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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) Emerging Science Journal Insect (Informatics and Security) : Jurnal Teknik Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS Literasi Hukum Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Journal of Advanced Health Informatics Research Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Control Systems and Optimization Letters
Purwono, Purwono
Universitas Harapan Bangsa
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Comparison of Classification and Regression Model Approaches on the Main Causes of Stroke with Symbolic Regression Feyn Qlattice Purwono, Purwono; Agung Budi Prasetio; Burhanuddin bin Mohd Aboobaider
Journal of Advanced Health Informatics Research Vol. 1 No. 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i2.87

Abstract

Stroke is one of the deadliest diseases in the world, caused by damage to brain tissue resulting from a blockage in the cerebrovascular system. Proper treatment is essential to avoid worsening complications in patients. Several main triggering factors for stroke include hypertension, obesity, smoking habits, lack of physical activity, excessive alcohol consumption, diabetes, and high cholesterol levels. The advancement of information technology allows for early disease prediction through the utilization of AI and Machine Learning technology. The vast amount of data available on medical and health services worldwide can be maximized to identify risk factors for various diseases, including stroke. Machine learning techniques can be employed to predict the causes of stroke. In this study, we were inspired to use the Feyn Qlattice model approach to address stroke. Both classification and regression models were tested in this study. The results indicate that the classification model performs better, achieving an accuracy rate of 0.95. In contrast, the regression model yielded less satisfactory results, with R2, MAE, and RMSE values considered inadequate. This conclusion is supported by the regression plot and residual plot, both of which indicate suboptimal performance. Hence, maximizing the use of the Feyn Qlattice regression model in datasets related to the causes of stroke is recommended
Artificial Intelligence-Based Mobile Health Solutions in the Health 4.0 Era Ariefah Khairina Islahati; Purwono, Purwono; Bala Putra Dewa
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.166

Abstract

In the Health 4.0 era, technological advances continue to bring major changes to the health care industry. Artificial intelligence (AI)-based Mobile Health (mHealth) solutions are an important innovation that will meet modern needs. As time goes by, modern society is increasingly dependent on health technology in everyday life, especially to manage their health conditions. The use of Mobile Health, especially via smartphone devices, has been proven to provide more personalized and affordable health services. Additionally, AI makes diagnosis and health monitoring easier. In this article, the concept of mHealth AI 4.0 is discussed, with particular emphasis on its critical role in providing responsive, proactive, and patient-focused healthcare. In the Health 4.0 era, it is hoped that we can make a positive contribution to improving the quality of health services by using this solution
Challenges and Opportunities: Integration of Data Science in Cancer Research Through A Literature Review Approach Purwono, Purwono; Ariefah Khairina Islahati; Yuslena Sari; Dewi Astria Faroek; Muhammad Baballe Ahmad
Journal of Advanced Health Informatics Research Vol. 1 No. 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v1i3.167

Abstract

Several research articles in this journal relate to various aspects of cancer, such as treatment, patient outcomes, caregiver responsibilities, and the use of AI and liquid biopsy in cancer research. Covers a wide range of topics, including valuable insights into the latest developments in cancer research as well as potential future opportunities and issues. Several articles discuss the impact of non-coding RNA on gastric cancer, machine learning decision support systems for cancer survival factors, economic impact of cancer mortality, nausea in children diagnosed with cancer, protein-RNA variations in cancer clinical analysis, integration and proteomic data analysis in the context of cancer genomics, personalized cancer medicine, mass spectrometry-based clinical proteomics, cancer proteogenomics, subtype-based This journal provides an in-depth overview of various aspects of current cancer research and future research prospects
Implementation of Intelligent Pneumonia Detection Model, Using Convolutional Neural Network (CNN) and InceptionV4 Transfer Learning Fine Tuning Anggit Wirasto; Purwono, Purwono; Muhammad Baballe Ahmad
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.180

