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All Journal Jurnal Ilmu Komputer dan Informasi Tematik : Jurnal Teknologi Informasi Komunikasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Muhamad Koyimatu
Unknown Affiliation
Computer Science & IT Library & Information Science

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Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System Herminarto Nugroho; Meredita Susanty; Ade Irawan; Muhamad Koyimatu; Ariana Yunita
Jurnal Ilmu Komputer dan Informasi Vol 13, No 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (579.884 KB) | DOI: 10.21609/jiki.v13i1.761

Abstract

This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.
Penerapan Metode SDLC Waterfall Pada Sistem Pemesanan Makanan Menggunakan QR-Code Berbasis Website Yolla Putri Ervanisari; Muhamad Koyimatu; Kristine Angelina Simanjuntak; Intan Oktafiani
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Café Sudut Temu by Toastea, located in Cirahayu Tegalega Street, Central Bogor, was established in 2015. The manual booking system in place at Café Sudut Temu is currently inefficient and vulnerable to error, resulting in lengthy waiting times, order errors, and challenges in inventory management. Therefore, this research is planning to build a QR code-based food ordering system through a website. The ordering system in this study was developed using the Waterfall methodology and the application was designed using the Laravel PHP framework. The design phase begins with the collection of requirements, the creation of design, the implementation or development of the system and ends with testing. This research results in a system that simplifies the ordering process, reduces errors, and improves operational efficiency.In addition, the responsive design with a mobile-friendly interface allows customers to order directly without having to wait for the help of the server. The system is expected to increase customer satisfaction and contribute to coffee growth amid increasing competition in the industry. The results of this research are a website-based application for ordering food and beverages using a QR Code on Café Sudut Temu.
Identifikasi Opini Publik Terhadap Kendaraan Listrik dari Data Komentar YouTube: Pemodelan Topik Menggunakan BERTopic Kristine Angelina Simanjuntak; Muhamad Koyimatu; Yolla Putri Ervanisari; Tasmi
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2096

Abstract

The Indonesian government is encouraging the transition to electric vehicles to reduce the use of fossil fuels and the negative environmental impact. This transition sparked controversy because Indonesia is still heavily dependent on coal-fired power plants, and many argue that the transition is not ready without adequate renewable energy and supporting infrastructure. Public opinion analysis is crucial in considering the introduction of electric vehicles in Indonesia due to the controversial nature of the transition. The opinion is transmitted through YouTube by taking comment data, then grouped into a topic to identify public opinion. The topic modeling method used is a BERTopic transformer model using IndoBERTweet in embedding. Once public opinion is modeled into a topic, changes in public opinion are evaluated using coherence score metrics and topic diversity as a measure of the consistency and diversity of the topic. The resulting topics have a coherence value of around 0.6 to 1 and a diversity value of 0.95838. This indicates that the resulting themes have strong semantic similarities and high diversity in terms of word usage and capture various aspects of text documents well.
Co-Authors Ade Irawan Ariana Yunita Herminarto Nugroho Intan Oktafiani Kristine Angelina Simanjuntak Meredita Susanty Tasmi Yolla Putri Ervanisari