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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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Heart Disease Classification Using Extreme Learning Machine (ELM) Method With Outlier Handling One-Class Support Vector Machine (OCSVM) Ariyanto, Dimas; Novitasari, Dian Candra Rini; Hamid, Abdulloh
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.v9i5.9763

Abstract

Heart disease remains the leading cause of death globally, accounting for approximately 32% of all deaths. Developing countries are particularly affected due to prevalent risk factors such as hypertension, diabetes, and poor lifestyle habits. Accurate and early diagnosis is essential for effective treatment and prevention. Technological advancements have enabled the precise analysis of complex clinical data. This study investigates the application of the Extreme Learning Machine (ELM) algorithm combined with outlier handling using One-Class Support Vector Machine (OCSVM) for heart disease classification. The dataset, obtained from the University of California, Irvine Machine Learning Repository, consists of 1190 clinical records with 12 numerical features. The ELM model was evaluated using the Tanh activation function and 10-fold cross-validation. Among the tested configurations, the best performance was achieved using 450 hidden neurons, yielding a sensitivity of 92,52% with a standard deviation of 4,00%. These results indicate that ELM, when paired with effective outlier handling and properly tuned parameters, can provide reliable and stable performance in heart disease classification.
Comparison of Logistic Regression, Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) Algorithms in Diabetes Prediction Kurniawan, M. Fadli; Megawaty, Dyah Ayu
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.v9i5.9815

Abstract

Diabetes mellitus is a prevalent chronic illness that continues to grow in incidence worldwide, placing significant strain on healthcare systems. The timely prediction of diabetes is crucial for early intervention and management. This study explores the comparative effectiveness of four machine learning algorithms Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in identifying diabetes cases using a large public dataset containing 100,000 patient records obtained from open source Kaggle. The dataset includes nine clinical variables, such as age, gender, body mass index (BMI), blood glucose level, and HbA1c levels, among others. To address class imbalance, which showed less than 10% positive (diabetic) cases initially, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training data after an 80:20 stratified split. All models were evaluated using 5-fold stratified cross-validation, measuring their performance through accuracy, precision, recall, F1-score, area under the ROC curve (AUC), and training time. Among the models, Random Forest achieved the highest classification accuracy (96.88%) and AUC (99.70%), indicating superior overall performance. Furthermore, McNemar statistical tests revealed that the differences in performance between Random Forest and the other models were statistically significant. An analysis of feature importance highlighted that HbA1c, glucose level, and BMI were the most influential predictors. These results demonstrate that Random Forest offers the most balanced combination of accuracy, interpretability, and robustness, making it highly suitable for real-world clinical screening scenarios where early detection of diabetes is critical.
Enhancing The Security of E-Invoicing for Distribution Companies Through Image-Based PDF Conversion and QR Verification Rochmatullah, Arief Noor; Wibowo, Ignatius Aris; Sarwosri, Sarwosri
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.v9i5.9974

Abstract

Distribution companies face significant challenges in securing electronic invoices, as PDF files are susceptible to unauthorized text extraction and manipulation. Prior solutions include SHA-256 digital signatures, and QR code-based verification. There are often require specialized tools, stable internet, or user intervention posing barriers for general trade customers with limited digital access. To address these limitations, this study proposes a hybrid e-invoicing method by converting invoices into image-based PDFs embedded with QR codes. This approach enhances document security, increases resistance to text manipulation, and ensures file sizes remain under 1 MB for smooth distribution via WhatsApp. A dataset of 1000 invoices was tested using OCR and FuzzyWuzzy string similarity to compare extractability between text-based and image-based formats. A composite score was calculated by combining file size and manipulation resistance metrics. Results show that image-based PDFs achieve a significantly higher score (0.595) compared to text-based PDFs (0.005), confirming their superiority in terms of size efficiency and data security. The findings demonstrate that this method provides a robust, low-cost, and scalable solution for secure invoice distribution in environments with limited infrastructure and technical literacy.
Smart Valve Irrigation System Using Fuzzy Logic for Mustard Pranidana, Abdi Mulia; Qamal, Mukti; Risawandi, Risawandi
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.v9i5.10024

