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All Journal Jurnal Sarjana Teknik Informatika Scientific Journal of Informatics Journal of Information System JRST (Jurnal Riset Sains dan Teknologi) Mobile and Forensics Jurnal Sains Komputer dan Teknologi Informasi Journal of applied statistics and data mining
Nurul Huda
Institut Teknologi Statistika Dan Bisnis Muhammadiyah Semarang
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience Social Sciences

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ANALISIS PERFORMA INCEPTIONV3 CONVOLUTIONAL NETWORK PADA KLASIFIKASI VARIETAS DAUN GRAPEVINE: Performance Analysis of InceptionV3 Convolutional Network Used for Grapevine Leaves Varieties Classification Nurul Huda; Adiyah Mahiruna; Wellie Sulistijanti; Rina Chandra Noor Santi
Jurnal Sains Komputer dan Teknologi Informasi Vol. 5 No. 2 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

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Abstract

Daun Grapevine digunakan dalam berbagai masakan tradisional di seluruh dunia. Mengenali berbagai jenis daun Grapevine menjadi semakin penting karena harga dan rasanya bervariasi. Akan tetapi, identifikasi jenis daun ini secara manual akan sulit dan membutuhkan waktu yang lama. Sehingga, beberapa penelitian tentang klasifikasi daun ini dilakukan dengan memanfaatkan metode machine learning. Penelitian ini bertujuan untuk mengklasifikasikan 5 jenis daun Grapevine menggunakan arsitektur InceptionV3 yang merupakan salah satu arsitektur Convolutional Neural Network (CNN). Dataset yang digunakan adalah dataset publik yang terdiri dari 500 gambar, dimana untuk masing-masing kelas terdiri dari 100 gambar yaitu Ak (100), Ala Idris (100), Buzgulu (100), Dimnit (100), Nazli (100). Tahapan pertama dari penelitian ini dengan cara membagi dataset menjadi data training dan data testing. Prosentase data training sebesar 80% (400 gambar) dan data testing 20% (100 gambar). Tahapan selanjutnya dengan melakukan preprocessing gambar, dimulai dengan augmentasi gambar kemudian merubah ukuran gambar menjadi 300x300 pixel. Hasil dari preprocessing gambar inilah yang digunakan untuk uji coba model. Jika peneliti sebelumnya mengusulkan model berbasis Densenet-30 dan menghasilkan akurasi 98%, peneltian ini dengan menggunakan model InceptionV3 Convolutional Network berhasil mencapai akurasi sebesar 99.5%.
Plasmodium falciparum Identification Using Otsu Thresholding Segmentation Method Based on Microscopic Blood Image Nurul Huda; Alfa Yuliana Dewi; Adiyah Mahiruna
Scientific Journal of Informatics Vol 10, No 4 (2023): November 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i4.47924

Abstract

Purpose: Malaria, particularly the Falciparum strain, is a deadly disease caused by Plasmodium falciparum. Malaria diagnosis heavily relies on a microscopist's expertise. To expedite identification, machine learning research has been extensively conducted. However, it has not yet achieved high accuracy, partly due to the use of microscopic images as the dataset. Otsu thresholding, an optimal image segmentation method, maximizes pixel variance to separate the foreground (objects of interest) from the background, which is especially effective in various lighting conditions. Otsu thresholding aims to enhance accuracy and reliability in detecting and classifying Plasmodium falciparum parasites in blood samples.Methods: Dataset of microscopic images of thin blood smears with Plasmodium falciparum, taken from several sources, such as the UC Irvine Machine Learning Repository (UCI), National Institutes of Health (NIH), Kaggle, and Public Health Image Library (PHIL). The methodology combines various image processing techniques, including illumination correction, contrast enhancement, and noise filtering, to prepare the images effectively. The subsequent segmentation using Otsu thresholding method isolates the parasite regions of interest. The classification process involving CNN and SVM evaluates the performance of accuracy to identify different stages of Plasmodium falciparum parasites.Results: The research found the effectiveness of the Otsu thresholding segmentation method in identifying Plasmodium falciparum parasites in microscopic images. By utilizing Convolutional Neural Network (CNN) and Support Vector Machine (SVM) classifiers, the study achieved impressive accuracy rates, with the CNN achieving 96.5% accuracy and the SVM achieving 95.7%.Novelty: This research recorded significant improvement over previous studies that utilized feature extraction and selection methods. At the same time, previous research achieved an accuracy of 82.67. The key innovation here is the adoption of the Otsu thresholding segmentation method, which enhances the identification of Plasmodium falciparum parasites in microscopic images by integrating traditional image processing techniques like Otsu thresholding with modern machine learning methods like CNN and SVM. This significant improvement suggests that the proposed approach offers a more robust and reliable solution for malaria diagnosis.
Klasifikasi Malaria Menggunakan Metode Image Processing Dari Sel Darah Merah Dengan Algoritma Convolutional Neural Network Huda, Nurul; Prayogi, Sukmono Yogi; Ahmad, Muna Adilah; Dewi, Alfa Yuliana
JOINS (Journal of Information System) Vol. 7 No. 2 (2022): Edisi November 2022
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v7i2.7068

