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All Journal Jurnal Teknik Informatika (JUTIF) Jurnal Media Digital
Mutiarawan, Rezza Anugrah
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Computer Science & IT Decision Sciences, Operations Research & Management

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Optimasi Data Log Audit Master dan Audit Payload Menggunakan Metode KRaft Mutiarawan, Rezza Anugrah; Wiranata, Ade Davy; Iswahyudi
Jurnal Media Digital Vol. 1 No. 02 (2025): Media Digital November 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas LIA

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Abstract

Saat ini, banyak aplikasi yang digunakan untuk berbagai keperluan memerlukan tingkat integritas data yang tinggi, yang sering kali menyebabkan kompleksitas program semakin meningkat. Seiring bertambahnya kompleksitas sistem, mekanisme audit log yang efektif menjadi sangat penting untuk menjaga integritas data. Salah satu solusinya adalah dengan merekam aktivitas pengguna dalam sistem, sehingga semua tindakan dapat dimonitor secara menyeluruh. Dalam penelitian ini, KRaft digunakan sebagai alat untuk mengelola proses audit log. Implementasi KRaft bertujuan untuk memastikan bahwa proses audit tidak mengganggu operasi utama dari program. Berdasarkan skenario pengujian yang dilakukan, hasilnya menunjukkan bahwa penerapan KRaft untuk audit master data dan audit payload data dapat dilakukan dengan mudah dan tidak mengganggu proses utama aplikasi.
Efficient Waste Classification in Cisadane River Using Vision Transformer and Swin Transformer Architectures Surahmat, Asep; Mutiarawan, Rezza Anugrah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4451

Abstract

The increasing volume of waste in rivers has become a serious environmental problem. This study proposes the implementation of Artificial Intelligence (AI)-based models, specifically Vision Transformer (ViT) and Swin Transformer, for an automatic waste sorting system in the Cisadane River, Tangerang. The dataset used combines public sources and field data, processed through preprocessing and augmentation to improve robustness. Model training was conducted using k-fold cross-validation, pruning, and deployment testing on edge devices to ensure generalization and efficiency. Several architectural innovations were introduced, including Dynamic Patch Size for adapting to various waste shapes and sizes, and Spatial-Aware Attention to enhance focus on waste objects against complex river backgrounds. The evaluation involved a confusion matrix and statistical analysis using a paired t-test to validate the significance of the results. Experimental findings show that Swin Transformer achieved the highest accuracy of 94.2%, surpassing ViT at 91.8%, with precision of 93.5%, recall of 92.7%, and F1-score of 93.1%. Swin Transformer also proved more reliable in dynamic lighting and cluttered environments. This study demonstrates the potential of Transformer-based architectures in automatic waste classification, contributing to smarter and more efficient AI-based environmental management technologies.
Co-Authors Ade Davy Wiranata Iswahyudi Iswahyudi Surahmat, Asep