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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 269 Documents
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User Experience Analysis on Facebook Marketplace Pekanbaru using User Experience Questioner Method Mukti, Raihan; Megawati; Angraini; Fronita, Mona
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/r0qp2735

Abstract

The integration of e-commerce features into social media platforms has created unique user experience challenges, particularly in localized buying and selling communities. This study evaluates the user experience of Facebook Marketplace within the Pekanbaru Jual Beli Online (PJBO) community using the User Experience Questionnaire (UEQ) method. Through a quantitative approach with 400 active users selected via simple random sampling, this research reveals significant user experience gaps in a platform that accommodates over 136,000 members. The results demonstrate that four core UX dimensions fall below industry benchmarks: attractiveness (0.85), clarity (0.81), efficiency (0.82), and dependability (0.79), all with narrow confidence intervals (±0.14-0.18), indicating consistent user dissatisfaction across these dimensions. While stimulation (0.80) achieved "good" ratings and novelty (0.78) scored "above average," the overall UX profile reveals a platform that provides adequate innovative features but struggles with fundamental usability aspects. This study contributes to UX literature by examining the unique intersection of social network-based e-commerce in localized Indonesian markets, and provides platform developers with actionable recommendations to enhance system navigation, transaction efficiency, and trust mechanisms. However, this study employs only the UEQ method and does not extend to a more comprehensive evaluation, such as assessing system effectiveness and application interface performance.
Comparison of K-Means++ and Agglomerative Hierarchical Methods in Clustering Healthcare Workers Handayani, Citra Tjipta Nur; N. N. Sitokdana, Melkior
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/pcbrs043

Abstract

As an archipelagic country, Indonesia faces disparities in the distribution of healthcare workers, influenced by its diverse geographical conditions. These disparities impact the equitable access to healthcare services across the country. This study aims to compare the effectiveness of two clustering methods, namely K-Means++ and Agglomerative Hierarchical Clustering, using secondary data from Statistics Indonesia (BPS) on the Number of Healthcare Workers by Province in 2023, covering 38 provinces and 13 categories of healthcare professions.The evaluation was conducted using three metrics: Silhouette Score to measure cluster cohesion, Davies-Bouldin Index to assess inter-cluster separation, and Calinski-Harabasz Index to compare inter-cluster variance. The results show that Agglomerative Hierarchical outperformed K-Means++ in Silhouette Score (0.550) and Davies-Bouldin Index (0.457), while K-Means++ performed better in the Calinski-Harabasz Index (63.630). A 2D PCA visualization further illustrates the structural differences between the clusters formed by each method. These findings provide insights into selecting the most appropriate clustering method for analyzing the distribution of healthcare workers and can support data-driven decision-making by policymakers
Performance Comparison Of BERT Metrics and Classical Machine Learning Models (SVM,Naive Bayes) for Sentiment Analysis Adib Ulinuha El Majid; Reflan Nuari
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/wmh3rg23

Abstract

Sentiment analysis is one of the important methods in understanding public opinion from large amounts of text, such as product reviews or user comments. Many studies have shown that the BERT (BiDirectional Encoder Representations from Transformers) model has advantages over classical machine learning models such as Support Vector Machine (SVM) and Naïve Bayes. However, there are still few studies that systematically compare the performance of the two on datasets from various topics and languages, especially those with imbalanced label distributions. This study compares four BERT variants (bert-base-uncased, distilbert-base-uncased, indobert-base-uncased, and distilbert-base-indonesian) with two classical models using three datasets of IMDb 50K (English), Amazon Food Reviews (English), and Gojek App Review (Indonesian). The classical model uses the TF-IDF vectorisation method, while the BERT model is optimised through a further training process (fine-tuning) with a layer freezing technique. The evaluation is carried out using accuracy, precision, recall, and F1-score. The results show that the BERT model excels on English data, while on imbalanced Indonesian data, SVM and Naïve Bayes produce higher F1-score results. These findings indicate that the selection of the right model must be adjusted to the characteristics of the data used.
Website-Based Management and Financial Information System using Prototyping Method at GITJ Puncel Church Yehezkiel Febri Kurniawan; Aditya Akbar Riadi; Evanita
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/p9rvkh62

