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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL FISIKA PAPUA
Haay, Happy Alyzhya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Analysis of Water Flow Rate in the Kemiri River, Jayapura District Yoku, Rosdiana; Irawan, Sakka; Kopeuw, Agnes Julia; Haay, Happy Alyzhya; Daullu, Melissa Aeudia
Jurnal Fisika Papua Vol. 4 No. 1 (2025): Jurnal Fisika Papua
Publisher : Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31957/jfp.v4i1.234

Abstract

Kali Kemiri Sentani frequently experiences flooding, particularly during periods of intense rainfall. Given the critical importance of flood risk mitigation, this study aims to analyze the water flow rate in Kali Kemiri and investigate the key factors influencing its dynamics. The research was conducted within the Kali Kemiri watershed in Jayapura Regency, focusing on a watershed area with a total study area of 1,640 m², mapped at a small scale across two measurement points. Flow rate measurements were performed at two locations along Kali Kemiri using the float method. The study recorded key hydrological parameters, including water depth, river width, and flow velocity. These data were utilized to calculate the volumetric flow rate at the designated measurement sites. This methodology provides a comprehensive understanding of the hydrodynamic factors affecting water flow, which is essential for assessing flood hazards in the region. The results indicate that the highest discharge rate was observed at the first measurement point, with an average flow discharge of 0.188 m³/s and a velocity of 0.52 m/s. In contrast, the second measurement point recorded a discharge of 0.115 m³/s with a flow velocity of 0.29 m/s. These findings suggest a direct correlation between flow velocity and discharge, wherein an increase in velocity corresponds to an increase in discharge. Furthermore, the study highlights that the flow rate is influenced by watershed area, watershed volume, and watershed slope, which collectively govern the hydrological behavior of the river system.
LATENT DIRICHLET ALLOCATION (LDA) METHOD ANALYSIS ABOUT COVID-19 VACCINE ON TWITTER SOCIAL MEDIA Haay, Happy Alyzhya; Setiawan, Adi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (788.349 KB) | DOI: 10.30598/barekengvol16iss1pp189-196

Abstract

Twitter is one social media that often provides much information for its users, one of which is information regarding the COVID-19 vaccination. This study aimed to explore and find out what topics are often discussed on Twitter social media. One of which is the topic of COVID-19 vaccination using the Latent Dirichlet Allocation (LDA) method and analysis of the frequency of keywords that often appear with this topic. The Tweet data used in this study was taken from Twitter users worldwide in November 2021. In this study, the results of sentiment analysis were obtained from the tweet data taken, which was divided into positive sentiment and negative sentiment, namely "vaccination" with 40 words and "'Covid19" with 35 words
INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK Haay, Happy Alyzhya; Trihandaru, Suryasatriya; Susanto, Bambang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.962 KB) | DOI: 10.30598/barekengvol17iss1pp0211-0224

Abstract

In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.
Co-Authors Adi Setiawan Bambang Susanto Daullu, Melissa Aeudia Kopeuw, Agnes Julia Rosdiana Yoku Sakka Irawan Suryasatriya Trihandaru