This research analyzes the growing public distrust towards the Indonesian Government expressed on social media platforms during the COVID-19 outbreak. It aims to answer (1) why has there been a growth of disappointment among citizens towards both the central Government and the local Government during the COVID-19 outbreak; (2) what facts and factors are objectively considered sufficient to underlie the expression of “disappointment” that arises in the public domain; and (3) how can the Indonesian Government regain public trust during this Pandemic. This study uses the transformative concurrent mixed methods design and utilizes the Disappointment Theory and the Belief Theory. The results show that the Indonesian Government tried to overcome public distrust by increasing essential indicators: benevolence, reliability, competence, honesty, and openness. These five indicators were maximized in several government policies through the Covid-19 Task Force formation in Indonesia. It is then concluded that the Indonesian Government’s policies and protocols during the COVID-19 had been deemed ineffective in meeting society’s needs, resulting in significant economic loss and a rapid increase in the death rate. To regain public trust, the Government must increase the quality of its public relations and publish data that can be held accountable.

Penularan SARS-CoV-2 di Indonesia semakin mengkhawatirkan. Pandemik COVID-19 tidak hanya berdampak pada individu yang terinfeksi langsung, tetapi juga berimbas kepada para penyintas kolateral, yaitu mereka yang kehidupannya ikut terdisrupsi akibat krisis. Pulau Jawa adalah episentrum penularan COVID-19 di Indonesia. Beberapa studi menunjukkan bahwa kejadian COVID-19 memiliki hubungan yang erat dengan tingginya konsentrasi zat partikulat di udara. Penelitian ini bertujuan untuk mengetahui hubungan antara kualitas udara dan risiko penularan COVID-19 di Pulau Jawa. Kualitas udara diukur dengan konsentrasi NO2, sedangkan risiko penularan COVID-19 direfleksikan oleh Indeks Kewaspadaan COVID-19. Penelitian ini menggunakan empat model untuk melihat pengaruh kadar NO2 terhadap Indeks Kewaspadaan COVID-19, dimana variabel kepadatan penduduk ditambahkan sebagai kontrol. Model-0 adalah model dasar tanpa efek kelompok dan efek spasial. Model-1 adalah pengembangan dari Model-0 dengan menambahkan efek spasial. Model-2 adalah pengembangan dari Model-0 dengan menambahkan efek kelompok. Sedangkan, Model-3 adalah model lengkap dengan efek kelompok dan efek spasial. Hasil analisis menunjukkan bahwa Model-3 adalah model terbaik dalam menjelaskan pengaruh kadar NO2 terhadap Indeks Kewaspadaan COVID-19. Model-3 mampu meningkatkan kinerja model secara substansial. Hasil pengujian mengindikasikan bahwa kadar NO2 berpengaruh positif dan signifikan terhadap Indeks Kewaspadaan COVID-19, setelah efek kelompok dan efek spasial diperhitungkan. Dengan demikian, disimpulkan bahwa semakin tinggi konsentrasi NO2 yang mencerminkan semakin buruk kualitas udara, semakin tinggi pula risiko penularan COVID-19. Hasil ini memiliki implikasi penting untuk strategi pencegahan penyebaran COVID-19, khususnya pada daerah-daerah dengan tingkat pencemaran udara yang tinggi.

The increasing needs for more disaggregated data motivates National Statistical Offices (NSOs) to develop efficient methods for producing official statistics without compromising on quality. In Indonesia, regional autonomy requires that Sustainable Development Goals (SDGs) indicators are available up to the district level. However, several surveys such as the Indonesian Demographic and Health Survey produce estimates up to the provincial level only. This generates gaps in support for district level policies. Small area estimation (SAE) techniques are often considered as alternatives for overcoming this issue. SAE enables more reliable estimation of the small areas by utilizing auxiliary information from other sources. However, the standard SAE approach has limitations in estimating non-sampled areas. This paper introduces an approach to estimating the non-sampled area random effect by utilizing cluster information. This model is demonstrated via the estimation of contraception prevalence rates at district levels in North Sumatera province. The results showed that small area estimates considering cluster information (SAE-cluster) produce more precise estimates than the direct method. The SAE-cluster approach revises the direct estimates upward or downward. This approach has important implications for improving the quality of disaggregated SDGs indicators without increasing cost. The paper was prepared under the kind mentorship of Professor James J. Cochran, Associate Dean for Research, Prof. of Statistics and Operations Research, University of Alabama.

COVID-19 is a health crisis that is experienced by the world. Many countries decided to close schools, colleges, and universities. The Indonesian government decided to close schools and educational institutions and change the teaching and learning process through face-to-face turns into online learning. The transformation in this process impacts online learning for most teachers and students who have not adapted well. This study aims to analyze the relevant ministries in the education sector, which is less capable and less responsive, creating the right policy to face changes that occur and policy alternatives that should be taken by the government. This study uses the research method mixed method, that is a mixture of qualitative and quantitative with the type of concurrent triangulation to analyze each variable’s relationship more accurately. The data source is secondary data sources derived from journals, online news, and previous research survey results. This study shows that there is still the occurrence of inequality of internet access and technology capabilities, especially in rural areas; therefore, online learning facilities and infrastructure should be improved so that there is no aggrieved party.

Stochastic frontier analysis (SFA) is the favorite method for measuring technical efficiency. SFA decomposes the error term into noise and inefficiency components. The noise component is generally assumed to have a normal distribution, while the inefficiency component is assumed to have half normal distribution. However, in the presence of outliers, the normality assumption of noise is not sufficient and can produce implausible technical efficiency scores. This paper aims to explore the use of Student’s t distribution for handling outliers in technical efficiency measurement. The model was applied in paddy rice production in East Java. Output variable was the quantity of production, while the input variables were land, seed, fertilizer, labor and capital. To link the output and inputs, Cobb-Douglas or Translog production functions was chosen using likelihood ratio test, where the parameters were estimated using maximum simulated likelihood. Furthermore, the technical efficiency scores were calculated using Jondrow method. The results showed that Student’s t distribution for noise can reduce the outliers in technical efficiency scores. Student’s t distribution revised the extremely high technical efficiency scores downward and the extremely low technical efficiency scores upward. The performance of model was improved after the outliers were handled, indicated by smaller AIC value.