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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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Real-Time Detection of Coffee Cherry Ripeness Using YOLOv11 Ilyana, Anis; Nurdin, Nurdin; Maryana, Maryana
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.v9i4.9735

Abstract

This study aims to develop a real-time coffee fruit ripeness detection system using the YOLOv11 algorithm to assist farmers in determining the optimal harvest time. The dataset comprises 302 images categorized into three ripeness levels: ripe, semi-ripe, and unripe. Model training was conducted on Google Colab with data augmentation to enhance dataset variability and prevent overfitting. After 20 epochs, the model demonstrated strong performance in the ripe category (mAP50: 0.774, Precision: 0.645, Recall: 0.812) and satisfactory results for semi-ripe fruits (mAP50: 0.695, Precision: 0.624, Recall: 0.679). However, detection performance for unripe fruits was lower (mAP50: 0.4). The system achieved an inference time of 183.4 ms per image, with fast preprocessing and postprocessing (0.5 ms each), indicating its suitability for real-time applications. While the model performs well overall, further improvement is needed in detecting unripe coffee fruits for enhanced system effectiveness.
Enhancing the Encryption Capabilities of the Generalization of the ElGamal Algorithm for Document Security Zega, Imanuel; Yulianto, Bagas Dwi
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.v9i4.9737

Abstract

The development of cryptographic algorithms that are efficient in terms of computation and resource usage, in addition to maintaining the confidentiality, integrity, and authentication of information, is driven by the growing need for digital document security. The generalization of the ElGamal, an expansion of the traditional ElGamal algorithm with more adaptable encryption features, is one algorithm with a lot of promise in this situation. The implementation of the technique of splitting the plaintext into three-digit blocks to lower the complexity of encryption per character and the use of large prime numbers to increase the algorithm's mathematical complexity are the two main ways that this study seeks to increase the algorithm's efficiency and security. It is anticipated that this method will speed up computation time and simplify the encryption process per character without compromising security. The overall findings demonstrate that, without compromising security, this method dramatically cuts down on computation time and ciphertext enlargement. Therefore, in the age of digital transformation, the findings of this study aid in the creation of contemporary cryptographic algorithms that are more flexible and effective and serve as a strategic guide when creating a strong digital data security system.
Hierarchical Clustering of Education Indicators in Papua Island: A Ward’s Method Approach Prastika, Ifa; Sari, Devni Prima
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.v9i4.9740

Abstract

Education development aims to ensure inclusive, equitable education and increase learning opportunities for all Indonesian citizens. Papua Island is still not an island with a high education level; data on education indicators indicate this in each Regency / City on the island of Papua, with a value below the national average. Identifying districts/cities is needed to improve education, so clustering is carried out using the Ward method. This research aims to group and map regencies/cities on the island of Papua based on education indicators. The results of this study are expected to be a consideration and benchmark for the government in making decisions regarding education in districts/cities on the island of Papua, considering the region's characteristics. This is an applied research with the data type used, namely secondary data on education indicators in Papua Island in 2022. Data sources are obtained from the official website of the Central Bureau of Statistics of each province on the island of Papua. Four education indicators are taken into account in this research, namely the School Participation Rate (SPR), the Gross Enrollment Rate (GER), the Net Enrollment Ratio (NER), and the Average Years of Schooling (AYS), which are then detailed into 10 variables. The cluster analysis process uses Euclidean distance and cluster validation using the Dunn Index. The results showed that 3 clusters formed. Cluster 1 consists of 27 districts/cities; this first group is classified as a high level of education. Cluster 2 consists of 7 districts/cities with a medium level of education, and Cluster 3 has eight districts/cities with a low level of education—cluster results based on the highest Dunn Index validation value of 0.414.
Dendritic ShuffleNetV2 Model for Alzheimer’s Disease Imaging Classification Riandika Fathur Rochim; I Gusti Ngurah Lanang Wijayakusuma
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.v9i4.9742

