Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Infact: Jurnal Sains dan Komputer
Yo’el Pieter Sumihar
Universitas Kristen Immanuel
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Implementation of K-Means Clustering Method for Network Traffic Anomaly Detection Haeni Budiati; Antonius Bima Murti Wijaya; Barita Suci Vernando Zebua; Jatmika; Yo’el Pieter Sumihar
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Anomalies may degrade network performance for specific network traffic. Because of its nature, it causes abnormal network traffic. Using the K-means clustering method, this study addresses the formulation of the problem of detecting network bandwidth usage anomalies. The objective of this study is to identify potential network traffic anomalies. This study uses the K-Means Method to predict the value of the network traffic anomalies that will appear. K-Means operates by repeatedly iterating based on the initial cluster entered, until the same cluster results are discovered. The results of the study indicate that predicting the occurrence of anomalies with K-Means will help suppress activities that impede network traffic.
SISTEM INFORMASI AKADEMIK BERBASIS MOBILE MENGGUNAKAN FLUTTER Studi Kasus: Sistem Akademik Universitas Kristen Immanuel Yo’el Pieter Sumihar; Arnan Abdiel Theopilus
JURNAL SAINS DAN KOMPUTER Vol. 6 No. 01 (2021): Jurnal Sains Dan Teknologi Informasi
Publisher : Universitas Kristen Immanuel

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

Abstract

Sistem informasi akademik (SIA) adalah sebuah sistem untuk mengelola dan menyebarkan informasi yang berkaitan tentang akademik mahasiswa. SIA harus memberikan informasi yang lengkap dan dapat diakses oleh semua mahasiswanya. Oleh karena itu perlu di lakukan pengembangan pada SIA berbasis android versi beta yang dimiliki oleh Universitas Kristen Immanuel (UKRIM) yang masih memiliki beberapa kekurangan. Flutter merupakan sebuah mobile app SDK (Software Development Kit) untuk membuat aplikasi android dan ios dari satu codebase dengan performa tinggi. Artinya kita hanya perlu mempelajari flutter untuk membangun aplikasi mobile untuk dua platform. Flutter memiliki design pattern yaitu bloc pattern, yang digunakan untuk memisahkan komponen presentation dan business logic yang akan memudahkan dalam perawatan ataupun penambahan fitur pada pengembangan aplikasi selanjutnya. Hasil dari penelitian ini aplikasi SIA UKRIM dapat dikembangkan dengan mengimplementasikan hampir semua fitur yang ada pada SIA UKRIM berbasis web kecuali upload tugas dengan menggunakan bloc pattern pada flutter.
Pengembangan Model Klasifikasi Sentimen Dengan Pendekatan Vader dan Algoritma Naive Bayes Terhadap Ulasan Aplikasi Indodax Agus Dirgahayu Zendrato; Sunneng Sandino Berutu; Yo’el Pieter Sumihar; Haeni Budiati
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5050

Abstract

Cryptocurrency trading applications such as Indodax have grown rapidly, the understanding of user sentiment towards the platform is still lacking, so it is interesting to analyze user sentiment towards the platform. To measure sentiment, this research proposes a combined approach of Vader and Naïve Bayes methods. The data used is a collection of user comments on the google play store platform related to user experience using Indodax. The Vader method is used to analyze sentiment directly from the comment text, while Naïve Bayes is adopted to improve accuracy in sentiment classification. The sentiment analysis process involves various steps, starting from data preparation, data pre-processing, labeling of training and testing data and performance evaluation of the Naive Bayes model. At the sentiment analysis stage with the Vader Sentiment method, the positive category obtained the highest percentage of 63.5%, followed by the neutral category at 18.9% and negative at 17.6%. Meanwhile, based on the performance evaluation of the Naïve Bayes model, the accuracy value is 78% while the highest precision value is achieved by the negative sentiment category at 80% and recall in the positive sentiment category at 44%.
Co-Authors Agus Dirgahayu Zendrato Antonius Bima Murti Wijaya Arnan Abdiel Theopilus Barita Suci Vernando Zebua Haeni Budiati Jatmika Sunneng Sandino Berutu