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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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Implementation of Deep Learning with Multilayer Perceptron (MLP) for Heart Disease Prediction Using the SMOTE-ENN Technique Erliyan Redy Susanto; Erik Saputra
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9337

Abstract

Heart disease is a leading cause of global mortality, with its prevalence increasing annually. This study aims to develop a heart disease prediction model using a Multilayer Perceptron (MLP) combined with the SMOTE-ENN resampling technique to address data imbalance issues. The dataset used was obtained from the UCI Machine Learning Repository and includes patients' clinical and demographic features. The initial dataset consisted of [number of data] records, with an imbalanced class distribution between patients with and without heart disease. After applying SMOTE-ENN, the class distribution became more balanced, allowing the model to learn patterns more effectively. The MLP model was designed with two hidden layers comprising 64 and 32 neurons, respectively, using the ReLU activation function in the hidden layers and a sigmoid function in the output layer. Evaluation results showed that the model achieved an accuracy of 89.47%, precision of 77.78%, recall of 100%, and an F1-score of 87.5%. To validate the effectiveness of SMOTE-ENN, comparisons were made with other methods such as SMOTE and undersampling, as well as baseline models like Logistic Regression and Decision Tree. The results demonstrate that SMOTE-ENN outperforms other techniques in handling class imbalance, leading to better overall model performance.
Web-Based Makeup Recommendation System Using Hybrid Filtering Utami, Putu Mia Setya; Trisna, I Nyoman Prayana; Vihikan, Wayan Oger
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9339

Abstract

The increasing use of makeup products in the modern era, driven by evolving beauty trends and e-commerce accessibility, presents challenges in selecting products suited to individual skin types and conditions. A recommendation system addresses this issue by enhancing selection efficiency. This study explores the implementation of Content-Based Filtering (CBF) using TF-IDF and Cosine Similarity, Collaborative Filtering (CF) with Singular Value Decomposition (SVD), and a Hybrid Filtering approach integrating both methods through Weighted Hybrid techniques. The system's performance is evaluated across two user scenarios: new users (without prior ratings) and old users (with rating history). The evaluation method includes Precision, Normalized Discounted Cumulative Gain (NDCG), and accumulation of the best scenario based on user opinion. Results show that Hybrid Filtering outperforms CBF and CF, with notable differences between user groups. For new users, 32% prefer Scenario 1, which emphasizes CBF, achieving 80.8% Precision and 89.73% NDCG. For old users, 23% favor Scenario 2, attaining 83.4% Precision and 90.31% NDCG.
Integrating the CNN Model with the Web for Indonesian Sign Language (BISINDO) Recognition Kelana, Enisda Libra; Anshori Prasetya, Muhammad Riko; ., Mambang; Zulfadhilah, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9345

Abstract

Effective communication is challenging for deaf individuals in Indonesia, most of whom use Indonesian Sign Language (BISINDO). Sign Language Recognition (SLR) can bridge this communication gap. While Convolutional Neural Networks (CNNs) show high potential for SLR, their practical accessibility remains limited. This research aims to develop a CNN architecture for recognizing BISINDO alphabet signs from static images (still images) and integrate it into an accessible web platform. Using a static vision-based approach, a CNN model was trained on a public dataset (312 images, 26 classes) following standard pre-processing including data augmentation. The model was subsequently integrated into a web interface using Python and the Gradio library. Results demonstrated strong model performance, with validation accuracy reaching 97.44% and a macro-average F1-score of approximately 97.12%. However, classification challenges were identified for visually similar signs ('M' and 'N'). The resulting integrated web application proved functional, exhibited low prediction latency, and showed cross-platform compatibility. This study successfully demonstrates the development of an accurate DL model for static BISINDO alphabet recognition and its practical implementation via a web platform. This contributes to reducing the accessibility gap in SLR technology. Future research is recommended to utilize larger, more varied datasets and explore dynamic sign recognition.
Comparative Analysis of VGG16 and ResNet50 Model Performence in Cardiac ECG Image Classification Rizaqi, Hanif; Tahyudin, Imam
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9350