Abstract

This study presents the design and implementation of a smart irrigation system using Mamdani fuzzy logic integrated with IoT-based environmental sensors. The system utilizes an ESP32 microcontroller, DHT22 temperature sensor, capacitive soil moisture sensor, and a solenoid valve to perform adaptive irrigation based on real-time environmental conditions. The fuzzy logic engine processes sensor inputs and determines the irrigation intensity through centroid-based defuzzification. A web-based dashboard was developed using PHP and JavaScript to monitor temperature, soil moisture, and irrigation status in real time. The system was tested on mustard greens (Brassica juncea L.) for 12 hours, resulting in a 35% water usage reduction compared to manual watering methods while maintaining optimal soil moisture. This approach demonstrates a promising solution for sustainable and efficient smart agriculture.
Cybersecurity Awareness in Government Institutions: A Systematic Review of Behavioral Strategies and Policy Readiness Mwansa, Gardner
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.v9i5.10041

Abstract

This study presents a systematic literature review of cybersecurity awareness in government institutions, focusing on how public agencies perceive, implement, and sustain awareness initiatives. It critically examines behavioral strategies, training methods, and policy frameworks that influence employee engagement and institutional readiness. Findings highlight the fragmentation of awareness efforts, gaps in policy coordination, and the need for behaviorally informed, role-specific interventions to build cyber resilience in the public sector. Recommendations include strengthening institutional infrastructure, leadership engagement, and adaptive learning strategies to enhance cybersecurity awareness and long-term resilience.
A Predictive Model for Crop Irrigation Schedulling Using Machine Learning and IoT-Generated Environmental Data Syahputra, Rizki Agam; Andriani, Dewi
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.v9i5.10193

Abstract

This study develops and evaluates a machine learning model for predicting optimal irrigation schedules using real-time environmental data collected from an Internet of Things (IoT) system. Building upon a previously validated smart farming monitoring system that provided real-time data on temperature, humidity, and soil moisture, this research addresses the next step: moving from monitoring to predictive analytics. Data collected over a six-day period from DHT11 temperature and humidity sensors, as well as soil moisture sensors, were used to train a predictive model. The model is designed to forecast future soil moisture levels, thereby providing farmers with proactive recommendations for irrigation. A Long Short-Term Memory (LSTM) neural network was employed to capture the temporal dependencies between atmospheric conditions and soil moisture. The model was trained on a portion of the collected data and then validated on a separate, unseen dataset. The evaluation yielded a Mean Absolute Error (MAE) of 2.5%, a Root Mean Square Error (RMSE) of 3.1%, and an R-squared (R2) value of 0.92, demonstrating high predictive accuracy. This approach aims to enhance water resource management, reduce manual intervention, and improve crop health by ensuring water is supplied only when necessary. The results indicate that the machine learning model can accurately predict irrigation needs, offering a significant improvement over traditional, reactive monitoring systems and marking a substantial step towards data-driven, precision agriculture.
Performance Comparison of Machine Learning Algorithms Using EfficientNetB0 Feature Extraction on Dental Disease Classification Mustafa, Mohammad Faiq Ruliff; Wardhana, Ajie Kusuma
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.v9i5.10286

Abstract

Oral health conditions such as dental caries, calculus, gingivitis, and ulcers are prevalent globally and require accurate early detection to prevent further complications. Traditional diagnostic methods such as visual inspection and manual radiograph analysis often rely on subjective judgment, leading to inconsistencies, delayed treatment, and limited accessibility, particularly in underserved areas. This study proposes an intelligent classification framework for dental disease detection based on intraoral images. Deep features were extracted using EfficientNetB0, followed by classification through eleven machine learning algorithms, including SVM, XGBoost, and K-Nearest Neighbors. Preprocessing steps included image augmentation, SMOTE for class balancing, and feature normalization. Among all models, SVM achieved the highest accuracy of 92,9%, while XGBoost and LightGBM followed closely at 91.3%. Using K-Fold Cross Validation, KNN algorithm has an increasing value with accuracy of 91,24%. This indicate the KNN algorithm able to tackle generalization problem towards the classification. The results demonstrate that features extracted using CNNs, when classified using machine learning algorithms, can provide a scalable and effective alternative to conventional diagnostic practices. Hence, Machine Learning algorithms provide a promising result towards dental disease classification.
Transformer-Based Deep Learning Model for Coffee Bean Classification Ekowicaksono, Imam; Wisesa, I Wayan Wiprayoga; Fitriani, Vita
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.v9i5.10301

