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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Matematika MATEMATIKA Jupiter Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Sinergi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Momentum: Physics Education Journal Jurnal Elektronika dan Telekomunikasi Jurnal Informatika dan Rekayasa Elektronik Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal INFOTEL Media Elektrik
Samsuryadi Samsuryadi
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Physics

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Journal : Sinergi

 1 
CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM Ardina Ariani; Samsuryadi Samsuryadi
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.009

Abstract

The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.
REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK Dwi Lydia Zuharah Astuti; Samsuryadi Samsuryadi; Dian Palupi Rini
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (504.826 KB) | DOI: 10.22441/sinergi.2019.3.008

Abstract

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases.  This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.
Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network Kurniawan, Rudi; Samsuryadi, Samsuryadi; Mohamad, Fatma Susilawati; Wijaya, Harma Oktafia Lingga; Santoso, Budi
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.019

Abstract

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.
Co-Authors Agus Mistiawan Ahmad Fali Oklilas Ahmad Heryanto Akbar, M. Agung Ali Firdaus Anna Dwi Marjusalinah Apit Fathurohman Apriansyah Putra Aprilisa, Shinta Archibald Hutahaean, Jerrel Adriel Ardina Ariani Ardina Ariani Ariani, Ardina Arnelawati, Arnelawati Astuti, Dwi Lydia Zuharah Ayu Luviyanti Tanjung Azhar Azhar Bambang Tutuko Barlian Khasoggi Buchari, Muhammad Ali Cahyadi, Gabriel Ekoputra Hartono Darmawahyuni, Annisa Darmawijoyo, Darmawijoyo Dedy Fitriady Fitriady Deris Stiawan Desty Rodiah Dewy Yuliana Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Dian Palupi Rini Duano Sapta Nusantara Dwi Budi Santoso Dwi Lydia Zuharah Astuti Dwi Lydia Zuharah Astuti Dwi Meylitasari Tarigan Ermatita - Erni Erni Esti Susiloningsih Fatma Susilawati Mohamad Firdaus Firdaus gasim, Gasim Hadipurnama Satria Hadipurnawan Satria Hasby Rifky Indah Permatasari Islami, Anggun Jambak, Muhammad Ihsan Jayanti Jayanti Julian Supardi Khairun Nisa Kurniabudi, Kurniabudi Leni Marlina Lingga Wijaya, Harma Oktafia Lintang Auliya Kurdiati Lintang Auliya Kurdiati M. Nejatullah Sidqi Marlina Sylvia Meryansumayeka Meryansumayeka Mohamad, Fatma Susilawati Muhammad Fachrurrozi Muhammad Haviz Irfani Muhammad Naufal Rachmatullah Mukhlis Febriady Murniati . Nur Rachmat Oktafia Lingga Wijaya, Harma Primanita, Anggina Purnama, Benni Purnamasari, Evi Rahmat Budiarto Ramadhan, Muhammad Fajar Ratu Ilma Indra Putri Rifkie Primartha Risda Intan Sistyawati Riszky Pabela Pratiwi Rizq Khairi Yazid Rossi Passarella Rudi Heriansyah, Rudi Rudi Kurniawan Rudi Kurniawan Saparudin Saparudin Sapitri, Ade Iriani Serrano, Philip Alger M. Sharipuddin, Sharipuddin Shinta Puspasari Sisca Puspita Sepriliani Siti Nurmaini Sukemi Sukemi Sukemi Sukemi Susilawati Mohamad, Fatma Sutarno Sutarno Tri Kurnia Sari Vincen, Vincen Willy, Willy Yesinta Florensia Yogi Tiara Pratama Yulia Hapsari Yundari, Yundari Zahra Alwi Zulkardi Zulkardi