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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Virtual Local Area Network for Optimizing Network Performance using Simplified Form of Action Research Abdillah, Muhammad Fahmi; Wijayanto, Agus; Pamungkas, Wisnu Hera
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10141

Abstract

Virtual Local Area Network (VLAN) is an effective solution for improving network performance and efficiency in educational environments, particularly in computer laboratories. This study aims to implement and evaluate VLAN at the Computer Laboratory of Universitas Mulia using a simplified action research approach involving a single intervention cycle. The initial diagnosis revealed that the conventional flat network topology resulted in a broad broadcast domain and reduced performance under high traffic conditions. Through VLAN segmentation, the laboratory was logically divided into five segments based on functionality. Evaluation was conducted using throughput measurement and Quality of Service (QoS) indicators, including packet loss, delay, and jitter, based on TIPHON standards. The results indicated consistent improvements in data flow and stability, with reduced packet loss under 0,3% and average delay and jitter remaining below 20 ms across all segments. In addition to technical improvements, interviews with the network administrator highlighted significant benefits in bandwidth management and traffic control during examinations. This study demonstrates that a simplified approach can be replicated in similar educational institutions with limited resources and provides a foundation for more structured and controlled network development.
Comparison of Hyperparameter Tuning in Decision Tree and Random Forest Algorithms for Song Genre Classification Maitsa, Anindita; Winarsih , Nurul Annisa
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10142

Abstract

This research applies Decision Tree and Random Forest algorithms for music genre classification based on audio numerical features such as tempo, energy, loudness, and valence. The dataset used comes from Kaggle and consists of 7,958 song entries from eight genres. The data was processed through pre-processing stages that included duplication removal, empty value handling, normalization, outlier removal, and class balancing using the SMOTE technique. In the initial test, Random Forest showed an accuracy of 85%, higher than Decision Tree which recorded 76%. After hyper parameter tuning using GridSearchCV, Decision Tree's accuracy increased to 79%, while Random Forest experienced a slight decrease to 84%. This decrease does not reflect a decrease in performance, but rather a more balanced redistribution of predictions to minor classes, as reflected by the stable F1-score macro value at 0.84. In terms of efficiency, tuning the Random Forest took much longer (806.81 seconds) than the Decision Tree (17.42 seconds), indicating that model complexity has a direct impact on training time. These findings suggest that data quality, tuning strategy and time efficiency are important factors in building a reliable and balanced music genre classification system.
Usability Analysis of Online Travel Agent Applications Using System Usability Scale and Electroencephalography Larasati, Ayunda; Tranggono, Tranggono
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10145

Abstract

In the digital era, technology has transformed the travel and tourism industry, with Online Travel Agents (OTAs) like Traveloka, Tiket.com, and Agoda offering convenient trip planning through digital platforms. Despite their popularity, issues such as navigation difficulties and unclear information still affect user satisfaction. This study aims to evaluate the usability of OTA applications using the System Usability Scale (SUS) based on the ISO 9241-11 standard and Electroencephalography (EEG) to analyze user physiological responses. The test involved 10 respondents and 4 task scenarios. The results showed that Traveloka achieved a SUS score of 85, with 95% effectiveness and 0.023 goals per second efficiency. Tiket.com scored 79 with 92.5% effectiveness and 0.026 goals per second efficiency, while Agoda scored 70 with 87.5% effectiveness and 0.016 goals per second efficiency. EEG data revealed that Traveloka and Tiket.com had the highest average alpha wave values, indicating respondents felt nervous or anxious, whereas Agoda showed higher beta wave values, suggesting respondents were calm and aware without full concentration. This study highlights that the usability of OTA applications is influenced by user experience, which can be measured subjectively through SUS and more deeply using EEG data to understand physiological responses when interacting with the application.
Sentiment Classification of Indonesian-Language Roblox Reviews Using IndoBERT with SMOTE Optimization Ansyah, Ferdi; Suryono, Ryan Randy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10155

