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All Journal Jurnal Inspiration IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics Jurnal Teknologi Elekterika
Muhammad Nur Yasir Utomo
Politeknik Negeri Ujung Pandang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Pengembangan Sistem Survei Tracer Study Berbasis Web Menggunakan Arsitektur Model View Controller (MVC) Muhammad Nur Yasir Utomo; Irmawati Irmawati; Rini Nur
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 11, No 2 (2021): Jurnal Inspiration Volume 11 Issue 2
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v11i2.2633

Abstract

Tracer study sebagai metode untuk mendapat umpan balik dari alumni perguruan tinggi saat ini masih memiliki berbagai masalah dari sisi pelaksanaan. Masalah pelaksanaan tracer study umumnya berupa keterbatasan sumber daya, format survei dan cara pelaksanaannya. Untuk mengatasi masalah tersebut, teknologi informasi mulai dimanfaatkan sebagai solusi. Namun demikian, penelitian terkait tracer study saat ini masih sangat berfokus pada pengumpulan data alumni saja, sedangkan penelitian terkait pembuatan survei untuk mengetahui situasi dan kompetensi alumni didunia kerja masih sangat terbatas. Oleh karena itu, penelitian ini mengajukan sistem survei tracer study berbasis web dengan arsitektur Model-View-Controller (MVC) sebagai solusi. Sistem dikembangkan dengan spesifikasi memiliki dua modul utama yaitu modul survei alumni dan modul staf dan admin. Berdasarkan pengujian dan evaluasi yang dilakukan dengan menggunakan metode Black Box Testing, sistem yang diajukan berhasil memperloleh nilai kelayakan sebesar 90.9% sehingga dapat disimpulkan bahwa sistem yang diajukan pada penelitian ini dapat bekerja dengan baik untuk digunakan dalam melalukan survei tracer study alumni.
Pengembangan Model Migrasi Database Relational ke NoSQL Memanfaatkan Metadata SQL Muhammad Nur Yasir Utomo
Jurnal Teknologi Elekterika Vol 17, No 2 (2020): Nopember
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v4i2.2212

Abstract

Penyimpanan data merupakan isu krusial pada teknologi Big Data karena membutuhkan teknologi penyimpanan data yang profisien agar dapat menyimpan data (terstruktur dan tidak terstruktur) secara cepat dalam jumlah besar. Hal ini sudah tidak bisa lagi dipenuhi oleh model database relational (SQL) yang saat ini masih banyak digunakan. Kelemahan tersebut dapat diatasi dengan menggunakan database NoSQL, namun sayangnya proses migrasi data dari relational/SQL database ke NoSQL masih sulit dilakukan karena perbendaan skema dan format penyimpanan data. Berdasarkan masalah tersebut, maka penelitian mengenai migrasi database relational ke NoSQL sangat diperlukan. Penelitian ini mencoba mengajukan pengembangan model perangkat lunak untuk migrasi database relational ke NoSQL menggunakan pendekatan aturan migrasi dan data transformasi yang memanfaatkan metadata SQL. Berdasarkan eksperimen yang telah dilakukan aturan migrasi yang diterapkan pada model yang dikembangkan berhasil melakuakn migrasi database SQL ke NoSQL dengan kecepatan rerata 0.978 detik untuk 5 table dalam 1 database.
Coraza-Based WAF with OWASP CRS for SQL Injection in Multi-Domain Web System Muhammad Zaedil; Irfan Syamsuddin; Muhammad Nur Yasir Utomo
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1475

Abstract

This research aims to design and implement a Web Application Firewall (WAF) based on the OWASP Core Rule Set (CRS) to enhance web application protection against SQL Injection attacks. The study was conducted in the web environment of the State Polytechnic of Ujung Pandang, which has more than 80 active subdomains with uniform server configurations, mostly using vulnerable CMSs such as WordPress. The proposed solution integrates Coraza, a Go-based WAF engine, into the Nginx reverse proxy system. The system includes a web-based control panel, JSON-formatted logging, and Redis support for efficient traffic mapping and storage, enabling flexible management of multiple domains. A key contribution of this study is the implementation of a centralized WAF management approach capable of securing more than 80 subdomains within a unified configuration environment. Tests were carried out using five SQL Injection scenarios: URL parameters, form-data, x-www-form-urlencoded, JSON API, and automated tools such as SQLMap. Without WAF, all attacks successfully penetrated the system, whereas with WAF activated, all tested payloads were successfully blocked, manual and automated, was effectively blocked, indicating a significant improvement in defense capability. These results demonstrate that the developed WAF system provides strong protection against SQL Injection attacks and indicate strong potential for enhancing web application security against SQL Injection attacks.
Face Mask Wearing Detection Using Support Vector Machine (SVM) Muhammad Nur Yasir Utomo; Fajrin Violita
IJID (International Journal on Informatics for Development) Vol. 10 No. 2 (2021): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.3038

Abstract

As an effort to prevent the spread of the Covid-19, various countries have implemented health protocol policies such as work-from-home, social distancing, and face mask-wearing in public places. However, monitoring compliance with the policy is still difficult, especially for the face mask policy. It is still managed by humans and is costly. Thus, this research proposes a face mask-wearing detection using a soft-margin Support Vector Machine (SVM). There are three main stages: feature selection and preprocessing, model training, and evaluation. During the first stage, the dataset of 3833 images (1915 images with face masks and 1918 images without face masks) was prepared to be used in the training stage. The training stage was conducted using SVM added with the soft-margin objective to overcome images that could not be separated linearly. At the final stage, evaluation was conducted using a confusion matrix with 10 folds cross-validation. Based on the experiments, the proposed method shows a performance accuracy of 91.7%, a precision of 90.3%, recall of 93.5%, and an F-measure of 91.8%. Our method also worked fast, taking only 0.025 seconds to process a new image. It is 7.12 times faster than Deep Learning which requires 0.18 seconds for one classification.
Co-Authors Irfan Syamsuddin Irmawati Irmawati Muhammad Zaedil Rini Nur Violita, Fajrin