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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Computer Science and Engineering
Anggarah, Rengga
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Studi Perbandingan Penerapan Pola Model-View-Controller (MVC) dalam Lima Framework Web Populer: Laravel, Django, Ruby on Rails, Asp.net MVC, Spring MVC Kenia Nurma Feblia; Anggarah, Rengga; Villareal, Yovanza; Amanda, Khalisa Rizgita; Mardiyah, Qonita Adzkiatul; Amanda, Filya Chiara; Mursyidah, Anis Syarifatul; Aritonang, M D Valgiyos
Indonesian Journal of Computer Science and Engineering Vol. 2 No. 01 (2025): IJCSE Volume 02 Nomor 01, Mei 2025
Publisher : CV. Cendekiawan Muda Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70656/ijcse.v2i01.306

Abstract

Artikel ini menganalisis dan membandingkan implementasi arsitektur Model View Controller (MVC) pada lima framework web populer: Laravel, Django, Ruby on Rails, ASP.NET MVC, dan Spring MVC. Meskipun MVC bertujuan untuk memisahkan logika bisnis, antarmuka pengguna, dan kontrol aplikasi, setiap framework mengadopsi pendekatan dan karakteristik unik dalam menangani komponen Model, View, dan Controller. Analisis ini mengeksplorasi perbedaan dalam kemudahan pengembangan, fleksibilitas, performa, serta aspek-aspek lain seperti konvensi dan fitur bawaan. Hasil perbandingan ini diharapkan dapat menjadi panduan bagi pengembang dalam memilih framework MVC yang paling sesuai dengan bahasa pemrograman, kompleksitas proyek, serta kebutuhan fitur dan skalabilitas aplikasi web yang mereka kembangkan.
Efficient CNN-Based Classification of SARS-CoV-2 Spike Gene Sequences Using Alignment-Free Encoding Anggarah, Rengga; Ernawati, Ernawati; Oktoeberza, Widhia KZ
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15691

Abstract

The COVID-19 pandemic caused by SARS-CoV-2 continues to challenge the global health system through the emergence of various variants with genetic characteristics that affect vaccine transmission and effectiveness. Conventional identification methods such as Whole-Genome Sequencing (WGS) have high accuracy but are constrained by significant cost and time. Most classification studies today still rely on complex hybrid architectures such as CNN-LSTM or image-based representations that increase computational load. This study aims to develop  an  efficient and lightweight pure Convolutional Neural Network model based on alignment-free encoding to classify five Variant of Concern (VOC) variants of SARS-CoV-2 (Alpha, Beta, Delta, Gamma, and Omicron) with an exclusive focus on the Spike gene sequence. The dataset consists of 5,000 Spike gene sequences that are represented using integer encoding and standardized with zero-padding. CNN  proposed Lightweight architecture  consists of four 1D convolution layers with a total of approximately 1.6 million parameters. The test results show that the model achieves excellent performance with an overall accuracy of 98.93%. The precision, recall, and F1-score values averaged 0.99, while the analysis of the ROC curve showed AUC values above 0.99 for all variants. This approach has proven to be efficient and effective, offering a fast, scalable, and resource-efficient solution to support real-time genomic surveillance systems in future pandemic mitigation.
Co-Authors Amanda, Filya Chiara Amanda, Khalisa Rizgita Aritonang, M D Valgiyos Ernawati Ernawati Kenia Nurma Feblia Mardiyah, Qonita Adzkiatul Mursyidah, Anis Syarifatul Oktoeberza, Widhia KZ Villareal, Yovanza