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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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Information System Audit on the Simampu BPBD Web Application of Serang District using the COBIT 5 Framework Robby Irfansyah; Eris Dwi Purnama; Sigit Auliana
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/f3f9ky94

Abstract

This research was conducted to evaluate the effectiveness and efficiency of the Simampu information system implementation at the Regional Disaster Management Agency (BPBD) of Serang Regency using the COBIT 5 framework. The information system was designed to assist BPBD in disaster data management, reporting, monitoring, and facilitating quick and accurate decision-making. However, the current implementation of Simampu was found to be suboptimal as it is still under development, and several issues were identified, such as system errors, network disruptions, and inadequate data security, hindering effective disaster data processing and reporting. The research utilized a qualitative approach involving direct observations and in-depth interviews with the system management team at BPBD Serang Regency. This study focused on four COBIT 5 domains: BAI01 (Manage Programmes and Projects), DSS02 (Manage Service Requests and Incidents), DSS03 (Manage Problems), and DSS05 (Manage Security Services). These domains were selected due to their relevance to BPBD’s operational needs and strategic objectives in disaster management. Findings revealed several weaknesses in the management of the Simampu information system, particularly concerning IT service management processes, incident handling, problem management, and information security services. To address these weaknesses, the researcher provided technical recommendations, including improvements in data security, network infrastructure enhancements, and optimization of data processing procedures.
Application of Machine Learning in Analyzing Bandwidth Usage Patterns for Internet Service Providers Alfin Hilmy Nurmakhlufi; Eri Zuliarso
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/h2p5s858

Abstract

This study aims to address bandwidth management challenges faced by Internet Service Providers (ISP) through the application of machine learning techniques for analyzing usage patterns and forecasting future demand. A key novelty of this research lies in the combined use of K-Means clustering for dynamic customer segmentation based on real-time utilization patterns, followed by accurate short-term forecasting using Random Forest regression, specifically tailored for corporate client bandwidth planning. Data was collected from 12 corporate customers over a three-month period (January–March 2025) at five-minute intervals using the PRTG Network Monitor. The analytical workflow included data preprocessing, customer segmentation using K-Means clustering, and short-term bandwidth prediction using Random Forest regression. The clustering results classified customers into three main categories: underutilized, optimal, and overutilized, with a silhouette score of 0.663 indicating good cluster separation. The regression model achieved a coefficient of determination (R²) of 0.931, a Mean Absolute Error (MAE) of 0.036 Mbps, and a Root Mean Square Error (RMSE) of 0.062 Mbps, demonstrating high predictive accuracy for operational planning. This study is limited by the relatively short observation period and the exclusion of external variables in the modeling process. For future work, the use of deep learning methods such as Long Short-Term Memory (LSTM) or Temporal Convolutional Networks (TCN) is recommended, along with the integration of external features such as time-based traffic anomalies and customer profiles, to enhance model robustness, accuracy, and generalization.
Expert System in Analyzing Stress Levels in Factory Employees Using the Certainty Factor Method Aya Sofia Dinafa; Abdul Rohman
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/qc6ag389

Abstract

Factory employees are jobs that have high enough pressure, therefore they are prone to stress. Employees who experience stress have an impact on reducing productivity. This study aims to design and build to diagnose stress levels in factory employees with the Certainty Factor (CF) method. Data collection is done by means of a mental specialist and the distribution of questionnaires to factory employees. In this technological development, expert systems can be used to prevent employees from experiencing high work stress by identifying it early on so that advice can be given. This system is designed with the PHP programming language and MySQL database. The expert system with the Certainty Factor (CF) method has a fairly high level of accuracy, with a certainty level of 85% and can be a management tool in making decisions related to employee mental health.
Implementation of a Deep Learning Model Using Teachable Machine for Early Pneumonia Detection from X-Ray Images Primanto; Allwin M. Simarmata
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: 10.35314/7mjewe22