Abstract

This study systematically evaluates and compares the effectiveness of two deep learning architectures, VGG16 and ResNet50, in automating electrocardiogram (ECG) image classification for cardiac condition diagnosis. The dataset was obtained from a public source and consists of 2,898 color ECG images converted from raw signals, categorized into four classes: Abnormal Heartbeat, Myocardial Infarction, Normal Individual, and History of Heart Attack. The data underwent preprocessing steps including resizing to 224×224 pixels, pixel normalization to a 0–1 range, label encoding, one-hot encoding, and an 80:20 split for training and testing. Transfer learning was applied using feature representations from the VGG16 and ResNet50 models, employing the Adam optimizer and categorical cross-entropy loss function. To enhance training efficiency and prevent overfitting, early stopping was implemented based on validation loss performance. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that VGG16 achieved 95% accuracy with a loss of 0.1522, precision of 95%, recall of 94%, and F1-score of 94%. In contrast, ResNet50 attained 81% accuracy with a loss of 0.5730, precision of 82%, recall of 79%, and F1-score of 80%. These findings indicate that, within the context of this study, VGG16 consistently outperformed ResNet50 across all evaluation metrics in the ECG image classification task. Therefore, the application of transfer learning using the VGG16 model demonstrates strong potential as an effective approach for AI-based ECG image classification systems.
Comparison of Data Normalization Techniques on KNN Classification Performance for Pima Indians Diabetes Dataset Dimas Pratama, Yohanes; Salam, Abu
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9353

Abstract

This study analyzes the comparison of data normalization techniques in the K-Nearest Neighbors (KNN) model for diabetes classification using the Pima Indians Diabetes dataset. The three normalization techniques evaluated are Min-Max Scaling, Z-Score Scaling, and Decimal Scaling. After preprocessing, such as handling missing values and removing duplicates, as well as feature selection using the Random Forest method, the features removed include SkinThickness, Insulin, Pregnancies, and BloodPressure. The evaluation was carried out using accuracy, precision, recall, F1-Score, specificity, and ROC AUC metrics. The results show that Min-Max Scaling provides a significant improvement in all metrics, with the highest accuracy of 0.8117 and ROC AUC of 0.8050. Z-Score Scaling provides good results, but not as good as Min-Max Scaling. Decimal Scaling shows the lowest performance. Statistical tests using Paired T-Test show significant differences between Min-Max Scaling and without normalization on all metrics (P-Value <0.05), while Z-Score Scaling and Decimal Scaling are only significant on some metrics, with P-Values of 0.08363 and 0.43839 respectively for accuracy and ROC AUC. Overall, Min-Max Scaling proved to be the best normalization method for improving KNN performance in diabetes classification.
Optimizing Customer Data Security in Water Meter Data Management Based on RESTful API and Data Encryption Using AES-256 Algorithm Adrianto, Syahrul; Agus Herlambang, Bambang; Renaldy, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9358

Abstract

Good, accurate and secure data management is certainly one of the main needs for companies that provide public services. This research aims to develop a web application-based information system to manage customer water meter data at a regional water company in Semarang. This system was built using the RESTful API architecture using the PHP programming language framework, namely Laravel and the development of web page displays using the Javascripts framework. The data used is the original database managed by the company every month which is managed using a database management system by meter reader officers. To increase the security of customer data, a cryptographic algorithm is used, namely the Advanced Encryption Standard (AES) algorithm with a 256-bit key length to secure data that is considered sensitive and contains high privacy. This system is intended for meter readers to update customer water meter data per month in an efficient and structured manner. This research uses a Research and Development (R&D) based software development method with system testing using black-box testing method to ensure application functionality and data exposure testing method to ensure data security in the database. The test results show that the system successfully manages customer water meter data in realtime per data sent and secures customer data.
Hamming Code in JPEG Image Steganography within the Discrete Cosine Transform Domain Zulfikar, Dian; Hermanto, Hermanto
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9387

Abstract

This study proposes a novel JPEG image steganography method that combines Least Significant Bit (LSB) embedding in the Discrete Cosine Transform (DCT) domain with Hamming Code (2k − 1, 2k − k − 1) to minimize the number of modified DCT coefficients. Experiments were conducted on images with varying resolutions (512×512, 1024×1024, 2048×2048) and JPEG quality factor of 75, where PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) parameters are used to measure the quality and similarity between the original image and the stego image. The method achieved an embedding capacity of up to 524,288 bits, with an average PSNR of 39–41 dB and SSIM above 0.98. Compared to conventional techniques such as JSteg and F5, the proposed approach demonstrates improved embedding capacity, better visual quality, and higher resistance to statistical steganalysis, making it suitable for secure and efficient data hiding applications.
Implementation of CNN Algorithm for Indonesian Hoax News Detection on Online News Portals Hati, Clifansi Remi Siwi; Sulistiani, Heni
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9403

