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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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Myopia Identification by Fundus Photo Image Classification Using Convolutional Neural Network Laksono, Giffari Ilham; Winarno, Sri
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10624

Abstract

Myopia is a significant vision problem worldwide, requiring early detection to prevent further damage. This study aims to develop an image classification model using a Convolutional Neural Network (CNN) to identify myopia based on fundus images. The dataset used was 124,749 fundus images, divided into 80% for training and 20% for testing. The applied architecture was EfficientNetB0, chosen for its ability to achieve high performance with efficient computation. Experimental results showed that this model successfully achieved a classification accuracy of 97% in distinguishing between myopic and non-myopic images. These findings demonstrate the potential of CNN, especially EfficientNetB0, as a diagnostic tool for automatic myopia identification, which can accelerate the detection process and improve the accuracy of clinical diagnosis.
Method Design of an IoT-Based Automatic Pest Repellent System Prototype for Agriculture Kamaruzzaman, Hilda Zulfira; Ula, Munirul; Meiyanti, Rini
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10632

Abstract

Indonesia, as an agricultural country, still faces serious challenges in the farming sector, particularly pest attacks from birds and insects that significantly reduce rice productivity and may lead to crop failure. The use of traditional methods and chemical pesticides is considered ineffective and has negative impacts on health and the environment. This study aims to design a prototype of an automated pest repellent system for agriculture based on the Internet of Things (IoT) that is environmentally friendly, energy-efficient, and easy to operate by local farmers. The research method employed a prototyping approach, which includes problem identification, hardware and software design, testing, and system evaluation. The device consists of a NodeMCU ESP32 microcontroller, a PIR sensor to detect pest movement, relay, ultrasonic speaker, electric net, and solar panel as the main power source. Testing on a miniature rice field model showed that the system could detect pest movement at a distance of approximately 5 meters and automatically activate the ultrasonic speaker with a range of 50–100 meters to repel birds, and the electric net to catch insects at night. Energy consumption is primarily supplied by the solar panel, and a fully charged battery can power the system for about 3 hours without sunlight. The detection success rate reached more than 85% with consistent actuator response. This system has proven to reduce pesticide dependency, is environmentally friendly, and has the potential to increase rice farming efficiency.
Impact of SMOTE and ADASYN on Class Imbalance in Metabolic Syndrome Classification Using Random Forest Algorithm Nurhayati, Lutfiana Deka; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10657

Abstract

Metabolic Syndrome is a collection of medical conditions that can increase the risk of stroke, cardiovascular disease, and type 2 diabetes. Early detection of this condition requires a machine learning model capable of accurate classification to support timely treatment. However, class imbalance in data often hampers the performance of classification algorithms, particularly in recognizing minority classes, namely individuals diagnosed with Metabolic Syndrome. This study aims to analyze the effect of applying the SMOTE and ADASYN data balancing techniques in classifying Metabolic Syndrome using the Random Forest algorithm. These algorithms were chosen for their ability to produce accurate predictions, although their performance can decline when faced with imbalanced class distributions. The results showed that the model without data balancing techniques achieved 86% accuracy with a minority class recall of 75%. The application of SMOTE increased accuracy to 91% and recall to 93%, while ADASYN achieved 92% accuracy and a minority class recall of 95%. These findings indicate that the ADASYN technique combined with the Random Forest algorithm provides significant performance improvements in the classification of Metabolic Syndrome on imbalanced data.
Application of Naïve Bayes Classifiers for Family Risk Identification and Stunting Intervention Planning Kurniawan, Wildan Indra; Triloka, Joko
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10721

Abstract

Stunting remains a significant public health concern influenced by a combination of social, economic, and environmental factors. This study aims to implement the Naïve Bayes algorithm to support the determination of appropriate intervention strategies for families identified as being at risk of stunting in Metro City. Risk data were obtained from the BKKBN Metro City and underwent preprocessing steps, including handling missing values, encoding categorical variables, and feature selection. The dataset was then divided into training, validation, and testing subsets to develop and evaluate models using three Naïve Bayes variants: Gaussian, Multinomial, and Bernoulli. Evaluation metrics of accuracy, precision, recall, and F1-score indicate that the Multinomial Naïve Bayes model achieved the best performance with 99% accuracy, followed by the Bernoulli Naïve Bayes model with 98% accuracy. Both models effectively classified families at risk of stunting with minimal misclassification, while the Gaussian Naïve Bayes variant demonstrated lower performance with an accuracy of 60%. These results highlight the potential of the Naïve Bayes algorithm, particularly the Multinomial and Bernoulli models, as practical and efficient tools to support data-driven decision-making for stunting interventions.
Comparative Study of Manual and Generated Data Transfer Object Implementation Performance Pardede, Chandro; Sihombing, Wilson; Nainggolan, Winfrey
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10818

