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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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Comparison of Oversampling Techniques on Minority Data Using Imbalance Software Defect Prediction Dataset Hidayat, Deni; Manik, Lindung Parningotan
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8605

Abstract

Software Defect Prediction Dataset as a component of the Software Defect Prediction model has a very vital role. However, NASA Software Defect Prediction has a problem with imbalance in minority data. This study compares the performance of oversampling techniques in overcoming this. A total of 90 oversampling techniques in the form of SMOTE and its variants were used. The results of this study indicate that there is no oversampling technique that is able to overcome this. The original dataset without oversampling shows good performance at the level of accuracy and f1-score but has low performance on auc-score and g-score. Several oversampling techniques show increased performance on auc-score and g-score, unfortunately at the same time showing a decrease in performance on accuracy and f1-score.
Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset Sabella, Cindy Dinda; Pristyanto, Yoga
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8611

Abstract

Air pollution is an urgent global environmental problem, with significant impacts on public health and ecosystem stability. This research aims to develop an air quality classification model using the Global Air Pollution dataset from Kaggle, which consists of 23,463 rows of data and 12 features, including important variables such as Air Quality Index (AQI), PM2.5, NO2, and O3. Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms are applied to perform classification, with a focus on hyperparameter tuning to increase model accuracy. The research results show that the Decision Tree provides the best results with an accuracy of 99.89% after tuning hyperparameters using the Grid Search method. The SVM model showed an improvement of 94.89% to 99.32%, while Random Forest recorded an accuracy of 96.87% with no significant improvement after tuning. Importance feature analysis identified PM2.5 and AQI as the dominant factors in influencing air quality, with PM2.5 having the highest importance value of 0.93. This research confirms that machine learning can be an effective tool for integrating and classifying air pollution. It is hoped that the integration of this model into a real-time air quality monitoring system can help make more responsive and precise decisions in dealing with air pollution problems.
Optimization of Tourism Destination Recommendations in Batang Regency Using Content-Based Filtering Yulfihani, Ilmira; Zakariyah, Muhammad
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8618

Abstract

In an era where tourism plays a pivotal role in economic development, the need for effective navigation through diverse attractions has never been more critical. This research presents a cutting-edge tourism recommendation system tailored for Batang Regency, leveraging Content-Based Filtering (CBF) to deliver personalized suggestions that enhance the tourist experience. By categorizing tourist attractions into Culinary, Culture, Accommodation, Nature, and Leisure, and employing the Haversine formula for precise geographical calculations, our system prioritizes recommendations based on user preferences and proximity. Recommendation testing yielded an impressive average F1 Score of 0.965, underscoring the system's accuracy and relevance, particularly in straightforward user scenarios. However, the research also identifies challenges in more complex cases, suggesting the need for future enhancements through hybrid models and the integration of user feedback. This innovative approach not only streamlines the decision-making process for tourists but also aims to boost local tourism, making it an invaluable tool for both visitors and the Batang Regency community. Join us in exploring how technology can transform the way we experience travel, ensuring that every journey is tailored to individual desires and needs.
A Comparison of Convolutional Neural Network (CNN) and Transfer Learning MobileNetV2 Performance on Spices Images Classification Velarati, Khoirizqi; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8622

Abstract

This research was conducted to analyze the performance of the CNN algorithm without transfer learning in classifying spice images and compare it with the CNN algorithm using transfer learning on the MobileNetV2 architecture. This comparison aims to evaluate both methods' accuracy, efficiency, and overall performance and analyze the impact of transfer learning on classification results in the context of spices. The dataset consists of 1500 spice images divided into 10 classes, with each class of 150 images. In the first experiment, CNN without transfer learning resulted in 93% accuracy performance. For the second experiment using MobileNetV2, there was an increase in accuracy, reaching a value of 99% for all spice classes. The results of this study confirm that MobileNetV2 architecture significantly improves the accuracy and performance of spice classification compared to CNN without transfer learning, which can be recommended for spice image classification.
Optimization of Direct Sales and Sales Canvasser Sales Target Monitoring With RESTful API Implementation on Web-Based Monitoring System Friwaldi, Restian Dwi; Widiono, Suyud
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8644

Abstract

RESTful API offers advantages in real-time data exchange, scalability, and ease of integration with other systems. This research aims to develop a web-based monitoring system using RESTful API to optimise the monitoring of direct sales targets and sales canvassers at. This system is built with Laravel framework and Agile method, focusing on ease of use and real-time data access. Tests were conducted using Apache JMeter with load scenarios of 500, 750, and 1000 users. The test results showed response times of 758 ms for 500 users, 762 ms for 750 users, and 880 ms for 1000 users, all below the target of 900 ms. The error rate was recorded at 0.00%, indicating the high reliability of the system. The throughput achieved was 90.30 requests per second for 500 users, 124.30 requests per second for 750 users, and 159.10 requests per second for 1000 users, exceeding the target of 150 requests per second. Recommendations for further development are the integration of mobile applications for accessibility and real-time monitoring of sales performance.
Sentiment Analysis of Online Vehicle Tax Renewal Application Users Using Support Vector Machine Algorithm Fauzy, Muhamad Ilham; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8654

