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Jutech: Jurnal Teknologi Informasi
Published by Institut Teknologi dan Bisnis Ahmad Dahlan Jakarta
ISSN : 27974111     EISSN : 27974111     DOI : 1032546
Core Subject : Science,
Computer Science & IT
Rekayasa Perangkat Lunak; Sistem Informasi; Jaringan dan Keamanan Komputer; Pengolahan Citra; Sistem Pendukung Keputusan (SPK); Sistem Pakar; Kecerdasan Buatan; Aplikasi Mobile; Perancangan web; Basis data; dan banyak lagi topik-topik lain terkait bidang teknologi informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 63 Documents
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Implementasi Smart Building IoT Identifikasi Wajah, Sensor Pir, dan Sensor Gas Untuk Keamanan Showroom Andika Alhaetami; R Tommy Gumelar; Shevti Arbekti Arman
Jurnal Teknologi Informasi (JUTECH) Vol. 7 No. 1 (2026): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v7i1.2987

Abstract

Security and energy efficiency are crucial aspects of a car showroom that can be improved using an IoT-based smart building system. This research develops a system integrating a PIR sensor for automatic lighting, an MQ2 sensor for gas detection, and an ESP32-CAM for facial recognition to enhance security. The ESP32 microcontroller controls the system, enabling real-time monitoring via the Telegram application. Testing results show that the PIR sensor optimizes energy use by activating lights only when motion is detected. The MQ2 sensor provides early warnings for hazardous gases, while the ESP32-CAM ensures restricted access by identifying authorized personnel. IoT integration allows stable data transmission and remote control. This system effectively enhances showroom security and energy efficiency, offering fast response times and reliable performance. The findings provide a foundation for further smart building innovations applicable to various industries.
Implementasi Front-End Aplikasi Absensi Teknoid Menggunakan Tailwind CSS Studi Kasus ITB Ahmad Dahlan Muhammad Hendrik Tarigan; Elliya Sestri
Jurnal Teknologi Informasi (JUTECH) Vol. 7 No. 1 (2026): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v7i1.3206

Abstract

The digital transformation in higher education institutions demands more efficient administrative systems, including staff attendance systems. ITB Ahmad Dahlan still faces challenges with its current attendance system, such as manual recapitulation, undeleted inactive employee data, and limited automation features. This study aims to implement the front-end interface of a web-based attendance system using Tailwind CSS with an Agile approach. The system is designed to be responsive, modular, and integrated with features such as attendance validation, employee account management, and automated recapitulation. The development process includes requirement analysis, interface design, implementation with an organized folder structure, and functionality testing through Black Box and System Usability Scale (SUS) methods. The test results indicate that the system is accessible across various devices, performs well, and receives positive feedback from users. This front-end implementation is expected to support administrative efficiency and serve as a foundation for future integration with RFID- and IoT-based attendance systems.
Prediksi Tingkat Pemahaman Siswa SMP Pada Pembelajaran Digital Menggunakan Algoritma Machine Learning Munadi Fauzan; Dinda Permata Sukma; Eriwandi Eriwandi; Diana Kemala Odang
Jurnal Teknologi Informasi (JUTECH) Vol. 7 No. 1 (2026): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v7i1.3543

Abstract

With the proliferation of digital learning platforms—including Learning Management Systems (LMS)—data on student learning activities has been generated that is useful for supporting the evaluation of student learning. A total of 200 data points were analyzed, with key variables including login frequency, study duration, quiz scores, assignment scores, timeliness in submitting assignments, attendance, and engagement during learning. These variables can be analyzed using machine learning to predict students’ comprehension levels early on, enabling teachers to identify which students require additional support. This study focuses on the application of machine learning to predict students’ comprehension levels at SMP Negeri 18 Padang as an effort to support more accurate decision-making in the learning process. This study utilized three algorithms: Random Forest, Decision Tree, and Naïve Bayes. The results of the testing revealed that the Decision Tree algorithm achieved the highest accuracy rate at 88.52%, followed by the Random Forest algorithm at 83.61%, and the Naïve Bayes algorithm at 62.30%. Of the three algorithms, the Decision Tree algorithm achieved the highest accuracy. This algorithm can be utilized as an early warning system for teachers to identify which students have high, moderate, or low levels of understanding, allowing teachers to provide appropriate support.

Page 7 of 7 | Total Record : 63

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