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All Journal International Journal of Electrical and Computer Engineering Jurnal Dedikasi Jurnal Gamma TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Inovasi Teknologi Pendidikan International Journal of Advances in Intelligent Informatics POSITIF CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control KOMPUTIKA - Jurnal Sistem Komputer Jurnal Repositor Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Makara Journal of Technology
Agus Eko Minarno
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health

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Classification of Malaria Cell Image using Inception-V3 Architecture Minarno, Agus Eko; Aripa, Laofin; Azhar, Yufis; Munarko, Yuda
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1301

Abstract

Malaria is a severe global public health problem caused by the bite of infected mosquitoes. It can be cured, but only with early detection and effective, quick treatment. It can cause severe conditions if not properly diagnosed and treated at an early stage. In the worst scenario, it can cause death. This study aims at focusing on classifying malaria cell images. Malaria is classified as a dangerous disease caused by the bite of the female Anophles mosquito. As such, it leads to mortality when immediate action and treatment fails to be administered. In particular, this study aims to classify malaria cell images by utilizing the Inception-V3 architecture. In this study, training was conducted on 27,558 malaria cell image data through Inception-V3 architecture by proposing 3 scenarios. The proposed scenario 1 model applies the SGD optimizer to generate a loss value of 0.13 and an accuracy value of 0.95; scenario 2 model applies the Adam optimizer to generate a loss value of 0.09 and an accuracy value of 0.96; and lastly scenario 3 implements the RMSprop optimizer to generate a loss value of 0.08 and an accuracy value of 0.97. Applying the three scenarios, the results of the study apparently indicate that the Inception-V3 model using the RMSprop optimizer is capable of providing the best accuracy results with an accuracy of 97% with the lowest loss value, compared to scenario 1 and scenario 2. Further, the test results confirms that the proposed model in this study is capable of classifying malaria cells effectively.
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Eko Minarno, Agus; Setiyo Kantomo, Ilham; Setiawan Sumadi, Fauzi Dwi; Adi Nugroho, Hanung; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor Minarno, Agus Eko; Hasanuddin, Muhammad Yusril; Azhar, Yufis
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1299

Abstract

Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.
Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network Minarno, Agus Eko; Cokro Mandiri, Mochammad Hazmi; Azhar, Yufis; Bimantoro, Fitri; Nugroho, Hanung Adi; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.857

Abstract

Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.
Co-Authors Abu Abbas Mansyur Achmad Fauzi Saksenata Ahmad Annas Al Hakim Ahmad Faiz, Ahmad Ahmad Heryanto, Ahmad Akbi, Denar Regata Alfarizy, Muhammad Rifal Alfian Yuniarto Anbiya, Dhika Rizki Andhika Pranadipa Andrian Rakhmatsyah Aria Maulana Eka Mahendra Arif Bagus Nugroho Aripa, Laofin Arrie Kurniawardhani arrie kurniawardhany, arrie AULIA ARIF WARDANA Ayu Septya Maulani Bagaskara, Andhika Dwija Basuki, Setio Bayu Yudha Purnomo Bella Dwi Mardiana Chandranegara, Didih Rizki Cokro Mandiri, Mochammad Hazmi Deris Stiawan Dwi Rahayu Dyah Ayu Irianti Eko Budi Cahyono Fachry Abda El Rahman Feny Aries Tanti Firdhansyah Abubekar Fitri Bimantoro Galang Aji Mahesa Gita Indah Marthasari Hanung Adi Nugroho Haqim, Gilang Nuril Hardianto Wibowo Hariyady Hariyady Harmanto, Dani Hasanuddin, Muhammad Yusril Hazmi Cokro Mandiri, Mochammad Ibrahim, Zaidah Indah Soesanti Iqbal Fairus Zamani Irfan, Muhammad irma fitriani Izzah, Tsabita Nurul Lailis Syafa'ah Lailis Syafa’ah Linggar Bagas Saputro Lusianti, Aaliyah Mandiri, Mochammad Hazmi Cokro Moch Ilham Ramadhani Moch. Chamdani Mustaqim Muhammad Afif Muhammad Azhar Ridani Muhammad Hussein Muhammad Nafi Maula Hakim Muhammad Nasrul Tsalatsa Putra Muhammad Nuchfi Fadlurrahman Nanik Suciati Naser Jawas, Naser Nia Dwi Nurul Safitri Noor Aini Mohd Roslan Norizan Mat Diah Prabowo, Christian Ramadhani, Moch Ilham Rangga Kurnia Putra Wiratama Ratna Sari Riksa Adenia Rizalwan Ardi Ramandita Rizka Nurlizah Sabrila, Trifebi Shina Sari, Veronica Retno Sari, Zamah Sasongko Yoni Bagas Setiyo Kantomo, Ilham Sumadi, Fauzi Dwi Setiawan Suryani Rachmawati Suseno, Jody Ririt Krido Toton Dwi Antoko Trifebi Shina Sabrila Tsabitah Ayu Ulfah Nur Oktaviana Veronica Retno Sari Vizza Dwi Wahyu Andhyka Kusuma Wahyu Budi Utomo Wicaksono, Galih Wasis Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Yesicha Amilia Putri Yoga Anggi Kurniawan Yuda Munarko Yudhono Witanto Yufis Azhar Yundari, Yundari Zaidah Ibrahim Zaidah Ibrahim Zamah Sari Zamani, Iqbal Fairus