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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 67 Documents
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Search results for , issue "Vol. 10 No. 2 (2025): July" : 67 Documents clear
Comparison of SVM, Naïve Bayes, and Logistic Regression Algorithms for Sentiment Analysis of Fraud and Bots in Purcashing Concert Ticket Agresia, Vania; Suryono, Ryan Randy
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/npyfdh47

Abstract

Music concerts are highly anticipated entertainment events, but they are often subject to fraud and the use of bots in online ticket purchases, to the detriment of fans and organisers. Fans may lose confidence in the ticket system and reduce interest in the event. For organizers, it can reduce the event's reputation and finances. This research aims to analyse public sentiment regarding this issue by comparing three classification algorithms: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. Data taken from Twitter which contains comments related to fraud and bots. The methods used include data crawling, preprocessing, sentiment labelling, and model evaluation. Preprocessing includes data cleaning, case folding, tokenising, stopwords, and stemming. Sentiment labelling is done manually or by human annotators. The results showed that SVM had the best accuracy of 91.27%, followed by Logistic Regression (90.03%) and Naïve Bayes (77.70%). Applying SMOTE to overcome class imbalance and improve the performance of negative sentiment models. This research emphasizes the importance of choosing the right algorithm and using SMOTE to improve the accuracy of sentiment analysis regarding fraud and bots in concert ticket purchases. The research results can be applied to improve bot usage detection systems and provide insight for organizers.
Togaf Analysis in Bengkalis State Polytechnic Laboratory Information Systems Design Agustin, Wirta; Rahmaddeni; Rio, Unang; Suhada, Khairus
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0hz5q194

Abstract

Laboratories in educational institutions have an important role in supporting learning and research. However, effective and efficient laboratory management is still a challenge, especially in recording inventory and managing consumables (BHP). The Bengkalis State Polytechnic Informatics Engineering Laboratory already has an application system, but there are still limitations in recording monthly BHP usage, borrowing facilities, and proposing equipment procurement. This research aims to design a blueprint for an integrated laboratory information system using The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). This approach is applied to phase F: Migration Planning, which includes requirements analysis, business architecture design, information system architecture, as well as strategy and migration implementation. The results of this research produce a blueprint for a system information laboratory that includes application design, technology recommendations, and implementation stages that can be used as a guide in system development. This blueprint is expected to increase laboratory management efficiency by optimizing inventory recording, procurement planning and maintenance of laboratory services. In addition, the TOGAF ADM approach used can be adapted for laboratories in other educational settings that have similar needs.
The Comparison Between The Apriori Algorithm And The FP-Growth Algorithm In Determining Frequent Pattern Syah Zikri, Farid; Ikhsan, Muhammad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/s1yanj03

Abstract

This study aims to compare the efficiency and accuracy of the Apriori and FP-Growth algorithms in determining frequent patterns from sales transaction data. In addition to evaluating execution time and the quality of the generated association rules, the study also examines the algorithm's advantages on small to large-scale datasets. The data used were collected from G Coffee’s sales transactions during the period from September 1 to November 30, 2024. After undergoing preprocessing stages, both algorithms were tested using three dataset variations to identify common association patterns, such as the relationship between “Mineral 660ml” and “Kopi Susu Aren,” along with other product combinations with high confidence and significant lift values. The results show that FP-Growth had a faster execution time (0.3008) compared to Apriori (0.5833), without compromising the accuracy of the results. Although both algorithms generated identical association rules, FP-Growth was superior in computational efficiency due to its ability to avoid explicit candidate itemset generation. These findings offer strategic benefits for companies, particularly in enhancing product promotion through bundling, cross-selling, and product grouping based on consumer purchasing patterns. Results, a hybrid approach is recommended to combine the processing speed of FP growth with Apriori’s flexible parameter adjustment, enabling more optimal analysis of purchasing patterns in large and complex datasets.
Evaluation of Information Technology Governance Maturity Using COBIT 2019: Study of a Telecommunication Company Muhamad Rendi Novrian; Dazki, Erick
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r54e5n79

