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Agus Tedyyana
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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 543 Documents
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Implementation of Agile and Waterfall Methods in a Web-Based Admission System for Streamlined Registration and Communication Wafiq Lana Pradana; Agung Wibowo
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/gj9qa035

Abstract

This research discusses the development of a web-based New Student Admission (PPDB) system using a hybrid approach of Agile and Waterfall. The Waterfall method is used for structured system planning and design, while Agile allows for iterations during development. The integration of these two methods ensures that the system is developed with good documentation and flexibility in testing and feature adjustments. This system aims to improve operational efficiency, data transparency, and ease of communication between prospective students and educational institutions. Based on the test results, the system is able to reduce data input errors by up to 30%, speed up the registration process by up to 50%, and increase user satisfaction by 85% based on surveys conducted. Additionally, communication features such as real-time notifications and registration status updates help to improve interaction between users and the school. With the combination of Agile and Waterfall, this system can adapt to the needs of educational institutions and ensure a more efficient and transparent student admission process.
Analysis of User Satisfaction on the Shopeepay Application by Using the Usability Method Fitriasari Fatinah
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: 10.35314/8z385v27

Abstract

This study aims to analyse the level of user satisfaction with the ShopeePay digital wallet application using the Usability method. This method has five variables to evaluate the level of user satisfaction, namely Learnability, Efficiency, Memorability, Errors, and Satisfaction. Data was collected through a questionnaire of 262 data with case study specifications on Sriwijaya University students. The reason for the research is because of the population and diversity of Sriwijaya University students from various faculties and regions who study at Sriwijaya University. The results of the analysis show that the usability method applied contributes significantly to user satisfaction. The variables that have been set show positive and negative influences from users. The Shopee Pay application analysed aims to measure the level of user satisfaction, by understanding the factors that influence customer satisfaction to maintain the quality of the ShopeePay digital wallet (e-wallet) service.  
Comparison of Effectiveness of Machine Learning Methods in Predicting Chemical Compound Toxicity Enhance Pharmaceutical Product Safety Dufan Yuwana; Pulung Andono; Hendy 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/emkzcz13

Abstract

This study compares the effectiveness of machine learning methods in predicting the toxicity of chemical compounds using a dataset containing 5,000 samples with 14 key features. The dataset underwent preprocessing, including normalization, missing data handling, and oversampling to address data imbalance. The models used include Decision Tree, Random Forest, Extra Trees, and Gradient Boosting, validated using k-fold cross-validation. Evaluation based on accuracy, precision, recall, and F1-score showed that Gradient Boosting achieved the best performance with 92.3% accuracy, though it still faces challenges such as overfitting and interpretability limitations. Compared to in vitro and in vivo methods, machine learning is more efficient but still requires further experimental validation. This study recommends optimizing models through ensemble learning and explainable AI to improve prediction reliability.
Design and Development of a Make-Up Service Portal in Kudus Regency Using the Customer Satisfaction Index Method Umi Wahidasiana; Eko Darmanto; Arif Setiawan
Journal of Innovation and Technology Polbeng Series on Informatics (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/zga6c449

Abstract

Cosmetology services play an important role in enhancing an individual's self-confidence. In Kudus Regency, many makeup service providers still rely on manual ordering methods, which are prone to recording errors, limited information on service availability, and miscommunication between customers and service providers. This condition hampers operational efficiency and reduces the level of customer satisfaction. This research aims to develop a digital-based make-up service portal to improve service quality and customer satisfaction, which consists of the stages of needs analysis, system design, implementation, testing and maintenance The research method used is qualitative research; data is collected through in-depth interviews with customers, which consists of the stages of needs analysis, system design, implementation, testing, and maintenance. The system developed has main features such as online ordering and service catalogues, as well as CSI-based customer satisfaction evaluations that measure aspects of price, service quality, and user experience. Evaluation using the CSI method shows a customer satisfaction level of 88% with 300 respondents, which indicates that this system is effective in improving user experience and operational efficiency of service providers. In conclusion, the development of this digital-based make-up service portal has succeeded in increasing customer satisfaction and the competitiveness of make-up service providers in Kudus Regency. Further development recommendations are integration with digital payment systems and the use of artificial intelligence technology for more personalized service recommendations.  
From Data Imbalance to Precision: SMOTE-Driven Machine Learning for Early Detection of Kidney Disease Aldani Adi Bhirawa; Ucta Pradema Sanjaya
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: 10.35314/7jgjmg64

