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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 229 Documents
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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.
Analysis of User Satisfaction of SAINS Pahlawan Tuanku Tambusai University Using the EUCS Method Raihan Alfarisy; Idria Maita; Tengku Khairil Ahsyar; M. Afdal
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
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
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
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.
Implementation of a Teacher Development Website with Waterfall Development to Support the Teacher Promotion Process at SMP 2 Jekulo Kudus Ahmad Fahruddin; Endang Supriyati; Tri Listyorini
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/nnm9rf29

Abstract

This website is designed to support the promotion process of teachers at SMP 2 Jekulo Kudus by utilising digital technology. The main objective of this development platform is to improve the efficiency and quality of the management of teacher professional development programs. The problem to be overcome is the slow process of submitting promotion files which are often unstructured, thus hampering teacher career development. This platform provides a document management feature that makes it easier for users to submit promotion files. With this feature, teachers can upload the required documents, unify the application status in real time, and receive notifications regarding their application process. This not only speeds up the process but also reduces the possibility of loss or error in document management. The specific impact of implementing this website on the teacher promotion process includes increasing transparency and accountability in applying for promotions. With a structured system, teachers can more easily meet the requirements set, contributing to their increased professionalism. The use of MySQL as a database also ensures that data is managed quickly and efficiently. Overall, this website is expected to be an effective digital solution in supporting the teacher promotion process while improving the quality of education at SMP 2 Jekulo Kudus.
Measuring The Level of Cybersecurity Awareness of Social Media Users Among Students Muhammad Agung Al Affan; Mona Fronita; Eki Saputra; Muhammad Luthfi Hamzah; Zarnelly
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/vycq9t65

Abstract

The role of social media is increasingly important and easily accessible through mobile phones, slowly replacing conventional mass media. The development of Internet technology has made many users forget the importance of cybersecurity awareness, which impacts social media activities. As a result, cyber-attacks on social networks are now more frequent because many users still need help understanding cybersecurity well. Based on data BSSN, cyberattacks in Indonesia increased significantly from 290.3 million in 2019 to 495.3 million in 2020, representing a 41% rise. Social media users need to understand cybersecurity, a technology that protects data, networks, and programs from illegal access or digital attacks, known as cybercrime. Eighty-four respondents participated in the study. This study aims to measure the level of cybersecurity awareness and contribute to the existing literature by providing empirical insights into the level of cybersecurity awareness among university students. This study can also help raise awareness among the public, particularly young users, about the importance of protecting privacy and security while engaging in the digital world. This behaviour is measured using the five TPB variables: attitude, subjective form, perceived behaviour control, intention, and behaviour. Variables from TPB are then processed using SEM-PLS tools with the help of SmartPLS. Based on the study's results, it is concluded that the attitude and subjective form variables do not significantly affect the intention variable. In contrast, the perceived behaviour control variable significantly affects the intention variable, and the behaviour variable significantly affects intention.
Analysis of Rice Yield Prediction with Mlpregressor and Long Short-Term Memory Models Sunoto; Mangapul Siahaan
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/wnpm3846

Abstract

This research aims to analyse and compare the accuracy of rice productivity prediction using Multi-Layer Perceptron  Regressor (MLPRegressor) and Long Short-Term Memory (LSTM) models. The data used comes from the Badan Pusat Statistik (BPS) for the period 2018-2023, covering rice productivity from 34 provinces in Indonesia. The study employed six different architectural models for each model, with training data using the 2018-2020 period and testing data for 2021-2023. The results show that the LSTM model with 2-42-42-42-1 architecture achieved the highest accuracy rate of 94.12% with MSE 0.00305660, while the MLPRegressor model with 2-22-1 architecture achieved 91.18% accuracy with MSE 0.00471975. These results indicate that LSTM performs slightly better in predicting rice productivity, which can be used as a reference for agricultural planning and food policy in Indonesia.

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