Abstract

Roblox is a community-based gaming platform that is extremely popular among users of various age groups. Millions of user reviews available on the platform contain valuable information regarding user satisfaction, expectations, and criticisms of the gameplay experience. To extract insights from these reviews, a reliable natural language processing (NLP) approach tailored to the local language context is essential. This study aims to classify sentiments in Indonesian-language user reviews of Roblox into three categories: positive, negative, and neutral. The model used is IndoBERT, a transformer-based model specifically trained to understand the structure and vocabulary of the Indonesian language. One of the main challenges in this study is the imbalance in the number of data points across sentiment classes. To address this, the SMOTE (Synthetic Minority Over-sampling Technique) method is applied to strengthen the representation of minority classes. The dataset consists of thousands of reviews that have been manually labeled by annotators. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the combination of IndoBERT and SMOTE provides significant improvements compared to the baseline approach without oversampling. This research contributes to the development of automated sentiment analysis systems in the Indonesian language, which can be applied across various digital platforms. The implementation of this model can assist game developers and product analysts in efficiently understanding user opinions, thereby driving improvements in service quality and user satisfaction in the future.
Application of Artificial Neural Network (MLP) for Multivariate Analysis of Stunting Causes in Indonesia Irnanda, Muhammad Diva; Pratama, Ananta Surya; Putra, Fawwaz Azhima; Sugiyanto, Sugiyanto
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10205

Abstract

Stunting is a major public health challenge in Indonesia, primarily caused by prolonged malnutrition and recurrent infections during the First 1,000 Days of Life. This study utilizes the Multi-Layer Perceptron (MLP) neural network model to predict stunting, offering a new dimension in the analysis of complex data and identification of patterns influencing stunting. With its capabilities, the MLP model provides higher precision in detecting contributing factors to stunting. The evaluation results of the model show RMSE of 0.7231, MAE of 3.0313, and an R² value of 0.9463. The Food Security Index (IKP), feature X9, had the highest feature importance, followed by X5 (Lack of Clean Water) and X1 (NCPR). This study presents a novel approach to predicting stunting percentages and offers more objective insights to support evidence-based and effective health policies aimed at reducing stunting prevalence in Indonesia.
Image Classification of Red Dragon Fruit Ripeness Levels Using HSV Color Moments and the K-NN Algorithm Br Sembiring, Nadia; Fakhriza, M.
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10206

Abstract

Accurately determining the ripeness level of red dragon fruit (Hylocereus polyrhizus) is crucial for ensuring post-harvest quality and distribution efficiency. This study proposes a method for classifying red dragon fruit ripeness using color moment features in the HSV color space combined with the K-Nearest Neighbor (K-NN) algorithm. The dataset consists of 2,881 images of dragon fruit with a resolution of 800×800 pixels, categorized into three classes: ripe (886 images), unripe (1,241 images), and rotten (754 images). All images were captured under natural lighting conditions and underwent pre-processing to enhance color value consistency. Color features were extracted by calculating the mean, standard deviation, and skewness of the Hue, Saturation, and Value channels. The K-NN model was trained and tested on data randomly split in an 80:20 ratio. The testing results showed that the model achieved 100% accuracy in classifying the ripeness levels, demonstrating the effectiveness of this non-destructive method in distinguishing fruit ripeness. This approach holds strong potential to support efficient and consistent decision-making in the agricultural sector.
Enhancing Eye Diseases Classification Using Imbalance Training & Machine Learning Ihwan, Muhammad Azrul; Wardhana, Ajie Kusuma
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10207

