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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Jaring SainTek
Santoso, Ilham
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Studi Pengaruh Jenis Katalis, Waktu Reaksi dan Penurunan Bilangan IODINE pada Pembuatan Cocoa Butter Substitute dengan Proses Hidrogenasi Minyak Kelapa Widodo, Hernowo; Adhani, Lisa; Kustiyah, Elvi; Santoso, Ilham
Jurnal Jaring SainTek Vol. 1 No. 1 (2019): April 2019
Publisher : Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jaring-saintek.v1i1.183

Abstract

Vegetable fat can be used as a base for making chocolate butter substitutes in making chocolate coatings. The product is also called hard butter can be obtained using coconut oil. The fats used as a substitute for cocoa butter are coconut oil, palm oil and palm kernel oil that have been obtained by hydrogenation, the hydrogenation process is used to reduce iodine numbers to obtain oil in the form of plastic solids and to increase the consistency of oils and fats to reduce color and smell and to improve stability. In this study the hydrogenation reaction of coconut oil experienced an increase in speed when the commercial catalyst composition of Pricat 9910 was added with a 0.75 gram nickel catalyst and 0.75 grams of silica which was characterized by a drastic decrease in iodine number from 12.12 to 1.94 gr I2 / 100 gr example in the reaction time of 8 hours, It contained in hydrogenated coconut oil did not change or damage when the catalyst composition used in the hydrogenation process is increased.
Comparison of ResNet-50, EfficientNet-B1, and VGG-16 Algorithms for Cataract Eye Image Classification Santoso, Ilham; Manurung, Ayub Michaelangelo; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8968

Abstract

Cataract is a leading cause of blindness worldwide, emphasizing the need for an effective early detection approach. This study evaluates the capabilities of three widely-used deep learning models—ResNet-50, EfficientNet-B1, and VGG-16—in classifying visual data. The analysis was conducted on a dataset of 2,112 images, comprising 1,074 normal cases and 1,038 cataract cases. The findings reveal that ResNet-50 achieved the best accuracy at 98.61%, followed by EfficientNet-B1 at 96.64% and VGG-16 at 93.82%. In comparison, previous research using Convolutional Neural Network (CNN) techniques reported an accuracy of 92.93%. These results highlight ResNet-50's superior potential for image classification tasks in this domain. This study contributes significantly to the selection of robust models for building an automated cataract detection framework.
The Application of Deep Learning for Skin Disease Classification Using the EfficientNet-B1 Model Manurung, Ayub Michaelangelo; Santoso, Ilham; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9100

Abstract

The skin, being the largest organ in the human body, plays a vital role in protecting against various external threats. However, cases of skin diseases are steadily rising across countries, making it a significant global health concern. Diagnosis often faces challenges due to symptom variations and low public awareness, highlighting the need for automated technology in skin disease detection. This study developed an automated classification system for skin diseases using EfficientNet-B1, capable of categorizing five skin conditions: Acne and Rosacea, Eczema, Melanoma Skin Cancer Nevi and Moles, Normal, Vitiligo, Psoriasis pictures Lichen Planus and related diseases, Seborrheic Keratoses and other Benign Tumors, Tinea Ringworm Candidiasis and other Fungal Infections. The system utilized 1.571 plus 1641 JPG digital images resized to 224 x 224 pixels, with 80% of the data allocated for training and 20% for testing. The trained model achieved a high accuracy of 99%, demonstrating the system's potential to support faster and more accurate diagnostic processes.
Co-Authors Egia Rosi Subhiyakto, Egia Rosi Elvi Kustiyah, Elvi Lisa Adhani Manurung, Ayub Michaelangelo Widodo, Hernowo