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All Journal G-Tech : Jurnal Teknologi Terapan Jurnal Penelitian Inovatif
Istiqomah, Naharotul
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Engineering Public Health Social Sciences

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Integrasi Pendekatan Deep Learning dalam Pembelajaran Koding dan Kecerdasan Artifisial untuk Meningkatkan Critical Thinking dan Problem Solving Siswa Hidayati, Innasya' Putri; Priliyanti, Arumantika Eko Yulia; Istiqomah, Naharotul
Jurnal Penelitian Inovatif Vol 6 No 1 (2026): JUPIN Februari 2026
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jupin.2195

Abstract

Kemampuan critical thinking dan problem solving siswa Sekolah Menengah Kejuruan (SMK) masih belum sepenuhnya selaras dengan tuntutan pembelajaran abad ke-21 yang menekankan penguasaan keterampilan berpikir tingkat tinggi. Penelitian ini bertujuan untuk menganalisis pengaruh pembelajaran koding dan kecerdasan artifisial berbasis pendekatan deep learning terhadap peningkatan kemampuan berpikir kritis dan pemecahan masalah siswa SMK. Penelitian menggunakan pendekatan kuantitatif dengan desain kuasi-eksperimen pretest–posttest control group. Instrumen penelitian berupa tes uraian berbasis Higher Order Thinking Skills (HOTS). Analisis data dilakukan menggunakan uji t dan perhitungan N-gain. Hasil penelitian menunjukkan terdapat perbedaan peningkatan kemampuan critical thinking dan problem solving yang signifikan antara kelompok eksperimen dan kelompok kontrol (p < 0,05). Kelompok eksperimen memperoleh nilai N-gain pada kategori sedang hingga tinggi, sedangkan kelompok kontrol berada pada kategori rendah hingga sedang. Temuan ini menunjukkan bahwa integrasi pendekatan deep learning dalam pembelajaran koding dan kecerdasan artifisial efektif dalam meningkatkan kemampuan berpikir tingkat tinggi siswa serta memberikan kontribusi empiris terhadap pengembangan inovasi pembelajaran berbasis teknologi pada pendidikan vokasi.
Analysis of Software-Defined Networking Resilience to Link Failure through Failover Routing Simulation Istiqomah, Naharotul; Sholihin, Muhammad David Irsyadus; Hidayati, Innasya' Putri
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8971

Abstract

Software-Defined Networking (SDN) represents an advanced architectural paradigm that separates control logic from data forwarding, enabling centralized and adaptive network management. This study investigates SDN resilience under link failure conditions and evaluates the effectiveness of failover routing in restoring network performance. Simulation-based experiments are conducted on a redundant logical topology implemented using Python and the NetworkX library, covering three operational phases: normal operation, link failure, and failover recovery. The results show that link failures cause a noticeable degradation in network performance, with normalized link utilization increasing due to traffic concentration on limited paths. After failover routing is activated, network performance improves significantly, achieving up to approximately 15–20% higher normalized link utilization compared to the failure state, indicating successful traffic rerouting and service restoration. These findings demonstrate that failover routing enables rapid recovery and maintains service continuity despite link disruptions. This study contributes to SDN resilience research by providing a reproducible logical-topology simulation framework and quantitative evidence that proactive, controller-based failover routing effectively enhances network robustness under link-failure scenarios, offering practical insights for resilient SDN design in data center and enterprise environments.
Co-Authors Hidayati, Innasya' Putri Priliyanti, Arumantika Eko Yulia Sholihin, Muhammad David Irsyadus