Abstract

In Pneumonia is a type of contagious lung infection that has caused many human deaths in the form of inflammation of the alveoli. Based on WHO data, pneumonia is a type of acute infection that has caused more than 450 million cases and 4 million deaths each year. Covid-19 is one of the global pandemics that triggered many pneumonia incidents. Chest X-rays (CXR) are an important part of patient care. Radiologists can use CXR features to determine the type of pneumonia and the underlying pathogenesis. Machine learning and deep learning technologies are used to automatically detect various human diseases, thus ensuring smart healthcare. CXR features are more suitable to be analyzed by convolutional neural network (CNN). This algorithm is one of the typical deep learning architectures that has strong characteristics that are widely applied in the healthcare field. This study aims to develop a deep learning-based paradigm to distinguish Covid-19 patients from healthy and normal individuals by analyzing the presence of pneumonia disease symptoms on the CXR. This research provides an approach to the use of InceptionV4 transfer learning type in performing classification on CXR images. There are three main approaches carried out, namely making a standard CNN model, optimizing transfer learning xceptiion and fine tuning. The performance metrics results show a recall value close to 100% with a model accuracy value of 88%. Achieving a high enough recall value with a relatively small dataset makes the model built is considered to have good capabilities. The ability is also confirmed by the high ROC-AUC value with a value of 0.965
Smart Contracts for Data Sharing in Drug Development a Systematic Review of Security and Transparent Measurement Elsa Wulandari, Annastasya Nabila; Purwono
Journal of Advanced Health Informatics Research Vol. 2 No. 1 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i1.288

Abstract

This systematic literature review explores the role of smart contracts in improving data sharing for drug development, with an emphasis on security and transparency. Using blockchain technology, smart contracts offer a decentralized tracking mechanism for pharmaceutical supply chains, addressing challenges related to drug authentication and supply chain optimization. The review examined 52 studies using the PRISMA methodology, highlighting the automation of data exchange, reduced reliance on external parties, and acceleration of operational processes. Advanced encryption and strict access controls in smart contracts strengthen data security, ensuring patient confidentiality and compliance with medical data regulations. Despite technical and regulatory barriers, smart contracts promise significant improvements in operational efficiency, transparency, and collaboration among stakeholders in drug development. This study emphasizes the need for standardized protocols, further empirical research, and strategic implementation to fully leverage the potential of smart contracts in the pharmaceutical industry. Integration of these technologies can accelerate clinical trials and improve data reliability, thereby enhancing the safety and effectiveness of the drug development process.
Comparison of Transfer Learning Performance in Lung and Colon Classification with Knowledge Distillation Elsa Wulandari, Annastasya Nabila; Yudhistira , Aimar; Purwono; Sharkawy , Abdel-Nasser
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.289

Abstract

This research aims to apply the knowledge distillation method to medical image classification, specifically in the case of lung and colon image classification using various transfer learning models. Knowledge distillation allows the transfer of knowledge from a larger model (teacher) to a smaller model (student), which enables more efficient model building without sacrificing accuracy. In this research, the DenseNet169 model is used as the teacher model. The student model uses several alternative transfer learning architectures such as DenseNet121, MobileNet, ResNet50, InceptionV3, and Xception. The data used consists of 25,000 histopathology images that have been processed and divided into training, validation, and test data. Data augmentation was performed to enlarge the dataset from 750 to 25,000 images, which helped improve the performance of the model. Model performance evaluation was performed by measuring the accuracy and loss value of each student model compared to the teacher model. The results showed that the student models generated through the knowledge distillation process performed close to or even exceeded the teacher model in some cases, with the Xception model showing the highest accuracy of 96.95%. In conclusion, knowledge distillation is effective in reducing model complexity without compromising performance, which is particularly beneficial for implementation on resource-constrained devices.
Understanding Transformers: A Comprehensive Review Rahmadhani, Berlina; Purwono, Purwono; Safar Dwi Kurniawan
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.292