Abstract

Penyakit malaria merupakan infeksi penyakit yang ditularkan oleh nyamuk baik dari manusia ataupun hewan lain melalui protozoa parasit yang biasa disebut plasmodium. Malaria bisa berakibat fatal  jika terlambat dideteksi dikarenakan dapat mengakibatkan anemia akut,  gagal ginjal, hingga berujung kematian. Infeksi malaria sebenarnya bisa dideteksi dini menggunakan sampel sel darah merah, dikarenakan sel darah merah yang terinfeksi parasit akan tampak pola atau bercak. Dalam penelitian ini menggunakan data set publik berupa gambar sel darah merah yang terinfeksi protozoa parasit dan tidak terinfeksi. Beberapa peneliti sudah melakukan penelitian untuk mengklasifikasikan penyakit malaria dari sampel sel darah merah. Pada umumnya peneliti sebelumnya menggunakan metode image processing dengan mengubah gambar menjadi negatif untuk kemudian di klasifikasikan menggunakan metode klasifikasi tertentu. Di dalam penelitian ini penulis mencoba mengembangkan metode lain yaitu dengan melakukan pre-prosessing terlebih dahulu terhadap data set yang ada yaitu dengan mengubah ukuran gambar menjadi ukuran PIXEL 100x100, untuk kemudian dilakukan image augmentation untuk memperbanyak data set sehingga kemungkinan akurasi naik menjadi lebih tinggi. Proses selanjutnya yaitu mengubah dimensi gambar yang semula berdimensi tiga menjadi 1 dimensi dengan proses fitur reduksi menggunakan PCA (Principle Component Analysis) hasil dari fitur reduksi tersebut selanjutnya diklasifikasikan menggunakan metode Convolutional Neural Network (CNN). Hasil dari penelitian menunjukkan peningkatan akurasi yang lebih baik menjadi 98,30% dengan menggunakan metode tersebut.Kata kunci: image processing, CNN Classifier, Malaria, image augmentation, PCA
Identification of Plasmodium Vivax in Blood Smear Images Using Otsu Thresholding Algorithm Huda, Nurul; Aulia, Latifathul; Pandini, Maulany Citra
Mobile and Forensics Vol. 6 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v6i1.11261

Abstract

In this research, we explore the efficacy of Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) in identifying Plasmodium vivax from blood smear images. We utilized a dataset comprising images of Plasmodium vivax and non-infected cells, applying CNN for deep feature extraction and SVM with otsu’s thresholding for segmentation. The dataset was preprocessed and augmented to enhance model performance. The CNN architecture, consisting of multiple convolutional and dense layers, achieved an accuracy of 98.46% on the validation set. For comparison, features extracted using Otsu’s Thresholding were fed into an SVM classifier, yielding an accuracy of 82%. Confusion matrix was generated to evaluate the classification performance of both models. The CNN model demonstrated superior accuracy and robustness in classification tasks compared to the SVM model. This research shows how deep learning frameworks can be used to analyse medical images and how important it is to have methods for extracting and choosing features to make machine learning models work better.
Penerapan Algoritma Self Organizing Maps untuk Pengelompokkan Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah Chaerul Iqbal , Ilham; Istiawan, Deden; Ngatimin; Zaenah; Huda, Nurul
Journal of Applied Statistics and Data Mining Vol. 2 No. 2 (2021): Journal Applied Statistics and Data Mining
Publisher : Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63229/jasdm.v2i2.21

