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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
Analysis of Anthracnose Disease in Curly Chilli Using Fuzzy Logic Method simangunsong, Esterika; Situmeang, Johan Medi; Aikel; Barus, Ertina Sabarita
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/xtkp9c52

Abstract

Curly chilli (Capsicum annuum L.) is one of the horticultural products that has a high economic value and is often consumed by the people of Indonesia, both as a flavour enhancer for dishes and as a source of nutrition. However, until now, the production of chilli peppers has not been able to meet demand, one of which is caused by anthracnose disease that attacks plants through fungi of the genus Colletotrichum, potentially causing yield losses of 50 to 90%. Until now, there have not been many disease risk prediction systems that consider environmental variables adaptively. This research aims to develop an anthracnose disease risk prediction system based on the Mamdani fuzzy logic method that is able to handle the uncertainty of environmental data such as temperature, humidity, and soil pH. Data are obtained from trusted literature sources and have undergone a validation process before being used in modelling. The system was developed using MATLAB because it supports various features in the implementation of fuzzy logic. Simulation results show high consistency between manual calculations and software results, indicating that the system has a good level of accuracy and potential to be applied in agricultural management.
Forecasting Red Chilli Plant Growth using Time Series Method With Long Short-Term Memory Model Aritonang, Lastiur; Aryowindo, Brita; Syarif, Ridho; Barus, Ertina Sabarita
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: 10.35314/24mwkh42

Abstract

The growth of red chilli plants is a horticultural commodity whose growth is highly determined by environmental elements, as a result, it is very crucial to make predictions to help more effective agricultural planning. This study aims to examine the ability of the Long Short-Term Memory (LSTM) model in predicting the growth of red chilli plants (Capsicum annuum L.) according to 4 main parameters, namely stems, branches, leaves, and grains. The data used are red chilli plant growth data obtained from plantations located in Deli Serdang Regency, precisely in Namorambe District, namely Jatikusuma Village, over a period of 63 days and analyzed using the time collection method. The example provides high prediction accuracy for stem parameters (R² = 0.9796), branches (R² = 0.9618), and leaves (R² = 0.9489), but slightly low in fruit (R² = 0.8807) due to hyperbolic fluctuations. The consequences show the potential of LSTM in helping red chilli cultivation through better planning, green aid control, and early detection of growth anomalies. This study also demonstrates an integrative approach to four plant growth parameters using a single LSTM instance.
Implementation of Machine Learning in Business Intelligence for Customer Segmentation and Loyalty at PT. Inti Group Galuh Pandu Siwi Ambarsari; Ibrahim, Ichsan
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: 10.35314/5xwns554

Abstract

This study addresses the need for integrated data analytics and machine learning in PT Inti Group’s BI dashboard by implementing an unsupervised K‑Means clustering method on historical training data (January 2021–May 2025) extracted directly from a PostgreSQL database and analyzed using Python. The analysis process includes data preprocessing and feature engineering to create key variables: number of participants, training‑type frequency, recency (days since the last training), and engagement duration. Cluster determination was evaluated using the Elbow method (4 clusters), Silhouette score (2 clusters), and Davies–Bouldin index (9 clusters). Based on business interpretation and the balance between cluster compactness and separation, four clusters were selected: Loyal & High‑Value Customers, Inactive, Growing/Potential, and New/Sporadic. Customers who attended training more than ten times were classified as loyal. The segmentation results are visualized in a Power BI dashboard integrated directly with the data source, supporting rapid data‑driven managerial decisions. This study demonstrates that integrating unsupervised learning with BI effectively enhances understanding of customer characteristics and serves as a basis for designing more targeted marketing strategies. A limitation of this study is that the data cover only up to May 2025.
Implementation of an Artificial Neural Network Algorithm for Mental Illness Virtual Assistant Chatbot Development iqbal, Muhammad; darnila, eva; risawandi
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/wkj2ks31

