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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JREC (Journal of Electrical and Electronics) Jurnal Ilmu Komputer dan Agri-Informatika Journal of Students‘ Research in Computer Science (JSRCS) Journal of Computer Science Contributions (Jucosco)
Retno Nugroho Whidhiasih
Institut Pertanian Bogor
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Journal of Students‘ Research in Computer Science (JSRCS)

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Deteksi Emosi Menggunakan Convolutional Neural Network Berdasarkan Ekspresi Wajah Ekawati, Inna; Putra, Fadilla Nidya Riyanto; Sumadyo , Malikus; Whidhiasih, Retno Nugroho
Journal of Students‘ Research in Computer Science Vol. 5 No. 1 (2024): Mei 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/h0kayy31

Abstract

Facial expression recognition is an effective method for identifying someone's emotional expression. Emotional expressions can be recognized from changes in facial expressions, wrinkles on the forehead, blinking of the eyes, or changes in facial skin color. Facial expressions that a person generally has, such as neutral, angry, happy expressions. The problem that often occurs is the subjective assessment of a person's expression. This research examines how artificial intelligence can recognize facial expressions. The facial recognition process in the research uses a Convolutional Neural Network (CNN), which is a deep learning method capable of carrying out an independent learning process for object recognition, object extraction and classification and can be applied to high resolution images that have a nonparametric distribution model. The two main stages in CNN are feature learning and classification. The results of facial expression recognition can be used to detect a person's emotions. This research uses the FER2013 dataset which contains images of happy, sad, angry, afraid, surprised, disgusted and neutral emotions. The data set in the research received tests that had been carried out, namely the percentage of accuracy level in the model was 76%. It is hoped that the classification of emotions resulting from this research can contribute to the development of artificial intelligence technology and as a tool in various fields such as psychology, education and others. For further research, it can be developed further by adding other architectures such as VGG19, MobileNet, and ResNet-50 so that the resulting CNN model is more optimal.
Implementasi Sistem Monitoring Jaringan Mikrotik Dengan The Dude Jaelani, M; Whidhiasih, Retno Nugroho; Handayanto, Rahmadya Trias
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/15w1se86

Abstract

The internet service provider, CV. Sekarjaya Computindo, is currently still using a manual monitoring system. This manual monitoring system relies on the Windows command prompt. As a result, detecting network issues takes longer because the administrator must manually search for and ping IP addresses within the network or wait for user reports when network problems occur. In this study, a Mikrotik network monitoring system using The Dude was developed for CV. Sekarjaya Computindo to assist the administrator in monitoring network devices more efficiently, enabling faster identification and resolution of network disruptions. The results of the study show that The Dude monitoring system can quickly monitor the status of network devices when they are up or down due to damage or network disturbances. These statuses are displayed on The Dude’s network maps and real-time notifications are sent via Telegram.
Co-Authors Alfianto, Muhammad Gilang Anita Setyowati Srie Gunarti Dadan Irwan Dwi Yulianto Ekawati, Inna Haryono Haryono Hendharsetiawan, Andy Achmad Herlawati Herlawati Hodijah, Nur Saqinah Jaelani, M Jamaliah, Imma Maimunah Maimunah Malikus Sumadyo Nursinta Adi Wahanani Prapto Tri Suprio Prima Dina Atika Putra, Fadilla Nidya Riyanto Rahmadya Trias Handayanto Sugi Guritman Sumadyo , Malikus Supriyanto Supriyanto Yuliana, Upi