Abstract

This research aims to evaluate the effectiveness of various machine learning algorithms in classifying eye diseases based on retinal images. The dataset comprises four categories of eye diseases: Cataract, Diabetic Retinopathy, Glaucoma, and Normal. The feature extraction method employed a transfer learning approach using ResNet50, followed by SMOTE for data balancing, PCA for dimensionality reduction, and normalization for scaling data consistently. Eleven machine learning models were evaluated, including basic algorithms, ensemble methods, and neural networks. The evaluation utilized metrics such as accuracy, precision, recall, and F1-score. K-Fold Cross Validation is also employed to observe all models' generalisation. The results revealed that the XGBoost algorithm achieved the highest performance with an accuracy of 92.03%, followed by LightGBM 91.88% and MLP 91.50%. K-Fold Validation also improved the MLP performance, which achieved an average accuracy of 91.94% with a standard deviation of 0.0178. This study successfully enhanced classification accuracy compared to previous studies and shows significant potential for clinical applications in resource-limited environments.
Comparative Study of Linear Regression, SVR, and XGBoost for Stock Price Prediction After a Stock Split Andrika, Muhammad Yusuf; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10220

Abstract

This study aims to identify the most effective regression method for predicting the closing stock price of Bank Central Asia (BBCA) following the stock split event on October 12, 2021. Accurate post-split price predictions are crucial for helping investors comprehend new market dynamics, yet there is limited research evaluating the performance of regression models on BBCA’s stock after such corporate actions. Using data obtained through web scraping from the Indonesia Stock Exchange, this study tested three regression algorithms Linear Regression, Support Vector Regression, and XGBoost Regressor on post-split data. The selected input features were open_price, first_trade, high, low, and volume, while the target was close_price. The dataset was divided using an 80:20 train-test split and evaluated with RMSE, MAPE, and R-squared metrics. Results showed that Linear Regression achieved the best performance RMSE: 50.41, MAPE: 0.0048, R²: 0.9971, followed by XGBoost RMSE: 69.12, MAPE: 0.0058, R²: 0.9946, and SVR RMSE: 80.98, MAPE: 0.0069, R²: 0.9925. These findings indicate that BBCA’s post-split stock data exhibits a linear pattern, making Linear Regression the most suitable and efficient method. This suggests that simpler models can outperform more complex algorithms when applied to stable and structured financial datasets.
A Sentiment Analysis of Public Perception Toward Pets in Public Spaces Using Logistic Regression and Word Embedding Febianty, Dennita Noor; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10245

Abstract

Addressing the complex social debate over pets in public areas, this study assesses public sentiment by analyzing a dataset of YouTube comments. We employed a machine learning pipeline beginning with data collection via the YouTube API, followed by rigorous text preprocessing and SMOTE-based class balancing for the training data. For classification, a Logistic Regression model was trained on contextual features generated by Word Embeddings (Word2Vec) and optimized through hyperparameter tuning. The final model proved highly effective, yielding a test accuracy of 92.74% with F1-scores of 0.84 for the negative class and 0.95 for the positive class. Ultimately, this research establishes an effective approach to measuring public opinion on social issues in Indonesia, providing actionable insights for public space administrators and policymakers.
Evaluation of User Satisfaction on the Indonesian National Police Recruitment Website Using the EUCS Method Dwi Pratiwi, Dinda Malika; Sanglise, Marlinda; Baisa, Lorna Yertas
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10284

Abstract

The digitalization of public services encourages government institutions to provide efficient and responsive information systems, including in the recruitment process of the Indonesian National Police (Polri). The Polri recruitment website was developed as an online registration platform to improve transparency, accessibility, and service effectiveness. However, systematic evaluations of user satisfaction with this website are still limited. This study aims to measure user satisfaction using the End User Computing Satisfaction (EUCS) model. A quantitative approach was applied, with data collected through questionnaires from 144 prospective applicants in the West Papua Regional Police area. Data were analyzed using the Partial Least Squares - Structural Equation Modelling (PLS-SEM) method. The findings reveal that ease of use and timeliness significantly influence user satisfaction, while content, accuracy, and format do not. This indicates that usability and information timeliness play a more critical role. The study encourages system developers to focus on enhancing functional and responsive features to improve digital public services.

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