Article Per Year (5 Year)
p-Index From 2019 - 2024
0.983
P-Index
This Author published in this journals
All Journal Jurnal Inspiration IJID (International Journal on Informatics for Development) INTEK: Jurnal Penelitian Jurnal Teknologi Elekterika
Muhammad Nur Yasir Utomo
Politeknik Negeri Ujung Pandang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
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.
Improving Speed Performance of Select Random Query in SQL Database Muhammad Nur Yasir Utomo; Alvian Bastian; Anggun Winursito
INTEK: Jurnal Penelitian Vol 7, No 1 (2020): April 2020
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (868.105 KB) | DOI: 10.31963/intek.v7i1.1536

Abstract

Select random is a query in a SQL database that can retrieve data randomly from a table. Select random is often used to present data in various applications such as websites, data mining and others. Unfortunately, ordinary select random query is inefficient in terms of processing time if used in large table. This paper, tries to solve this problem by proposing two optimized methods of select random query, namely the Small Percentage Order by Rand (SPO-Rand) and the Filtered Column Order by Rand (FCO-Rand). The two proposed methods are then compared in terms of processing speed with a standard Select Random query or Normal Order by Rand (NO-Rand). The scenario of the experiment is to collect five random data from several data sets, ranging from 10.000 to 200.000 data. Based on the results of experiments that have been conducted, the proposed FCO-Rand method obtained the best process speed with 0.074 seconds at 200.000 data, followed by SPO-Rand with 0.265 seconds. These result are much faster than the standard random select method (NO-Rand) which takes up to 7,035 seconds for the same task.
Sistem Manajemen Konferensi Ilmiah Berbasis Web Menggunakan Metode Pengembangan Waterfall Muhammad Nur Yasir Utomo
Jurnal Teknologi Elekterika Vol 19, No 1 (2022): Mei
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/elekterika.v6i1.2751

Abstract

Scientific publications are a means of delivering research results and innovations of researchers. Recently, researchers' interest in publishing publications is also increasing. This increase occurred in the publication of papers through scientific conferences. This encourages the need for the use of information technology to facilitate conference management. However, the application of the conference management system is still minimal. Based on these problems, this study proposes a web-based conference management method. The system is built using a MySQL database and Model-View-Controller (MVC) code architecture in PHP. Therefore, every conference process can be included in the system. the system is designed with three main modules, namely the participant/presenter module, reviewer module, and committee/admin module, which covers the entire conference process starting from registration, paper submission, review, payment and distribution of presentation rooms. Trials and evaluations are carried out using the Black Box Testing method to ensure the system can work well. The results of the tests and evaluations that have been carried out show that the proposed system is proven to have good performance with a score of 100% or successfully fulfills all existing test scenarios
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.
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, 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 Alvian Bastian Anggun Winursito Fajrin Violita Irmawati Irmawati Rini Nur