Abstract

This study aims to design and implement a web-based information system to manage ministry and financial activities at GITJ Puncel Church, focusing on improving data accuracy, reporting transparency, and operational efficiency. The system was developed using the Prototyping method with an iterative approach, while data were collected through observations, structured interviews with church administrators, and document analysis. The system was built using PHP with the Laravel framework and MySQL database, and tested using the Black Box Testing method. This research addresses gaps in previous studies that did not effectively integrate ministry management features with financial administration, nor evaluate the quantitative impact on operational efficiency. The results show that the system successfully reduced financial recording time from 3–4 hours to 30 minutes (an 87.5% efficiency improvement), decreased recording errors from 15% to 2% (an 86.7% reduction in error rate), and enhanced reporting transparency through an automatic PDF export feature. Testing confirmed that all system functionalities operated 100% according to specifications. The study's limitations include its scope being limited to a single church with 150 congregants, a three-month implementation period, and no evaluation of long-term impacts on congregation participation. Overall, this research contributes a model for integrating ministry and financial systems for small to mid-sized religious organizations, and offers a framework for evaluating the effectiveness of web-based information systems in non-profit contexts.
Comparison of VGG16 and VGG19 Models in the Classification of Down Syndrome in the European Region with Transfer Learning Bima Evansyah, Excel; Sri Kusuma Aditya , Christian
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/pz35e881

Abstract

Down syndrome detection by utilizing facial images as the main data has been widely developed through deep learning approaches, especially Convulutional Neural Network (CNN). However, most studies only classify the disorder without paying attention to regional factors. This has limited the effectiveness of the model in the classification of Down syndrome, especially in populations in European regions that have different morphological characteristics. This study examines the performance of two pretrained CNN models, namely VGG16 and VGG19, in classifying facial images of children from Europe who are divided into 2 categories of Down Syndrome and Healthy. The dataset used in the study consists of 1,543 images from the Down syndrome class 671 images and the Healthy class 872 images. It was then expanded to 1570 images to balance the data between both Down syndrome and Healthy classes. The evaluation results of this research by applying augmentation show that the VGG16 model has superior performance compared to VGG19, with accuracy reaching 94%. Meanwhile, the VGG19 model obtained an accuracy of 90%. This difference shows that the VGG16 model has a more stable performance in detecting both categories with a better balance between precision and recall. This research is limited to European children's image data and still does not exist for ethnic teenagers or the elderly. This provides a basis for the development of facial image-based early detection systems, particularly for clinical applications or early screening in areas with similar populations.
Utilization of Satellite Imagery and GIS for Mapping Potential Anchovy Fishing Areas in East Lampung Rifki, Rifki Arif; Chairani, Chairani; Sriyanto, Sriyanto
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/t4fqfq25

Abstract

This study utilises Aqua MODIS satellite imagery from January to December 2023 to analyse Sea Surface Temperature (SST) and chlorophyll-a as primary indicators in mapping Potential Fishing Zones (PFZ) for anchovy in East Lampung. Images were filtered based on minimal cloud cover and seasonal completeness using Level 3 daily data with 1 km resolution. The spatial analysis was conducted using Geographic Information Systems (GIS) to identify areas with SST between 29–31°C and chlorophyll-a concentrations above 0.2 mg/m³, which are considered optimal for anchovy habitat. The results show dynamic seasonal shifts in fishing zones influenced by oceanographic conditions. Compared to previous studies, this research provides more detailed seasonal maps and incorporates local fishing data to strengthen relevance. Despite limitations in temporal continuity due to cloud coverage, the approach demonstrates potential for efficient and sustainable fisheries management in Lampung.
Schizophrenia Classification using Fuzzy K-Nearest Neighbour on Patient Data from RSJD Dr. Amino Gondohutomo Ozagastra Caluella Prambudi; Ajib Susanto; Christy Atika Sari
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/t2mfvf14

