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All Journal Jurnal Rekayasa Sistem Informasi dan Teknologi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Yulisara, Ekatri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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AUDIT SISTEM INFORMASI SRIKANDI PADA KANTOR PEMERINTAHAN MENGGUNAKAN FRAMEWORK COBIT 2019 Yulisara, Ekatri; Falhamilat Wikron; Nur Alawiyah Hasibuan; Megawati
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 2 No. 4 (2025): Mei
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jrsit.v2i4.2293

Abstract

ini bertujuan untuk mengevaluasi tingkat kapabilitas tata kelola Sistem Informasi Kearsipan Dinamis Terintegrasi (SRIKANDI) di Kantor Camat Tuah Madani Kota Pekanbaru dengan menggunakan framework COBIT 2019. Penelitian ini menggunakan pendekatan deskriptif kuantitatif melalui observasi langsung, wawancara terstruktur, serta penyebaran kuesioner berbasis self-assessment kepada para pemangku kepentingan, yang diklasifikasikan dalam RACI Chart. Domain COBIT 2019 yang dianalisis mencakup EDM01 (Ensure Governance Framework Setting and Maintenance), BAI03 (Manage Solutions Identification and Build), dan BAI04 (Manage Availability and Capacity). Hasil penelitian menunjukkan bahwa ketiga domain berada pada tingkat kapabilitas Level 3 (Established) dengan status Largely Achieved, yang mengindikasikan bahwa proses tata kelola telah berjalan cukup baik namun belum mencapai optimalisasi penuh. Beberapa area yang perlu ditingkatkan antara lain dokumentasi kebijakan, konsistensi implementasi proses, serta pengukuran kinerja berkelanjutan. Hasil audit ini memberikan gambaran objektif bagi perbaikan tata kelola TI di lingkungan pemerintahan.
Comparative Study of Convolutional Neural Network Architectures and Optimizers for Flower Image Classification Yulisara, Ekatri; Husna, Nayla; Martin, David; Ariesta, Candrawati
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2110

Abstract

This study aims to comparatively evaluate the performance of different Convolutional Neural Network (CNN) architectures and optimization algorithms for flower image classification. Three widely used CNN architectures DenseNet201, InceptionV3, and MobileNetV2 are implemented using transfer learning with pre-trained ImageNet weights and tested with two optimizers, Adam and RMSProp. The experiments are conducted on the Flowers Recognition dataset consisting of five flower classes: daisy, dandelion, rose, sunflower, and tulip. Image normalization and data augmentation are applied to improve model generalization, while performance is evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study lies in a systematic comparison of CNN architectures and optimizers within a unified experimental framework, which is rarely addressed in previous studies. The results show that DenseNet201 combined with the Adam optimizer achieves the highest classification accuracy of 90%, followed by MobileNetV2 with RMSProp, while InceptionV3 yields the lowest accuracy of 85%. These results confirm that the research objective is achieved, demonstrating that both CNN architecture and optimizer selection significantly influence flower image classification performance.
Co-Authors Ariesta, Candrawati Falhamilat Wikron Husna, Nayla Martin, David Megawati Nur Alawiyah Hasibuan