Abstract

Pneumonia is one of the leading infectious lung diseases that continues to cause high morbidity and mortality rates, particularly among children and the elderly. Early detection is crucial to prevent severe complications; however, the limited availability of radiologists in Indonesia poses a significant challenge. This study implements a deep learning model using Teachable Machine to detect pneumonia from chest X-ray images. The dataset was obtained from an open-source repository on Kaggle, consisting of 1,341 normal lung images and 3,875 pneumonia lung images. The training process was carried out with 50 epochs, a learning rate of 0.001, and a batch size of 16. The experimental results demonstrated that the model achieved 100% accuracy, precision, recall, and F1-score in detecting pneumonia. These findings indicate that Teachable Machine can serve as an effective solution for developing early pneumonia detection models without requiring advanced programming skills. Nevertheless, the relatively small dataset size may lead to potential overfitting, highlighting the need for further research with larger and more diverse datasets. The contribution of this study lies in providing an alternative approach to artificial intelligence implementation that is simple, fast, and cost-effective, particularly for healthcare facilities in resource-limited regions.
Rainfall Prediction using the SARIMAX and LSTM Methods in Semarang City Rudi setyo P; Eri Zuliarso
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: 10.35314/6sg7m889

Abstract

The purpose of this study is to predict the decade rainfall in Semarang City using two main methods, namely Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Long Short Term Memory (LSTM). The methodology of this study begins with data preprocessing, which includes data deletion analysis using dropna and data normalization using Min-Max Scaling to reduce the scale to between 0 and 1. The dataset is then divided into 80% training data and 20% test data. The validity of the data (X_test, Y_test) using the best 56-epoch data validation (val_loss) is better than the validity of the training data (loss). On the other hand, SARIMAX uses the (2,1,2), (2,1,2,36) model, and its validation techniques include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Specifically, the RMSE of the LSTM model is 19.6, and the RMSE of the SARIMAX is 31.05. The MAE of LSTM is 15.0, SARIMAX is 24.5, the R2 of LSTM is 0.814, and SARIMAX is 0.52. Lower RMSE and MAE values indicate lower prediction errors, but a higher R2 value of 1 indicates that LSTM can explain 81% of the actual data variation, which is better than SARIMAX, which is only about 52%. The main finding of this study is that the LSTM model performs better when recommending rainfall datasets.
Design of a Website-Based Employee Absence Information System Using Laravel at PT Hesed Indonesia Wimar Ardana Gulo; Agung Wibowo
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/ynjh9g19

Abstract

This study aims to design and implement a web-based employee attendance information system using the Laravel framework at PT Hesed Indonesia, with the main contribution being the integration of real-time attendance data management features, automatic reports, and centralized access rights settings that were not available in the previous system. This system was developed to replace the manual attendance method, which was prone to recording errors, data manipulation, and slowed down the reporting process. Development was carried out using the Waterfall method, which includes the stages of requirement definition, design, coding & testing, integration & system testing, and operation & maintenance. A trial was conducted on 25 employees over one month, resulting in an attendance recording accuracy rate of 98% and a 70% reduction in recapitulation time compared to the previous method. The implementation results show that the system can improve efficiency and ease of data access, although it still has limitations such as the lack of integration with fingerprint devices or payroll systems. In the future, this system can be developed with attendance notification features, IoT integration for automatic attendance, and attendance data analysis to support managerial decisions.
Improving Butterfly Fish Image Classification Accuracy using HSV Feature Extraction and SMOTE-Based Data Balancing I Putu Arya Putra; I Made Agus Wirawan; I Gede Aris Gunadi
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/vw52nn48

Abstract

Class imbalance in image data can reduce the accuracy of classification models, especially when the minority class data is much smaller than the majority class. This research focuses on enhancing the classification accuracy of butterflyfish images through the application of the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, combined with the K-Nearest Neighbor (KNN) algorithm utilizing HSV-based feature extraction. The datasets were collected in two conditions, namely conditioned (controlled background and lighting) and unconditioned (varied background and natural lighting). The research stages include preprocessing, HSV feature extraction, data balancing with SMOTE, and classification using KNN with various k values (3, 5, 7, 9) and cross-validation (k-fold 5 and 10). The experimental results show that SMOTE consistently improves accuracy on both types of datasets, with the best performance at k = 3 and k-fold = 10, namely 85.32% (conditioned) and 87.59% (unconditioned). This improvement occurs because a more balanced data distribution allows the model to optimally recognize features between classes. This study proves that the integration of SMOTE and KNN is effective in overcoming class imbalance in image classification, with potential applications in the fields of digital image technology, ecosystem management, and species identification.  
Application of Machine Learning for Classifying and Identifying Security Threats Using a Supervised Learning Algorithm Approach Yudhi Arta; Suzani Mohamad Samuri; Nesi Syafitri; Anggi Hanafiah; Wina Oktaria; Maripati Maripati; Eka Pandu Cynthia
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/aqjdbj22

