Assessing public satisfaction of public service application using supervised machine learning

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

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.
利用监督机器学习评估公众对公共服务应用的满意度
COVID-19 大流行极大地影响了全球的经济形势,包括印度尼西亚在内的 3000 万印尼人因此陷入贫困。社会事务部启动了一项针对最贫困家庭的社会援助分配计划。Aplikasi Cek Bansos "是一个公共服务应用程序,旨在验证他们在社会援助计划中的地位。了解公众情绪和影响公众满意度的因素至关重要。本研究的目标是对有监督的机器学习进行比较研究,以了解公众的情绪和导致公众满意度的主要变量。支持向量机、Naïve Bayes 和 K-nearest neighbor (KNN) 算法都是为了寻求最高的准确性。实验发现,KNN 算法的准确率高达 99.21%,表现出色。情感预测显示负面情感占多数,达到 83.81%。通过三段论分析,可以了解影响对应用程序满意度的主题。负面主题分为以下几类:应用程序不稳定、希望改进、导航问题和低质量内容。为社会事务部和开发人员提出了一些建议,以克服负面反馈并提高公众对应用程序的满意度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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