Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Jurnal Media Informatika Journal of Biomedical and Techno Nanomaterials Journal of World Future Medicine, Health and Nursing
Justam Justam
Universitas Mega Buana Palopo
Biochemistry, Genetics & Molecular Biology Computer Science & IT

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AI POWERED DIGITAL HISTOPATHOLOGY: PREDICTING IMMUNOTHERAPY RESPONSE USING DEEP LEARNING Loso Judijanto; Som Chai; Ming Pong; Justam Justam; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers. Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.
Development of a Motor Vehicle Rearview Image Pattern Recognition System for Detection of Traffic Flow Violations on One-Way Roads: Image processing Sitti Mawaddah Umar; Justam Justam
Jurnal Media Informatika Vol. 6 No. 3 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i3.6052

Abstract

This study aims to detect traffic violations, specifically motorcycles riding against the flow on one-way roads, by utilizing computer vision technology to recognize the rearview patterns of vehicles. The method employed involves applying the deep learning model Faster-RCNN for object detection, using image data captured from an IP camera mounted on a pole at a height of 2.5 meters with a 45-degree tilt angle to optimally monitor vehicles from behind. Image labeling was performed using the LabelImg application, while model training and classification were conducted using the TensorFlow framework. The developed system achieved a detection accuracy of 88%, demonstrating the effectiveness of this approach in identifying motorcycles violating traffic direction. These findings highlight the potential of computer vision as an automatic and real-time solution for traffic monitoring, which can help reduce dangerous violations and enhance road safety. Therefore, this research contributes significantly to the development of more advanced and efficient traffic violation detection systems.
UTILIZING MACHINE LEARNING ALGORITHMS TO PREDICT PATIENT SURGES IN FUTURE EMERGENCY DEPARTMENTS Justam Justam; Luis Santos; Josefa Flores
Journal of World Future Medicine, Health and Nursing Vol. 4 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/health.v4i4.4278

Abstract

Emergency departments face increasing pressure from unpredictable patient surges that can intensify overcrowding, treatment delays, staff workload, and resource shortages. This study aimed to develop and evaluate machine learning algorithms for predicting emergency demand across multiple hospitals and forecast horizons. A quantitative multi-site predictive modeling design combined retrospective records from 1,427,860 emergency encounters with prospective validation data from 151,632 visits. Seasonal ARIMA, Poisson regression, support vector regression, random forest, long short-term memory, extreme gradient boosting, and hybrid ensemble models were compared using temporal and external validation. Results showed that the hybrid ensemble achieved the strongest twenty-four-hour performance, with a mean absolute error of 21.84 patients, mean absolute percentage error of 6.58%, and an R-squared value of 0.91. The model also produced high six-hour surge sensitivity, while recent arrivals, occupancy, ambulance activity, infectious disease indicators, bed availability, weather, air quality, and public events emerged as influential predictors. Predictive accuracy declined for longer horizons and less frequent clinical categories. The study concludes that machine learning can strengthen emergency preparedness when forecasts are explainable, continuously validated, and connected to predefined staffing, triage, bed-management, and ambulance-response protocols.  
Co-Authors Ardi Azhar Nampira Josefa Flores Loso Judijanto Luis Santos Ming Pong Sitti Mawaddah Umar Som Chai