Abstract

Coffee is one of the most popular beverage commodities consumed worldwide. The process of selecting high-quality coffee beans plays a vital role in ensuring that the resulting coffee has superior taste and aroma. Over the years, various deep learning models based on Convolutional Neural Networks (CNN) have been developed and utilized to classify coffee bean images with impressive accuracy and performance. However, recent advancements in deep learning have introduced novel transformer-based architectures that show great promise for image classification tasks. By incorporating a self-attention module, transformer models excel at generating global context features within images. This ability demonstrate improved and more consistent performance compared to CNN-based models. This study focuses on training and evaluating transformer-based deep learning models specifically for the classification of coffee bean images. Experimental results demonstrate that transformer models, such as the Vision Transformer (ViT) and Swin Transformer, outperform traditional CNN-based models. Swin Transformer model achieves excellent on the coffee bean image classification task, with 95.13% Accuracy and 90.21% F1-Score, while ViT achieves 94.47% Accuracy and 88.93% F1-Score. It indicates their strong capability in accurately identifying and classifying different types of coffee beans. This suggests that transformer-based approaches could be a better alternative for coffee bean image classification tasks in the future.
Development of an IoT-Based Smart Greenhouse with Fuzzy Logic for Chrysanthemum Cultivation Khairina, Jikti; Nurdin, Nurdin; Fikry , Muhammad
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.v9i5.10313

Abstract

Conventional cultivation of Chrysanthemum plants in greenhouses faces serious challenges such as inefficiency, response delays, and errors in temperature and humidity settings due to manual management. These conditions result in unsuitable growing environments that can reduce the quality and quantity of harvests. To overcome these problems, this study developed a smart greenhouse system based on the Internet of Things (IoT) and cloud computing with the application of fuzzy logic. The system is designed to automatically monitor and control temperature, humidity, and light intensity using NodeMCU ESP32, DHT22 and BH1750 sensors, as well as relay-based actuators and mini air conditioners. Environmental data is sent to the cloud and processed using the Sugeno fuzzy method to produce adaptive and precise control decisions. Test results show that the system can maintain stable and optimal environmental conditions with an average temperature control difference of 30.341% and an actuator efficiency of 9.34% against microcontroller commands. This system provides a modern solution to the limitations of traditional methods, and supports smart agriculture in tropical climates such as Lhokseumawe.
Exploration of Machine Learning Algorithms and Class Imbalance Handling on Plant Disease Detection Aditya, Ervin; Wardhana, Ajie Kusuma
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.v9i5.10338

Abstract

Plant leaf diseases pose a significant threat to agricultural productivity, necessitating accurate and efficient identification systems for timely intervention. This study proposes an approach that leverages deep feature extraction using a pretrained ResNet50 model combined with traditional machine learning algorithms to recognize 38 types of plant leaf diseases. Each image was transformed into a 2048-dimensional feature vector, followed by normalization and dimensionality reduction using Principal Component Analysis (PCA). To mitigate the issue of class imbalance in the dataset, random under-sampling was applied at the feature level to ensure equal representation across all classes. Eleven machine learning models were trained and evaluated using 5-fold cross-validation, with performance assessed through accuracy, precision, recall, F1-score, and ROC AUC score. Among the evaluated models, the Support Vector Machine (SVM) achieved the highest accuracy of 99.63%, followed by Logistic Regression at 97.33%, and LightGBM at 96.25%. These models demonstrated strong generalization capabilities in multiclass settings, while simpler classifiers like AdaBoost and Decision Tree yielded lower performance. A comparative analysis of training and test accuracy further highlighted model robustness and overfitting tendencies. The findings emphasize the potential of combining pretrained convolutional neural networks for feature extraction with conventional classifiers to address complex agricultural classification tasks. Future work may explore the inclusion of healthy leaf samples, alternative CNN architectures, and deployment in real-time diagnostic tools to support precision farming and improve crop health monitoring.

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