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All Journal JURNAL KAJIAN TEKNIK ELEKTRO
EE Lailatul Putri
Universitas 17 Agustus 1945 Jakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perancangan Sistem Informasi Penyewaan Kontrakan Berbasis Website untuk Mendukung Digitalisasi Menajemen Pengelolaan EE Lailatul Putri; Zahra Nasywa Zain; Ath Thaariq; Eka Sofiati
Jurnal Kajian Teknik Elektro Vol 10, No 2 (2025): JKTE VOL 10 NO 2 (SEPTEMBER 2025)
Publisher : Universitas 17 Agustus 1945 Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52447/jkte.v10i2.8835

Abstract

The rapid development of information technology has encouraged digitalization in various sectors, including property rental management such as boarding houses. The manual management process often leads to several issues, such as disorganized tenant data, delays in monitoring payments, and errors in calculating late fees. This study aims to design and develop a web-based rental information system that automates tenant data management, payment recording, and real-time financial reporting. The research method used is the Agile Development Method, which includes planning, design, development, testing, and implementation stages. The system was developed using the PHP programming language and a MySQL database, and it can be operated both locally and online. The implementation results show that the system can facilitate tenant data management, automatically calculate late fees, and generate financial reports effectively and efficiently. Based on the testing results, all main features functioned properly according to user requirements. With this system, rental management becomes more structured, transparent, and easily accessible for both landlords and tenants in real-time.
Tinjauan Komprehensif Jaringan Syaraf Tiruan RNN: Karakteristik, dan Aplikasi dalam Peramalan Energi Bangunan Gedung Ahmad Rofii; EE Lailatul Putri
Jurnal Kajian Teknik Elektro Vol 10, No 2 (2025): JKTE VOL 10 NO 2 (SEPTEMBER 2025)
Publisher : Universitas 17 Agustus 1945 Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52447/jkte.v10i2.8645

Abstract

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has introduced various approaches to processing time series data, particularly for energy consumption forecasting. One of the most prominent architectures is the Recurrent Neural Network (RNN) and its variants, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), which are designed to capture temporal dependencies in sequential data. This study examines the development, characteristics, and performance of RNN and its variants across various domains, with a specific focus on building energy consumption forecasting. The reviewed research spans from 1990 to 2024 and was selected based on relevance, citation count, and novelty of contribution. The findings indicate that LSTM and GRU generally outperform standard RNNs in handling long-term dependencies, while BiLSTM is effective for complex data patterns. However, challenges such as the need for high-quality data, computational complexity, model interpretability, and integration into Energy Management Systems (EMS) remain significant barriers. This study reaffirms the importance of RNN and its variants in energy prediction systems while opening opportunities for further research on hybrid architectures and the development of more user-friendly interfaces.
Co-Authors Ahmad Rofii Ath Thaariq Eka Sofiati Zahra Nasywa Zain