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All Journal Jurnal Teknik Sinkron : Jurnal dan Penelitian Teknik Informatika
Rokhayadi, Wakhid
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Rainfall Monitoring Using Aloptama Automatic Rain Gauge And The Network Development Life Cycle Method Nugroho, Kristiawan; Afandi , Afandi; Rokhayadi, Wakhid; Budiarto, Indri; Hermawan, Taufan
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13908

Abstract

Examining the role of rainfall data management in monitoring and reducing natural disasters. Between the observation post and the coordinating office of the Central Java Meteorology, Climatology and Geophysics Agency, there are problems in managing rainfall data. To increase the accuracy and efficiency of rainfall monitoring, the Central Java BMKG Coordinator has used various platforms that are considered very good, such as Grafana, Node-RED, Xampp, and MQTT. Previous research has shown that the use of the Automatic Rain Gauge (ARG) and the Network Development Life Cycle (NDLC) method is very effective in creating an accurate and reliable rainfall monitoring system. This research uses the NDLC model, which consists of analysis, design, prototype simulation, implementation, monitoring and management stages. It is hoped that the research results will help improve visual monitoring of rainfall in local areas and increase understanding of rainfall patterns, flood prediction, water resource management and mitigation measures. This will serve as a reference for governments and institutions working together to make decisions to avoid catastrophic climate change.
Pengembangan Chatbot AI untuk Layanan Pelanggan PLN Menggunakan Algoritma Long Short Term Memory (LSTM) Afandi, Afandi; Rokhayadi, Wakhid; Susanto, Edi
Jurnal Teknik Vol 23 No 1 (2025): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v23i1.553

Abstract

Digital transformation requires customer service to be fast, responsive, and continuously accessible. To address this demand, this study presents the development of an AI-based chatbot employing the Long Short-Term Memory (LSTM) algorithm to enhance customer support for PLN. LSTM was chosen due to its effectiveness in capturing conversational context and understanding natural language patterns. The development process includes data preprocessing, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. Experimental results on 133 test samples demonstrate an accuracy of 82.71%, with an average precision of 82%, recall of 77%, and F1-score of 77%, indicating reliable model performance. The chatbot is designed to handle common customer inquiries, including billing information, service disruptions, and other general services. This innovation is expected to improve PLN’s operational efficiency while delivering faster, more personalized, and dependable customer service, aligning with the demands of the digital era.
Co-Authors Afandi , Afandi Afandi Afandi Budiarto, Indri Dian Kristiawan Nugroho Edi Susanto Hermawan, Taufan