Abstract

Transformers have been recognized as one of the most significant innovations in the development of deep learning technology, with widespread application to Natural Language Processing (NLP), Computer Vision (CV), and multimodal data analysis. The self-attention mechanism, which is at the core of this architecture, is designed to capture global relationships in sequential and spatial data in parallel, enabling more efficient and accurate processing than Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN)-based approaches. Models such as BERT, GPT, and Vision Transformer (ViT) have been used for a variety of tasks, including text classification, translation, object detection, and image segmentation. Although the advantages of this model are significant, the high computing power requirements and reliance on large datasets are major challenges. Efforts to overcome these limitations have been made through the development of lightweight variants, such as the MobileViT and Swin Transformer, which are designed to improve efficiency without sacrificing accuracy. Further research is also directed at the application of transformers for multimodal data and specific domains, such as medical image analysis. With its high flexibility and adaptability, transformers continue to be regarded as a key component in the development of more advanced and far-reaching artificial intelligence.
A Comprehensive Review of Knowledge Distillation for Lightweight Medical Image Segmentation Asmat Burhan; Purwono, Purwono
Journal of Advanced Health Informatics Research Vol. 2 No. 2 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v2i2.294

Abstract

Medical image segmentation plays a crucial role in computer-aided diagnosis by enabling precise identification of anatomical and pathological structures. While deep learning models have significantly improved segmentation accuracy, their high computational complexity limits deployment in resource-constrained environments, such as mobile healthcare and edge computing. Knowledge Distillation (KD) has emerged as an effective model compression technique, allowing a lightweight student model to inherit knowledge from a complex teacher model while maintaining high segmentation performance. This review systematically examines key KD techniques, including Response-Based, Feature-Based, and Relation-Based Distillation, and analyzes their advantages and limitations. Major challenges in KD, such as boundary preservation, domain generalization, and computational trade-offs, are explored in the context of lightweight model development. Additionally, emerging trends, including the integration of KD with Transformers, Federated Learning, and Self-Supervised Learning, are discussed to highlight future directions in efficient medical image segmentation. By providing a comprehensive analysis of KD for lightweight segmentation models, this review aims to guide the development of deep learning solutions that balance accuracy, efficiency, and real-world applicability in medical imaging
Understanding Generative Adversarial Networks (GANs): A Review Purwono, Purwono; Wulandari, Annastasya Nabila Elsa; Ma'arif, Alfian; Salah, Wael A.
Control Systems and Optimization Letters Vol 3, No 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i1.170

Abstract

Generative Adversarial Networks (GANs) is an important breakthrough in artificial intelligence that uses two neural networks, a generator and a discriminator, that work in an adversarial framework. The generator generates synthetic data, while the discriminator evaluates the authenticity of the data. This dynamic interaction forms a minimax game that produces high-quality synthetic data. Since its introduction in 2014 by Ian Goodfellow, GAN has evolved through various innovative architectures, including Vanilla GAN, Conditional GAN (cGAN), Deep Convolutional GAN (DCGAN), CycleGAN, StyleGAN, Wasserstein GAN (WGAN), and BigGAN. Each of these architectures presents a novel approach to address technical challenges such as training stability, data diversification, and result quality. GANs have been widely applied in various sectors. In healthcare, GANs are used to generate synthetic medical images that support diagnostic development without violating patient privacy. In the media and entertainment industry, GANs facilitate the enhancement of image and video resolution, as well as the creation of realistic content. However, the development of GANs faces challenges such as mode collapse, training instability, and inadequate quality evaluation. In addition to technical challenges, GANs raise ethical issues, such as the misuse of the technology for deepfake creation. Legal regulations, detection tools, and public education are important mitigation measures. Future trends suggest that GANs will be increasingly used in text-to-image synthesis, realistic video generation, and integration with multimodal systems to support cross-disciplinary innovation.
Implementasi Market Basket Analysis dengan Algoritma Apriori untuk Analisis Pendapatan Usaha Retail Imam Ahmad Ashari; Anggit Wirasto; Deny Nugroho Triwibowo; Purwono Purwono
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1439

