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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi TIERS Information Technology Journal Journal of Information Systems and Technology Research Jurnal PETISI (Pendidikan Teknologi Informasi)
Mohammad Idhom
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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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. 
Implementation of Multi-Level Association Rule Mining Based on Concept Hierarchy in Drug Transaction Analysis to Support Inventory Management Nurul Kamalia Zahra; Mohammad Idhom; Andri Fauzan Adziima
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1606

Abstract

Drug inventory planning is a crucial aspect in logistics management in first-level health care facilities, as the availability of the right type and quantity of drugs greatly affects the quality of service and patient safety. However, in practice, there are still various problems, such as the mismatch between the amount of stock and the real need for limitations in the use of historical data on drug transactions, and the lack of optimal systems in identifying drug use patterns in a structured and data-based manner. This condition has the potential to cause stockouts and overstock, which has an impact on delays in the delivery of therapy, wasted budgets, and increased risk of expired drugs. In addition, the lack of analysis of drug use combinations also hinders more accurate decision-making in inventory planning and control. Based on these problems, this study aims to apply the Multi-Level Association Rule Mining (MLARM) method in identifying combinations of drug use based on transaction data at health facilities. The results of the study showed that the MLARM method  was effective in identifying patterns of drug use associations at various levels of the hierarchy. At the drug class level, it was found that there was a relationship between hard drugs, over-the-counter drugs, and limited over-the-counter drugs which reflected the use of combinations between drug classes in health services. At the group level of therapy, the association involves analgesics, antibiotics, antihistamines, corticosteroids, and decongestants. Meanwhile, at the level of drug names, specific combinations were found, such as Mms with Kalk, Fe with Paracetamol, Ctm with Paracetamol, and Mefenamic Acid with Amoxicillin. This information can be used to support more optimal management of drug supplies and reduce the risk of stockout and overstock.
Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP) I Gede Susrama Mas Diyasa; Hajjar Ayu Cahyani Kuswardhani; Mohammad Idhom; Prismahardi Aji Riyantoko; Deshinta Arrova Dewi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5954

Abstract

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.
Prediksi Harga Saham Menggunakan Model Mixture Autoregressive (MAR) (Studi Kasus : Saham Perusahaan Rokok) Sintiya Ristiyani; Mohammad Idhom; Dwi Arman Prasetya
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.2721

Abstract

Memprediksi harga saham merupakan tantangan besar dalam dunia penelitian karena tingginya risiko yang terlibat, meskipun potensi keuntungannya juga sangat besar. Hal ini mencerminkan bahwa pergerakan saham sangat dipengaruhi oleh perubahan faktor eksternal maupun internal. Oleh karena itu, memperoleh prediksi harga saham yang tepat menjadi sangat krusial guna meminimalkan kemungkinan kerugian bagi para investor. Penelitian ini dilakukan dengan tujuan untuk memprediksi harga saham dari tiga perusahaan rokok besar di Indonesia, yaitu PT. Hanjaya Mandala Sampoerna Tbk (HMSP), PT. Gudang Garam Tbk (GGRM), dan PT Wismilak Inti Makmur Tbk (WIIM). Penelitian ini menggunakan suatu model statistika bernama Mixture Autoregressive (MAR), dipilih karena kemampuannya dalam menangkap pola tidak linier serta perubahan kondisi yang sering muncul pada pergerakan harga saham. Menggunakan model MAR diperoleh model MAR dengan nilai Mean Absolute Percentage Error (MAPE) untuk masing-masing perusahaan yaitu Gudang Garam (5.46%), Sampoerna (4.52%), dan Wismilak (3.58%).
Co-Authors Andri Fauzan Adziima Anggraini Puspita Sari Deshinta Arrova Dewi Dwi Arman Prasetya Hajjar Ayu Cahyani Kuswardhani I Gede Susrama Mas Diyasa Maulana, Hendra Muchammad Fadika Naddiyanto Nurul Kamalia Zahra Prismahardi Aji Riyantoko Prismahardi Aji Riyantoko Sintiya Ristiyani Tresna Maulana Fahrudin