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All Journal Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) REKAYASA Jurnal Ilmiah Pangabdhi Proceeding of the Electrical Engineering Computer Science and Informatics Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Layanan Masyarakat (Journal of Public Service) Indonesian Journal of Cultural and Community Development
Rima Tri Wahyuningrum
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Efficient Thoracic Abnormalities Detection Using Mobile Deep Learning Models Bauravindah, Achmad; Fudholi, Dhomas Hatta; Wahyuningrum, Rima Tri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2268

Abstract

Indonesia faces a critical shortage of radiologists, with only 1.2 radiologists per 100,000 individuals. This shortage leads to delays in diagnosing thoracic abnormalities such as pneumothorax, cardiomegaly, nodule/mass, consolidation, and infiltration. Chest X-ray (CXR) interpretation remains challenging due to overlapping radiological features, necessitating AI-assisted solutions. This study evaluates three lightweight deep learning models—MobileNetV2, ShuffleNetV2, and EfficientNetB0—for automated thoracic abnormality detection using the ChestX-ray8 dataset. We assessed model performance using accuracy, precision, recall, F1-score, and AUC-ROC, selecting the best model based on the highest per-fold F1-score. EfficientNetB0 emerged as the top-performing model, achieving a macro-average F1-score of 0.556 and AUC-ROC of 0.765, outperforming MobileNetV2 (0.494, 0.719) and ShuffleNetV2 (0.481, 0.713). Grad-CAM analysis revealed strong localization for pneumothorax and consolidation but misclassifications in cardiomegaly and nodule/mass detection due to poor feature differentiation. The findings highlight EfficientNetB0’s potential as an AI-assisted diagnostic tool for low-resource settings while also underscoring the need for segmentation-based pretraining and multi-scale feature extraction to enhance detection accuracy. Future work should focus on optimizing sensitivity to subtle abnormalities and ensuring clinical trust through improved interpretability techniques.
Segmentasi Citra X-Ray Dada Menggunakan Metode Modifikasi Deeplabv3+ Wahyuningrum, Rima Tri; Jannah, Maughfirotul; Satoto, Budi Dwi; Sari, Amillia Kartika; Sensusiati, Anggraini Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106754

Abstract

COVID-19 is a disease that affects the human respiratory system. The latest developments in September 2022 the number of confirmed cases of COVID-19 worldwide reached 608,328,548 with 6,501,469 patients who died. While in Indonesia confirmed COVID-19 reached 6,408,806 with 157,892 patients who died. Reserve Transcription Polymerase Chain Reaction (RT-PCR) is the most widely used tool. However, the latest RT-PCR test report shows that the RT-PCR test is inadequate. As an alternative, radiographic images such as chest x-rays and CT scans can help detect this. Radiographic images, especially x-rays, need processing to be able to make a diagnosis. Computer Aided Diagnosis (CAD) is a computer assisted diagnosis system that can be used as supporting information in making a diagnosis. To make it easier to make a diagnosis, we need a deep learning model that can help with this. DeepLabV3+ is a method that can carry out the segmentation process. DeepLabV3+ which is an extension of DeepLabV3 with the aim of improving the segmentation results. DeepLabV3+ uses a modified Xception as the backbone. In this study, 1,500 chest x-ray image data were used which were then divided into 80% for training data and 20% for testing data. There are 4 test scenarios in this study, namely with a learning rate of 0.01 without CLAHE, a learning rate of 0,01 and using CLAHE, a learning rate of 0,0001 without CLAHE, and a learning rate of 0,0001 using CLAHE. Of the 4 scenarios the learning rate scenario is 0,01 and using CLAHE gets the highest evaluation results using the Dice Similarity Coefficient (DSC) of 96.91%. 
Deteksi Penyakit Pada Daun Tanaman Padi Berbasis Yolov8 Yasid, Achmad; Ni’mah, Ana Tsalitsatun; Ramadhaningtias, Risma; Wahyuningrum, Rima Tri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Padi (Oryza sativa) merupakan sumber pangan utama di Indonesia yang rentan terhadap serangan penyakit daun seperti Brown Spot, Hispa, dan Leaf Blast. Penyakit ini menghambat fotosintesis dan berdampak pada produktivitas, sehingga deteksi dini sangat penting. Penelitian ini menerapkan metode deteksi objek berbasis YOLOv8n untuk mengidentifikasi tiga penyakit daun padi. Dataset yang digunakan terdiri dari 1.999 citra, yang mencakup 1.867 citra dari Kaggle Repository dan 132 citra hasil pengambilan data lokal di Bangkalan, Jawa Timur. Dataset dibagi menjadi 70% untuk pelatihan, 20% validasi, dan 10% pengujian. Evaluasi model menggunakan metrik mean Average Precision (mAP) dengan delapan skenario pelatihan, yakni kombinasi batch size (16, 32), epoch (100, 300), serta penggunaan augmentasi dan tanpa augmentasi. Hasil menunjukkan bahwa konfigurasi batch size 32 dengan 100 epoch, tanpa augmentasi menghasilkan performa terbaik dengan mAP sebesar 76,5%. Temuan ini menunjukkan bahwa YOLOv8n merupakan metode yang akurat, efisien, dan potensial untuk diimplementasikan pada perangkat mobile sebagai sistem peringatan dini penyakit daun padi.   Abstract Rice (Oryza sativa) is a major food source in Indonesia that is susceptible to leaf diseases such as Brown Spot, Hispa, and Leaf Blast. These diseases inhibit photosynthesis and impact productivity, so early detection is very important. This study applies the YOLOv8n-based object detection method to identify three rice leaf diseases. The dataset used consists of 1,999 images, which includes 1,867 images from the Kaggle Repository and 132 images from local data collection in Bangkalan, East Java. The dataset is divided into 70% for training, 20% validation, and 10% testing. The evaluation model uses the mean Average Precision (mAP) metric with eight training scenarios, namely a combination of batch size (16, 32), epochs (100, 300), and the use of augmentation and without augmentation. The results show that the configuration of batch size 32 with 100 epochs, without augmentation produces the best performance with an mAP of 76.5%. These findings indicate that YOLOv8n is an accurate, efficient, and potential method to be implemented on mobile devices as an early warning system for rice leaf diseases.
Analisis Penggunaan Teknik Load Balancing untuk Optimalisasi Jaringan Ahmad Irsyad Amru Rosyidi; Rima Tri Wahyuningrum
Jurnal Teknik Elektro dan Komputer TRIAC Vol 9, No 2 (2022): Special Edition
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v9i3.15389

