. The research results indicate that AI technology has significantly contributed to improving operational efficiency, speed, and accuracy in claim resolution. The implementation of AI enables more effective process automation, faster decision-making, and more accurate identification of potential fraud. Furthermore, the use of AI also supports the enhancement of compliance with Sharia principles, ensuring that all processes and decisions are in line with Sharia law. This research provides valuable insights for insurance companies looking to implement AI technology in their operations, particularly within the scope of Sharia insurance.
Examining the Impact of Software Testing Practices on Software Quality in Batam Software Houses Suwarno; Aklani, Syaeful Anas; Purwandi, Nellsen
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5bmdas03

Abstract

This research aimed to investigate the impact of software testing practices on software quality in software companies in Batam, Indonesia. It focused on identifying key factors such as software testing knowledge, software testing approach, and software testing complexity and analysing their correlation with software quality. Data was collected from 48 respondents, including project managers, developers, and QA teams, using a questionnaire distributed via Google Forms and convenience sampling. The questionnaire was designed based on related studies to ensure relevance to the respondents’ roles. Regression analysis identified significant impacts of testing complexity, approach (p = 0.000), and knowledge (p = 0.003) on software quality. The F-test result (F = 32.622) confirmed a strong relationship between testing practices and software quality. These findings emphasise the critical role of robust testing strategies in enhancing software quality. For companies in Batam, the study offers actionable insights, including adopting structured frameworks and preferable action on testing approaches. Implementing these strategies can help organisations improve software outcomes and maintain competitiveness in the evolving software development landscape.
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Jauhar Vikri, Muhammad; Wisma Dwi Prastya, Ifnu; Pradema Sanjaya, Ucta; Agung Barata, Mula
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
Design and Construction of a Website-Based Tourist Bus Rental System Using the Extreme Programming Method Karima, Nida; Kurniawan , Defri
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2ck7ea61

Abstract

Technological developments encourage companies to implement structured systems to improve operational efficiency. PMJ Trans, a tourist bus company located in Kudus, is faced with the problem of manual administrative data management, such as recording customer orders using Excel and inappropriate bus maintenance schedules. This study aims to design and build a website-based bus rental system using the XP method, which allows for short iterations and fast feedback. This system includes online booking features, booking history management, bus placement, and booking notifications from customers to admins. User Acceptance Testing (UAT) testing showed 100% system success, as measured by the aspects of Learnability, Efficiency, Memorability, Errors, and Satisfaction. The constraints during development were the limited number of respondents during the testing stage, where only the company owner was involved due to unsupportive time and location. Implementing this system can help the company's operational efficiency, reduce manual errors, and provide a good experience to customers. Further research suggests adding a payment feature integrated with the bank to automate payment confirmation and transaction security and a chatbot so that bookings via WhatsApp are well organised.
The Implementation of AWS Cloud Technology to Enhance the Performance and Security of the Pharmacy Cashier Management System Hendy Kurniawan; L. Budi Handoko; Valentino Aldo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/x0rctv54

Abstract

 This study examines the implementation of Amazon Web Services (AWS) in the MEKATEK pharmacy cashier management system to address the limitations of traditional systems, such as slow transaction processing, data loss risks, and challenges in handling transaction surges. The prototyping method was employed, involving user requirements analysis through interviews and observations, followed by iterative development of core features like inventory management, transactions, reporting, and data backups. Black box testing demonstrated a 100% success rate for core functionalities. Performance analysis recorded stable CPU utilisation below 5% under normal workloads and the ability to handle throughput up to 2532 packets/minute. System optimisation reduced AWS operational costs to IDR 150,000–160,000 per month. AWS implementation improved operational efficiency, strengthened data security through encryption and role-based access control, and minimised human errors. Initial user feedback indicated faster workflows, although adjustments are needed for users with limited technical backgrounds. This study recommends further development, including AI-based analytics and digital payment integration, to enhance MEKATEK’s functionality and competitiveness in the future.
Optimization of Variable Combinations for Household Electricity Consumption Prediction Using a Multivariate Time Series Machine Learning Approach Akhmad Faeda Insani; Ahmad Mushawir; Zainuddin; Aditya Adiaksa; Sparisoma Viridi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/hd6bv378