Abstract

The Data Transfer Object (DTO) is a fundamental component in Flutter application development, particularly in managing data serialization and deserialization. This study compares two DTO implementation methods—manual and generated—focusing on execution speed and memory efficiency. Testing was conducted across three levels of data complexity (Small, Medium, and Large) over 100 iterations using Flutter DevTools. The findings reveal that the generated approach (utilizing libraries such as json_serializable) consistently outperforms the manual approach. Specifically, it achieves a 1:1.147 ratio in parsing speed and a 1:1.42 ratio in memory efficiency compared to manual DTOs. Although the manual method provides greater flexibility for implementing conditional parsing logic, it tends to be more error-prone and less efficient when handling large datasets. In contrast, the generated approach offers faster performance, better scalability, and reduced human error potential, making it the preferred option for projects demanding technical efficiency and rapid development cycles. Consequently, this study recommends adopting generated DTOs for applications dealing with large-scale and complex data, while reserving manual DTOs for cases requiring highly dynamic or conditional data parsing.
Comparative Study of Logistic Regression, Random Forest, and XGBoost for Bank Loan Approval Classification Putra, Hamdika; Rumini, Rumini
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10862

Abstract

Bank loan approval plays a vital role in ensuring financial institutions can minimize credit risk while supporting economic growth. Default prediction is a crucial aspect of banking credit risk management. This study compares three machine learning algorithms Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) to classify bank loan approvals using a combination of application, previous application, and bureau datasets. The workflow includes data merging, cleaning, missing value imputation, handling unknown values, feature engineering (such as converting day-based variables into years, calculating total submitted documents, income-to-annuity ratio, and employment-to-income ratio), encoding (label and one-hot), scaling (min-max normalization), feature selection based on correlation analysis, handling class imbalance with SMOTE, as well as modeling and evaluation using Accuracy, Precision, Recall, F1-score, and AUC. The results show that Logistic Regression yields the highest AUC of 0.741498, outperforming Random Forest (0.713758) and XGBoost (0.715944). From a business perspective, implementing the best model reduced the Loss Given Default (LGD) by 39.77 %, from $1,705,098,055.50 to $1,026,944,185.50. This finding confirms that simpler models remain competitive on imbalanced datasets when supported by appropriate preprocessing and balancing strategies.
Comparison of Support Vector Machine (SVM) and Random Forest Algorithms in the Analysis of SOcial Media X User Sentiment Towards the TNI Bill Rochmawati, Nur; Zyen, Akhmad Khanif; Widiastuti, Nur Aeni
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10883

Abstract

The rapid advancement of information technology has enabled the public to openly express their views through social media, including on strategic national issues such as the Draft Law on the Indonesian National Armed Forces (RUU TNI). This study aims to map public sentiment toward the RUU TNI and to compare the effectiveness of two popular sentiment analysis algorithms, Support Vector Machine (SVM) and Random Forest (RF). A total of 525 relevant tweets collected between February and May 2025 were analyzed and classified into three sentiment categories: positive, negative, and neutral. The results reveal that neutral opinions dominate at 81.4%, followed by negative sentiments at 11.1% and positive sentiments at 7.4%. The performance comparison shows that SVM achieved an accuracy of 92%, outperforming RF which obtained 91%. These findings highlight that strategic defense issues tend to generate predominantly informative public opinions, while critical voices show an increasing trend as the discourse evolves. The novelty of this study lies in the application of three-class sentiment classification and the comparative evaluation of SVM and RF within the domain of defense policy. This research contributes to the academic discourse by extending sentiment analysis beyond electoral and marketing topics, while also providing practical insights for policymakers in understanding and responding to public aspirations more effectively.
Sentiment Analysis customer Towards Cinema Services in Semarang Using Naive Bayes Classifier on Google Reviews Brian Maualan, Husni; Rini Handayani , Maya; Umam, Khotibul
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10974

