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All Journal Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia Journal Of Artificial Intelligence And Software Engineering
Rhemadanu, Andreas
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Humanities Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Transportation

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Perancangan Sistem Pengamanan Sepeda Motor Berbasis Kontrol Telegram Cahyo Nugroho, Nur; Rhemadanu, Andreas; Aryo Putra Prayoga, Ferdianto; Pramono
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 2 No. 1 (2024): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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Abstract

Motorcycles are very popular in Indonesia, with the number reaching 125,305,332 units in 2022 according to the Central Statistics Agency (BPS). However, this popularity also increases the risk of theft, which rose from 106 cases in 2021 to 183 cases in 2022 according to BAPPEDA Jogja. To address this issue, this study proposes a motorcycle security system controlled via Telegram. This system uses WiFi technology, allowing users to monitor and control their motorcycles remotely. The prototype employs an Arduino Uno microcontroller, an ESP8266 module for WiFi connectivity, vibration and PIR sensors to detect suspicious activity, and relays to activate alarms and cut off power to the motorcycle. The development method used is prototyping, which involves iterative feedback and refinement. The resulting design effectively reduces the risk of theft by providing early detection and quick response capabilities, thereby enhancing motorcycle security.
Peningkatan Kinerja Perpustakaan Melalui Penerapan Sistem Knowledge Rizqy Alfiansyah, Rafif; Octavia Rahmawati, Anin; Hafid Krisna Wahyu Wijaya, Muhammad; Farid Faqih, Muhammad; Rhemadanu, Andreas; Nurjanah, Umi
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 2 No. 2 (2024): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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This research focuses on improving library performance through the implementation of an information-based system. The system is designed to help libraries manage information resources more effectively, enhance accessibility, and support learning and research processes. The research methods include problem identification, literature review, knowledge-based system design, and prototype evaluation before full implementation. The benefits of implementing this system include increased operational efficiency, improved service quality, support for learning and research, knowledge management, and information literacy. The data model used applies case-based techniques with similarity calculations between user needs and the information of the books owned, involving features such as genre and publication year. The similarity calculation results are used to provide book recommendations to library visitors. For example, the system recommends the book "To Kill a Mockingbird" because it shares the same genre and publication year as "The Alchemist". Process modeling has proven to be an essential tool in developing an effective and efficient system. This research also includes relevant references to support the findings and methodologies used.
Implementation of a Food Menu Recommendation System at Ndalem Uti Restaurant Using Collaborative Filtering Based on User Preferences Rhemadanu, Andreas; Susanto, Rudi; Asri, Anindhiasti Ayu Kusuma
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7119

Abstract

This study aims to implement a menu recommendation system at Ndalem Uti Restaurant using a Collaborative Filtering approach based on user preferences. The main problem faced is that customers have difficulty choosing the best-selling menu because of the many choices and minimal information regarding the popularity of each menu. To overcome this, the Collaborative Filtering method is used with the User-Based Cosine Similarity calculation to measure the similarity of preferences between users. The test results show that the recommendation accuracy level reaches 96%, including for new users. This system is able to provide more personalized menu recommendations based on customer order history and ratings. This implementation is expected to improve user experience, optimize sales, and be a solution for the development of culinary businesses in the future.
Co-Authors Anindhiasti Ayu Kusuma Asri Aryo Putra Prayoga, Ferdianto Cahyo Nugroho, Nur Farid Faqih, Muhammad Hafid Krisna Wahyu Wijaya, Muhammad Octavia Rahmawati, Anin Pramono Rizqy Alfiansyah, Rafif Rudi Susanto Umi Nurjanah, Umi