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Contact Name
Adli Abdillah Nababan
Contact Email
admitechsolutions@gmail.com
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+62 811 6556 192
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sisfotekjar@journal.itisd.org
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Jl. Pintu Air Gg. Langgar, Siti Rejo I, Kec. Medan Kota, Kota Medan, Sumatera Utara 20219
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Jurnal Sistem Informasi dan Teknologi Jaringan
Published by CV. ADMITECH SOLUTIONS
ISSN : 28087917     EISSN : 28079259     DOI : https://doi.org/10.63703/sisfotekjar
Core Subject : Science,
Computer Science & IT
Jurnal SISFOTEKJAR adalah jurnal yang diterbitkan oleh CV. ADMITECH SOLUTIONS yang bertujuan untuk mewadahi hasil penelitian tentang Sistem Informasi dan Teknologi Jaringan . Jurnal SISFOTEKJAR (Sistem Informasi dan Teknologi Jaringan) adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis dibidang berbagai ilmu. Jurnal SISFOTEKJAR (Sistem Informasi dan Teknologi Jaringan) terbit 2 kali dalam satu tahun yaitu di bulan Maret dan September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 2 Documents
 1 
Search results for , issue "Vol 5 No 2 (2024): September" : 2 Documents clear
Pengembangan Sistem Informasi Pengelolaan Jadwal dan Ruangan berbasis Website Adhitya Pratama, Yudhistira; Pratama, Yudhistira Adhitya; Nababan, Adli Abdillah; Maulana, Ade; Dulianto, Des; Harefa, Ade May Luky
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 5 No 2 (2024): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63703/sisfotekjar.v5i2.42

Abstract

The rapid development of information technology has significantly impacted various aspects of life, including management and administration. Efficient scheduling and room management remain a major challenge for educational, governmental, and private institutions. Manual processes are often time-consuming, prone to errors, and lack flexibility in responding to dynamic changes. This research aims to design and develop a web-based information system for scheduling and room management to address these challenges effectively. The system provides features such as building and room management, schedule management, and user account handling, enhancing accessibility and reducing overlapping schedules and allocation errors. The development process involves system requirement analysis and modeling using Use Case and Entity Relationship Diagrams. The resulting system simplifies real-time monitoring, automates manual processes, and improves institutional operational efficiency. Testing through black-box methods confirmed the system's functionality and user-friendliness, ensuring reliable implementation for users. This study contributes to technological advancement by offering a practical solution to operational inefficiencies while laying the groundwork for further enhancements in system functionality and user interface design.
Pengembangan Model Predictive Maintenance Untuk Kendaraan Menggunakan Algoritma Pembelajaran Mesin Hasugian, Penda Sudarto; Kumar, Prasanth; Aispriyani; Sagala, Jijon Raphita
Jurnal Sistem Informasi dan Teknologi Jaringan Vol 5 No 2 (2024): September
Publisher : CV. ADMITECH SOLUTIONS

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Vehicle maintenance is an important aspect to ensure optimal performance and vehicle life. Conventional maintenance approaches based on time or mileage (Time-Based Maintenance) are often ineffective, because they do not consider the actual condition of the vehicle. Predictive Maintenance supported by machine learning algorithms offers a more accurate solution in detecting potential vehicle damage before failure occurs, so that maintenance can be carried out according to actual needs. This study aims to develop a machine learning-based predictive maintenance model with a case study at the Payung Auto Solution workshop, which leads to the repair of Nissan, Datsun, and other vehicle brands. The methods used in this study include collecting operational data and vehicle maintenance history at Payung Auto Solution. This data is analyzed and processed using machine learning algorithms, such as Random Forest and Neural Network, to build a predictive model that is able to identify damage patterns in vehicle components. This model is tested and evaluated using prediction accuracy metrics, to determine the effectiveness of the model in predicting maintenance needs.

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