Article Per Year (5 Year)
p-Index From 2021 - 2026
4.309
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) TELKOMNIKA (Telecommunication Computing Electronics and Control) Abdimas Lembaran Ilmu Kependidikan Scientific Journal of Informatics Jurnal Kajian Informasi & Perpustakaan JURNAL PROFESI KEGURUAN Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) Journal of Innovation in Educational and Cultural Research Journal of Soft Computing Exploration International Journal of Engineering, Science and Information Technology Journal of Advances in Information Systems and Technology Proceeding of International Conference on Science, Education, and Technology Recursive Journal of Informatics Scientific Journal of Informatics Jurnal Abdi Negeri Recursive Journal of Informatics
Aji Purwinarko, Aji
Unknown Affiliation
Arts Humanities Astronomy 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 Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 44 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 44 Documents
Search

 1   2   3   4   5 
Neural Network Optimization Using Hybrid Adaptive Mutation Particle Swarm Optimization and Levenberg-Marquardt in Cases of Cardiovascular Disease Rima Ayu Cahyani; Aji Purwinarko
Recursive Journal of Informatics Vol. 2 No. 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/192vyt90

Abstract

Abstract. Cardiovascular disease is a condition generally characterized by the narrowing or blockage of blood vessels, which can lead to heart attacks, chest pain, or strokes. It is the leading cause of death worldwide, accounting for approximately 31% or 17.9 million deaths each year globally. Deaths caused by cardiovascular disease are projected to continue increasing until 2030, with the number of patients reaching 23.3 million. As cases of death due to cardiovascular disease become more prevalent, early detection is crucial to reduce mortality rates. Purpose: Many previous researchers have conducted studies on predicting cardiovascular disease using neural network methods. This study extends these methods by incorporating feature selection and optimization with Hybrid AMPSO-LMA. The research is designed to explore the implementation and predictive outcomes of Hybrid AMPSO-LMA in optimizing MLP for cases of cardiovascular disease. Methods/Study design/approach: The first step in conducting this research is to download the Heart Disease Dataset from Kaggle.com. The dataset is processed through preprocessing by removing duplicates and transforming the data. Then, data mining processes are carried out using the MLP algorithm optimized with Hybrid AMPSO-LMA to obtain results and conclusions. This system is designed using the Python programming language and utilizes Flask for website access in HTML. Result/Findings: The research results demonstrate that the method employed by the author successfully improves the accuracy of predicting cardiovascular disease. Predicting cardiovascular disease using the MLP algorithm yields an accuracy of 86.1%, and after optimization with Hybrid AMPSO-LMA, the accuracy increases to 86.88%. Novelty/Originality/Value: This effort will contribute to the development of a more reliable and effective cardiovascular disease prediction system, with the goal of early identification of individuals exhibiting symptoms of cardiovascular disease.
Application of C4.5 Algorithm Using Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO) for Diabetes Prediction Dela Rista Damayanti; Aji Purwinarko
Recursive Journal of Informatics Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/yjy1tw93

Abstract

Abstract. Diabetes is the fourth or fifth leading cause of death in most developed countries and an epidemic in many developing countries. Early detection can be a preventive measure that uses a set of existing data to be processed through data mining with a classification process. Purpose: Investigate the efficacy of integrating the C4.5 algorithm with Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO) for improving the accuracy of diabetes prediction models. By employing SMOTE, the study aims to address the class imbalance issue inherent in diabetes datasets, which often contain significantly fewer instances of positive cases (diabetes) than negative cases (non-diabetes). Furthermore, by incorporating PSO, the research seeks to optimize the decision tree construction process within the C4.5 algorithm, enhancing its ability to discern complex patterns and relationships within the data. Methods/Study design/approach: This study proposes the use of the C4.5 classification algorithm by applying the synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO) to overcome problems in the diabetes dataset, namely the Pima Indian Diabetes Database (PIDD). Result/Findings: From the research results, the accuracy obtained in applying the C4.5 algorithm without the preprocessing process is 75.97%, while the results of the SMOTE application of the C4.5 algorithm are 80%. Meanwhile, applying the C4.5 algorithm using SMOTE and PSO produces the highest accuracy, with 82.5%. This indicates an increase of 6.53% from the classification results using the C4.5 algorithm. Novelty/Originality/Value: This research contributes novelty by proposing a hybrid approach that combines the C4.5 decision tree algorithm with two advanced techniques, Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO), for the prediction of diabetes. While previous studies have explored the application of machine learning algorithms for diabetes prediction, few have examined the synergistic effects of integrating SMOTE and PSO with the C4.5 algorithm specifically.
Implementation of Convolutional Neural Network Algorithm Using Vgg-16 Architecture for Image Classification in Facial Images Renita Arianti Hapsari; Aji Purwinarko
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/4mqqh981

