Article Per Year (5 Year)
p-Index From 2020 - 2025
1.819
P-Index
This Author published in this journals
All Journal Indonesian Journal on Software Engineering (IJSE) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics
Paramita, Cinantya
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models Paramita, Cinantya; Rakasiwi, Sindhu; Andono, Pulung Nurtantio; Shidik, Guruh Fajar; Shier Nee Saw; Rafsanjani, Muhammad Ivan
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26257

Abstract

Purpose: The study evaluates EfficientNetB3 and VGG16 deep learning architectures for image classification, focusing on stability, accuracy, and interpretability. It uses Gradient-weighted Class Activation Mapping to improve transparency and robustness. The research aims to create reliable AI-based diagnostic tools. Methods: The study used a dataset of 4,217 color retinal fundus images divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. The dataset was divided into 70% for training, 10% for validation, and 20% for testing. The researchers used a transfer learning approach with EfficientNetB3 and VGG16 models, pretrained on ImageNet. Real-time augmentation was applied to prevent overfitting and improve generalization. The models were compiled with the Adam optimizer and trained with categorical cross-entropy loss. Early stopping was implemented to allocate computational resources efficiently and reduce overfitting. A learning rate scheduler (ReduceLROnPlateau) was added to adjust the learning rate if no significant improvement was made concerning validation loss. EfficientNetB3 was more efficient in model size, possessing only 12 million parameters compared to VGG16's 138 million, making it suitable for resource-constrained mobile or embedded systems. The final evaluation was done on the held-out test set. Result: The EfficientNetB3 architecture outperforms VGG16 in classification accuracy and loss value stability, with an average accuracy of 93%. It also exhibits better transparency and predicted accuracy, making it a reliable model for medical image categorization. Novelty: This work introduces a novel framework integrating EfficientNetB3 architecture, stratified cross-valuation, L2 regularization, and Grad-CAM-based interpretability, focusing on openness and explainability in model evaluation.
Analisis Pengaruh SMOTE terhadap Kinerja Model KNN untuk Prediksi Risiko Stroke Paramita, Cinantya; Simbolon, Calvin Samuel; Pamungkas, Azriel Sebastian; Triono, Justin Matthew; Widi Utomo, Emanuel Pinesthi; Subhiyakto, Egia Rosi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8809

Abstract

Penelitian ini membahas masalah ketidakseimbangan data dalam klasifikasi risiko stroke, di mana kasus non-stroke secara signifikan lebih rendah daripada kasus stroke. Ketidakseimbangan kelas cenderung menimbulkan bias terhadap kelas mayoritas, yang menyebabkan berkurangnya efektivitas klasifikasi. Untuk mengatasi hal ini, SMOTE (Synthetic Minority Over-sampling Technique) digunakan untuk mengatasi ketidakseimbangan kelas dalam dataset dan algoritma K-Nearest Neighbor (KNN) digunakan untuk klasifikasi. Dataset mengalami preprocessing, aplikasi SMOTE, dan algoritma KNN dilatih dan dievaluasi menggunakan metrik standar termasuk akurasi, presisi, recall, dan F1-score. Penerapan SMOTE bersama dengan KNN menghasilkan peningkatan yang signifikan dalam hasil klasifikasi, mencapai akurasi 91,87%, presisi 94,27%, recall 89,20%, dan F1-score 91,66%. Temuan ini menegaskan bahwa pendekatan yang diimplementasikan berkinerja baik dalam mendeteksi risiko stroke meskipun ada set data yang tidak seimbang. Tujuan dari penelitian ini adalah untuk menginformasikan kemajuan teknologi deteksi dini stroke yang lebih kuat dan mendukung peningkatan dalam penyediaan layanan kesehatan.
Pelatihan Penggunaan Aplikasi Screen Reader JAWS Bagi Tunanetra Untuk Meningkatkan Kemampuan Dalam Pengelolaan Administrasi Paramita, Cinantya; Sudibyo, Usman; Muljono, Muljono; Supriyanto, Catur
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.68 KB) | DOI: 10.33633/ja.v2i2.46

Abstract

Salah satu permasalahan yang dihadapi Perkumpulan Penyandang Disabilitas Indonesia (PPDI), Dewan Pengurus Cabang (DPC) Kota Semarang yakni terbatasnya media dan prasarana untuk mendukung kegiatan belajar mereka dalam mengoperasikan komputer serta tingkat perekonomian yang hanya cukup untuk memenuhi kebutuhan hidup, meraka pun belum sepenuhnya paham dalam perkembangan teknologi hingga sampai saat ini semua masih diolah dalam bentuk manual yakni melaporkan dengan lisan dan dengan cara mengingat. Untuk mengatasi permasalahan tersebut, pengabdian ini mengusulkan untuk mengadakan pelatihan penggunaan Job Access with Speech (JAWS) bagi para tuna netra. Pengabdian dilakukan dengan mengajarkan penggunaan dasar keyboard yang didukung oleh aplikasi JAWS dan pengetahuan dasar Microsoft Excel.
Co-Authors Abu Salam Adhitya Nugraha Amalia Amalia Andreas Wilson Setiawan Astuti, Yani Parti Catur Supriyanto Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Fauzi Adi Rafrastara Finoe, Albireo Musyaffa Guruh Fajar Shidik Junta Zeniarja Kencana, Lisdi Inu Manaf, Humam Wajdi Muljono Muljono Mulyanto, Edy Octaviani, Dhita Aulia Pamungkas, Azriel Sebastian Pradana, Naufal Arsyaputra Pulung Nurtantio Andono Purwanto, Purwanto Putra Hartanto, Octaviano Ryan Eka Rafsanjani, Muhammad Ivan Rahadian, Arief Saputra, Filmada Ocky Savicevic, Anamarija Jurcev Shier Nee Saw Simbolon, Calvin Samuel Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja Tanjung, Mohamad Aprilian Tasleem, Faathir El Triono, Justin Matthew Widi Utomo, Emanuel Pinesthi Wildanil Ghozi ‘Aini, Fariida Qurrota