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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Journal of Vocational, Informatics and Computer Education
Andi Jamiati Paramita
Institut Teknologi dan Bisnis Kalla, Indonesia
Humanities Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Pendeteksian Code Smell pada Website Perusahaan Andi Jamiati Paramita; Andi Hutami Endang; Dian Aprilia Khairunnisa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (98.068 KB) | DOI: 10.36418/syntax-literate.v7i1.6019

Abstract

Website merupakan pusat penyajian informasi yang penting dimiiki oleh perusahaan untuk dapat menjangkau konsumen lebih luas. Pengembangan website harus memperhatikan pemeliharaan dimulai dari penulisan kode. Oleh karena itu, penelitian ini mengembangkan sebuah platform untuk mendeteksi code smell pada file HTML website perusahaan/institusi dengan menggunakan metode Crowdsourcing. Hasil pendeteksian ini akan melewati fase voting untuk mendapatkan hasil yang lebih meyakinkan.
Classification of Students' Facial Expressions Utilizing Convolutional Kolmogorov–Arnold Networks (C-KANs) and Classroom Teaching Methods Classification Employing ResNet-152 Hutami Endang; Annahl Riadi; Alif Fauzan; Andi Jamiati Paramita; Furqan Zakiyabarsi; Hany Alexanders
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.859

Abstract

Purpose - This study proposes a deep learning parallel dual-classification framework utilizing a lightweight Convolutional Kolmogorov–Arnold Network (C-KAN) to recognize six classes of students' facial expressions and a ResNet-152 to classify three types of teaching methods.Methods - The parallel framework routes micro-student expressions and macro-classroom contexts separately. The C-KAN model features a two-pronged architecture combining 96×96 pixel grayscale facial images with 9 geometric features from 68 landmarks, while ResNet-152 processes 256×256 pixel RGB full-classroom frames. Findings - Evaluated on elementary school recordings (9,130 teaching method images and 2,069 facial expression instances), ResNet-152 achieved 95% accuracy (0.94 macro F1-score). Meanwhile, C-KAN achieved 87% accuracy (0.86 macro F1-score) across six facial expressions.Research implications - By leveraging learnable B-spline functions, C-KAN models complex non-linear micro-expressions accurately with only 4.1 million parameters. This structural efficiency slashes inference time to 11 ms, proving its high viability for real-time edge-computing analytics.Originality - His framework introduces a dual-perspective routing approach. Integrating spline-based C-KAN provides high discriminative power with low computational overhead, supporting evidence-based, automatic evaluation of classroom learning.
Co-Authors Alif Fauzan Andi Hutami Endang, Andi Hutami Annahl Riadi Dian Aprilia Khairunnisa Furqan Zakiyabarsi Hany Alexanders Hutami Endang