Abstract

Abstract: Face Recognition has now become a technology capable of recognizing facial patterns, facial image recognition is also used in various applications, for example in biological data recognition applications, digital image and video search, room security, and other applications. Purpose: This study aims to find out how the implementation of the CNN method with the VGG-16 architecture affects the classification of gender in facial images and how it affects the results. Methods/Study design/approach: In this study, we use the CNN method for data processing and build the program and use VGG-16 Architecture to build the model, then the tensorflow library for calling the required features such as when optimizing or for statistical plots and using the Confusion Matrix to determine the level of accuracy obtained. The desired results in this study are accuracy, precision, recall, and Fscore. Result/Findings: Classifying facial images using CNN with VGG-16 architecture provides an accuracy rate of 94%. From the results of this study it can be concluded that the model with the best accuracy is at epoch 20 compared to epoch 60, epoch 80, and epoch 100 which have previously been tested. Novelty/Originality/Value: The level of accuracy resulting from the implementation of the CNN method using the VGG-16 Architecture for image classification in facial images is quite good, resulting in an accuracy of 94%. Accuracy results were obtained from tests carried out by comparing several epoch values to produce the best accuracy of 94% using epoch 20.
Implementation of Synthetic Minority Oversampling Technique and Two-phase Mutation Grey Wolf Optimization on Early Diagnosis of Diabetes using K-Nearest Neighbors Fathan Arsyadani; Aji Purwinarko
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/s6qsz079

Abstract

Abstract. Diabetes is a disease attacking the endocrine system characterized by high blood sugar levels. International Diabetes Federation (IDF) estimates that there were 451 million people with diabetes globally in 2017. Without treatment, this number is expected to rise to 693 million by 2045. One method for preventing increases in the number of diabetics is by early diagnosis. In an era where technology has developed rapidly, early diagnosis can be made with the machine learning method using classification. In this study, we propose a diabetes classification using K-Nearest Neighbors (KNN). Before classifying the data, we select the best feature subset from the dataset using Two-phase Mutation Grey Wolf Optimization (TMGWO) and balance the training data using Synthetic Minority Oversampling Technique (SMOTE). After dividing the dataset into training and testing sets using 10-fold cross validation, we reached an accuracy of 98.85% using the proposed method. Purpose: This study aims to understand how to apply TMGWO and SMOTE to classify the early stage diabetes risk prediction dataset using KNN and how it affects the results. Methods/Study design/approach: In this study, we use TMGWO to make a feature selection on the dataset, K-fold cross validation to split the dataset into training and testing sets, SMOTE to balance the training data, and KNN to perform the classification. The desired results in this study are accuracy, precision, recall, and f1-score. Result/Findings: Performing classification using KNN with only features selected by TMGWO and balancing the training data using SMOTE gives an accuracy rate of 98.85%. From the results of this research, it can be concluded that the proposed algorithm can give higher accuracy compared to previous studies. Novelty/Originality/Value: Implementing TMGWO to perform feature selection so the model can perform classification with fewer features and implementing SMOTE to balance the training data so the model can better classify the minority class. By doing classification using fewer features, the model can perform classification with a shorter computational time compared to using all features in the dataset.
Co-Authors Adi, Mahargjo Hapsoro Aditya Marianti Akhmad Munawar Alfa Narendra Alfandy Himawan Bagus Rafli Amalia Langgundi, Fitri Amidi Amidi Amin Retnoningsih Amirul Mukminin Anggyi Trisnawan Putra Annindya Ardiansari Ardhi Prabowo Arif Widiyatmoko, Arif Arsyadani, Fathan Aryono Adhi, Aryono Bambang Sugiantoro Budi Prasetiyo, Budi Budiman, Kholiq Cahyani, Rima Ayu Damayanti, Dela Rista Dela Rista Damayanti Dimarti, Safira Chairani Edi Subkhan, Edi Fathan Arsyadani Fianti Fianti, Fianti Fitri Arum Sasi, Fitri Arum Fridiyanto, Fridiyanto Hakim, M. Faris Al Hapsari, Renita Arianti Hasan Mukhibad Hasani, Azharu Alfi Ian Yulianti, Ian Indah Urwatin Wusqo Insan , Shiva Maulana Khoiru Kasinyo Harto Langlang Handayani Lutfia Nur Hadiyanti, Lutfia Nur Mahargjo Hapsoro Adi Majidah Majidah Martien Herna Susanti Melati, Inaya Sari Melissa Salma Darmawan MHD. Reza M.I. Pulungan Muhamad Taufiq Munawar, Akhmad Mutoriq, Alfat Nor Hafizah Ngajikin Nuni Widiarti Nur Kusuma Dewi Putra, Ngurah Made Darma Rahmawati, Sinta Kurnia Renita Arianti Hapsari Rima Ayu Cahyani Ririanti, Novia Puji Sri Siswati Subagja, Mona Subhan Subhan Sugiyarta Stanislaus, Sugiyarta Sujarwata Sujarwata Sukestiyarno Sukestiyarno Susilo Susilo Syuja Zhafran Rakha Krishandhie Syukron Salam, Syukron Tris Sugiarto Tsabit Azinar Ahmad Utis Sutisna Wahyu Hardyanto Widiyanto Widiyanto Woro Sumarni Yanuarto, Alfath YB. Praharto Yuliantika, Atika