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All Journal Sistemasi: Jurnal Sistem Informasi Geosains Kutai Basin
Manik, Ni Kadek Sri
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering

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Mapping the Golden Hour: A Spatiotemporal Analysis of Ambulance Response Time in Urban Jakarta Jailani, Zakiul Fahmi; Nurmadewi, Dita; Syumanjaya, Raden Bambang; Manik, Ni Kadek Sri
GEOSAINS KUTAI BASIN Vol. 6 No. 2 (2023)
Publisher : Geophysics Study Program, Faculty of Mathematics and Natural Sciences, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/geofisunmul.v6i2.1188

Abstract

This study aims to map the ambulance response time in Jakarta city and assess the current ambulance to population ratio. The data used in this study include hospital point data retrieved from OpenStreetMap (OSM), manually digitized data points, and a database from community/non-government organizations that manage their own ambulance. The analysis was conducted using a combination of buffer, service area, and overlay tools in GIS (Geo-information System) software. The results show that the current ambulance to population ratio in Jakarta is inadequate, with only 78 ambulances available that can only serve a total population of 5,598,058 out of all 10,748,230 people in Jakarta. This means that at least 215 ambulances are needed to provide comprehensive coverage for the entire population. Furthermore, the golden time for ambulance response, as set by the Ministry of Health in Indonesia, is less than 15 minutes. However, the current ambulance to population ratio in Jakarta makes it difficult to meet this standard.
Comparison of the Performance of the VADER and RoBERTa Algorithms on Twitter Nurmadewi, Dita; Jailani, Zakul Fahmi; Manik, Ni Kadek Sri
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4198

Abstract

This research compares the performance of two sentiment analysis algorithms, namely VADER (Valence Aware Dictionary and Entiment Reasoner) and RoBERTa (Robustly Optimized BERT Pretraining Approach), using a dataset of public opinions regarding climate change on twitter. Analysis is carried out to determine the sentiment distribution of the tweets described, whether they are positive, negative or neutral. In addition, this research identifies the keywords that appear most frequently from the collection of tweets that have been analyzed. Time series analysis was also carried out to see the distribution of sentiment over 12 months. The relationship between the two models was evaluated using matrix scatter plot analysis for tweets per two months, to assess the correlation and consistency of sentiment results between VADER and RoBERTa. The results show that VADER is more effective in situations that require rapid responses to changes in public sentiment, while RoBERTa is superior in in-depth analysis of more complex and ambiguous content.
Co-Authors Jailani, Zakiul Fahmi Jailani, Zakul Fahmi Nurmadewi, Dita Syumanjaya, Raden Bambang