Abstract

Penyandang Masalah Kesejahteraan Sosial (PMKS) adalah seseorang atau keluarga karena suatu hambatan, kesulitan atau gangguan tidak dapat melaksanakan fungsi sosialnya dan karenanya tidak dapat menjalin hubungan yang serasi dan kreatif dengan lingkungannya sehingga tidak dapat memenuhi kebutuhan hidupnya (jasmani, rohani, sosial) secara memadai dan wajar. Berdasarkan data penyandang masalah kesejahteraan sosial (PMKS) di Dinas Sosial Provinsi Jawa Tengah, menampilkan tiap daerah memiliki jumlah yang bervariasi dari 26 indikator tersebut. Untuk itu diperlukan pengelompokkan, supaya diketahui karakteristik dari tiap kota atau kabupaten yang memiliki jumlah yang banyak maupun sedikit, oleh karena itu dibutuhkan metode untuk memudahkan dalam pengelompokkan. Data cenderung berdimensi tinggi daripada umumnya dan memiliki atribut lebih dari sepuluh. Sehingga pada penelitian ini menggunakan metode algoritma Self Organizing Maps (SOM) karena efektif untuk visualisasi data berdimensi tinggi. Penelitian ini memperoleh bahwa Kabupaten atau Kota di Provinsi Jawa Tengah yang tidak sejahtera yaitu Kabupaten Purbalingga dan Kabupaten Cilacap. Hasil pengelompokkan Kabupaten/Kota di Jawa Tengah menjadi 3 cluster dimana cluster 1 terdiri dari 4 daerah dikategorikan cukup sejahtera, cluster 2 terdiri dari 13 daerah dikategorikan tidak sejahtera, dan cluster 3 terdiri dari 18 daerah dikategorikan sejahtera.
Chronological review of medical imaging innovations and their transformative impact on healthcare Huda, Nurul; Safitri Windiarti, Ika
Jurnal Sarjana Teknik Informatika Vol. 14 No. 1 (2026): Februari
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i1.30846

Abstract

Over the past two decades, the rapid evolution of medical imaging technologies has become a critical driver of innovation in healthcare. The urgent need for faster, more accurate, and non-invasive diagnostic tools has led to the development of high-field MRI, multislice CT, 3D and 4D ultrasound, and hybrid modalities such as PET/CT and PET/MRI. This review aims to provide a chronological overview of these technological advancements and to evaluate their broader social and clinical impacts. Using a systematic literature review of publications indexed in major databases between 2000 and 2023, the study identifies key milestones in imaging innovation and assesses their influence on diagnostic precision, patient outcomes, and healthcare accessibility. The findings show that the integration of artificial intelligence and machine learning has substantially enhanced image interpretation, early disease detection, and treatment personalization, while also introducing new ethical and policy challenges. The novelty of this work lies in its dual emphasis on technological evolution and societal transformation, offering a comprehensive understanding of how medical imaging continues to shape modern medicine and public health.
Optimization of CNN Architectures for Accurate Brain Tumor Classification: A Comparative Study Nurul Huda; Herman Yuliansyah; Maulany Citra Pandini
JRST (Jurnal Riset Sains dan Teknologi) Volume 9 No. 2 September 2025: JRST
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/jrst.v9i2.26398

Abstract

Automatic classification of brain tumors from MRI images is crucial for supporting early diagnosis and improving treatment planning. However, manual diagnostic processes remain limited by subjectivity and resource constraints. This study aims to optimize brain tumor classification by conducting a comparative analysis of six Convolutional Neural Network (CNN) architectures: VGG16, VGG19, MobileNet, InceptionV3, AlexNet, and Xception. The MRI datasets were sourced from open repositories and processed through normalization, noise reduction, segmentation, and data augmentation. All CNN models were implemented using transfer learning and trained under consistent parameters. Model performance was evaluated based on accuracy, sensitivity, specificity, and F1-score. The results revealed that the Xception and InceptionV3 architectures achieved the highest classification performance, with validation accuracies of 97.9% and 96.1%, respectively. MobileNet also performed competitively at 95.6%, offering notable computational efficiency. In contrast, VGG19 and AlexNet yielded lower validation accuracies and exhibited signs of overfitting. These findings highlight the effectiveness of modern CNN architectures that incorporate depthwise separable convolutions and residual connections in extracting complex features from brain MRI images. Therefore, models such as Xception and MobileNet are strong candidates for implementation in computer-aided diagnosis systems in resource-constrained clinical environments.
Co-Authors Adiyah Mahiruna Adiyah Mahiruna Ahmad, Muna Adilah Aulia, Latifathul Chaerul Iqbal , Ilham Dewi, Alfa Yuliana Herman Yuliansyah Ika Safitri Windiarti Istiawan, Deden Maulany Citra Pandini Ngatimin Pandini, Maulany Citra Prayogi, Sukmono Yogi Rina Chandra Noor Santi Wellie Sulistijanti Zaenah