The Pillars of Survival in the COVID-19 Pandemic: The Case of Indonesia

Soesilo, Primidya K. M.; Rahman, Fathony

doi:10.1007/978-3-030-98152-5_13

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The potency to use telemedicine will be increased in the future. In Indonesia, the health services digitalization be the one of six pillars that have been developed to strength health resilience [12]. The millennial generation that was born when technology developed massively also be the most of population in Indonesia.…”

Section: Resultsmentioning

confidence: 99%

Policy Recommendation for Telemedicine Services after Covid-19 Pandemic

Budiyanti

Suryawati²,

Murni³

et al. 2022

BIO Web Conf.

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During Coronavirus Disease 2019 (COVID-19) pandemic, the use of telemedicine services in Indonesia rose. Telemedicine service policies were released as an emergency condition during the pandemic. Meanwhile, the post-pandemic policies on telemedicine have never been formulated. This study aimed to construct the policy recommendation related to telemedicine services in Indonesia in the COVID-19 post-pandemic. The policy recommendations were developed after an initial analysis of user needs. The respondents were 102 telemedicine users who were over 18 years old. Results showed that most telemedicine users (61%) were worried about misdiagnosis, and others (52.9%) were worried about data security and confidentiality. The conceptual policy recommendation was developed based on the situation analysis. Multi-stakeholder cooperation across institutions such as the Indonesian Ministry of Health, the Ministry of Information Technology, the Food and Drug Administration, the Association of Health Service Facilities, professional organizations, and telemedicine startups. Such cooperation may harmonize the implementation of policies.

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Section: Resultsmentioning

confidence: 99%

Policy Recommendation for Telemedicine Services after Covid-19 Pandemic

Budiyanti

Suryawati²,

Murni³

et al. 2022

BIO Web Conf.

View full text Add to dashboard Cite

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“…Indonesia is a country located in Southeast Asia, consisting of more than 17,000 islands and home to over 270 million people. It is the fourth most populous country in the world and has a rich cultural heritage with over 300 ethnic groups [1]. This diversity extends to the country's linguistic landscape, as Indonesia is home to more than 700 languages [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of language identification algorithms for regional Indonesian languages

Sujaini,

Bijaksana Putra

2024

IJ-AI

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Detecting local languages in Indonesia is essential for recognizing linguistic diversity, promoting intercultural understanding, preserving endangered languages, and improving access to education and services. By identifying and documenting these languages, we can support language preservation efforts, provide tailored resources for communities, and celebrate the unique cultural heritage of different ethnic groups. Ultimately, this encourages a more accepting and open-minded society, prioritizing various languages and cultural customs. This research aims to identify the most suitable algorithm for language detection in Indonesian regional languages and gain insights into their unique characteristics through n-gram analysis. By understanding language diversity, the study contributes to preserving Indonesia's cultural and linguistic heritage and improving language detection techniques. This study compares the performance of five algorithms (Naïve Bayes, K-nearest neighbors (KNN), least-squares, Kullback Leibler divergence, and Kolmogorov Smirnov test) to determine the most accurate and efficient method for language identification. Incorporating trigram features alongside unigrams and bigrams significantly improved the model's performance, with F1 scores increasing from 0.923 to 0.959. The study found that using more features leads to better accuracy, with Naïve Bayes and KNN emerging as the top-performing algorithms for language identification.

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The Pillars of Survival in the COVID-19 Pandemic: The Case of Indonesia

Cited by 2 publications

References 19 publications

Policy Recommendation for Telemedicine Services after Covid-19 Pandemic

Policy Recommendation for Telemedicine Services after Covid-19 Pandemic

Analysis of language identification algorithms for regional Indonesian languages

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