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All Journal Jurnal Dedikasi Jurnal Ilmu Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Simantec Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Teknologi Informasi dan Ilmu Komputer SMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Pilar Nusa Mandiri Network Engineering Research Operation [NERO] Jurnal Komputer Terapan Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika Kaputama (JIK) JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Repositor Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Perempuan & Anak Jurnal Dinamika Informatika (JDI) Makara Journal of Technology Jurnal Sistem Informasi Jurnal Informatika: Jurnal Pengembangan IT
Yufis Azhar
Universitas Muhammadiyah Malang
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Implementasi Data Augmentation untuk Klasifikasi Sampah Organik dan Non Organik Menggunakan Inception-V3 Bintang, Rahina; Azhar, Yufis
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.192-204

Abstract

The surge in global waste, particularly in Indonesia, with a total of 36.218 million tons per year, has become an urgent issue. Challenges in waste management are increasingly complex due to the lack of public understanding and awareness in classifying types of waste. One systemic approach to address waste classification issues involves the use of machine learning technology to categorize waste into two main types: organic and non-organic. The data used in this study comes from a Kaggle website dataset comprising 25,500 entries. This research employs a transfer learning approach with the Inception-V3 architecture and data augmentation implementation. Transfer learning is chosen for its proven performance in image data classification, while data augmentation is implemented to introduce diversity to the dataset. The research stages include business understanding, data preprocessing, data augmentation, data modelling, and evaluation. The results show that the use of transfer learning with the Inception-V3 approach and data augmentation implementation achieves an accuracy rate of 94%, which falls into the excellent category.
Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval Azhar, Yufis; Akbi, Denar Regata
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2179

Abstract

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.
The Implementation of Pretrained VGG16 Model for Rice Leaf Disease Classification using Image Segmentation Suseno, Jody Ririt Krido; Azhar, Yufis; Minarno, Agus Eko
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 1, February 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i1.1592

Abstract

Rice is an agricultural sector that produces rice which is one of the staple foods for the majority of the population in Indonesia. In the cultivation of rice plants there are also factors that affect rice production and are not realized by farmers causing that they are late in handling and diagnosing symptoms and making rice production decline. Therefore, it is necessary to have an early diagnosis of rice plants to identify them correctly, quickly and accurately. Machine learning is one of the classification techniques to detect various plant diseases such as rice plants. There are several studies on machine learning using the Convolutional Neural Network with the VGG16 model to classify rice leaf diseases and using Image Segmentation techniques on rice leaf datasets for make the image becomes a form that is not too complicated to analyze. The data used in this research is Rice Leaf Disease which consists of 3 classes including Bacterial leaf blight, Brown spot, and Leaf smut. Then segmentation is carried out using two techniques, namely threshold and k means. Then data augmentation for make dataset used has a large and varied number and training using VGG16 model with hyperparameter tuning and obtained 91.66% accuracy results for scenarios with the k-means dataset.
Classification of Coffee Leaf Diseases using CNN Sucia, Dara; Shintya Larasabi , Auliya Tara; Azhar, Yufis; Sari, Zamah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 3, August 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i3.1745

Abstract

Indonesia’s coffee industry plays a crucial role as a major export, making a significant contribution to the country’s economy by generating foreign exchange. The quality and quantity of coffee production depend on various factors such as humidity, rain, and fungus that can cause rust diseases on coffee leaves. These diseases can spread quickly and affect other coffee plants quality, leading to decreased production. To address this issue, CNN with VGG-19 architecture model was utilized to identify coffee plant diseases using image data and the python programming language, which in previous studies used MATLAB as their platform. In addition, VGG-19 with image enhancement and contouring data for pre-processing step has a more profound learning feature than the method used in the previous studies, AlexNet which makes the structure of VGG- 19 more detailed. The dataset used in this paper is Robusta Coffee Leaf Images Dataset which have three classes, namely health, red spider mite, and rust. The VGG-19 model attained F1-Score of 90% when evaluated using the testing data with ratio 80:20, where 80% is training data, and 20% is validation data. This paper employed 0.0001 learning rate, batch size 15, momentum 0.9, 12 training iteration, and RMSprop optimizer.
Comparison of Transfer Learning Models in Classification Dental and Tongue Disease Images Azhar, Yufis; Setiono, Fauzan Adrivano; Chandranegara, Didih Rizki
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.487

