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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) U-NET Jurnal Teknik Informatika
Samsir
Universitas Al Washliyah
Computer Science & IT Engineering

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Implementasi Max Length dan Input Type Number Pada Form Login Website Untuk Mencegah Penetrasi SQL Injeksi Secara Paksa Zulkifli; Samsir; Azrai Sirait
U-NET Jurnal Teknik Informatika Vol. 4 No. 1 (2020): U-NET Jurnal Teknik Informatika | Februari
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52332/u-net.v4i1.223

Abstract

Dalam dunia IT pada website sangat rentan akan serangan hacker dengan berbagai jenis cara agar mereka dapat membobol kemanan website target. Serangan SQL Injeksi sering dilakukan pada pembobolan website dari form login dengan menginputkan username dan password khusus injeksi sehingga website dapat dibobol dengan mudah. Dalam mengamankan sebuah website dari serangan injeksi beragam caranya salah satunya dengan menggunakan teknik maxlength dan input type number. Teknik maxlengntht dan input type number ini dibuat dalam bentuk source code php atau html yang disisipkan kedlalam source code form login pada bagian input username dan password. Salah satu keunggulan teknik maxlengnth dan input type ini akan membuat batasan inputan username dan mengubah format inputan password hanya bertipekan angka saja yang artinya akan mencegah hacker dalam melakukan penetrasi secara paksa dalam serangan SQL Injeksi pada website
Temporal Semantic Flow Networks for Analyzing Topic Evolution in Educational Data Mining Firman Edi; Ambiyar; Waskito; Samsir
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7755

Abstract

This study aims to provide a comprehensive longitudinal analysis of the evolution of Educational Data Mining (EDM) research from 2014 to 2024, with a particular emphasis on learning analytics and student success prediction. We have developed and implemented the Temporal Semantic Flow Networks with Attention-based Topic Evolution (TSFN-ATE) methodology, which reconceptualizes the evolution of scientific discourse as continuous semantic flows. This framework integrates multi-scale temporal attention mechanisms and dynamic network representations to analyze Scopus-indexed keyword data from 436 EDM articles. The analysis identified significant transformations within the discourse of EDM, with a 333% increase in research intensity related to predictive modeling and an unprecedented 2,650% growth in the application of deep learning from 2014 to 2024. Deep learning attained the highest semantic flow score (0.94), signifying its successful integration across various EDM subfields, while machine learning emerged as the central methodological bridge, exhibiting the strongest network centrality (0.74). Multi-scale temporal attention analysis revealed differential patterns across time scales, with machine learning receiving the highest recent attention (0.85 at a 1-year scale), whereas learning analytics demonstrated sustained long-term influence (0.75 at a 5-year scale). Concept drift detection identified four major paradigm shifts, with the 2018-2019 deep learning integration representing the most significant conceptual disruption (semantic stability index 0.45). Collectively, these four identified paradigm shifts — from traditional machine learning through deep learning integration to the emergent ethical turn — reveal that EDM is not simply accumulating methods but undergoing continuous conceptual reorganisation; understanding and anticipating these shifts is therefore essential for researchers, institutions, and policymakers seeking to align educational technology development with evolving scientific and societal priorities.
Co-Authors Ambiyar, Ambiyar Azrai Sirait Firman Edi Waskito Zulkifli