cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Agus Tedyyana
Contact Email
agustedyyana@polbeng.ac.id
Phone
+6285289866666
Journal Mail Official
jurnaoinformatika@polbeng.ac.id
Editorial Address
Jl. Bathin alam, Sungai Alam Bengkalis-Riau 28711
Location
Kab. bengkalis,
Riau
INDONESIA
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 543 Documents
 35   36   37   38   39 
Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Muhammad Jauhar Vikri; Ifnu Wisma Dwi Prastya; Ucta Pradema Sanjaya; Mula Agung Barata
Journal of Innovation and Technology Polbeng Series on Informatics (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 Nida Karima; Defri Kurniawan
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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
Journal of Innovation and Technology Polbeng Series on Informatics (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.
Analysis of User Satisfaction of SAINS Pahlawan Tuanku Tambusai University Using the EUCS Method Raihan Alfarisy; Idria Maita; Tengku Khairil Ahsyar; M. Afdal
Journal of Innovation and Technology Polbeng Series on Informatics (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/n7ene473

Abstract

  Abstract - Smart Academic Integrated System (SAINS) is an information system used to improve the quality and facilitate students, lecturers and staff in carrying out lecture activities at Universitas Pahlawan Tuanku Tambusai. Nevertheless, certain challenges persist, as revealed through interviews and observations with the Head of Student Affairs and Active Students. These include insufficient details regarding the KRS filling schedule and lecture information (content), an unappealing SAINS appearance (format), an inaccessible forgotten password menu (case of use), and a lengthy login process (timeliness). In order to gauge how happy SCIENCE users are with the system, this study used the End User Computing Satisfaction (EUCS) approach and polled 97 people. The results showed that three variables had a positive effect, namely accuracy, format and ease of use, and two variables had a negative effect, namely content and timeliness. The variables that have a positive influence have T-statistic values ​​of 2.804, 2.414, and 3.528, while the variables that have a negative influence have T-Statistic values ​​of 0.576, and 0.326. Research recommendations can add information about the KRS filling schedule and lectures on the SAINS system homepage, as well as increase the speed of access to the SAINS system by users.
Application of Waterfall Method in Sales System using Laravel 10 Framework in Bella Grocery Store Fernanda Bagus Dwi Prastyo; Abdul Rohman
Journal of Innovation and Technology Polbeng Series on Informatics (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/rndvs873

Abstract

Bella grocery store does not yet have a cashier system that can record transactions and manage stock items so it faces various challenges in managing stock items and recording transactions. Our research aimed to build a web-based sales system at the store using the waterfall method and the Laravel 10 framework. To identify the main needs including stock management, transaction recording and financial reports we collect data through direct interviews and observations. The system is designed with the waterfall method starting from requirements analysis, system design, and implementation to testing.  The results of the implementation of this system can help manage stock items, and record transactions and financial reports at the store. The results of black box testing ensure that all features function properly. with this system, Bella grocery stores can manage stores more accurately and organized.
User Experience Evaluation of e-Puskesmas in Payakumbuh City using the User Experience Questionnaire Method Furqan Anwari; Eki Saputra; Arif Marsal; Mona Fronita; Muhammad Jazman; Syafril Siregar
Journal of Innovation and Technology Polbeng Series on Informatics (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/z7dkt524

Abstract

Puskesmas in Indonesia are responsible for providing primary health services to the local community. The Community Health Centers (Puskesmas) in Indonesia utilise e-Puskesmas, a web-based system that enables electronic data management and patient services. The newest version, e-Puskesmas Next Generation (NG), was made to allow online interactions, but it has had some technical problems, including duplicate medical data and wrong medication dosages because it doesn't have an allergy detection feature. The UEQ method will be used to rate the user experience of e-Puskesmas. The UEQ measures six factors: attractiveness, efficiency, perspicuity, dependability, stimulation, and novelty. Twenty-one people participated in the evaluation, and the results show that all variables were close to neutral. This means that using the system doesn't make people very happy or sad, but instead stays at an average level. This study confirms that, although e-Puskesmas has the potential to be an effective tool, there is still significant room for improvement, especially in terms of feature customisation and user interface. The "poor" score in the benchmark evaluation indicates that significant improvements in the system's design and functionality are necessary to enhance user satisfaction and healthcare service efficiency. It is hoped that these findings can encourage further development that addresses the existing shortcomings and effectively improves the management of health services at the community health centre
Public Sentiment Analysis on Dirty Vote Movie on YouTube using Random Forest and Naïve Bayes Christ Mario; Ryan Randy Suryono
Journal of Innovation and Technology Polbeng Series on Informatics (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/ev9j2g33

Abstract

In early 2024, the film Dirty Vote attracted public attention, sparking discussions on YouTube. Understanding public sentiment towards this film is important for evaluating the reception of the work and its impact on public opinion. This study analyses 4,551 YouTube comments using the Random Forest and Naïve Bayes algorithms. The data was collected using the Apify platform, which allows the extraction of comment data based on video links and the desired amount of data. The analysis results show that the film received more negative comments than positive, reflecting the public's reception of the socio-political issues raised in the film. This dominance of negative sentiment is important for understanding how the film's message is received, which could influence marketing strategies and the film's reception in the digital media industry. This study also compares the effectiveness of both algorithms in sentiment analysis, with Random Forest being more effective at identifying positive sentiment, while Naïve Bayes is more efficient, though less accurate at capturing positive sentiment. These findings provide insights for developers and analysts in selecting the appropriate algorithm for sentiment analysis applications on social media.

Page 37 of 55 | Total Record : 543

 35   36   37   38   39 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 11 No. 3 (2026): August (Inpress) Vol. 11 No. 2 (2026): May Vol. 11 No. 1 (2026): February Vol. 10 No. 3 (2025): November Vol. 10 No. 2 (2025): July Vol. 10 No. 1 (2025): March Vol. 9 No. 2 (2024): November