Abstract

The development of information technology drives the need for effective IT governance, especially in the telecommunications sector, which faces challenges such as 5G, Iot, and strict regulations. This research aims to evaluate the maturity level of IT governance at PT Wahana Sukses Makmur and identify gaps against the COBIT 2019 standard to support business strategy. Quantitative methods were used through structured questionnaires to 15 respondents selected by purposive sampling, as well as internal document analysis. The data was analysed using the COBIT 2019 toolkit and triangulated to increase validity. The results show that IT governance maturity is at levels 1 and 2, far from the level 4 target in domains APO13, APO07, and DSS01. Recommendations include the formulation of risk-based information security policies, the development of an IT HR competency roadmap based on organisational needs, and continuous digitisation of operational processes. This research faced limitations in the number of respondents and access to certain internal documents, which affected the depth of analysis. The contribution of the research lies in the holistic evaluative approach based on COBIT 2019 in the real context of the Indonesian telecommunications sector, as well as practical implications in strategic decision-making related to IT governance.
Evaluation of Employee Payroll Decision-Making System at PT Morich Indo Fashion using Machine Learning Efaforito Gulo; Yoannes Romando Sipayung
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5p7nse86

Abstract

PT Morich Indo Fashion is a company that focuses on clothing production and includes fabric inspection, accessories, and heat transfer storage. This study aims to evaluate the use of payroll information systems in internal control. The method used in this research is descriptive with a qualitative approach, and the data obtained comes from secondary and primary sources. The problem faced by the Cooperative is that there are errors in calculating employee salaries and lack of clarity in the payroll process, where employees are only told the total amount of salary each month without knowing the amount of deductions caused by lateness or absenteeism in a month. To overcome this problem, a web-based payroll information system is needed. This research aims to design and develop a web-based employee payroll system at PT Morich Indo Fashion, with the aim of speeding up and simplifying the salary payment process effectively. This research uses a qualitative method with the Design and Creation approach and applies the waterfall development method. Testing is done with White-box Testing and Black-box Testing techniques. The results of the test show that the objectives of this research have been achieved. This application successfully simplifies and accelerates the process of calculating employee salaries in a transparent, accurate, effective, and efficient way.
Comparison of User Experience and Satisfaction in Digital Payment Applications Using the PSSUQ and EUCS Methods Wulandari, Anak Agung Ayu Ratna; Wibawa, K Suar; Rosiana Dewi, N W Emmy
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/t53mb880

Abstract

The rapid adoption of digital wallets such as DANA and GoPay has not yet been matched by comprehensive evaluations of user experience and satisfaction. National survey data indicates that GoPay is currently Indonesia's most widely used digital payment application. This study aims to compare the two most popular digital payment apps, DANA and GoPay, using the Post-Study System Usability Questionnaire (PSSUQ) and End-User Computing Satisfaction (EUCS) methods. A total of 136 respondents (68 users for each application), aged 18–40 and residing in Bali Province, were surveyed. Additionally, 2,366 user reviews from the Google Play Store were analysed using the Random Forest algorithm for sentiment classification. The results reveal that GoPay outperforms DANA in terms of usability (PSSUQ score of 2.04) and user satisfaction (EUCS average score of 4.34), compared to DANA (2.32 and 3.33, respectively). Information quality emerged as the most influential factor affecting user satisfaction, with Spearman correlation scores of 0.949 for DANA and 0.948 for GoPay. This study offers practical insights for developers and users to better understand and improve the digital payment user experience. However, the research is limited by the sample size of user reviews and the regional scope of respondents.
Public Facility Loan System Based On Laravel To Improve Transparency In Banjaranyar Village Arfan Maulana Adam; Isa Faqihuddin Hanif
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qgp5hg04

Abstract

Banjaranyar Village faces challenges in managing public facilities such as village halls and sports fields, which are still handled manually through notebooks or verbal communication. This manual approach is prone to recording errors and lacks transparency, making it difficult for residents to access real-time facility availability and hindering administrative efficiency. This study aims to develop a web-based facility reservation system using the Laravel framework to improve transparency and management efficiency. The research adopts a combination of case studies, surveys, and observations. The survey was conducted with 25 participants, consisting of villagers and village officials selected based on their active involvement in community activities, during October to November 2024. The system enables users to check facility availability, submit reservation requests, and manage booking data in a structured manner using a MySQL database. Testing results indicate the system performs well and improves access to information for the community. In the future, the system can be enhanced with Artificial Intelligence features and mobile applications to increase service reach and operational efficiency.
Design and Development of Customer Relationship Management in a Construction Company Pertiwi, Ni Kadek Puja Ari; Sutramiani, Ni Putu; Wibawa, K Suar
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qhkc3j28