Abstract

This study investigates the integration of a dendritic neural model (DNM) into the ShuffleNetV2 architecture to enhance Alzheimer’s stage classification from MRI scans. The proposed “Dendritic ShuffleNetV2” retains the original network’s computational cost (0.31 GFLOPs) while incurring only a 1.6% increase in parameter count (from 2.48 M to 2.52 M) and achieves faster convergence (15 epochs versus 22 epochs). Experiments were conducted on a four‑class Alzheimer’s MRI dataset comprising Non‑Demented, Very Mild Demented, Mild Demented, and Moderate Demented categories. Compared to the baseline ShuffleNetV2, the Dendritic variant yielded an average accuracy improvement of 0.79%, with corresponding gains of approximately 0.8% in weighted precision, recall, and F1‑score. Confusion matrix analysis revealed persistent overlap between the Very Mild and Mild Demented classes, although overall discrimination—particularly for the majority and early‑stage classes—remained robust. Training stability was maintained without significant overfitting.
Eye Disease Classification Using EfficientNet-B0 Based on Transfer Learning Pratiwi Tentriajaya, I Dewa Ayu Pradnya; Wijayakusuma, I Gusti Ngurah Lanang
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.v9i4.9743

Abstract

This study focuses on developing and evaluating a deep learning approach employing EfficientNet-B0 based on transfer learning to classify retinal fundus images into four categories: Cataract, Diabetic Retinopathy, Glaucoma, and Normal. The model was trained using a retinal image dataset and demonstrated stable training performance, indicated by a consistent decrease in both training and validation loss without signs of overfitting. The training accuracy reached 92%, while the validation accuracy ranged between 94–95%. Model performance evaluation using a confusion matrix and classification report showed excellent classification results, particularly for the Diabetic Retinopathy class, with an F1-Score of 0.98. The Cataract and Normal classes also achieved high performance, with F1-Scores of 0.94 and 0.92, respectively. However, classification accuracy slightly declined for the Glaucoma class, which experienced some misclassification with the Normal class. Overall, the model achieved a classification accuracy of 94% on the test dataset, indicating good generalization capability. These findings suggest that the model holds strong potential for implementation in automated medical image-based diagnostic support systems. Nonetheless, performance improvement in classes with relatively higher misclassification rates is still required to ensure model reliability in clinical practice.
Implementation of Convolutional Neural Networks (CNN) for Breast Cancer Detection Using ResNet18 Architecture Siden, Hagia Sofia; Wijayakusuma, I Gusti Ngurah Lanang
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.v9i4.9746

Abstract

Early detection of breast cancer is crucial for improving patient survival rates. This study implements a Convolutional Neural Network (CNN) architecture based on ResNet18 using a transfer learning approach to classify breast ultrasound (USG) images into three categories: normal, benign, and malignant. The dataset, comprising 1,578 grayscale images collected from Baheya Hospital in Egypt, underwent preprocessing steps including image conversion, normalization, and augmentation. The ResNet18 model was fine-tuned using selective layer unfreezing to better adapt to the medical imaging domain. Evaluation was conducted using stratified 5-fold cross-validation and assessed with accuracy, precision, recall, F1-score, and AUC metrics. The best results were achieved by fine-tuning layer2, layer3, and the fully connected layer, yielding 95% accuracy, a macro F1-score of 0.93, and an AUC of 0.9906. The findings demonstrate that ResNet18, when properly fine-tuned with transfer learning, delivers high performance in breast cancer detection via ultrasound and holds strong potential as a reliable clinical decision-support tool.
Comparative Analysis of ResNet50V2, ResNet152V2, and MobileNetV2 Architectures in Monkeypox Classification Mustaqim, Habi Jiyan; Ozzi Suria
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.v9i4.9756

Abstract

Convolutional Neural Networks (CNN) are recognized for their high accuracy in image classification, but large-scale datasets and significant computer resources are needed to train them from scratch, though. Transfer learning offers a practical solution by leveraging pre-trained models to accelerate training even when data is limited. Although CNNs have been widely applied to skin disease classification, specific evaluations of architectures such as ResNet50V2, ResNet152V2, and MobileNetV2 for monkeypox image classification remain scarce. Therefore, this study aims to comprehensively compare the effectiveness and trade-offs of these architectures in detecting monkeypox through transfer learning. The evaluation focuses on balancing accuracy and computational efficiency across stages, including data collection, preprocessing, model design, training, and testing. The dataset, obtained from Kaggle, consists of 2,310 images across four classes: monkeypox, chickenpox, measles, and normal. Transfer learning was implemented using fine-tuned weights from ImageNet. According to the results, ResNet152V2 needed the most training time but had the lowest loss and the greatest validation accuracy (98.28%). ResNet50V2 maintained a good compromise between accuracy (97.84%) and training efficiency, while MobileNetV2 yielded the best overall classification metrics (97.86% for accuracy, precision, recall, and F1-score), indicating strong generalization. These findings highlight the distinct strengths of each model, offering insights into architecture selection based on specific operational constraints and goals.
Visual Segmentation and Classification of Coffee Beans After Roasting Firdaus Alamanda; Rudy Susanto; Wiji Lestari
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.v9i4.9758