Abstract

Pada era teknologi sekarang hampir semua bisnis ritel sudah menggunakan teknologi Point of Sale (PoS), dimana semua transaksi di rekap dalam sebuah database sistem. Data yang disimpan di dalam database dapat diolah untuk meningkatkan penjualan. Dengan mengetahui asosiasi data penjualan, aplikasi dapat memberikan rekomendasi produk yang memungkinkan pelanggan untuk membeli rekomendasi produk tersebut. Tujuan dari penelitian ini adalah mengetahui pola asosiasi yang terdapat pada sebuah toko yang sudah menerapkan teknologi PoS. Apabila pola asosiasi tersebut membentuk keterhubungan produk yang relevan dan mendatangkan keuntungan lebih maka metode yang di usulkan akan di terapkan pada aplikasi toko. Algoritma Apriori dapat menemukan pola hubungan produk antar satu atau lebih item dalam suatu dataset. Hanya saja Algoritma Apriori memiliki kelemahan dalam performa. Penerapan algoritma apriori dapat memperlambat akses transaksi, sehingga perlu pengkajian lebih dalam tentang kebermanfaatan pola asosiasi ini. Pada penelitian ini pola asosiasi dianalisis apakah berpengaruh terhadap peningkatan penjualan. Dalam penelitian ini didapatkan bahwa pola asosiasi memiliki peran penting dalam peningkatan penjualan. Didapatkan rata - rata asosiasi dengan nilai confidence tertinggi terjadi pada bulan maret, yaitu 0.61 dengan nilai minimal support 0.003. Hal ini sesuai dengan hasil penjualan tertinggi, yaitu sebesar Rp. 295.509.934 pada bulan maret, tahun 2021. Berdasarkan penelitian ini maka penggunaan algoritma apriori pada aplikasi POS perlu diterapkan.
Co-Authors Agung Budi Prasetio Agung Pangestu Ahmad Toha Alfian Ma’arif Amanah Wulandari Anggit Wirasto Anggit Wirasto Anton Suhendro Ariefah Khairina Islahati Arif Setia Sandi A. Asmat Burhan Asmat Burhan Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Barlian Kristanto Berliana Rahmadhani Burhanuddin bin Mohd Aboobaider Deny Nugroho Triwibowo Dewi Astria Faroek Dimas Febri Kuncoro Dimas Herjuno Eko Ariyanto Elsa Wulandari, Annastasya Nabila Endang Setyawati Hadi Jayusman Hesti Ayu Wahyuni Iin Dyah Indrawati Iis Setiawan Mangkunegara Iis Setyawan Mangku Negara Imam Ahmad Ashari Imam Ahmad Ashari, Imam Ahmad Imam Riadi Imam Riadi Isa Ali Ibrahim Jatmiko Indriyanto Jihad Rahmawan Khoirun Nisa Khoirun Nisa Khoirun Nisa Lutviana Lutviana Lutviana Mangku Negara, Iis Setiawan Mangkunegara, Iis Setiawan Marlia Hafny Afrilies Maya Ruhtiani Muchammad Naseer Muhammad Ahmad Baballe Muhammad Baballe Ahmad Muhammad Haikal Satria Muntiari, Novita Ranti Musafa Widagdo Pramesti Dewi Qazi Mazhar ul Haq Rahmadhani, Berlina Riska Suryani Riyanarto Sarno Rosyid Ridlo Al-Hakim Rubaeah, Siti Rusydi Umar Safar Dwi Kurniawan Salah, Wael A. Sandi Najib Iskandar Sharkawy , Abdel-Nasser Slamet Slamet Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Supriyatin Supriyatin Tohari Ahmad Tusaria Tri Wahyu Ningrum Wahyu Caesarendra Wahyu Rahmaniar Windu Gata Wulandari, Annastasya Nabila Elsa Yanuar Zulardiansyah Arief Yudhistira , Aimar Yuris Tri Naili Yuris Tri Naili Yuslena Sari, Yuslena Yusuf Fadlila Rahman