Abstract

This study examines user sentiment towards online vehicle tax renewal applications by utilizing the Support Vector Machine (SVM) algorithm. The data was collected from user reviews on the Google Play Store for three major applications: New Sakpole, Sapawarga, and Timsalut. The reviews were preprocessed through steps including normalization, case folding, tokenization, and stopword removal. The SVM algorithm was then applied to classify the reviews into positive or negative sentiments. A comparative analysis was performed with K-Nearest Neighbors (KNN) and Naïve Bayes, with SVM demonstrating the best performance, achieving an accuracy of 76.5%. In addition to accuracy, metrics such as precision, recall, and F1-score were also evaluated to provide a more comprehensive assessment of the models. The results indicate that while these applications help facilitate vehicle tax payments, there remains significant user dissatisfaction, particularly related to technical issues and usability concerns. This study offers valuable insights for application developers, highlighting areas for improvement in functionality and user experience to better meet public expectations.
Game-Based Learning for Mathematics Lesson on 3rd Grade Elementary School Tanudidjaja, Miquel Jan; Pranata, Caraka Aji; ., Bernadhed
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8661

Abstract

Integrating cutting-edge strategies to improve students' learning experiences has become increasingly important in the ever-changing world of education. This study investigates how third-grade students at SD Pius Purbalingga can benefit from using game-based learning as an instructional strategy to improve their mathematical education. The study focuses on how to hold children' attention and improve their knowledge of mathematics. The main subject of this study is the effectiveness of educational games in enhancing elementary school student's understanding of mathematics. A mathematics game was created to solve this problem by actively involving pupils and reiterating key mathematical ideas. This game-based strategy aimed to create an engaged and enjoyable learning experience for third-grade pupils with acceptable cognitive capacities. The findings suggest that students who played the math game significantly increased their involvement, comprehension, and memorization of mathematical ideas. This study adds to the growing evidence supporting using educational games as useful tools in mathematics instruction. The study's findings revealed increased academic performance among students, with male students experiencing a rise of 2.4% in their overall scores. In contrast, female students demonstrated a significantly higher increase of 8.5%, indicating a more pronounced advancement in their academic performance.
Implementation of the Naive Bayes Classifier Algorithm for Classifying Toddler Nutritional Status Kamil, Muhammad Insan; Wibowo, Adityo Permana
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8669

Abstract

This research addresses the pressing issue of malnutrition among toddlers in Indonesia, aiming to classify their nutritional status using the Naive Bayes Classifier (NBC). The study utilizes a dataset comprising 958 records from Puskesmas Cilandak and categorizes nutritional status into six class labels: good nutrition, at risk of excess nutrition, excess nutrition, obesity, undernutrition, and severe malnutrition. The methodology includes data preprocessing techniques such as class weighting to tackle class imbalance and Principal Component Analysis (PCA) for effective feature extraction. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, achieving an impressive accuracy of 85.76% when class weighting is applied, which significantly enhances the recall and F1 scores for minority classes. The findings highlight the critical importance of robust preprocessing and evaluation metrics in improving machine learning models for public health applications. Furthermore, they suggest that further exploration of alternative algorithms and dataset expansion could yield more comprehensive insights into the classification of toddler nutritional status.
Comparison of Naïve Bayes Classifier and Decision Tree Algorithms for Sentiment Analysis on the House of Representatives' Right of Inquiry on Twitter Wahyuni, Putri; Romli, Moh. Ali
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8670

Abstract

This research analyzes public sentiment towards the topic of the House of Representatives' Right of Inquiry on Twitter using Naive Bayes Classifier and Decision Tree algorithms. The goal is to compare the effectiveness of the two algorithms in political sentiment analysis. . The research methodology includes data collection from Twitter, data pre-processing, sentiment classification, and result analysis. Sentiment analysis reveals the dominance of positive sentiment related to the DPR's Right of Inquiry. However, this study has limitations in terms of dataset size and depth of text-based sentiment analysis. This research contributes to a better understanding of public sentiment towards political issues in Indonesia and highlights the importance of proper algorithm selection in social media sentiment analysis.  Development suggestions include exploration of deep learning techniques, integration of multimodal analysis, data balancing (oversampling or undersampling) and improvement of pre-processing so that the model is better able to capture negative contexts. The results of the study showed excellent performance of both Naive Bayes Classifier and Decision Tree algorithms with accuracy above 95%. Decision Tree excels with an accuracy of 99%, while Naive Bayes Classifier performs better with an accuracy of 96%. The results with the Confusion Matrix test are precision 0.98, recall 1.00, and F1-Score 0.99.
Comparison of Clustering Algorithms: Fuzzy C-Means, K-Means, and DBSCAN for House Classification Based on Specifications and Price Putra, Dhendy Mardiansyah; Abdulloh, Ferian Fauzi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8671

Abstract

This study aims to compare the performance of three clustering algorithms, namely Fuzzy C-Means, K-Means, and DBSCAN, in grouping houses based on their specifications and prices. The data used includes features such as price, building area, land area, number of bedrooms, number of bathrooms, and availability of garages. The performance of these algorithms was evaluated using Silhouette Score and Davies-Bouldin Score to determine the quality of cluster separation. The results indicate that K-Means achieved the best performance with the highest Silhouette Score of 0.7702 for two clusters, followed by Fuzzy C-Means, which excelled in handling overlapping clusters. DBSCAN, while effective in detecting outliers, showed suboptimal performance for this housing dataset. These findings suggest that K-Means is the most suitable clustering method for housing data, while Fuzzy C-Means and DBSCAN can serve as alternatives depending on the data characteristics. This research is expected to assist in making the house searching and classification process more efficient and provide additional insights for developers in shaping housing market strategies.

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