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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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A Roasted Coffee Bean Identification Using ResNet50 Model Aqsel, Aryasatya Muhammad; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11460

Abstract

Identification of coffee types after roasting is a major challenge because visual changes make the appearance of coffee beans diverse. Subjective assessment methods are time-consuming, so digital image processing and CNN techniques show potential to solve complex classification problems. This study develops a ResNet50-based CNN model to identify four types of coffee beans (Robusta, Arabica, Excelsa, and Liberica) after roasting and analyzes the effectiveness of pre-processing and augmentation techniques in improving classification performance. The research employed quantitative methodology with three phases: data collection, pre-processing with augmentation, and CNN implementation. The dataset consisted of 2,000 coffee bean images, with 500 images for each class: Arabica, Excelsa, Liberica, and Robusta, ensuring balanced representation across all coffee varieties  from a local Indonesian coffee supplier, using smartphone. Preprocessing included normalization and resizing, while augmentation comprised various image transformation techniques. Model performance was evaluated using performance metrics. Results showed an overall accuracy of 94.50%, with Liberica demonstrating exceptional performance (100% precision, 98% recall). Robusta achieved 97% precision and 98% recall, while Arabica showed 86.5% precision with 96% recall. Excelsa achieved 95.6% precision and 86% recall. The model successfully classified 378 out of 400 test samples, with Excelsa representing the primary classification challenge due to visual similarity with other varieties post-roasting. Analysis of misclassifications revealed improved distinction between coffee varieties, with the model demonstrating strong generalization capabilities across all classes. The ResNet50 model successfully identified coffee beans with good accuracy but experienced difficulty distinguishing varieties with similar visual characteristics. Future work should explore improved methods and larger datasets for accuracy.
Analysis of Naive Bayes Algorithm for Lung Cancer Risk Prediction Based on Lifestyle Factors Vabilla, Sheila Anggun; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11463

Abstract

Lung cancer is one of the types of cancer with the highest mortality rate in the world, which is often difficult to detect in the early stages due to minimal symptoms. This study aims to build a lung cancer risk prediction model based on lifestyle factors using the Gaussian Naive Bayes algorithm. Data fit is addressed using the Synthetic Minority Over-sampling Technique (SMOTE), and feature selection is carried out using the Mutual Information. The dataset used consists of 1000 patient data with 24 features related to lifestyle and environmental factors. Model validation is carried out using 5-fold Stratified Cross Validation, and evaluated based on accuracy, precision, recall, and confusion matrices. The results show that the application of SMOTE successfully increases the model accuracy to 91.00% with high precision and recall values in all risk classes (Low, Medium, High). The features "Passive Smoker" and "Coughing up Blood" are identified as the most influential factors in the prediction. The results of this study indicate that the combination of Gaussian Naive Bayes with SMOTE and Mutual Information is able to produce an accurate prediction model.
Classification of Cat Skin Diseases Using MobileNetV2 Architecture with Transfer Learning Saputra Aji, Dian; Ashari, Wahid Miftahul; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11469

Abstract

Skin diseases in cats often present similar visual symptoms across different conditions, making early and accurate diagnosis challenging for pet owners and veterinarians. This study develops a classification model for cat skin diseases: Fungal Infection, Flea Infestation, Scabies, and Healthy, using the MobileNetV2 architecture with a transfer learning approach. A total of 1,600 RGB images were collected from public datasets and divided into 1,280 training and 320 validation samples. The dataset underwent preprocessing, normalization, and data augmentation techniques such as rotation, shear, zoom, and flipping to enhance model generalization and reduce overfitting. Several experiments were conducted to analyze the impact of input size and learning rate adjustments on model performance. The optimal configuration was achieved using an input size of 224×224 pixels, a learning rate of 0.001, and augmentation applied to the training data. The resulting model achieved a validation accuracy of 91.8%, with an average precision, recall, and F1-score of 91%, demonstrating balanced performance across all classes. These results indicate that the MobileNetV2 architecture, combined with appropriate hyperparameter tuning and augmentation, provides a reliable and computationally efficient method for automatic identification of cat skin diseases. This approach can support early diagnosis, improve animal welfare, and serve as a foundation for the development of practical veterinary diagnostic applications.
Improving News Text Classification Using a Hybrid C5.0-KNN Model Wikarsa, Liza; Ngenget, Algy; Tumewu , Andrew; Kalempouw , Miracle; Oley , Edgard
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11478