Abstract

CV. Puspa Karya is a construction company that faces challenges in customer management and marketing activities. This study aims to design and implement a Customer Relationship Management (CRM) system using the Flectra framework to support marketing, sales, and customer‐service processes more effectively, with the specific objectives of improving both operational efficiency and customer satisfaction. The research employs the Accelerated SAP (ASAP) methodology, chosen for its systematic, result‐oriented approach that is well suited to projects requiring structured planning and rapid execution. ASAP was applied in five tailored phases: Project Preparation, Business Blueprint, Realisation, Final Preparation, and Go-Live & Support. The developed system was validated through User Acceptance Testing (UAT), achieving a final score of 167. User satisfaction was further assessed via the Post-Study System Usability Questionnaire (PSSUQ), yielding an overall mean score of 1.60 on a 1–7 scale (where lower scores indicate higher satisfaction): System Usefulness 1.55, Information Quality 1.66, and Interface Quality 1.61. These results exceed typical industry benchmarks for comparable systems. The implications include qualitative enhancements in customer‐service quality and quantitative gains in process speed and prospect‐tracking accuracy, leading to heightened operational professionalism, increased client trust, and stronger potential for customer loyalty.
Application of Machine Learning and Deep Learning to Predict Financial Product Subscriptions Based on Customer Features Prayoga, Harditya; Ignatus Moses Setiadi, De Rosal; Hendy Kurniawan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/cyvzwk14

Abstract

The financial industry faces challenges in predicting consumer behaviour, especially in forecasting decisions related to subscribing to financial products like term deposits. This study applies machine learning and deep learning to predict subscriptions based on demographic and behavioural data from the Bank Marketing dataset from the UCI Machine Learning Repository. The models tested include Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM). Model performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Results show that BiGRU achieves the highest accuracy of 92.52%, outperforming other models, with SVM and BiLSTM also showing strong performance. However, all models still face limitations in detecting subscribing customers, as evidenced by the high false negative rate. These findings highlight the potential of machine learning and deep learning to support data-driven decision-making in financial marketing, despite limitations such as the use of a single data source and the lack of consideration for external factors affecting customer decisions.
Applying KNN, NBC, and C4.5 Algorithms to Identify Eligibility for Non-Cash Food Aid Rizki Pratama Putra Agri; Permana, Inggih Permana; Salisah, Febi Nur; Jazman , Muhammad; Afdal, Muhammad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/89xvxf70

Abstract

The Indonesian government has implemented the Non-Cash Food Assistance (BPNT) program as an effort to improve people's welfare. However, in its implementation, there are still obstacles in the process of determining the right beneficiaries. Determining the right BPNT recipients is important to ensure that the assistance is received by people who really need it and to prevent budget misuse. This research aims to help the government to easily process data using three classification algorithms, namely K-Nearest Neighbour (K-NN), Naïve Bayes Classifier (NBC), and C4.5 in classifying BPNT recipient data in Air Molek Village, Indragiri Hulu Regency. K-NN, NBC, and C4.5 were chosen because they represent different approaches: K-NN is distance-based, NBC is probability-based, and C4.5 uses decision trees. The stages of the methodology used include data collection, data preprocessing, data splitting (Hold-Out), data balancing and model testing. The results showed that the K-NN algorithm got an accuracy of 70.45%, precision 68.34% recall 72.42%, NBC got an accuracy of 60.58%, precision 58.21%, recall 85.42%, and C 4.5 with an accuracy of 62.56%, precision 59.17%, recall 63.33%. The results of this study can help the government in developing a more objective and data-based decision support system for determining BPNT recipients. The limitation of this research is the use of data that is limited to only one of the data sources.

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