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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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Forecasting Red Chilli Plant Growth using Time Series Method With Long Short-Term Memory Model Lastiur Aritonang; Brita Aryowindo; Ridho Syarif; Ertina Sabarita Barus
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: 10.35314/24mwkh42

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; Ichsan Ibrahim
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: 10.35314/5xwns554

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 Muhammad iqbal; eva darnila; risawandi
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/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 Nafi' Ikhsan Burrhanuddin; Anief Fauzan Rozi
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/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 Muhammad Jihad Saputra; Bustami; Maryana
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/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
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/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.
Decision Support System for Inventory Prediction using Fuzzy Tsukamoto Method (Case Study: UMKM Bayou Indonesia) Galih Agil Febri Hidayatullah; Sri Mujiyono
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/sfyymk96

Abstract

Bayou Indonesia, an MSME engaged in acrylic product manufacturing, faces overproduction issues due to manual production planning, leading to stockpiling and wasted resources. This study aims to develop a decision support system using the Fuzzy Tsukamoto method to predict production quantities more accurately by analyzing historical data such as orders, shipments, and final stock. Data processing is performed with fuzzy logic to generate reliable production forecasts for the upcoming periods. The novelty of this research lies in the real-world integration of the Fuzzy Tsukamoto method within a CodeIgniter-based web application, which is directly implemented in the MSME environment, moving beyond the purely theoretical simulations of prior studies. The system significantly improves production planning accuracy, reducing manual errors (MAPE) from 21.5% to 8.7%, with an RMSE of 11.2 units. Furthermore, it helps decrease excess production discrepancies by up to 30% per month, raises prediction precision to 85%, and accelerates the decision-making process from two to three days to real-time. The resulting operational efficiency gains are estimated at 60–70%. These findings indicate that the system provides a practical solution for MSMEs to minimize overproduction risks, optimize resource usage, and enhance production planning through data-driven methods.
Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Ahmad Zamsuri; Nurliana Nasution; Maya Ramadhani
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/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
Development of a Website-Based Facilities and Infrastructure Rental System using the Rapid Application Development Method Valentino Aldo; L. Budi Handoko
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/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
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: 10.35314/7bdz0a54

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.

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