The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs ...initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.
The Indonesian national police (Polri) offer public services through mobile apps: Digital korlantas polri (DigiKorlantas) and samsat digital nasional (SIGNAL). Sentiment analysis gauges public ...perceptions, serving as a basis for e-government evaluation using user ratings and comments from app stores. Keyword relevance is assessed via feature extraction and Naïve Bayes classification. Thematic analysis is implemented using N-grams methods to identify the factors affecting the effectiveness based on user experiences. The accuracy of the model reaches 81.09% where it indicates a high performance. DigiKorlantas acquires slightly more negative reviews in comparation with positive reviews which are 51% and 49% respectively. In contrast, positive sentiment is dominant on SIGNAL which reach 58%, compared with negative sentiment that in 42%. N-grams reveal similar review patterns for both apps. Some of the solutions are Korlantas Polri should enhance the verification functionality with several techniques such as retinex algorithms or optical character recognition pipeline and increase the capacity of supporting server then releasing an updated version of application to address errors or bugs. This analysis can be alternative evaluation by the Polri to measure the success of the application and find out the continuous improvement of the process and the system.
Implementing the COVID-19 vaccination program was not easy because it received various responses from the public. This study will explore public sentiment on the program which was taken based on ...public comments on several videos on YouTube using data crawling techniques using Coberry tools. The data was public opinions in Bahasa Indonesia that will be collected based on videos from official news channels with high engagement in the last eight months. Sentiment analysis was carried out using the Naive Bayes Classifier method that can be used to make deep analysis through filtering and classifying the opinions of the Indonesian people, as stated on YouTube. This research showed that public sentiment was dominated by negative sentiment based on some public doubts regarding the side effects of vaccines and the government follow-up regarding the country's economic recovery. Compared with the previous studies, the conclusions of sentiment obtained by this study were not the same due to differences in data sources and the selection of timeframes for community responses. An analysis of the public responses that have been carried out using a data collection method like this one will be very effective in providing an overview of the public desire to facilitate policymakers in formulating policy designs for the public interest
Various discussions and studies from different sectors have emerged as a reaction to the relocation of the Indonesian capital city from economic, transportation, and environmental perspectives. ...However, not many articles discuss civil servants as one of the groups directly affected by the relocation policy. This paper describes the condition and correlation of civil servants' knowledge, perceptions and attitudes toward the IKN relocation policy. This research uses quantitative descriptive research methods, which were conducted by distributing surveys to Central Government Civil Servants based in Greater Jakarta. The survey results stated that civil servants had relatively excellent knowledge regarding IKN relocation, while civil servants' perceptions of IKN transfers tended to be negative. Regarding attitude, 48% of respondents wanted to be transferred with special compensation. Based on the analysis of the relationship between three aspects, it is known that crosstabulation analysis on knowledge and perception affects the attitudes of civil servants regarding the IKN relocation policy. Meanwhile, cross-tabulation analysis between knowledge and perception does not affect each other. Finally, from the results, to increase the knowledge and perception of civil servants, the government needs to increase socialization among civil servants regarding the IKN relocation policy and update the information regularly and massively, especially in the compensation that is more in favour of civil servants
