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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Informasi dan Terapan (J-TIT) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Cinantya Paramita
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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EARLY DETECTION OF DOWN SYNDROME BASED ON FACIAL IMAGES USING A HYBRID CONVOLUTIONAL NEURAL NETWORK CINANTYA PARAMITA; Gifari Hilal Hilmi Nashif
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7196

Abstract

Down Syndrome is a multisystem genetic disorder serving as a primary cause of intellectual disability, where delayed diagnosis often obstructs access to crucial developmental interventions. Conventional clinical diagnosis, which relies on the subjective observation of dysmorphic features, is frequently constrained by the scarcity of medical experts, necessitating the development of automated systems based on facial images to support objective and accessible early detection. This study aims to evaluate and compare the performance of single deep learning models with an innovative hybrid architecture to enhance the accuracy of screening systems. The research methodology employs a dataset of 2,666 facial images of toddlers, processed using Convolutional Neural Networks (CNN) through a transfer learning approach. Comprehensive experiments compared InceptionV3 and EfficientNetB3 architectures both as standalone models and within a hybrid ensemble while assessing the efficacy of feature extraction versus fine-tuning strategies. The results demonstrate that fine-tuning significantly outperforms feature extraction, yielding a 10-12% performance increase due to more specific feature adaptation. The hybrid ensemble model utilizing fine-tuning emerged as the superior approach, achieving a peak validation accuracy of 92.32% and an ghF1-Score of 92.33%. This model proved robust against pose and expression variations while effectively minimizing false negatives. Consequently, integrating computational strengths through a hybrid architecture produces rich feature representations, establishing this method as a reliable and precise solution for medical screening.
YOLOV8S AS THE OPTIMAL MODEL FOR AUTOMATED PNEUMONIA DETECTION: A COMPARATIVE STUDY CINANTYA PARAMITA; Nila Farihah; Anamarija Jurcev Savicevic
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7213

Abstract

Pneumonia remains a leading cause of global mortality, accounting for nearly 15% of deaths among children under five worldwide, thus requiring early and accurate detection. This research examines three YOLOv8 variants: YOLOv8n, YOLOv8s, and YOLOv8m to determine their effectiveness in classifying Chest X-Ray (CXR) images into normal, bacterial pneumonia, and viral pneumonia categories. The dataset was divided into training 80%, validation 10%, and testing 10%, with each image resized to 640 × 640 pixels. The models were checked using three measures: Precision, Recall, and mAP@50. The experimental evaluation revealed that all models produced high accuracy, each achieving mAP@50 scores above 0.90. Among the tested variants, YOLOv8s delivered the most optimal results, obtaining mAP@50 of 0.917, precision of 0.873, and recall of 0.889. YOLOv8m showed consistent performance and strong detection capability for normal lung images, while YOLOv8n achieved the highest recall value (0.894) with efficient computational demands. The superior outcome of YOLOv8s is attributed to its well-balanced architectural design, enabling enhanced detection while minimizing overfitting. Overall, the findings indicate that the moderately complex YOLOv8s model provides the most effective combination of speed, a
COMPARATIVE PERFORMANCE ANALYSIS OF YOLOv5, YOLOv8, AND YOLOv9 SMALL AND MEDIUM VARIANTS FOR EXPLAINABLE PNEUMONIA DETECTION IN CHEST X-RAY IMAGES Faishal Luthfi Maulana Hakim; Cinantya Paramita
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8099

Abstract

Pneumonia is a lung infection disease that remains one of the leading causes of death worldwide, particularly among children and vulnerable populations. The diagnosis process using Chest X-Ray (CXR) images still faces several challenges, including the limited number of radiologists and the potential for human error in medical image interpretation. This study aims to perform a comparative analysis of YOLOv5, YOLOv8, and YOLOv9 small and medium variants for bounding box-based pneumonia detection on chest X-Ray images. The dataset was obtained from Kaggle and processed using Roboflow through preprocessing stages including auto-orient and image resizing to 640×640 pixels. The dataset was divided into 70% training, 20% validation, and 10% testing data. The training process was conducted using the Ultralytics YOLO framework for 50 epochs on an NVIDIA Tesla T4 GPU in Google Colaboratory. Model evaluation was carried out using Precision, Recall (Sensitivity), mAP50, and mAP50-95 metrics. The testing results showed that the YOLOv9m model achieved the best performance with a Recall value of 0.982759 and an mAP50-95 value of 0.717609. In addition, YOLOv5m produced the highest Precision value of 0.948602 and mAP50 of 0.969271. Based on the experimental results, the YOLOv9 architecture demonstrated the most optimal performance for pneumonia detection on Chest X-Ray images compared to the other models.  
Lightweight Deep Learning Approach for Sugarcane Leaf Disease Classification Using MobileNetV2 Cinantya Paramita; Rifky Bintang Pradana; Nurul Anisa Sri Winarsih; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.456