Abstract

According to the Global Burden of Disease Study, dental caries is the most prevalent oral health ailment, affecting around 3.5 billion individuals globally. According to the Ministry of Health of the Republic of Indonesia, 93% of children in the country suffer from oral health issues, making poor oral health a serious public health concern. The tongue and teeth in the mouth are particularly vulnerable to a wide range of illnesses, and the condition of the mouth is a key sign of the health of the body as a whole. The CNN algorithm has been utilized in numerous studies to classify disorders of the tongue and teeth. Nevertheless, no study has classified tongue and dental diseases using merged datasets as of yet. This research addresses this gap by focusing on the classification of dental and tongue diseases using transfer learning techniques with CNN architecture models VGG16, VGG19, and ResNet50. The primary aim is to compare these three models to identify the one with the most optimal performance in handling related cases. Based on the results, the best accuracy was achieved with data augmentation and models trained for 75 epochs. The VGG16 model attained 94% accuracy, VGG19 achieved 93% accuracy, and ResNet50 also reached 94% accuracy. These findings suggest that transfer learning with CNN architectures can effectively classify dental and tongue diseases. The implications are significant for developing automated diagnostic tools that can aid in the early detection and treatment of oral health issues globally.
Improving Image Retrieval Performance with SCS and MCS Clustering Techniques Fikri, Ulul; Prakoso, Rahmat; Azhar, Yufis
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 2 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i2.75643

Abstract

This paper presents two methods, Single Cluster Search (SCS) and Multiple Cluster Search (MCS), aimed at enhancing image retrieval performance on the Corel1k, Corel5k, and Corel10k datasets, which has a wide variation of images. The Multi Texton Co-Occurrence Descriptor (MTCD) method is used for feature extraction, and the K-Medoids and DBSCAN methods are used for dataset clustering. The clusters are then ranked based on the distance of their medoids to the query image. The most relevant images are retrieved from the highest-ranking clusters. SCS selects the cluster with the highest ranking as the search area and expands the search area to the next ranking cluster if the number of images is less than 6, which is the desired number of retrieval results. MCS merges several clusters with the highest ranking and combines clusters as the search area. Both methods are evaluated using several metrics, such as AP, MRR, and retrieval time. The results are also compared with the original method, which does not use clustering (the query image and the dataset are only extracted with MTCD, and their distance is calculated). The findings indicate that both methods improve the retrieval time. In Corel1k, the SCS method reduces the time complexity by 0.001s, while the MCS method, although not surpassing the original method, still shows potential. In Corel5k, both methods reduce the time complexity by 0.052s in the SCS method and 0.015s in the MCS method. In Corel10k, both methods reduce the time complexity by 0.122s in the SCS method and 0.058s in the MCS method, compared to the original method. These results have practical implications for improving image retrieval efficiency. The paper discusses the reasons behind these results and suggests possible directions for future research.
Classification of Skin Cancer Images Using Convolutional Neural Network with ResNet50 Pre-trained Model Minarno, Agus Eko; Lusianti, Aaliyah; Azhar, Yufis; Wibowo, Hardianto
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2156

Abstract

The skin, an astonishingly expansive organ within the human body, plays a pivotal role in safeguarding us against the environment's harsh elements. It acts as a formidable barrier, shielding our delicate internal systems from the scorching heat of the sun and the harmful effects of relentless exposure to light. Nevertheless, it is not impervious to damage, especially when subjected to excessive sunlight and the potentially hazardous ultraviolet (UV) radiation that accompanies it. Prolonged UV exposure can wreak havoc on our skin cells, potentially setting the stage for the development of skin cancer. This condition demands prompt and accurate diagnosis for effective treatment. To address the pressing need for swift and precise skin cancer diagnosis, cutting-edge technology has come to the fore in the form of deep learning systems. These sophisticated systems have been meticulously designed and trained to classify skin lesions autonomously with remarkable accuracy. The Convolutional Neural Network (CNN) architecture is a formidable choice for handling image classification tasks among the array of deep learning techniques. In a recent breakthrough study, a CNN-based model was meticulously constructed to explicitly classify skin lesions, leveraging the power of a pre-trained ResNet50 architectural model to augment its capabilities. This groundbreaking ResNet50 architecture was meticulously trained to classify seven distinct skin lesions, surpassing the performance of its predecessor, MobileNet. The results achieved in this endeavor are nothing short of impressive. The overall accuracy of the ResNet50 model stands at a commendable 87.42% when tasked with classifying the seven diverse classes within the dataset. Delving further into its proficiency, we find that the Top2 and Top3 accuracy rates soar to an astounding 95.52% and 97.86%, respectively, illustrating the model's exceptional precision and reliability.
Leveraging ESRGAN for High-Quality Retrieval of Low-Resolution Batik Pattern Datasets Azhar, Yufis; Marthasari, Gita Indah; Regata Akbi, Denar; Minarno, Agus Eko; Haqim, Gilang Nuril
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3202

