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All Journal Jurnal Teknik Informatika UNIKA Santo Thomas Journal of Computer Science and Engineering (JCSE) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST)
Oktiana, Milda Safrila
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Metode Image Recognation pada Aplikasi Pengenalan Alat Musik Tradisional Yanda, Nafa; Purnamasari, Detty; Anam, M. Khoirul; Oktiana, Milda Safrila
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 2 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Alat musik tradisional merupakan salah satu identitas kesenian setiap daerah di Indonesia. Provinsi DKI Jakarta memiliki alat musik tradisional yang beraneka ragam. Namun seiring perkembangan zaman sudah jarang generasi muda yang memainkan alat musik tradisional. Semua ini terjadi karena adanya perubahan alat musik tradisional menjadi yang lebih modern. Penelitian ini menggunakan dataset public melalui pencarian google image sebanyak 1200. Selanjutnya, dilakukan pengembangan struktur jaringan CNN dengan menggunakan Bahasa pemrograman Dart dan text editor VisualStudio Code. Pembuatan aplikasi menggunakan salah satu teknologi machine learning yaitu Image Recognation diharapkan dapat membantu masyarakat mengetahui jenis alat musik tradisional DKI Jakarta. Metode yang digunakan dalam pembuatan aplikasi adalah CRISP-DM yaitu Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, dan Deployment. Model yang sudah dibuat dan dievaluasi, diimplementasikan menjadi sebuah aplikasi berbasis android sehingga dapat digunakan untuk membantu pengenalan alat musik tradisional DKI Jakarta agar tetap terjaga kelestariannya. Hasil pengujian menunjukan bahwa system dapat mendeteksi alat musik dengan akurasi sebesar 94%, presisi sebesar 79%, dan sensitifitas sebesar 83%.
Metode Image Recognation pada Aplikasi Pengenalan Alat Musik Tradisional Yanda, Nafa; Purnamasari, Detty; Anam, M. Khoirul; Oktiana, Milda Safrila
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 2 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Alat musik tradisional merupakan salah satu identitas kesenian setiap daerah di Indonesia. Provinsi DKI Jakarta memiliki alat musik tradisional yang beraneka ragam. Namun seiring perkembangan zaman sudah jarang generasi muda yang memainkan alat musik tradisional. Semua ini terjadi karena adanya perubahan alat musik tradisional menjadi yang lebih modern. Penelitian ini menggunakan dataset public melalui pencarian google image sebanyak 1200. Selanjutnya, dilakukan pengembangan struktur jaringan CNN dengan menggunakan Bahasa pemrograman Dart dan text editor VisualStudio Code. Pembuatan aplikasi menggunakan salah satu teknologi machine learning yaitu Image Recognation diharapkan dapat membantu masyarakat mengetahui jenis alat musik tradisional DKI Jakarta. Metode yang digunakan dalam pembuatan aplikasi adalah CRISP-DM yaitu Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, dan Deployment. Model yang sudah dibuat dan dievaluasi, diimplementasikan menjadi sebuah aplikasi berbasis android sehingga dapat digunakan untuk membantu pengenalan alat musik tradisional DKI Jakarta agar tetap terjaga kelestariannya. Hasil pengujian menunjukan bahwa system dapat mendeteksi alat musik dengan akurasi sebesar 94%, presisi sebesar 79%, dan sensitifitas sebesar 83%.
Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization Oktiana, Milda Safrila; Sulistyo, Satria Harya; Zahwa, Refina Nur; Chair, Luthfi Muhammad; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

The Plasmodium parasite, which spreads through the bite of the Anopheles mosquito, causes malaria, a significant global health concern. Notwithstanding attempts to curtail its proliferation, malaria continues to be a predominant cause of mortality in tropical nations, especially in Sub-Saharan Africa and certain regions of Southeast Asia. Timely identification and precise diagnosis are essential for effective treatment. This research seeks to create a malaria classification model using deep learning based on the EfficientNetV2B0 architecture. The model is engineered to identify malaria parasite infections in microscopic images of erythrocytes. The dataset used is an open-source collection of photographs depicting red blood cells categorised as either infected or uninfected with malaria. The development method encompasses multiple critical stages, beginning with data collection, followed by preprocessing, data augmentation, and modelling using transfer learning with the EfficientNetV2B0 model. Bayesian optimisation is used to improve the model's accuracy by adjusting its hyperparameters. Assessment metrics, including accuracy, precision, recall, and F1-score, are used to evaluate the trained model's performance. The results show that the model has an accuracy of 96%, with equivalent precision, recall, and F1-scores for both the infected (under the heading "Parasitised") and uninfected (under the heading "Uninfected") groups. The model is extremely effective in diagnosing malaria, making it a valuable diagnostic tool for malaria control and prevention, especially in resource-constrained locations.Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization
Measurement of Security Awareness Level in the Implementation of Quick Response Code Indonesian Standard Oktiana, Milda Safrila; Tarigan, Avinanta
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 2 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i2.26050

Abstract

Quick Response Code Indonesian Standard (QRIS) is a QR code standard adopted in Indonesia as an electronic payment method. QRIS allows users to make payment transactions easily using mobile phones. Although QRIS provides many benefits regarding ease and efficiency of transactions, there are some security risks from payment methods using QR codes. A lack of public awareness regarding the safety of using QRIS can open the risk of misuse by irresponsible parties. This study aims to measure the level of security awareness in using QRIS Merchants in the DKI Jakarta areaThe research data was collected using questionnaires based on the Kruger and Kearney models involving 3 (three) dimensions, namely knowledge, attitudes, and behavior, as well as 6 (six) focus areas. The number of samples obtained was 110 respondents. The results showed that the safety awareness of QRIS use in the DKI Jakarta area was 91, in the average value category. With an average score of 91, most respondents realize the importance of paying attention to security aspects in transacting using QRIS.
Co-Authors Avinanta Tarigan Avinanta Tarigan Chair, Luthfi Muhammad Detty Purnamasari M. Khoirul Anam Sulistyo, Satria Harya Yanda, Nafa Zahwa, Refina Nur