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All Journal Nusantara of Research : Jurnal Hasil-hasil penelitian Universitas Nusantara PGRI Kediri (e-journal) JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Puspitasari, Yuanita
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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Studi Komparasi Terhadap Resiliensi Siswa Berdasarkan Gender Muwakhidah, Muwakhidah; Lianawati, Ayong; Puspitasari, Yuanita
Nusantara of Research : Jurnal Hasil-hasil Penelitian Universitas Nusantara PGRI Kediri Vol 10 No 3 (2023)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nor.v10i3.19526

Abstract

Resilience is an individual's quality to rise and adapt to social change. Gender is one of the inconsistent resilience factors, so it needs to be tested. This study aims to prove the difference in resilience between male studens with female students. The research method used a comparative study of 188 male students and 188 female students from SMA, SMK, or MA in the city of Surabaya. The results of the ANOVA test showed that there was a significant difference between male and female resilience. Descriptive test shows women are superior in resilience scores than men. How to resolve and deal with change is the main indicator that makes the two groups have different resilience
Investigasi Hubungan Antara Resiliensi Akademik, Mindfulness dan Efikasi Diri Akademik Siswa SMP Se-Kota Surabaya Puspitasari, Yuanita; Darminto, Eko; Tri Hariastuti, Retno
Nusantara of Research : Jurnal Hasil-hasil Penelitian Universitas Nusantara PGRI Kediri Vol 10 No 4 (2023)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/nor.v10i4.21048

Abstract

Academic resilience, mindfulness, and academic self-efficacy are topics of interest to psychologists, but little is known about the relationship between the three. The main aim of this study was to explore the role of mindfulness and academic self-efficacy in predicting academic resilience among junior high school students. This research design uses quantitative correlation. The subjects in this study were 710 junior high school students in Surabaya. The research instrument used the Freiburg Mindfulness Inventory questionnaire, the academic self-efficacy questionnaire and the Casidi academic resilience questionnaire. The data analysis technique uses multiple regression techniques. The results showed that there was a significant relationship between mindfulness and academic self-efficacy, r = 0.43. Both mindfulness and academic self-efficacy were found to be significant predictors of academic resilience, F (1.139) = 110.39, p<.000, and F (2.138) = 104.44, p<.000. Based on the results of the study it can be concluded that mindfulness and academic self-efficacy have a significant influence on the academic resilience of junior high school students.
COMPARISON OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES EFFICIENTNET-B4 AND MOBILENETV2 IN CATARACT DISEASE DETECTION Puspitasari, Yuanita; Jong, Jek Siang; Prabawati, Andhika Galuh
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7502

Abstract

Cataracts are the leading cause of blindness worldwide, with 94 million cases reported in 2023. Conventional cataract identification relies on visual examination methods that are prone to error due to their subjective nature. This study compares the performance of two Convolutional Neural Network (CNN) architectures, MobileNetV2 and EfficientNet-B4, in detecting cataract images. The dataset used was sourced from Kaggle and consisted of 1,074 normal images and 1,038 cataract images. The stages included preprocessing, augmentation, and the application of transfer learning with weights from ImageNet. The models were evaluated using accuracy, loss, precision, recall, F1-score, error rate, and visual interpretation using Grad-CAM metrics. The results showed that MobileNetV2 achieved 96% accuracy with an error rate of 4.05%, balanced precision, recall, and F1-score of 0.96, and a loss of 0.60. Meanwhile, EfficientNet-B4 achieved an accuracy of 96.5% with an error rate of 3.47%, balanced precision, recall, and F1-score of 0.97, and a lower loss of 0.12. Further evaluation indicates that EfficientNet-B4 has the same error rate on both training and test data (3.47%) with a loss difference of 0.02, suggesting that the model performs well and does not experience overfitting. In MobileNetV2, the difference in error rate between training (3.28%) and test (4.05%) is relatively small (0.77%), indicating that this model also does not exhibit overfitting. Grad-CAM visualization reveals that EfficientNet-B4 focuses more on clinically relevant areas, whereas MobileNetV2 tends to capture global patterns. Thus, EfficientNet-B4 is considered superior in terms of accuracy and generalization, while MobileNetV2 is more computationally efficient
Co-Authors Eko Darminto Jong, Jek Siang Lianawati, Ayong Muwakhidah, Muwakhidah Prabawati, Andhika Galuh RETNO TRI HARIASTUTI