Abstract

In the digital era, the overwhelming volume of online news far exceeds readers’ ability to manually filter information, necessitating automated text classification. However, achieving high classification accuracy remains challenging, especially in low-resource languages like IndonesianThe C5.0 decision tree and K-Nearest Neighbors (KNN) offer complementary strengths but have not yet been jointly utilized for Indonesian news classification; therefore, this study proposes a hybrid C5.0–KNN model designed to enhance news classification performance. A dataset of 1.700 articles was collected from four Indonesian online news, namely CNN Indonesia, Okezone, Tribun Jakarta, and Tribun Jabar, covering five topical categories, namely economy/ekonomi, technology/teknologi, sport/olahraga, entertainment/hiburan, or life style/gaya hidup). The data underwent preprocessing and TF-IDF weighing before classification with the hybrid model. In this approach, C5.0 first generates interpretable decision rules, and KNN then refines borderline cases, combining rule-based and instance-based methods. The findings revealed that the hybrid model achieved a highest accuracy of 0.8847 (using 25% test data and k=5), outperforming standalone C5.0 (0.7426) and KNN (0.8735). Notably, it attained 100% recall for “sport/olahraga” and an F1-score of 0.89 for “entertainment/hiburan”. These results demonstrate the model’s novelty, efficiency, and strong potential for real-world news classification in low-resource language contexts, offering practical value for journalists, analysts, and media monitoring systems.
A Two-Stage Braille Recognition System Using YOLOv8 for Detection and CNN for Classification Setiawan, Tan Valencio Yobert Geraldo; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11483

Abstract

Automatic recognition of Braille characters remains a challenge in the field of computer vision, especially due to variations in shape, size, and lighting conditions in images. This research proposes a two-stage system to detect and recognize Braille letters in real time using a deep learning approach. In the first stage, the YOLOv8 model is used to detect the position of Braille characters within an image. The detected regions are then processed in the second stage using a classification model based on the MobileNetV2 CNN architecture. The dataset used consists of 7,016 Braille character images, collected from a combination of the AEyeAlliance dataset and annotated data from Roboflow. To address the class imbalance problem—particularly for letters T to Z which had fewer samples—oversampling and image augmentation techniques were applied that makes the final combined dataset contained approximately 7,616 images. The system was tested on 1,513 images and achieved strong results, with average precision, recall, and F1-score of 0.98, and an overall accuracy of 98%. This two-stage method effectively separates detection and classification tasks, resulting in an efficient and accurate Braille recognition system suitable for real-time applications.
Automatic License Plate Detection System with YOLOv11 Algorithm Kurniawan, Nicholas Alfandhy; Sari, Christy Atika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11484

Abstract

The increasing number of motor vehicles in Indonesia demands technological solutions to enhance efficiency and security, particularly in automatic license plate recognition systems. This study aims to develop an automatic license plate detection system using the YOLOv11 algorithm to detect license plates and their characters in real-time. The research methodology includes collecting datasets from Kaggle, RoboFlow, and manual acquisition, followed by annotation, data augmentation, model training, and interface development using Tkinter and OpenCV. The dataset comprises 4000 license plate images and 3000 characters images, divided for training, validation, and testing. Evaluation results demonstrate strong model performance, with precision of 0.891, recall of 0.911, mAP50 of 0.906, and mAP50-95 of 0.631 for license plate detection, and precision of 0.889, recall of 0.912, mAP50 of 0.907, and mAP50-95 of 0.629 for character detection. Real-time testing showed that 12 out of 12 license plates were successfully recognized, influenced by lighting conditions, distance, and plate orientation. This study produced an efficient system for parking security, with potential for further development.
Identification of Latent Dimensions of Digital Readiness and Typology of Districts/Cities in Indonesia Using PCA and K-Means Clustering Sari, Jefita Resti; Fahira, Fani; Zahra, Latifah; Fitrianto, Anwar; Alifviansyah, Kevin
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11487

Abstract

Digital transformation is a key agenda in Indonesia’s national development that requires balanced readiness across regions. However, the level of digital readiness among districts and cities still varies widely, highlighting the need for a typology that can comprehensively describe existing disparities. This study aims to identify the latent dimensions of digital readiness and to develop a regional typology of Indonesian districts/cities using Principal Component Analysis (PCA) and K-Means clustering. The data were obtained from the 2024 Indonesian Digital Society Index (IMDI), which consists of four pillars—Infrastructure and Ecosystem, Digital Skills, Empowerment, and Employment—with ten sub-pillars. PCA reduced these correlated indicators into two main latent components, namely Digital Capacity and Participation and Digital Infrastructure Foundation, which together explain 70.4% of the total variance. Cluster validation using the Silhouette Score and Davies–Bouldin Index (DBI) showed that K = 2 yielded the best internal validity (Silhouette = 0.402; DBI = 0.906), but a three-cluster configuration (K = 3) was adopted to obtain a more interpretable typology of high-, medium-, and low-readiness regions (Silhouette = 0.346; DBI = 1.007). Spatial mapping reveals that high-readiness districts are concentrated in Java, Bali, and parts of Sumatra, whereas low-readiness areas dominate eastern Indonesia. These findings confirm persistent digital inequality across regions and provide a quantitative basis for targeted policy interventions, including infrastructure development, digital literacy programs, and innovation ecosystem strengthening, to support an inclusive digital transformation in Indonesia.
Evaluating the Impact of Random Over Sampling on IndoBERT Performance for Indonesian Sentiment Analysis Alfinsyah, Dimas Ramadhan; Hartato, Bambang Pilu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11488