Abstract

Sugarcane is one of Indonesia’s strategic crops, yet its productivity is frequently disrupted by leaf diseases such as yellow leaf, rust, and red rot. Previous studies have shown that deep learning models are promising for plant disease detection, but many of them rely on heavy architectures that limit deployment in real-world agricultural settings. To address this gap, this study applies MobileNetV2, a lightweight Convolutional Neural Network, for the classification of sugarcane leaf diseases. Using the publicly available Kaggle dataset, the model was trained and evaluated on four classes: healthy, yellow leaf, rust, and red rot. The results demonstrate that MobileNetV2 achieved 97.0% test accuracy, with strong precision, recall, and F1-scores across all categories. These findings highlight that efficient deep learning architectures can deliver reliable disease classification while remaining practical for implementation on mobile or edge devices. Compared with previous approaches, this study contributes by demonstrating that lightweight model like MobileNetV2 can provide a balance of accuracy and efficiency, making them suitable for supporting precision agriculture practices in resource-limited environments
Optimization of Diabetes Mellitus Classification Using the Random Forest and SMOTE-ENN Methods Imam Fadhur Rahman; Egia Rosi Subhiyakto; Cinantya Paramita
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.11387

Abstract

Diabetes Mellitus is a non-communicable disease (NCD) that has turned into a worldwide health issue with a steadily rising prevalence. Timely identification is essential for minimizing the risk of complications and the financial strain on the healthcare system. This research focuses on creating a precise and dependable diabetes classification model through the Random Forest algorithm by implementing a series of systematic data preprocessing methods. This methodology utilizes a dataset obtained from Kaggle, consisting of 768 samples. The steps taken include addressing missing values using Multiple Imputation by Chained Equations (MICE) , removing outliers with the Z-Score and Interquartile Range (IQR) techniques , selecting features based on ANOVA F-value to identify the eight most significant features , and balancing classes through the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbours (SMOTE-ENN) to correct dataset imbalance. The assessment of the Random Forest model revealed outstanding performance, attaining an accuracy of 94.3% and an Area Under Curve (AUC) value of 0.98. These findings suggest that the model possesses strong discriminative capability to differentiate between diabetic and non-diabetic individuals. This research concludes that the Random Forest algorithm, when backed by suitable data preprocessing, is very efficient and could be utilized in clinical decision support systems as an early tool for diabetes screening.
Optimizing Pneumonia Detection on Edge Devices Using YOLOv5lu with Pruning and Quantization Muhammad Satriya Pratama Manggala Kusuma; Cinantya Paramita; Suryanti Chan
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10374

Abstract

Pneumonia continues to be a leading cause of childhood mortality worldwide, with the greatest impact in resource limited regions where access to radiologists is limited. Deep learning has emerged as a promising tool for automated screening, yet conventional object detection models demand high computational resources, restricting their use on low cost edge devices common in rural healthcare settings. To address this challenge, we developed a lightweight pneumonia detection framework designed for realtime inference on standard hardware. Our approach builds on the YOLOv5lu architecture, which incorporates an anchor free decoupled head, and was trained on a balanced dataset of 14,863 chest X-rays from the RSNA Pneumonia Detection Challenge.To enable deployment on edge devices, we applied a sequential compression pipeline. First, 30% of convolutional filters were removed through structured pruning, followed by post training quantization to INT8. These optimizations reduced the model size by nearly half (from 101 MB to 51.72 MB) and improved inference speed to 6.24 ms per image, equivalent to more than 160 frames per second on a standard CPU. Importantly, the quantized model preserved diagnostic performance, achieving a mean Average Precision (mAP@0.4–0.75) of 0.266 compared to the baseline score of 0.287. These findings confirm the practical feasibility of deploying advanced deep learning models in limited resource regions. By effectively balancing efficiency and accuracy, this framework offers a scalable solution for early pneumonia screening and establishes a foundation for extending detection to other diseases, including Tuberculosis and COVID-19.
Co-Authors Abu Salam Adhitya Nugraha Amalia Amalia Anamarija Jurcev Savicevic Aripin Catur Supriyanto Christy Atika Sari De Rosal Ignatius Moses Setiadi Dewi Pergiwati Dhita Aulia Octaviani Dwi Eko Waluyo Egia Rosi Subhiyakto Eko Hari Rachmawanto Erlin Dolphina Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Faishal Luthfi Maulana Hakim Fauzi Adi Rafrastara Gifari Hilal Hilmi Nashif Gigih Trapsilo Hayu Wikan Kinasih Imam Fadhur Rahman Junta Zeniarja Lisdi Inu Kencana Muhammad Satriya Pratama Manggala Kusuma Muljono, - Nasrudin Affandi Prasetyo Nila Farihah Nurul Anisa Sri Winarsih Rendra Gunawan Ricardus Anggi Pramunendar Rifky Bintang Pradana Suryanti Suryanti Suryanti Suryanti Usman Sudibyo Warih Prasetyaningtyas Yani Parti Astuti