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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 256 Documents
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Design of Android-Based Card Production Scheduling System Application using Rapid Application Development Method Barokah, Irma; Azrino Gustalika, Muhamad
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: 10.35314/9dksvc86

Abstract

PT. Cazh Teknologi Inovasi is a company focused on delivering excellent customer service. However, the current card production scheduling process, which is still managed manually through social media, has led to several issues, including a high risk of human error, product backlog, and long customer waiting times. These problems negatively impact service quality and customer satisfaction. To address this, an Android-based card production scheduling system was developed using the Rapid Application Development (RAD) method, which enables fast and flexible application development tailored to the company's needs. The system was tested using the black box method to ensure that each feature functions according to its specifications. The test results indicate that the system is feasible for use and can improve production efficiency while reducing customer waiting time.
Application of ARIMA and ARIMAX Methods to Predict the Number of Visitors to Hotel XYZ Pekanbaru Vernando, Julio; Insani, Fitri; Okfalisa, Okfalisa; Kurnia, Fitra
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/enrfna19

Abstract

Predicting the number of visitors to Hotel XYZ is one of the steps that can be taken by the hotel to find out how many visitors will increase in each upcoming holiday season. The purpose of this study is to forecast the number of visitors to Hotel XYZ from June 2023 to July 2024 using the ARIMA and ARIMAX comparison methods. The research methodology encompasses problem identification, data collection, data processing, and ARIMA and ARIMAX analysis, which involves testing the parameters (p, d, q) selected based on the ACF and PACF using the AIC Model. Based on the test results, ARIMAX (5, 0, 3) has the lowest AIC, which is 3495.2, followed by ARIMAX (3, 0, 5), which has a slightly higher AIC. The results showed that the ARIMAX (5, 0, 3) model is the most accurate model for predicting data (eg the number of hotel guests, room demand, or income), with an RMSE value of 15.80% and a MAPE of 18.90%. Therefore, research that applies the ARIMAX model can provide real benefits in supporting operational efficiency, resource management, and hotel business strategy, ultimately increasing the competitiveness and profitability of the hotel.
Virtual Tour Application for Cultural Heritage in North Aceh Regency using Augmented Reality Technology Melly, Melly Yani; Darnila, Eva; 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: 10.35314/3s7wyh46

Abstract

Cultural heritage refers to historical objects that must be preserved through protection, development, and utilisation. In North Aceh Regency, cultural heritage preservation faces challenges such as low interest among younger generations and the lack of interactive learning media. This study aims to design a virtual tour application using Augmented Reality (AR) and Geographic Information System (GIS) technologies as an interactive medium to digitally introduce cultural heritage sites. Data were collected from the Department of Education and Culture of North Aceh and through direct observation and documentation in the field. The application integrates AR features to display 3D cultural objects and GIS to present the geographical locations accurately. The development includes user interface design, motion-based navigation, and historical information panels. Testing results show that all markers successfully displayed 3D objects with an average detection time of 3.58 seconds, a detection distance of 75.71 cm, and a rotation angle of up to 360°. The objects appeared stable, and the historical information was well presented. The main contribution of this study is the implementation of AR technology in the local context of North Aceh, which has rarely been applied. Limitations include the small number of heritage sites and testing limited to a few AR devices. Future research is recommended to expand site coverage, improve device compatibility, and add gamification features to enhance user engagement.
Sentiment Analysis of Telegram Application User Satisfaction on Google Play Store Using Naïve Bayes, Logistic Regression and SVM Putri, Adellia Septiani; Fauzan Rozi, Anief
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/r6cyb589

Abstract

Sentiment analysis is a technique for finding out how people feel about something and putting the polarity of text into groups of documents or words so that they can be labeled neutral, positive, or negative. We will use the Naïve Bayes algorithm, logistic regression, and SVM to conduct sentiment analysis on how happy Telegram app users are. The purpose of this study is to see what people who use the app think and group their thoughts into three groups: neutral, positive, and negative. The three methods' results will be compared to see which is most accurate for this study. The results of this sentiment analysis show that many users are dissatisfied with the verification code they need to register or log in to their accounts. This makes it difficult for new users to get the verification code because the app itself sends it. The SVM approach has an accuracy value of 89.73%, which means it is more accurate in this study. The Naïve Bayes approach is accurate by 75.61%, while the logistic regression method is accurate by 87.49%.
Diabetes Detection Using Stacking Technique: A Combination of XGBoost, Gradient Boosting, and Meta Model Aden Rahmat, Aden Rahmat; Wahyu Utomo, Danang
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: 10.35314/48asdy77

Abstract

Type 2 diabetes mellitus is a chronic and progressively increasing global health issue that necessitates early detection to mitigate serious complications such as kidney failure, neuropathy, and cardiovascular disorders. While numerous studies have developed predictive models using machine learning techniques, many are limited by their reliance on single algorithms and inadequate handling of class imbalance. This research introduces a novel strategy by employing an ensemble stacking method that integrates Gradient Boosting, XGBoost, and Random Forest, with Random Forest acting as the meta-learner. The dataset, comprising 100,000 patient records, underwent preprocessing and was balanced using the SMOTE-Tomek approach to correct class distribution disparities. The stacking process is implemented in two phases: base models generate preliminary predictions, which are subsequently used as input for the meta-model to refine the final outcomes. The evaluation demonstrates that the stacking model achieves superior performance, recording 98% accuracy and an F1-score of 0.98, outperforming the individual models. The key distinction of this study lies in the effective application of ensemble stacking to enhance prediction accuracy, especially in dealing with imbalanced and complex medical data. This methodology has the potential to improve clinical decision support systems, making them more accurate and responsive.  
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.

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