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All Journal Prawara: Jurnal Pendidikan Bahasa dan Sastra Indonesia Horizon: Indonesian Journal of Multidisciplinary
Ramadhani, Muhammad Nabil
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Agriculture, Biological Sciences & Forestry Arts Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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PENGARUH PENGGUNAAN BAHASA INDONESIA TERHADAP KEPUASAN PENGGUNA PORTAL LAYANAN MAHASISWA FASILKOM UPN “VETERAN” JATIM Sholihatin, Endang; Ramadhan, Raihan; Jovian, Abraham Danar; Gabriel, Paskalis Reynaldy Elroy; Triwinanda, Rizqullah Sandya Yossie; Ramadhani, Muhammad Nabil; Bimantoro, Ryan Bagus
Prawara: Jurnal Pendidikan Bahasa dan Sastra Indonesia Vol 4 No 2 (2023): Desember 2023
Publisher : Fakultas Bahasa dan Sastra Indonesia Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jpbsi.2023.4.2.8854

Abstract

This study discusses the influence of the Indonesian language on user satisfaction in the development and use of student service portals at the Faculty of Computer Science at the National Development University "Veteran" East Java. (a) The purpose of this study is to determine the user's understanding and the effectiveness of using the Indonesian language used in the Student Service Portal of the Faculty of Computer Science UPN "Veteran" East Java. (b) To find out whether the use of the Indonesian language increases the interaction between the user and the Service Portal. (c) To find out whether the Indonesian language can minimize user misinformation so that it can affect the level of satisfaction with the Student Service Portal. The research method used was a survey using a questionnaire as a data collection tool. The research sample is UPN "Veteran" East Java Faculty of Computer Science students who actively use the student service portal. (a) The results of the study show that the use of the student service portal can be well understood by the student's family, as evidenced by the data that 99% of students understand the use of the language in the Student Service Portal. (b) Then students who use the Student Service Portal experience an increase in interaction with 98% agreeing because of the use of clear and effective language. (c) Most users report that they are more comfortable interacting with the portal because they use language that is familiar to them and also influences understanding in receiving information. Keyword: Indonesian language, user satisfaction, student service portal
Analisis Pengaruh Strategi Augmentasi Data Terhadap Performa Model Hybrid Cnn–Swin Transformer dalam Klasifikasi Citra Mikroskopis Malaria Ramadhani, Muhammad Nabil; Haromainy, M. Muharrom Al; Wahanani, Henni Endah
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 4 (2026): HORIZON: Indonesian Journal of Multidisciplinary (In-Press)
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.7178

Abstract

Malaria diagnosis through microscopic images still faces challenges due to variations in the shape, size, and quality of blood cell images that can reduce the generalization ability of deep learning models. One approach to address this problem is data augmentation, but the effectiveness of various augmentation strategies on a hybrid Convolutional Neural Network (CNN) and Swin Transformer model has not been systematically compared. This study aims to compare the effect of five data augmentation strategies on the performance of the Hybrid CNN–Swin Transformer model in classifying malaria microscopic images. The NIH Malaria dataset consisting of 27,558 blood cell images was divided using the stratified split method with a ratio of 80:10:10. The model was trained using five augmentation strategies, namely Baseline (A0), Basic (A1), Geometric (A2), Advanced (A3), and RandAugment (A4), then evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and Train-Val Gap. All strategies produced accuracy above 96% and ROC-AUC above 99%. Strategy A3 provided the highest classification performance with an accuracy of 97.79%, an F1-score of 97.78%, and an ROC-AUC of 99.52%, while strategy A2 showed the most stable generalization ability based on the Train-Val Gap value. The results of this study provide empirical evidence that the choice of augmentation strategy affects the performance and generalization of the model, and serve as a reference in the development of a Hybrid CNN–Swin Transformer-based malaria classification system.
Co-Authors Bimantoro, Ryan Bagus Endang Sholihatin Gabriel, Paskalis Reynaldy Elroy Henni Endah Wahanani Jovian, Abraham Danar Muhammad Muharrom Al Haromainy Ramadhan, Raihan Triwinanda, Rizqullah Sandya Yossie