Abstract

The rapid growth of harmful web content has intensified the demand for intelligent systems capable of accurately classifying cyber threats based on URL patterns. This study evaluates two widely used supervised learning algorithms, Random Forest and Naïve Bayes, for probabilistic classification of multi-class URL datasets. A synthetic dataset comprising 547,775 URLs was designed to reflect realistic threat distribution: benign (65.74%), phishing (14.46%), defacement (14.81%), and malware (4.99%). Each sample included simple structural features such as URL length, number of dots, HTTPS usage, and keyword indicators. Both models were tested using identical stratified train-test splits with varying sample sizes, including focused experiments on 15,000 and 100,000 entries. Results revealed that both models achieved high recall and precision only for the benign class, while failing to detect minority classes. For Random Forest, precision and recall for benign URLs reached 1.00 but dropped to 0.00 for phishing, defacement, and malware in all test scenarios. Naïve Bayes exhibited similar shortcomings, highlighting the impact of class imbalance and limited feature expressiveness. This research concludes that while Random Forest and Naïve Bayes are computationally efficient, they are inadequate for detecting cyber threats without preprocessing techniques such as SMOTE, cost-sensitive learning, or feature enrichment. Future work will explore adaptive hybrid models with contextual features and deep learning frameworks to enhance multi-class detection in real-world cybersecurity scenarios.
Decade Rainfall Prediction Using Prophet Algorithm and LSTM (Case Study in Banjarnegara Regency) Sulistiyowati sulis; Eri Zuliarso
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/j3mbxq89

Abstract

Hydrometeorological disasters such as floods and landslides in Banjarnegara Regency are closely related to fluctuating rainfall variability. This study aims to predict decadal (10-day) rainfall by comparing the performance of the Prophet algorithm and the Long Short-Term Memory (LSTM) model. The dataset comprises daily rainfall records from 14 observation stations spanning the period 2005–2024. The research stages included preprocessing, modelling, hyperparameter optimization using Optuna, and evaluation with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results indicate that the Prophet model outperformed LSTM in most locations, with an average RMSE of 69.55 and MAE of 53.05, lower than LSTM, which recorded 73.03 and 55.72, respectively. The ensemble averaging model produced competitive results at several locations, although it was less responsive to sharp fluctuations in rainfall. These findings confirm that Prophet is more effective in capturing seasonal patterns and long-term trends, thus providing significant potential to support climate-based disaster mitigation systems in vulnerable areas such as Banjarnegara
Implementation of IP SLA and Policy-Based Routing for Failover In a Multi-Homed Network Dysan Yusak; Wiwin Sulistyo
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/g2av9m56

Abstract

In the current digital era, stable and uninterrupted internet connectivity is the backbone of modern operations, demanding high availability and reliability. A key challenge is managing the inherent single point of failure associated with a single ISP connection. This study investigates the implementation and effectiveness of IP Service Level Agreement (IP SLA) and Policy-Based Routing (PBR) on a multi-homed network to achieve automated failover optimization. A virtual testbed was constructed using EVE-NG with a Cisco IOS C7200 image, and each test scenario was replicated five times to ensure data consistency. IP SLA was configured for proactive failure detection (parameters: timeout 5000 ms, threshold 500 ms, request-data-size 100, delay up 10, delay down 5), while PBR was used for VLAN-based traffic steering. The results indicate that this solution successfully steered traffic according to policy and achieved a consistent failover switchover time of 1.07–1.09 seconds (n=5), supported by Wireshark analysis, which documented up to 100% packet loss during the transition. The failure detection time, directly correlated with the IP SLA frequency, varied from 3.96 seconds (5-second frequency) to 45.63 seconds (60-second frequency). During the transition, router CPU load remained low at 3%, indicating high resource efficiency. This research concludes that the combination of IP SLA and PBR is an effective solution for enhancing the resilience and service continuity of multi-homed networks with minimal computational overhead.

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