Abstract

The Spread of hoax news in the Industrial Revolution 4.0 era has occurred in the world’s society, including Indonesia. Therefore, an effective method is needed to detect it. The purpose of this research is to apply deep learning with the Convolutional Neural Network (CNN) algorithm in detecting text-based hoax news in Indonesian. The dataset is taken from Kaggle, which has been scraped from CNN Indonesia, Tempo, and Turnbackhoax, which will be labeled as valid and hoax. The implementation of the dataset goes through several processes that include input dataset, data pre-processing using pre-trained embedding GloVe, data processing, model evaluation, also model deployment into the simple web. Data is divided into 80% training data and 20% test data for CNN model development. The results show that the CNN model can achieve high accuracy in detecting hoaxes with training accuracy values reaching 99.65% and validation accuracy reaching 99.88% with a loss of 0.0477 and 0.0435, which means that the model is effective in classifying text-based hoax news to the maximum. The model is evaluated using a confusion matrix, precision, recall, and heatmap as a visualization of results. For further research, it is recommended to increase additional variations for training data so the model can understand patterns well.
Development of ViScan: A Mobile Application for Skin Cancer Detection Using Ionic Framework and YOLOv10x Haresta, Alif Agsakli Haresta; Cinantya Paramita; William Dwiputra Tjahtjono
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9426

Abstract

Skin cancer is a common global health issue, with the number of cases continuing to rise worldwide. Early detection is crucial for improving patient outcomes, but traditional detection methods often require significant time, cost, and medical expertise. To address this challenge, this research focuses on developing a mobile application that leverages deep learning, specifically the YOLOv10x model, to enable fast and accurate detection of skin lesions. This application aims to provide an easy-to-use platform for self-monitoring skin health, particularly for individuals in remote areas with limited access to medical facilities. The system uses the HAM10000 dataset, which consists of a diverse collection of dermoscopy images of skin lesions, to train the YOLOv10x object detection model for real-time detection on mobile devices. By leveraging TensorFlow.js and Node.js, the model processes skin images and provides real-time results with precision and efficiency. The mobile application, developed using the Ionic Framework, ensures cross-platform compatibility and a responsive, intuitive user interface. System performance was evaluated using key metrics such as Precision (84.2%), Recall (86.3%), mAP (89.2%), and F1 Score (85.2%), demonstrating its effectiveness in early skin cancer detection. The potential of this application extends beyond detection, contributing to society by raising awareness and offering an accessible, low-cost screening solution.
HANA: An AI Chatbot for Islamic Jurisprudence on Menstruation using SBERT and TF-IDF Masuzzahra, Tsaura Rafah; Khothibul Umam; Hery Mustofa; Maya Rini Handayani
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9449

Abstract

The advancement of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), has opened new opportunities for religious technological innovation, especially in addressing practical Islamic jurisprudence issues such as menstruation (fiqh haid). This research proposes and implements HANA, an AI chatbot developed for Telegram, utilizing a hybrid approach combining Term Frequency-Inverse Document Frequency (TF-IDF) and Sentence-BERT (SBERT) models. A curated dataset of over 1000 question-answer pairs from classical and contemporary Islamic literature was used, primarily based on the Shafi'i school of thought. The chatbot matches user queries through a two-stage retrieval: initial keyword matching via TF-IDF and deeper semantic matching via SBERT embeddings. Evaluations were conducted by comparing TF-IDF, SBERT, and hybrid approaches using cosine similarity, precision, recall, and F1-score metrics, focused on top-1 retrieval accuracy. HANA achieved an average cosine similarity score of 0.6581 and a semantic relevance rating of 87% based on expert validation, while User Acceptance Testing (UAT) involving 15 respondents indicated 86.7% satisfaction. Although the system is deployed as a proof-of-concept on Google Colab without persistent hosting, it demonstrates the viability of lightweight AI chatbots for Shariah consultation services. Future improvements include multi-turn conversation handling and integration with large language models for better context understanding. This research contributes to expanding NLP applications within techno-dakwah initiatives, providing a scalable approach to enhance women's access to Islamic jurisprudence knowledge.

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