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All Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Pengabdian Magister Pendidikan IPA JURIKOM (Jurnal Riset Komputer) Ensiklopedia of Journal jurnal abdimas saintika Journal of Artificial Intelligence and Digital Business Jurnal Sistem Cerdas dan Rekayasa Indonesian Research Journal on Education Masyarakat: Jurnal Pengabdian
Fauzi, Dede
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Digitalisasi Klasifikasi Penyakit ICD-10 dalam Sistem Informasi Pencarian Diagnosis untuk Mendukung Rekam Medis Elektronik: Implementasi Metode String Matching pada Sistem Berbasis Web untuk Meningkatkan Efisiensi Pencarian Diagnosis Denos Imam Fratama; Dede Fauzi; Muhammad Ihksan
Jurnal Sistem Cerdas dan Rekayasa (JSCR) Vol 8 No 1 (2026): Jurnal Sistem Cerdas dan Rekayasa (JSCR) 2026
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Widya Kartika (LPPM UWIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61293/jscr.v8i1.951

Abstract

Penerapan rekam medis dalam format digital di institusi layanan kesehatan menuntut tersedianya mekanisme pengkodean diagnosis yang baku dan dapat dioperasikan dengan mudah. ICD-10 (International Classification of Diseases, 10th Revision) merupakan referensi klasifikasi internasional yang diadopsi secara global untuk mendokumentasikan dan mengkategorikan berbagai kondisi medis. Tujuan utama penelitian ini adalah membangun sebuah platform berbasis web yang mengonversi seluruh data klasifikasi ICD-10 menjadisistem pencarian diagnosis terdigitalisasi yang terorganisasi dan responsif. Proses pengembangan sistem mengacu pada model waterfall dengan memanfaatkan PHP sebagai bahasa pemrograman utama dan MySQL sebagai pengelola basis data. Kapabilitas utama sistem mencakup mekanisme pencarian berbasis kata kunci (string matching), kategorisasi kode penyakit, serta penyajian informasi kode secara sistematis. Pengujian dilaksanakanmenggunakan pendekatan blackbox testing, pengukuran kecepatan respons, serta instrumen System Usability Scale (SUS). Hasil evaluasi memperlihatkan sistem beroperasi optimal pada semua skenario pengujian fungsional, dengan rata-rata waktu pencarian di bawah 2 detik dan perolehan skor SUS 82,5 yang masuk dalam kategori "Excellent". Sistem ini diyakini mampu meningkatkan produktivitas dan ketepatan pengelolaan kode diagnosis ICD-10 di berbagai fasilitas pelayanan kesehatan.
Perbandingan Model Deep Learning DenseNet121, EfficientNetB0 dan Resnet-50 pada Klasifikasi Anemia Citra Telapak Tangan Muhammad Ihksan; Dede Fauzi
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9638

Abstract

Anemia is a global health problem affecting approximately 1.2 billion people worldwide, with the highest prevalence among pregnant women and adolescent girls. Conventional diagnosis through laboratory blood tests is invasive, requires trained medical personnel, and is unaffordable for communities in remote areas. This study aims to evaluate and compare the performance of three deep learning architectures, namely DenseNet121, EfficientNetB0, and ResNet-50, in classifying anemia and non-anemia conditions non-invasively based on palm images. The dataset used is a public dataset called anemiatangan from the Kaggle platform, consisting of 10,200 images with two classes, Anemia and Non-Anemia, divided into 80% training data (8,200 images), 10% validation (1,000 images), and 10% testing (1,000 images). All three models were trained using a transfer learning approach with pre-trained weights from ImageNet, accompanied by preprocessing and data augmentation. Evaluation was performed based on accuracy, precision, recall, F1-Score, and AUC-ROC (Area Under the Receiver Operating Characteristic Curve) metrics. The test results indicate that DenseNet121 and EfficientNetB0 achieved the highest accuracy of 99% with precision, recall, and F1-Score values approaching perfection at 0.99, while ResNet-50 recorded an accuracy of 97%. Therefore, DenseNet121 and EfficientNetB0 are proven to be the most optimal architectures for implementing a non-invasive anemia screening system based on palm images, with the potential to be integrated into mobile applications and telemedicine systems to support early detection of anemia in remote areas.
Co-Authors Abdur Rahman Andri Swandi Denos Imam Fratama Dewi, Alfita Hanifa, Amalia Hanim, Hafizah Herman Susilo Isnan, Mahmud M. Syahputra Muhammad Ihksan Nurul Abdillah Sari, Imrah Sulistia, Engla Sulrieni, Ilma Nuria Werleam, Ihsan Yeni , Anita Citra Yeni, Anita Citra