Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis

Hasna Melani Puspasari, Ilham Zharif Mustaqim, Avita Tri Utami, Rahmad Syalevi, Y. Ruldeviyani
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Abstract

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.
通过情感和主题分析评估印度尼西亚警察公共服务平台
印度尼西亚国家警察(Polri)通过移动应用程序提供公共服务:Digital korlantas polri (DigiKorlantas) 和 samsat digital nasional (SIGNAL)。情感分析可衡量公众的看法,并利用应用程序商店中的用户评分和评论作为电子政务评估的基础。关键词相关性通过特征提取和奈维贝叶斯分类进行评估。使用 N-grams 方法进行专题分析,根据用户体验确定影响有效性的因素。该模型的准确率达到 81.09%,显示出较高的性能。与正面评论相比,DigiKorlantas 获得的负面评论略多,分别为 51% 和 49%。相比之下,正面评价在 SIGNAL 上占主导地位,达到 58%,而负面评价为 42%。N-grams 显示这两款应用的评论模式相似。一些解决方案是,Korlantas Polri 应使用多种技术(如视网膜算法或光学字符识别管道)增强验证功能,并提高支持服务器的容量,然后发布更新版本的应用程序,以解决错误或漏洞。Polri 可以通过这种分析进行替代评估,以衡量应用程序的成功与否,并发现流程和系统的持续改进之处。
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