Abstract

Sentiment analysis is a prominent research area in natural language processing (NLP). For the Indonesian language, IndoBERT has emerged as a leading model due to its competitive performance. However, its effectiveness is strongly influenced by balanced class distribution. A common challenge arises because user reviews on digital platforms, such as the Google Play Store, often exhibit imbalanced classes. This study investigates the effectiveness of the Random Over Sampler (ROS) technique in improving IndoBERT’s performance under imbalanced data conditions. The dataset consists of 13,821 user reviews of the IDN App collected from the Google Play Store between 2015 and July 2025. Prior to modeling, data preprocessing was performed, including punctuation removal, case folding, stopword removal, tokenizing, normalization, and stemming to ensure textual consistency. Reviews were categorized into two sentiment classes: positive (3–5 stars) and negative (1–2 stars). Two experimental scenarios were conducted: (1) IndoBERT without ROS and (2) IndoBERT with a balanced dataset using ROS. Model performance was evaluated using accuracy, precision, recall, and F1-score, with data split into 70% training, 20% validation, and 10% testing. Results showed a significant improvement after ROS implementation: 94.55% accuracy, 94.61% precision, 94.53% recall, and 94.54% F1-score. Confusion matrix analysis indicated improved classification of the minority class, reducing the error rate by 49%. However, learning curve analysis revealed potential overfitting due to ROS. Further research is needed to optimize ROS strategies for better performance and generalization.
Sentiment Analysis of the TPKS Law on Twitter: A Comparative Study of Classification Algorithm Performance Mawar, Heni Sapta; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11503

Abstract

The enactment of Law Number 12 of 2022 concerning the Crime of Sexual Violence (UU TPKS) has sparked significant public discourse on social media, especially on Twitter. This study aims to identify the most effective classification algorithm for analyzing public sentiment regarding the UU TPKS. A total of 2,351 Indonesian-language tweets were collected, preprocessed, and manually labeled into positive and negative sentiments. The Term Frequency–Inverse Document Frequency (TF-IDF) method was used for feature extraction, followed by classification using six algorithms: Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The evaluation results show that SVM and Random Forest achieved the highest accuracy of 85.35%, precision of 0.85, recall of 0.85, and F1-score of 0.83, outperforming other models in handling high-dimensional and imbalanced data. These results demonstrate that the combination of TF-IDF with SVM and Random Forest provides an effective and reliable approach for sentiment analysis of Indonesian-language social media data, particularly in evaluating public responses to socio-legal policies such as the UU TPKS.
Segmentation of Generation Z Spending Habits Using the K-Means Clustering Algorithm: An Empirical Study on Financial Behavior Patterns Sylvester, Gunawan; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11506

Abstract

Generation Z, born between 1997 and 2012, exhibits unique consumption behaviors shaped by digital technology, modern lifestyles, and evolving financial decision-making patterns. This study segments their financial behavior using the K-Means clustering algorithm applied to the “Generation Z Money Spending” dataset from Kaggle. In addition to K-Means, alternative clustering algorithms—K-Medoids and Hierarchical Clustering—are evaluated to compare their effectiveness in identifying behavioral patterns. The dataset consists of 1,700 individuals with 15 numerical spending attributes, including rent, food, entertainment, education, savings, and investments. All data were normalized using Min-Max Scaling prior to clustering. The analysis identifies six distinct clusters, ranging from highly consumption-oriented groups (with higher spending on entertainment and online shopping) to financially conscious groups prioritizing savings and investments. A quantitative approach was used, incorporating exploratory data analysis, correlation testing, and the Elbow Method to determine the optimal number of clusters. The optimal cluster count of six is supported by a Davies-Bouldin Index (DBI) score of 2.412, indicating acceptable but improvable cluster separation. Each cluster displays unique characteristics: Cluster 0 (average age 20.6) focuses on savings and investments with moderate essential spending; Cluster 1 (average age 23.6) prioritizes education and higher rent expenses; Cluster 2 (average age 20.3) is digitally oriented, spending more on online shopping and entertainment; Cluster 3 (average age 25.2) demonstrates financial stability with balanced expenditures; Cluster 4 (average age 24.9) emphasizes savings and investments with moderate living costs; and Cluster 5 (average age 24.96) combines strong saving habits with balanced essential and leisure spending. Model performance was assessed using the Davies-Bouldin Index, Silhouette Score, and Calinski-Harabasz Index to ensure comprehensive evaluation of cluster quality. The findings highlight the diverse spending behaviors of Generation Z, offering valuable insights for businesses, policymakers, and financial service providers to develop targeted strategies aligned with each segment’s characteristics.

Page 66 of 70 | Total Record : 695

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