Abstract

Accurate household electricity consumption prediction is vital for effective energy planning in Indonesia, a nation facing rapid economic growth and technological advancements. Inaccurate predictions can lead to inefficiencies in resource allocation and energy shortages. Traditional methods like ARIMA struggle with non-linear patterns, long-term dependencies, and multivariate relationships critical in understanding electricity consumption dynamics. To address these challenges, this study employs the Long Short-Term Memory (LSTM) algorithm with a multivariate time series approach, chosen for its ability to capture complex patterns and long-term trends. The dataset comprises monthly electricity consumption data (2004–2023) from PT PLN, enriched with macroeconomic and environmental variables like Household Consumption GDP, inflation, and average temperature. The Denton-Chollete method was used to transform quarterly GDP data into monthly intervals, and correlation analysis identified Household Consumption GDP (r=0.98) and Power Contract Additions (r=0.64) as significant predictors. Testing 63 feature combinations, the best (Power Contract Additions, Household Consumption GDP, and Household Electricity Consumption) achieved a Mean Absolute Percentage Error (MAPE) of 3.54%. These results highlight LSTM's superiority in handling dynamic and complex electricity consumption patterns and provide a robust predictive tool for PT PLN. This study underscores the importance of exploring additional variables and advanced optimisation techniques to enhance predictive accuracy further.
Implementation of a Web-Based Student and Teacher Attendance System With QR Code Integration using the RAD Ganesh Lindung Nusantara; Rian Andrian; Nuur Wachid Abdulmajid
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/f2qvfs64

Abstract

This study aims to implement a web-based student and teacher attendance system with QR Code integration at SMK Muhammadiyah Campaka, Purwakarta. Currently, the school still uses a manual attendance system that is proven to be inefficient, time-consuming, prone to human error, and difficult to monitor in real-time. The proposed system is expected to overcome these problems by increasing the efficiency of managing and monitoring student attendance data. The Rapid Application Development (RAD) approach is used in developing this system, which allows for a faster and more flexible development process. QR Code was chosen as the attendance method because it can speed up attendance recording and reduce data input errors. The results showed that the new system succeeded in reducing attendance recording time by 50%, from an average of 10 minutes to only 5 minutes per class. In addition, the recording error rate was reduced by more than 70%, from the previous 36% to only 9% after the system was implemented. This system also allows attendance reports that can be accessed in real-time, supporting increased efficiency in the school environment. With the implementation of this system, it is hoped that the attendance process will be faster, more accurate, and easier to monitor, which in turn can improve the quality of education management at SMK Muhammadiyah Campaka.
Prediction of Electricity Bill Payment Delays for Customers Using a Machine Learning Approach Dyah Puspita Sari Nilam Utami; Mochamad Ikbal Arifyanto
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tc81dq58

Abstract

Electricity is a vital necessity in modern life, and the management of electricity bill payments is crucial for the continuity of services and the financial stability of electricity providers like PLN. Identifying potential delays in payments by customers is a strategic step to enable effective preventive actions. This study aims to develop a prediction model for payment delays using two machine learning methods, namely Random Forest Regressor and Bidirectional Long Short-Term Memory, based on historical customer data from the period of 2018–2023. The research process includes data preprocessing to ensure consistency and accuracy, dividing the data into training and testing sets, and training the models using both algorithms. The results show that the Random Forest model performed the best in recognizing long-term statistical patterns with the lowest Mean Absolute Error value of 0.00387 on the 12-month Moving Average feature, as well as optimal efficiency with a number of trees between 100–200. On the other hand, the Bidirectional LSTM model demonstrated competitive ability in capturing temporal patterns of sequential data, with the best configuration yielding a validation error value of 0.243 and the highest validation accuracy of 56.2%. Both models are effective in predicting customers who are likely to delay their electricity bill payments. This research provides significant contributions to PLN in supporting data-driven decision-making and facilitating mitigation strategies such as early notifications or rescheduling payment plans to reduce the risk of overdue payments.

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