Abstract

This research aims to develop an image-based system for segmenting and classifying coffee beans after roasting using deep learning. A U-Net architecture was applied to isolate coffee beans from the background with high spatial accuracy, achieving a mean Intersection over Union (IoU) of 0.8833 and Dice Coefficient of 0.9375. The segmented images were then classified into six roasting levels green, light, light to medium, medium, medium to dark, and dark using a modified ResNet-50 model, which reached an overall classification accuracy of 86%. The system demonstrates strong performance for clear categories but shows overlapping predictions for visually similar classes such as “medium” and its neighboring levels, indicating that boundaries between roasting stages can be ambiguous. This study provides an objective and automated alternative for roast quality inspection, reducing reliance on subjective human assessment. However, to meet industrial standards, further improvements are needed, such as integrating additional image features or ensemble models to increase discrimination power. This two-stage system serves as a promising foundation for future developments in automated coffee quality control.
Scientific Paper Recommendation System: Application of Sentence Transformers and Cosine Similarity Using arXiv Data Putra, Ananda Pannadhika; Singgih Putri, Desy Purnami; Wiranatha, AA.Kt.Agung Cahyawan
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.v9i4.9766

Abstract

Searching for relevant scientific literature faces complex challenges due to the proliferation of academic publications. This research develops a semantic similarity-based scientific paper recommendation system by utilizing Sentence Transformer (all-MiniLM-L6-v2 model) and cosine similarity algorithm on arXiv dataset (15,504 papers in Computer Science). The system is implemented as a Streamlit-based interactive web application that accepts user queries and recommends related papers based on semantic similarity. Performance evaluation using Precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG) metrics showed that embedding text from the Introduction section without pre-processing yielded the best performance (NDCG: 0.7590; MAP: 0.6960; MRR: 0.7254), outperforming Abstract-based or text combination approaches. A user test of 45 respondents confirmed the effectiveness of the system: 95.5% expressed satisfaction with the relevance of the recommendations, and 93.3% confirmed a significant reduction in manual search time. The findings prove that retaining the raw text structure in the Introduction is optimal for semantic representation. Development suggestions include multidomain dataset expansion and transformer model optimization for accuracy improvement.
Clustering of the Air Pollution Standard Index (ISPU) in the Province of DKI Jakarta Using the CLARANS Algorithm Azzahra, Adelia Ramadhina; Nabila, Nasywa Azzah; Idhom, Mohammad; Trimono, Trimono
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.v9i4.9783

Abstract

Air pollution has become a serious global issue. According to IQAir's 2024 report, DKI Jakarta ranked 10th among cities with the worst air quality worldwide, indicating that air pollution in DKI Jakarta has reached a concerning level. This research uses the CLARANS algorithm to cluster daily air quality in DKI Jakarta based on pollution parameters. CLARANS is chosen due to its advantages in terms of big data processing efficiency, outlier resistance, and medoid search capability. The novelty of this research lies in the application of CLARANS to overcome the limitations of clustering algorithms in previous research. This research comprises several stages, including data understanding, data preprocessing, building the CLARANS model, and evaluation using the silhouette score. The CLARANS clustering result using the most optimal parameter combination and k = 3 demonstrates well-separated cluster boundaries, with an overall average silhouette score across all regions and years of 0.6398. The analysis results indicate that air pollution in DKI Jakarta tends to worsen in 2024. Jakarta Barat and Jakarta Pusat are predominantly affected by PM10, CO, and O₃ pollution, whereas Jakarta Selatan and Jakarta Utara are more influenced by SO₂ and NO₂ pollution. On the other hand, air pollution in East Jakarta shows a balanced dominance from both pollutant categories.

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