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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi TIERS Information Technology Journal
Mohammad Idhom
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT

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Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets Mohammad Idhom; Dwi Arman Prasetya; Prismahardi Aji Riyantoko; Tresna Maulana Fahrudin; Anggraini Puspita Sari
TIERS Information Technology Journal Vol. 4 No. 1 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i1.4380

Abstract

This research focuses on accurately classifying pneumonia in children under the age of 5 using X-ray images, considering the challenge of an imbalanced dataset. A modified VGG-16 CNN architecture is evaluated for pneumonia classification in Chest X-Ray Images. The study compares testing results with and without data augmentation techniques and explores the potential application of the model in an Android-based machine learning system for pneumonia diagnosis assistance. Using a dataset of 5,856 Chest X-Ray images categorized as normal or pneumonia, obtained from Kaggle, the research conducts two test scenarios: one without data augmentation and another with data augmentation techniques. The modified VGG-16 CNN algorithm's performance is evaluated using the accuracy metric. The results highlight the effectiveness of data augmentation in improving pneumonia classification accuracy. The augmented tests outperform the non-augmented ones, achieving an impressive 92% accuracy, indicating a significant 15% improvement over the non-augmented scenario. This improvement underscores the efficacy of data augmentation techniques in enhancing the CNN's ability to accurately classify pneumonia, particularly when faced with an imbalanced dataset. Furthermore, the research explores the potential integration of the trained model into an Android-based machine learning system for pneumonia diagnosis assistance. This integration would enable doctors to analyze X-ray images and identify potential pneumonia cases in patients. The integration of advanced machine learning systems in healthcare holds promise for improving patient care and the accuracy of pneumonia diagnoses. In summary, this research contributes to the accurate classification of pneumonia in children under 5 years old using X-ray images. It emphasizes the efficacy of data augmentation techniques in enhancing classification accuracy and explores the practical application of an Android-based machine learning system for pneumonia diagnosis assistance. These findings underscore the importance of advanced machine learning systems in healthcare and their potential to improve pneumonia diagnosis accuracy and enhance patient care.
Prediksi Kadar Air Greenbeans Kopi Pra-Roasting Menggunakan Metode ANFIS Muchammad Fadika Naddiyanto; Mohammad Idhom; Hendra Maulana
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3568

Abstract

Moisture content of green coffee beans is a critical parameter that determines quality stability during storage and the pre-roasting stage; however, conventional measurement methods are destructive and unsuitable for continuous monitoring. This study aims to develop an Internet of Things (IoT)-based moisture content prediction system using the Adaptive Neuro-Fuzzy Inference System (ANFIS). Input variables include temperature, relative humidity (RH), and capacitive sensor ADC signals, while moisture content is used as the target variable. A dataset consisting of 1032 observations was divided into training and testing sets with an 80:20 ratio. The ANFIS model employed Gaussian membership functions and an early stopping mechanism, and its performance was evaluated using MAE, RMSE, MAPE, and the coefficient of determination (R²). Experimental results achieved MAE of 0.2648, RMSE of 0.4187, MAPE of 2.077%, and R² of 0.8109 with an accuracy of 97.923%. The proposed system enables accurate, non-destructive, and real-time moisture content prediction.Keywords: Moisture content; Green beans; Coffee; ANFIS; Prediction.AbstrakKadar air biji kopi hijau merupakan parameter penting yang menentukan stabilitas mutu selama penyimpanan hingga tahap pra-roasting, namun metode pengukuran konvensional bersifat destruktif dan tidak mendukung monitoring berkelanjutan. Penelitian ini bertujuan mengembangkan sistem prediksi kadar air berbasis Internet of Things (IoT) menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Variabel input meliputi suhu, kelembaban relatif (RH), dan sinyal ADC sensor, dengan kadar air sebagai variabel target. Dataset sebanyak 1032 data dibagi menjadi data latih dan data uji dengan rasio 80:20. Model ANFIS menggunakan fungsi keanggotaan Gaussian dan mekanisme early stopping, serta dievaluasi menggunakan MAE, RMSE, MAPE, dan koefisien determinasi (R²). Hasil pengujian menunjukkan MAE 0,2648, RMSE 0,4187, MAPE 2,077%, dan R² sebesar 0,8109 dengan akurasi 97,923%. Sistem yang diusulkan mampu melakukan prediksi kadar air secara akurat, non-destruktif, dan real-time. 
Co-Authors Anggraini Puspita Sari Dwi Arman Prasetya Maulana, Hendra Muchammad Fadika Naddiyanto Prismahardi Aji Riyantoko Tresna Maulana Fahrudin