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All Journal Journal of Innovation Information Technology and Application (JINITA) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Kukuh Muhammad
Politeknik Negeri Cilacap
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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Development of a Hybrid CNN–SVM-Based Acute Lymphoblastic Leukemia Detection System on Hematology Image Data Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.3002

Abstract

Acute Lymphoblastic Leukemia (ALL) is among the most common pediatric blood cancers and progresses rapidly, necessitating early and accurate detection. Manual diagnosis via microscopic analysis of blood samples is time-consuming and highly dependent on specialist expertise. This study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) to automatically detect ALL from blood-cell images. The CNN performs deep feature extraction from images, while the SVM serves as the classifier to determine ALL status. The dataset comprises microscopic images labeled as ALL or normal and is processed through preprocessing steps such as augmentation and normalization. The adopted CNN produces optimized feature representations. Experimental results show that the hybrid CNN–SVM model with an RBF kernel achieves the best performance, with an accuracy of 96.4%, precision of 95.8%, recall of 96.1%, and an F1-score of 96.0%, surpassing pure CNN-based baselines. Training converged at the 41st epoch, with a training accuracy of 97.2%, validation accuracy of 95.9%, training loss of 0.09, and validation loss of 0.11, indicating stable learning without overfitting. The model’s ROC curve lies well above the chance diagonal, with an Area Under the Curve (AUC) of 0.914, means there is a 91.4% chance the model assigns a higher score to a truly positive (leukemia) image than to a negative (normal) image.These findings suggest that the CNN–SVM hybrid approach enhances leukemia detection performance compared with conventional CNN-only methods and holds promise as a fast, accurate, and efficient image-based decision-support tool for early leukemia diagnosis in digital hematology.
Transformasi Digital Pengasapan Ikan: Pendampingan Implementasi IoT untuk Monitoring Suhu dan Meningkatkan Kualitas Lutfi Syafirullah; Fajar Mahardika; Adlan Nugroho; Joko Purwanto; Kukuh Muhammad; Laura Sari; Ratih Hafsarah Maharrani
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 8 No. 1 (2026): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v8i1.12247

Abstract

Digital transformation in the traditional food processing sector has become a strategic step to enhance efficiency and product quality. This study aims to design and assist in the implementation of an Internet of Things (IoT) system for fish smoking equipment, particularly focusing on real-time temperature monitoring. By utilizing temperature sensors, microcontrollers, and cloud-based IoT platforms, the system enables business actors to supervise the smoking process accurately and continuously. Assistance was provided to local fish-smoking groups to ensure that the implemented technology can be operated independently and tailored to field needs. The implementation results showed an improvement in temperature consistency during the production process, which directly impacts the final product quality, especially in terms of taste, color, and shelf life of smoked fish. This study demonstrates that the integration of digital technology into traditional processing can increase product added value and strengthen the adaptive capacity of business actors in the Industry 4.0 era. The developed system also opens opportunities for replication in other agro-industrial sectors as part of strengthening a technology-based appropriate economy
Co-Authors Abdul Rohman Supriyono Adlan Nugroho Annisa Romadloni Joko Purwanto Laura Sari Linda Perdana Wanti Lutfi Syafirullah Mahardika, Fajar Muhammad Nur Faiz Ratih Hafsarah Maharrani