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All Journal Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan Jurnal Publikasi Manajemen Informatika
Abdurrahman Afifi
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Earth & Planetary Sciences

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STUDY OF CONSUMER ACCEPTANCE OF INSTANT REBON Shrimp Shrimp (Acetes sp.) SHIPPING WITH DIFFERENT TYPES OF CHILLIES Abdurrahman Afifi; Suparmi Suparmi; Sumarto Sumarto
Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan Vol 8, No 1 (2021): Edisi 1 Januari s/d Juni 2021
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan

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Abstract

AbstrakThe purpose of this study was to determine the proximate difference of instant shrimp paste shrimp paste (Acetes sp.) with the addition of different types of chili and to determine the level of consumer acceptance of making instant chili paste with different types of chili treatment. The method used is an experimental method by conducting experiments on consumer acceptance of the instant rebon shrimp paste chili with red and green chili types with a weight of 84,6,6g or 47% of the weight of the shrimp paste that you want to make. Parameters assessed by proximate and organoleptic analysis were appearance, aroma, taste and texture performed by 80 untrained people, moisture content, ash content, fat content, protein content and carbohydrate content. The results showed that making chili paste using red chili was more preferred by consumers than green chili chili paste. The formulation for making red chili paste with 33% water content, 77% ash content, 83.49% fat content, 46.18% protein content and 52.33% carbohydrate content.Keywords: Acetes sp, red chili, green chili, instant, shrimp paste.
Klasifikasi Jenis Kelamin Berbasis Citra Mata Menggunakan Algoritma Support Vector Machine Abdurrahman Afifi; Talitha Sulfah
Jurnal Publikasi Manajemen Informatika Vol. 5 No. 1 (2026): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v5i1.4347

Abstract

This study aims to develop a gender classification model based on eye images using the Support Vector Machine (SVM) algorithm. The dataset consists of 13,499 eye images divided into two classes: male and female. The methodology includes preprocessing by converting images to grayscale and resizing them to 64×64 pixels, followed by feature extraction using raw pixel representation resulting in a 4,096-dimensional vector. The data is split into 80% for training and 20% for testing, and SVM parameters are optimized using grid search with 5-fold cross-validation. The SVM model employs an RBF kernel with parameters C=10 and gamma='scale'.Evaluation is carried out using accuracy, precision, recall, F1-score metrics, and a confusion matrix. A decision boundary is visualized using PCA to analyze data separability. The results show excellent performance with 99.96% accuracy, 100.00% precision, 99.95% recall, and 99.98% F1-score. The confusion matrix indicates near-perfect classification, with 648 male samples and 2,051 female samples correctly classified without misprediction. This study demonstrates that the SVM algorithm, even with simple preprocessing, can achieve high accuracy in gender classification based on eye images, showing strong potential for practical implementation in biometric systems
Co-Authors Sumarto Sumarto Suparmi Suparmi Talitha Sulfah