Abstract

Schizophrenia is a complex mental disorder with overlapping symptoms, making subtype diagnosis uncertain. This study aims to develop an automated classification method for schizophrenia subtypes using the Fuzzy K-Nearest Neighbour (FKNN) algorithm, which effectively handles uncertainty in medical data. The dataset includes 300 patients from RSJD Dr. Amino Gondohutomo, Central Java, aged 18–60 years, with balanced gender distribution. Four subtypes—paranoid, catatonic, hebephrenic, and undifferentiated—were classified. Symptom and demographic data were encoded and normalised using min-max scaling. The model was trained using k = 5 and evaluated via 10-fold cross-validation. The results achieved 94% accuracy with high precision and recall across all classes. However, limitations include a relatively small and single-source dataset and the lack of ROC/AUC analysis. These findings suggest that FKNN has strong potential as a data-driven decision support system for schizophrenia diagnosis, suitable for integration into psychiatric hospital information systems. Future research should explore oversampling techniques such as SMOTE and threshold tuning to improve model sensitivity.
Implementation of Convolutional Neural Network Algorithm in Recyclable Waste Recognition to Support Environmental Management Yuliana Fitriani; Evanita, Evanita; Akbar Riadi, Aditya
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/drcjhg64

Abstract

Waste remains one of the unresolved environmental problems, primarily due to ineffective waste management in sorting and recycling processes. Many individuals are unaware of or do not have the time to identify recyclable types of waste. This study aims to develop a web-based system capable of automatically classifying waste types to support raising public awareness of the importance of recycling. The method used is a CNN with a total of 1,800 images divided into six classes: glass, paper, metal, plastic, organic, and residual. The dataset is split into 1,296 images for training, 144 for validation, and 360 for testing. Unlike previous studies that classified only two to three types of waste or were not web-based, this system combines classification of six categories with an interactive web interface that can be directly used by the public. The results show that the developed model achieved an accuracy of 90%, with the best performance in classifying organic waste. However, the model still has limitations such as sensitivity to variations in lighting, varying image capture angles, and visual similarities among certain waste types that can affect classification accuracy. These findings indicate that the proposed system has the potential to help the community manage waste more effectively and sustainably.
Design of a Mobile Application for Real-Time Flood Information in North Aceh Region Based on GIS and Haversine Method Muhammad Naufal; Qamal, Mukti; LRosnita, Lidya
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/kwqt3q67

Abstract

This research focuses on the design and development of a mobile application for real-time flood information in the Aceh Utara region, utilizing Geographic Information Systems (GIS) and the Haversine method. The primary goal of this study is to provide an accessible and reliable tool for residents and local authorities to monitor flood events, allowing them to make timely and informed decisions. By integrating GIS technology, the application enables users to view flood-prone areas on an interactive map and receive real-time alerts based on proximity to flood locations. The Haversine method is applied to calculate distances between various flood points and user locations, ensuring the accuracy of the alerts. The methodology includes the design of a user-friendly interface and the implementation of real-time data processing. Results show that the application successfully integrates GIS and the Haversine method, providing accurate flood data and enhancing user experience in disaster management. The significance of this research lies in its potential to improve disaster preparedness and response in flood-prone regions, thus reducing the impact of floods on communities and infrastructure. This mobile application can be a crucial tool for managing flood risks and ensuring the safety of the population in Aceh Utara.
Development of Web-Based Multimedia Learning for Grade 3 Elementary School Mathematics Muharom, Ahmad; Rukhviyanti, Novi
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/sj1qng08

Abstract

The development of information technology has significantly contributed to the education sector, particularly in creating interactive and engaging learning media. Mathematics, as one of the core subjects, is often perceived as difficult and tedious by students. Therefore, there is a need for innovative learning media that can enhance students’ interest and understanding of the subject matter. This study aims to design and implement an interactive mathematics game website that presents basic math problems in an enjoyable and responsive format. The website consists of nine pages, including a landing page, login, dashboard, quiz selection, quiz display, and evaluation results. Each quiz contains 10 questions displayed in two horizontal rows (5 questions each), supported by a countdown timer and an automatic scoring system. The development methodology includes needs analysis, system design, implementation, and evaluation. The evaluation phase was conducted through internal testing and user observation targeting elementary school students.

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