Abstract

Saat ini internet berkembang sangat pesat dan membutuhkan penyediaan dalam penyeimbangan sarana internet. Internet juga membutuhkan koneksi untuk memenuhi pemakainya. Sehingga cadangan manajemen keberlangsungan koneksi dari internet sangat diperlukan. Saat ada koneksi yang mengalami masalah, tentu masih memiliki cadangan koneksi lainnya. Salah satu solusi yang ada yaitu pemakaian sistem jaringan Load Balancing. Agar kinerja internet menjadi lebih optimal diperlukan sistem Load Balancing. Load Balancing merupakan sarana yang bagus sebagai penunjang kinerja untuk internet supaya menjadi lebih optimal. Hal ini dikarenakan pembagian jalur traffic dilakukan secara merata. Dalam pemakaianya, mikrotik menjadi alat yang digunakan untuk menjalankan sistem Load Balancing. Ada beberapa metode yang dapat dijalankan, yaitu Per Connection Classifier (PCC), Equal Cost Multi Path (ECMP), dan juga NTH. Metode PCC menjadi metode yang sering dijalankan dalam pemakaian berulang kali. Hal itu disebabkan karena trafik koneksi yang melewati router dibagi secara berkelompok. Maka hubungan anatara client dan server menjadi terjamin, hal ini dikarenakan selalu berada di jalan yang sama.
Co-Authors Achmad Yasid Ahmad Irsyad Amru Rosyidi Albarizy, Ferdy Amillia Kartika Sari Ana Tsalitsatun Ni'mah Angkoso, Cucun Very Ari Kusumaningsih Arif Muntasa Arik Kurniawati Bagus Fajariyanto Bahrur Rosyid Bain Khusnul Khotimah Bauravindah, Achmad Budi Dwi Satoto Firli Irhamni Fitri Damayanti Fuad Hasan Fudholi, Dhomas Hatta Hendrawan, Yonathan Ferry I Ketut Eddy Purnama Ibrohim, Ikrom Maulana Malik Islam, Moh Idzul Jannah, Maughfirotul Khozaimi, Ach. Kurniawan Eka Permana Mauridhi Hery Purnomo Mochammad Kautsar Sophan Mohamad Mustajib, Mohamad Prasetyo, Reynaldi Putra, Wahyu Zainur Ramadhaningtias, Risma Rochman, Eka Mala Sari Rosyidi, Ahmad Irsyad Amru Sensusiati, Anggraini Dwi Siradjuddin, Indah Agustien Syafi?i, Slamet Imam Yonathan Ferry Hendrawan Yonathan Fery Hendrawan Yudiansyah, Hilman Tsaqif