Abstract

Mental health is a critical issue in modern society, yet access to psychological support remains limited. This study presents the development of a chatbot as a virtual assistant for individuals experiencing mental illness using the Artificial Neural Network (ANN) algorithm. The dataset was manually constructed and divided using an 80:20 ratio for training and testing. The ANN model employs one hidden layer with ReLU and softmax activation functions to classify user input into relevant mental health categories. The model achieved a training accuracy of 83.2% with a loss of 0.655, and a testing accuracy of 81.5%, indicating solid performance. Compared to rule-based methods, ANN provides better adaptability in recognizing diverse expressions and delivering context-aware, empathetic responses. This study also introduces a custom-built mental health dataset and integrates a crisis response module that is underexplored in previous research. The chatbot targets five categories of mental disorders: Schizophrenia, Bipolar Disorder, Depression, Anxiety, and Personality Disorders. Findings suggest that ANN-based chatbots can serve as reliable, accessible, and scalable early-stage mental health support tools.
Sentiment Analysis and Classification of User Reviews on the Redbus Application Using Logistic Regression And SVM Burrhanuddin, Nafi' Ikhsan; Rozi, Anief Fauzan
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/k6k6m469

Abstract

The increasing number of RedBus users in Indonesia has led to a growing volume of user reviews on digital platforms, especially the Google Play Store. These reviews reflect user perceptions and are valuable for sentiment analysis. This study aims to classify sentiments in RedBus user reviews using Logistic Regression and Support Vector Machine (SVM) algorithms. A total of 2,000 reviews were collected through automated web scraping and labelled using a lexicon-based approach. The data underwent preprocessing steps including normalisation, tokenisation, filtering, stemming, and labelling. Features were transformed using the TF-IDF method and split into 90% training and 10% testing sets. Evaluation results showed that SVM with a linear kernel outperformed Logistic Regression, achieving 91.10% accuracy and more balanced F1-scores across sentiment classes. Logistic Regression reached 86.39% accuracy but performed lower on positive sentiment. A paired t-test confirmed the statistical significance of the performance difference (p = 0.0005). These findings suggest that SVM is more effective in handling high-dimensional text data and can be recommended for real-world sentiment classification tasks, such as filtering negative reviews and improving customer service.
K-Medoids Clustering Method Iin Transaction Data Reports of UIN IB Padang With Bank Nagari Saputra, Muhammad Jihad; Bustami; Maryana
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/tq2tkw36

Abstract

Manual management of student financial transaction data remains a major challenge in many higher education institutions, including in the collaboration between Universitas Islam Negeri Imam Bonjol (UIN IB) Padang and Bank Nagari. Until now, no automated system has been developed to cluster student transaction data using the K-Medoids algorithm within higher education institutions in West Sumatra. This study aims to design a transaction clustering system that can identify student transaction patterns more efficiently. The K-Medoids algorithm is applied to transaction data that has been preprocessed through categorical transformation and normalization to address accuracy issues in distance-based analysis. The results show the formation of three main clusters: low (59 data points), medium (185 data points), and high (106 data points). This distribution reflects the variations in student transaction behavior and can be utilized by both the university and the bank to design more targeted service strategies, such as resource allocation and payment policy evaluation. This research provides an initial contribution to the application of K-Medoids-based data mining for optimizing transaction management in regional higher education institutions
Sentiment Analysis of BPD DIY Mobile Banking Application Using SVM and KNN Methods Nabil Fauzan; Putry Wahyu Setyaningsih
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/qyebc428