Abstract

Chronic Kidney Disease (CKD) has become a significant global health issue, with its prevalence rising sharply, particularly in developing countries like Indonesia. According to the Kementrian Kesehatan (KEMENKES), the Synthetic Minority Over-sampling Technique (SMOTE) has been widely adopted to address this. SMOTE generates synthetic samples for the minority class, enhancing the model’s ability to identify high-risk patients. Studies demonstrate SMOTE’s effectiveness, particularly when combined with ensemble learning algorithms like Random Forest and Gradient Boosting. The data collection focused on relevant medical parameters critical for the study, encompassing laboratory test results, diagnostic reports, and clinical observations related to kidney function. This dataset in kidney disease is used to predict whether someone has chronic kidney disease or not with a total sample of 400 data obtained from the Ungaran Regional Hospital and several clinics that can detect kidney disease. Recent research highlights that SMOTE significantly improves model accuracy, with Random Forest achieving 99.30% accuracy. These findings emphasise the importance of data balancing in enhancing diagnostic precision, offering promising avenues for early CKD detection and improved patient outcomes.
Website-Based Baduy Tourism Information System Using The Software Development Life Cycle Method Putri Adinda; Devita Eviliana; Novi Rukhviyanti
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/v8vtvt27

Abstract

Baduy cultural tourism has great potential, but limited information and a manual ticket booking system result in long queues and inadequate tourism services. This study aims to develop a Web-based Baduy Tour Ticket Booking Information System using the Software Development Life Cycle method for tourist ticket reservations using the waterfall method approach. System development includes needs analysis, system design, implementation, testing, and maintenance. The assessment of the system is carried out through functional testing and black-box testing methods to ensure the legality and confidentiality of the application. The development of this system utilises Laravel as its full-stack framework, which includes Javascript, PHP, blade and CSS with Bootstrap. The test results showed that the system was able to save reservation time by up to 60% compared to the manual method, and from the 50 users surveyed, the customer satisfaction rate was 85%. Thus, the development of the Tourism Information System provides a more effective and efficient solution to help Buduy's cultural tourism.
Minimarket Sales Optimization: Implementation of  FP-Growth dan MongoDB  With Python Nia kurniati; Novi Rukhviyanti
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: 10.35314/2qh79f26

Abstract

This study applies an integrated FP-Growth algorithm with MongoDB and Python to analyze 150,000 minimarket transaction records over a one-year period. The dataset includes transaction numbers, product names, quantities sold, transaction dates, purchase prices, and selling prices. The parameters of a minimum support of 0.001, a confidence of 0.01, and a lift above 1.0 are used to ensure relevant association rules. The analysis indicates that the discovered product association patterns can increase operational efficiency by up to 15%, particularly in instant food and ready-to-drink beverage categories. These data-driven strategies also boost sales volume by 12.3% and reduce dead stock by 8.7%. Beras MCS 5KG stands out as the most profitable product, with a margin of IDR 1,066,724,400. The main strength of this study lies in the integration of FP-Growth with MongoDB, enabling large-scale real-time analysis without generating candidate itemsets. This approach enhances data processing efficiency, allowing minimarkets to optimise inventory and promotional strategies more accurately.
Evaluation of Employee Payroll Decision-Making System at PT Morich Indo Fashion using Machine Learning Efaforito Gulo; Yoannes Romando Sipayung
Journal of Innovation and Technology Polbeng Series on Informatics (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.
Application of Random Forest Method for Television Malfunction Prediction Elfira Aulia Septrian; Erna Zuni Astuti
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/ymcj1j22

Abstract

 In repairing a television (TV), it is necessary to understand the symptoms experienced by the TV. Therefore, technicians need to conduct an initial analysis of the causes of these symptoms. Analysis of the causes of TV damage can be predicted using a technological approach, one of which is by using an expert system. This study will focus on developing an expert system to predict the causes of TV damage. This study will apply the Random Forest method to predict TV damage based on historical datasets obtained from company X. Company X is a company engaged in the repair of electronic devices, one of which is TV. The data obtained will be used as training data to create a model that can predict the causes of TV damage. Then the experiment was carried out with a quantitative approach with experiments to optimise the model in increasing prediction accuracy. The model was evaluated using accuracy metrics. The results of the study showed that Random Forest has very good performance in classifying the causes of TV damage with a high level of accuracy reaching 100%. However, this study is only limited to certain historical data and does not consider external factors that influence damage to the TV.
Classification of Rlderly Health Using K-Nearest Neighbor Comparison, Naive Bayes and Decision Tree Alviant Chandra Kusuma; Hari Soetanto
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: 10.35314/2q4a1524

Abstract

Health and nutrition in the elderly play a crucial role in determining the quality of human resources, especially for the elderly themselves. The ageing process causes a decrease in the ability of body tissues to regenerate, making the elderly more vulnerable to infections and organ damage. Indonesia is currently experiencing an increase in the number of elderly, from 18 million people (7.56%) in 2010 to 25.9 million people (9.7%) in 2019, and is predicted to reach 48.2 million people (15.77%) in 2035. This study aims to determine the most effective algorithm for identifying the nutritional status of the elderly, by comparing three algorithms, namely Decision Tree, K-Nearest Neighbor (KNN), and Naïve Bayes. The methodology applied is CRISP-DM, and algorithm performance evaluation is carried out using the accuracy metric of the Confusion Matrix. The results showed that Decision Tree achieved the highest accuracy (95.55%), followed by Naïve Bayes (94.18%) and KNN (94.01%). The combination of algorithms provides optimal results because each algorithm can capture different patterns in the data, so the integration of the results can reduce errors and increase accuracy in the classification of the nutritional status of the elderly.

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