Prediksi Pemakaian Pulsa Listrik Kamar Kos Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation
Abstract:
Purpose: Electricity is a crucial necessity, serving as a prerequisite for the smooth, efficient, safe, and comfortable conduct of human life activities. The energy requirements for each household, especially in shared living spaces like dormitories, vary, with residents' behaviors and habits influencing daily electricity usage patterns. Wasteful electricity habits can lead to disadvantages for various parties. To address this, control technology has been developed to reduce the risk of injustice in billing and electricity usage among dorm residents, aiming to optimize electrical load usage.
Research Methodology: This research utilizes the backpropagation method to develop a complex calculation model for predicting monthly electricity token expenditures based on daily electricity usage. The methodology involves understanding and analyzing the electricity consumption patterns of dorm residents, implementing the backpropagation algorithm, and testing the model's accuracy in predicting future electricity expenses.
Results: The study presents insights gained from applying the backpropagation method, demonstrating its effectiveness in predicting monthly electricity token expenditures. Results include the development of a model that takes into account daily electricity usage patterns, allowing residents to better control and plan their electricity expenses.
Limitation: It is important to note that this research is confined to the development of a predictive model using the backpropagation method. Practical implementation and testing of the model in real-world dormitory settings are beyond the scope of this study. Future research and application stages are warranted to validate the model's effectiveness and usability.
Contribution: This research contributes by providing a predictive model using the backpropagation method, offering dormitory residents a tool to estimate and control their monthly electricity expenses. The model aims to reduce wasteful electricity practices and promote fair billing, contributing to a more sustainable and equitable electricity usage environment.
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Antika, Z. R., Rusmana, O., & Widianingsih, R. (2023). Analisis Determinasi Minat dan Penggunaan Financial Technology Payment Menggunakan Theory of Planned Behavior: Studi pada Mahasiswa Unsosed. Jurnal Ilmu Siber dan Teknologi Digital, 1(2), 111-124. doi:10.35912/jisted.v1i2.2097
Dwisatya, R., Kirom, M. R., & Abdullah, A. G. (2015). Prediksi Beban Listrik Jangka Pendek Berbasis Algoritma Feedforward Backpropagation Dengn Mempertimbangkan Variasi Tipe Hari. eProceedings of Engineering, 2(3).
Kosasi, S. (2014). Penerapan metode jaringan saraf tiruan backpropagation untuk memprediksi nilai ujian sekolah. Jurnal teknologi, 7(1), 20-28.
Mulyadi, Y., Abdullah, A., & Harmaen, U. Prediksi Beban Listrik Jangka Pendek Berdasarkan Kluster Tipe Beban Hari Libur Menggunakan Algoritma Backpropagation.
Rachman, A. S., Cholissodin, I., & Fauzi, M. A. (2018). Peramalan Produksi Gula Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Pada PG Candi Baru Sidoarjo. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(4), 1683-1689.
Rian Handoko, R. H., & Tata Sutabri, T. S. (2023). Analisamachine Learningdengan Algoritma Multi-Layer Perceptronuntuk Penanganan Kejahatan Phishing. Jinteks (Jurnal Informatika Teknologi dan Sains), 5(1), 13-17.
Sakinah, N. P., Cholissodin, I., & Widodo, A. W. (2018). Prediksi Jumlah Permintaan Koran Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(7), 2612-2618.
Sari, M., & Prasetyo, D. E. (2002). Sistem Simulasi Meteran Listrik Prabayar Berdasarkan Penggunaan Token Terhadap Daya Listrik Berbasis Multimedia.
Sutabri, T. (2012). Konsep sistem informasi: Penerbit Andi.
Wanto, A., & Windarto, A. P. (2017). Analisis prediksi indeks harga konsumen berdasarkan kelompok kesehatan dengan menggunakan metode backpropagation. Sinkron: jurnal dan penelitian teknik informatika, 2(1), 37-43.
- Antika, Z. R., Rusmana, O., & Widianingsih, R. (2023). Analisis Determinasi Minat dan Penggunaan Financial Technology Payment Menggunakan Theory of Planned Behavior: Studi pada Mahasiswa Unsosed. Jurnal Ilmu Siber dan Teknologi Digital, 1(2), 111-124. doi:10.35912/jisted.v1i2.2097
- Dwisatya, R., Kirom, M. R., & Abdullah, A. G. (2015). Prediksi Beban Listrik Jangka Pendek Berbasis Algoritma Feedforward Backpropagation Dengn Mempertimbangkan Variasi Tipe Hari. eProceedings of Engineering, 2(3).
- Kosasi, S. (2014). Penerapan metode jaringan saraf tiruan backpropagation untuk memprediksi nilai ujian sekolah. Jurnal teknologi, 7(1), 20-28.
- Mulyadi, Y., Abdullah, A., & Harmaen, U. Prediksi Beban Listrik Jangka Pendek Berdasarkan Kluster Tipe Beban Hari Libur Menggunakan Algoritma Backpropagation.
- Rachman, A. S., Cholissodin, I., & Fauzi, M. A. (2018). Peramalan Produksi Gula Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Pada PG Candi Baru Sidoarjo. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(4), 1683-1689.
- Rian Handoko, R. H., & Tata Sutabri, T. S. (2023). Analisamachine Learningdengan Algoritma Multi-Layer Perceptronuntuk Penanganan Kejahatan Phishing. Jinteks (Jurnal Informatika Teknologi dan Sains), 5(1), 13-17.
- Sakinah, N. P., Cholissodin, I., & Widodo, A. W. (2018). Prediksi Jumlah Permintaan Koran Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(7), 2612-2618.
- Sari, M., & Prasetyo, D. E. (2002). Sistem Simulasi Meteran Listrik Prabayar Berdasarkan Penggunaan Token Terhadap Daya Listrik Berbasis Multimedia.
- Sutabri, T. (2012). Konsep sistem informasi: Penerbit Andi.
- Wanto, A., & Windarto, A. P. (2017). Analisis prediksi indeks harga konsumen berdasarkan kelompok kesehatan dengan menggunakan metode backpropagation. Sinkron: jurnal dan penelitian teknik informatika, 2(1), 37-43.