Abstract

This study aims to conduct sentiment analysis on user reviews of the BPD DIY Mobile Banking application available on the Google Play Store. The analysis is crucial due to the increasing number of user complaints regarding technical performance and user experience that have not been systematically addressed. Two machine learning algorithms, the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN), were used to classify reviews into positive and negative sentiments.  The dataset comprises 1,211 user reviews collected through web scraping and processed with comprehensive preprocessing stages, including cleaning, tokenizing, case folding, stopword removal, normalization, and stemming. The novelty of this research lies in the integration of Indonesian-specific preprocessing techniques and a comparative evaluation of two classification models, which are rarely applied in similar studies focused on regional banking applications.  The results indicate that SVM outperforms KNN, achieving 81.48% accuracy, 82.30% precision, and 88.50% recall, while KNN only reaches 55.56% accuracy, 63.00% precision, and 65.50% recall. With this level of accuracy, the SVM-based model can be effectively utilized for real-time sentiment monitoring and to identify critical issues in user experience. These findings offer strategic insights for BPD DIY to enhance application quality, particularly in addressing technical problems frequently highlighted by users.
Development of a Website-Based Facilities and Infrastructure Rental System using the Rapid Application Development Method Valentino Aldo; L. Budi Handoko
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/wcqyg231

Abstract

To improve the efficiency and transparency of the management of facilities and infrastructure at the Semarang City Youth and Sports Office, a web-based rental system was developed with the RAD approach. Evaluation using the time measurement technique showed that the booking process time was reduced from 10 minutes to 3 minutes, and payment validation, which previously took up to 1 hour, now takes place automatically in seconds. The system was built using Express.js based on Node.js for an efficient and structured backend, React.js for an interactive and responsive frontend, and MySQL as the main database. The system design uses visual aids such as use case diagrams, activity diagrams, and entity relationship diagrams. Testing was carried out using black box testing using the equivalence partitioning technique. As a result, the system meets all functional requirements and increases operational efficiency by up to 70% through payment gateway integration. Further development, it is recommended to add reporting and analysis features to support decision making.
Analysis of WAN Network Reliability Based on Response Time and Downtime at the Faculty of Information Technology UKSW Kevin; Indrastanti R. Widiasari
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: 10.35314/7bdz0a54

Abstract

Wide Area Network reliability is crucial in supporting academic and administrative activities in higher education institutions. This study aims to evaluate the reliability of the WAN network at the Faculty of Information Technology, UKSW, using response time and Downtime as the main indicators. The research employed a quantitative descriptive approach by utilizing PRTG Network Monitor, Ping, and Zabbix to measure network performance. The results showed that the average response time was 104.31 ms, with a maximum response time of 614.0 ms. The total Downtime recorded was 22 hours and 42 minutes, with a network uptime percentage of 80.16%. These findings indicate that while the network remains operational, optimization is needed to reduce latency fluctuations and minimize Downtime. Recommendations include enhancing network infrastructure and implementing proactive monitoring strategies.
Android Application Prototype for Detecting Mould on Bread using Machine Learning Siregar, Frissy; Barus, Daniel Haganta; Piay, Clara Stephanie Bernadeth; Indra, Evta
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/bptwhn82

Abstract

Mould contamination in bread poses a serious health risk if not detected early, especially in the food industry, which still relies heavily on manual visual inspection. This study aims to develop a prototype Android application capable of automatically detecting mould on bread using a machine learning approach based on the MobileNetV2 architecture. The classification model was trained on a dataset of 666 bread images, consisting of 533 training and 133 validation samples. Training was carried out over 37 epochs using data augmentation techniques and a learning rate of 0.0001. The results demonstrated consistent accuracy improvements and loss reductions without signs of overfitting. The model achieved 94% testing accuracy, with a precision, recall, and F1-score of 0.94 for both "Mouldy" and "Non-Mouldy" classes. The confusion matrix showed 125 correct predictions out of 133 test images. This research addresses the gap in lightweight and practical solutions for mobile-based mould detection. Unlike previous studies that used heavier models such as VGG16 or ResNet, this study shows that MobileNetV2 can achieve high performance with lower computational demands, making it suitable for real-world Android applications. The trained model was integrated into a simple Android interface, allowing users to upload images and instantly receive classification results. For future improvement, this prototype can be enhanced by incorporating object detection or image segmentation techniques such as YOLOv5 or U-Net to enable not only classification but also the localisation of mould areas in real-time.

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