Abstract

As one of the world's cultural heritages in Indonesia, batik is one of the quite interesting research subjects, including in the realm of image retrieval. One of the inhibiting factors in searching for batik images relevant to the query image input by the user is the low resolution of the batik images in the dataset. This can affect the dataset's quality, which automatically also impacts the model's performance in recognizing batik motifs with complex details and textures. To address this problem, this study proposes using the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method to increase the resolution of batik images. By increasing the resolution, it is hoped that ESRGAN can clarify the details and textures of the initial low-resolution image so that these features can be extracted better. This study proves that ESRGAN can produce high-resolution batik images while maintaining the details of the batik motif itself. The resulting image's high PSNR and low MSE values confirm this. The implementation of ESRGAN has also been proven to improve the performance of the image retrieval system with an increase in precision and average precision values between 1-5% compared to other methods that do not implement it.
Improving Classification of Medical Images Using ESRGAN-Based Upscaling and MobileNetV2 Masluha, Ida; Azhar, Yufis
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.636

Abstract

Low-resolution photos are frequently problematic in the medical field when diagnosing skin and eye conditions since they can induce noise and lower the precision of classification algorithms. To overcome this, this research implements the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method which is used to perform upscaling, namely increasing the resolution of a low image to a high-resolution image. The research results show that ESRGAN is able to improve the quality of eye and skin images, as proven by accuracy consistency tests on the two datasets. For image classification, the MobileNetV2 model is used because this model is suitable for eye and skin datasets. Evaluation of the image retrieval system using a high-resolution dataset resulting from ESRGAN Upscaling shows an increase in accuracy of 4-17% on both datasets. In this research, the improvement in visual image quality is also proven by the high Peak Signal-to-Noise Ratio (PSNR) value, so that ESRGAN is proven to be effective in increasing image resolution and clarity, both for eye medical image datasets and skin images.
MRI Image Based Alzheimer’s Disease Classification Using Convolutional Neural Network: EfficientNet Architecture Ujilast, Novia Adelia; Firdausita, Nuris Sabila; Aditya, Christian Sri Kusuma; Azhar, Yufis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5457

