Mahadhni, Jhingga (2025) {JALUR PROFESIONAL LOMBA} UNITY COMPETITION - 2024. S1 - Sarjana thesis, Universitas AMIKOM Yogyakarta.
![[img]](https://eprints.amikom.ac.id//style/images/fileicons/text.png) |
Text (COVER-ABSTRAK)
COVER.pdf Download (2MB) |
|
Text (BAB I)
BAB I.pdf Download (183kB) |
|
Text (BAB II)
BAB II.pdf Restricted to Registered users only Download (199kB) |
|
Text (BAB III)
BAB III.pdf Restricted to Registered users only Download (223kB) |
|
Text (BAB IV)
BAB IV.pdf Download (39kB) |
|
Text (DAFTAR PUSTAKA-LAMPIRAN)
Daftar Pustaka dan Lampiran.pdf Restricted to Registered users only Download (140kB) |
![[img]](https://eprints.amikom.ac.id//style/images/fileicons/archive.png) |
Archive (SOURCE CODE)
Sourcecode - 21.83.074.zip Restricted to Repository staff only Download (20MB) |
Abstract
Yogyakarta State University (UNY) organized the UNITY Competition #12, a national-level information technology competition participated in by students from various universities across Indonesia. The competition aims to encourage technological innovation that supports digital transformation and fosters an inclusive and empowered society. One of the contested categories was IoT Smart Device, focusing on the development of Internet of Things (IoT)-based solutions for industrial and societal needs. The preliminary round was conducted online through proposal selection, while the final round was held offline with prototype presentations and demonstrations before a panel of judges. In this event, the team from Universitas Amikom Yogyakarta won 1st Place in the IoT Smart Device category with their innovation, SESA (Smart Energy System Analysis). SESA is an IoT and deep learning-based system designed to analyze real-time energy consumption patterns and provide optimization recommendations to improve energy efficiency. This study includes a literature review on electricity consumption prediction using machine learning, as well as the effectiveness of real-time data analysis in energy optimization. The results indicate that integrating IoT and machine learning offers promising solutions for both the energy industry and households. This research is expected to serve as a reference for the development of similar systems to enhance energy efficiency in the future.
| Item Type: | Thesis (S1 - Sarjana) | ||
|---|---|---|---|
| Contributor: |
|
||
| Uncontrolled Keywords: | Machine Learning, Iot, Analisis Energi, Efisiensi Energi | ||
| Subjects: | 000 - Komputer, Informasi dan Referensi Umum > 000 Ilmu komputer, ilmu pengetahuan dan sistem-sistem > 005 Pemrograman komputer, program dan data 000 - Komputer, Informasi dan Referensi Umum > 000 Ilmu komputer, ilmu pengetahuan dan sistem-sistem > 006 Metode komputer khusus |
||
| Divisions: | Fakultas Ilmu Komputer > Teknik Komputer | ||
| Depositing User: | RC Universitas AMIKOM Yogyakarta | ||
| Date Deposited: | 16 Sep 2025 04:56 | ||
| Last Modified: | 16 Sep 2025 04:56 | ||
| URI: | http://eprints.amikom.ac.id/id/eprint/30701 |
Actions (login required)
 |
View Item |