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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Halal Research
Jamaludin, Khairur Rijal
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Religion Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Other

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Designing Food Safety Management and Halal Assurance Systems in Mozzarella Cheese Production for Small-Medium Food Industry Putri, Nilda Tri; Kharisman, Arif; Arief, Ikhwan; Abdul Talib, Hayati Habibah; Jamaludin, Khairur Rijal; Ismail, Elsayed Ali
Indonesian Journal of Halal Research Vol. 4 No. 2 (2022): August
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/ijhar.v4i2.12996

Abstract

Indonesia's small and medium-sized enterprises (SMEs) are having difficulty implementing a food safety management and halal assurance system. This article aims to design a food safety and halal assurance system for Dairy Farm SMEs. This research designed a food system by identifying the application of Good Manufacturing Practices (GMP) and the HACCP to Dairy Farm SMEs based on the survey, in-depth interviews, and document standard review. The food safety system was implemented using HACCP, and six Critical Control Point (CCP) processes were identified, including milking (raw material), storage, pasteurization, curd filtering, and cheese packaging. The halal assurance system is implemented at Dairy Farm SMEs by identifying and improving the company's business processes and the mozzarella cheese production process. In addition, a Standard Operating Procedure (SOP) was developed, including a food safety system and a halal assurance system. The research results can be used wisely by Dairy Farm SMEs to assist in obtaining recommendations from the Food and Drug Supervisory Agency and halal certification.
Classification of Cervical Cell Types Based on Machine Learning Approach: A Comparative Study Wan Mustafa, Wan Azani; Khiruddin, Khalis; Jamaludin, Khairur Rijal; Khusairi, Firdaus Yuslan; Ismail, Shahrina
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.829

Abstract

Cervical cancer remains a major global health issue and is the second most common cancer affecting women worldwide. Early detection is crucial for effective treatment, but remains challenging due to the asymptomatic nature of the disease and the visual complexity of cervical cell structures, which are often affected by inconsistent staining, poor contrast, and overlapping cells. This study aims to classify cervical cell images using Artificial Intelligence (AI) techniques by comparing the performance of Convolutional Neural Networks (CNNs), Support Vector Machine (SVMs), and K-Nearest Neighbors (KNNs). The Herlev Pap smear image dataset was used for experimentation. In the preprocessing phase, images were resized to 100 × 100 pixels and enhanced through grayscale conversion, Gaussian smoothing for noise reduction, contrast stretching, and intensity normalization. Segmentation was performed using region-growing and active contour methods to isolate cell nuclei accurately. All classifiers were implemented using MATLAB. Experimental results show that CNN achieved the highest performance, with an accuracy of 85%, a precision of 86.7%, and a sensitivity of 83%, outperforming both SVM and KNN. These findings indicate that CNN is the most effective approach for cervical cell classification in this study. However, limitations such as class imbalance and occasional segmentation inconsistencies impacted overall performance, particularly in detecting abnormal cells. Future work will focus on improving classification accuracy, especially for abnormal samples , by exploring data augmentation techniques such as Generative Adversarial Networks (GANs) and implementing ensemble learning strategies. Additionally, integrating the proposed system into a real-time diagnostic platform using a graphical user interface (GUI) could support clinical decision-making and enhance cervical cancer screening programs.
Co-Authors Abdul Talib, Hayati Habibah Ikhwan Arief Ismail, Elsayed Ali Ismail, Shahrina Kharisman, Arif Khiruddin, Khalis Khusairi, Firdaus Yuslan Nilda Tri Putri Wan Mustafa, Wan Azani