Abstract

Alzheimer's disease is a neurodegenerative disorder or a condition characterized by degeneration and damage to the nervous system. This leads to a decline in cognitive abilities such as memory, thinking, and focus, which can impact daily activities. In the medical field, a technology called Magnetic Resonance Imaging (MRI) can be used for the initial diagnosis of Alzheimer's disease through image procedures-based recognition methods. The development of this detection system aims to assist medical professionals, including doctors and radiologists, in diagnosing, treating, and monitoring patients with Alzheimer's disease. This study also aims to classify different types of Alzheimer's disease into four distinct classes using the convolutional neural network method with the EfficientNet-B0 and EfficientNet-B3 architectures. This study used 6400 images that encompass four classes, namely mild demented, moderate demented, non-demented, and very mild demented. After conducting testing for both scenarios, the exactness outcomes for scenario 1 utilizing EfficientNet-B0 reveryed 96.00%, and for scenario 2 utilizing EfficientNet-B3, the exactness was 97.00%.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Fauzi Saksenata Adhigana Priyatama Aditya Dwi Maryanto Adnan Burhan Hidayat Kiat Afdian, Riz Agus Eko Minarno Agus Zainal Arifin Ahmad Annas Al Hakim Ahmad Darman Huri Ahmad Hanif Nurfauzi Ahmadu Kajukaro Akbi, Denar Regata Akmal Muhammad Naim Al asqalani, Sheila Fitria Al-rizki, Muhammad Andi Alfin Yusriansyah Ali Sofyan Kholimi Amelia, Putri Juli Ananda Ayu Dianti Andhika Ade Verdiyanto Andhika Pranadipa Andi Shafira Dyah Kurniasari Andreawana, Andreawana Andriani Eka Pramudita Annisa Annisa Annisa Fitria Nurjannah Aria Maulana Aripa, Laofin Aris Muhandisin arrafiq, ubay hakim Arya, Tri Fidrian Audi Bayu Yuliawan Aulia Ligar Salma Hanani Bagas Aji Aprian Basuki, Setio Bayu Yuliawan, Audi Bintang, Rahina Chandranegara, Didih Rizki Chita Nauly Harahap Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Cokro Mandiri, Mochammad Hazmi Denny Risky Delis Putra Dewi Agfiannisa Diana Purwitasari Doni Yulianto Dwi Anggraini Puspita Rahayu Dwi Kurnia Puspitaningrum DWI RAHMAWATI Dyah Anitia Dyah Ayu Irianti Eko Budi Cahyono Elsyah Ayuningrum Elza Norazizah Evi Febrion Rahayuningtyas Fahrur Rozi Faizun Nuril Hikmah Faldo Fajri Afrinanto Fatimah Defina Setiti Alhamdani Fenny Linsisca Putri Feny Novia Rahayu Feranandah Firdausi Ferin Reviantika Ferin Reviantika Fikri, Ulul Fiqri Azmi Fachir Firdausi, Feranandah Firdausita, Nuris Sabila Firdausy, Aidia Khoiriyah Firdhansyah Abubekar Fitri Bimantoro Galang Aji Mahesa Galang Aji Mahesa Gita Indah Marthasari Hanung Adi Nugroho Haqim, Gilang Nuril Hardianto Wibowo Haris Diyaul Fata Harmanto, Dani Hasanuddin, Muhammad Yusril Hermansyah Adi Saputra Hiu Adam Abdullah Hussin Agung Wijaya Ibrahim, Zaidah Ilham Rahmana Syihad Imam Halimi Irfan, Muhammad Ivan Dwi Nugraha Jahtra Hidayatullah Jalu Nusantoro Khoirir Rosikin Kiki Ratna Sari Lina Dwi Yulianti Linggar Bagas Saputro Lusianti, Aaliyah M Syawaluddin Putra Jaya M. Randy Anugerah Mahar Faiqurahman Maskur Maskur Maskur Maskur Masluha, Ida Maulina Balqis Meilina Agustina Mentari Mas'ama Safitri Moch Shandy Tsalasa Putra Moch. Chamdani Mustaqim Mochammad Hazmi Cokro Mandiri Moh. Badris Sholeh Rahmatullah Muhammad Aji Purnama Wibowo Muhammad Al Reza Fahlopy Muhammad Andi Al-Rizki Muhammad Athaillah Muhammad Bima Al Fayyadl Muhammad Fadliansyah Muhammad Hussein Muhammad Misbahul Azis Muhammad Nuchfi Fadlurrahman Muhammad Riadi Muhammad Rifal Alfarizy Muhammad Rivaldi Asyhari Muhammad Rizki Muhammad Rizky Iman Permana Muhammad Shalahuddin Zulva Mujaddid Izzul Fikri Nabillah Annisa Rahmayanti Nina Mauliana Noor Fajriah Novandha Yudyanto Noviani Sintia Duwi Trisna Nur Hayatin Nur Putri Hidayah Nuryasin, Ilyas Oktavia Dwi Megawati Otto Endarto Prakoso, Rahmat Pratama, Dhimas Rama Anthony Navy Putri, Ira Ekanda Rahma Ningsih Rangga Kurnia Putra Wiratama Ratna Sari Rifky Ahmad Saputra Riksa Adenia Riska Septiana Putri Rista Azizah Arilya Riz Afdian Rizal Arya Suseno Rizal Rakhman Mustafa S, Vinna Rahmayanti Saputri, Indah Sari Wahyunita Sari, Veronica Retno Sari, Zamah Satrio Hadi Wijoyo Septiyan Andika Isanta Setiono, Fauzan Adrivano Shintya Larasabi , Auliya Tara Silcillya Ayu Astiti Siti Maghfiroh Sucia, Dara Suryani Rachmawati Suseno, Jody Ririt Krido Susi Ekawati Syaifuddin Syaifuddin Syaifudin Zuhri Taufik Nurahman Tri Fidrian Arya Trifebi Shina Sabrila Trifebi Shina Sabrila Ujilast, Novia Adelia Ulfah Nur Oktaviana Veronica Retno Sari Vinna Utami Putri Wahyu Priyo Wicaksono Wana Salam Labibah Wicaksono, Galih Wasis Widya Rizka Ulul Fadilah Wildan Suharso Wildan Suharso Wildan Suharso Yesicha Amilia Putri Yuda Munarko Yudhono Witanto Yurizal Rizqon Rifani Yusuf, Achmad Zamah Sari Zulva, Muhammad Shalahuddin