Abstract

The development of the entertainment industry, especially in the field of cinema, encourages every service provider to continuously maintain the quality of their services. One method of assessing customer satisfaction is through sentiment testing. The main objective of this study is to examine customer sentiment towards cinema services in Semarang by applying the Naive Bayes Classifier method. The research data was taken from 600 customer reviews on Google Review, which were then divided into two groups: training data consisting of 480 reviews (80%) and testing data consisting of 120 reviews (20%). Before the classification process, the data underwent pre-processing stages involving data cleaning, case folding, tokenization, stopword removal, and stemming, followed by data labeling into two sentiment categories, namely positive and negative. This study took five cinemas as objects, namely CitraXXI, Cinépolis Java Mall, Paragon XXI, XXI Uptown Mall, and XXI DP Mall. The classification results show that the Naive Bayes algorithm is able to group sentiments quite well, with model accuracy ranging from 0.90 to 0.94. Of the five cinemas, Cinépolis Java Mall achieved the highest accuracy, which was 0.94.
Sentiment Analysis of E-Commerce Product Reviews on Tokopedia Using Support Vector Machine Alaiya, Azna; Nurdin, Nurdin; Agusniar, Cut
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10977

Abstract

This research aims to analyze the performance of Support Vector Machine (SVM) algorithm in classifying sentiment of e-commerce product reviews on the Tokopedia platform using web scraping data of 571 reviews from the 2024 period. The data includes review text variables, publication dates, and usernames processed through text preprocessing (text cleaning, stopword removal, stemming with Sastrawi), auto-labeling using a lexicon-based approach, and TF-IDF feature extraction with optimal parameters (max_features=5000, ngram_range=(1,2)) resulting in 1,187 features. Data splitting was performed using stratified method with proportions of training (80%) and testing (20%) on 461 reviews from binary classification filtering (positive vs negative). The research results demonstrate that Support Vector Machine with linear kernel achieved excellent performance with accuracy 95.70%, precision 95.89%, recall 95.70%, and F1-score 94.89% on the testing set. Despite the imbalanced dataset characteristics (92.4% positive vs 7.6% negative), SVM effectively handled the classification task by identifying negative sentiment with 100% precision and 42.86% recall, demonstrating its robustness in handling skewed data distribution. TF-IDF feature analysis identified the highest discriminative words such as "suitable", "goods", and "good" that are relevant for classifying consumer sentiment towards e-commerce products. The results indicate that SVM algorithm is highly effective for sentiment classification of e-commerce product reviews, making it suitable for practical implementation in automated sentiment analysis systems for online marketplaces.
Development of an IoT-Based Smart Cane with Non-Invasive Health Monitoring for Elderly Care in Batam Putera, Dimas Akmarul; Adi, Roni; Kurniawan, Dwi Ely; Leman, Abdul Mutalib; Raynold, Raynold
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11107

Abstract

The rapid growth of the elderly population requires assistive technologies that support mobility, health, and safety. This study presents the development of an IoT-based smart cane designed to enhance elderly independence and health monitoring in Batam, Indonesia. The prototype integrates non-invasive health sensors (MAX30102 for heart rate and SpO₂, MLX90614 for temperature, and a non-invasive glucose sensor), a GPS module, a mini-CCTV with two-way audio, and a solar-powered energy system, all controlled by an ESP32 microcontroller connected to the Blynk IoT platform. Ergonomic design was guided by anthropometric data of Indonesian elderly to ensure user comfort and usability. Experimental results demonstrated stable performance of the integrated modules. Heart rate values ranged from 86–103 BPM (mean 89.5 ± 6.2 BPM), blood glucose estimations from 110–112 mg/dL (mean 111 ± 0.9 mg/dL), and body temperature from 36.9–37.1 °C (mean 37.0 ± 0.1 °C), all of which aligned closely with clinical references. Oxygen saturation readings, however, averaged 89 ± 0.8%, slightly below the clinical norm (≥95%), highlighting the need for sensor calibration. Dynamic testing of the GPS module across a 500-meter route achieved positional accuracy within 3–5 meters, while the CCTV system successfully streamed live video but was dependent on WiFi stability.The novelty of this research lies in the unique combination of locally adapted ergonomic design, multi-sensor non-invasive health monitoring, two-way visual and audio communication, GPS tracking, and renewable energy integration within a single portable device. These contributions not only enrich IoT-based healthcare research but also provide practical solutions tailored to elderly care in Indonesia. Future work will focus on clinical-grade validation of sensors, extended field trials, and the integration of predictive analytics using Machine Learning and Fuzzy Logic.

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