Determining Citizen Complaints to The Appropriate
Government Departments using KNN Algorithm
Suhatati Tjandra*,
Amelia Alexandra Putri Warsito**
Department of Informatics Engineering Sekolah Tinggi Teknik Surabaya
Surabaya, Indonesia *tati@stts.edu, **sandra@stts.edu
Judi Prajetno Sugiono
Department of Industrial Engineering Sekolah Tinggi Teknik Surabaya
Surabaya, Indonesia jpsugiono@stts.edu
Abstract—Participation of citizens in the process of city development is very important. To achieve good governance and democratic, the citizen can participate by providing complaints, information, or advices. In the current system, complaints are handled manually by 1-2 operators, whereas speed and accuracy are needed. The problem is this manual handling causes errors in the determination of appropriate government departments that handle the complaint. This research will propose a system that aims to determine the appropriate government department with complaints given by the citizen with the implementation of K-Nearest Neighbor (KNN) algorithm, to reduce human errors. This algorithm is one of text classification algorithms, which in this research, is used to classify complaints which the texts in Indonesian language. The input of the system is complaint given by the citizen and the output is the name of the appropriate government department, which is in accordance with the contents of the complaint.
Keywords—text classification, k-nearest neighbor, supervised learning, indonesian language, complaint
I. INTRODUCTION
The growth of information technology that is rapidly increasing makes the elements in it are affected. This information technology greatly affects the processing, distribution, keeping and communicating of information. Existing institutions, such as companies, organizations, and governments require information in the development of institution itself, especially information that can support the progress of an institution. However, to communicate this information is sometimes not supported by good infrastructure, so that there is no communication running smoothly.
Citizen likes to give complaints against what has happened to the related institution. Citizen does not only convey a complaint, but there also conveyed appreciation for what happened. To accommodate complaints from the citizen, there are institutions that provide the facilities to convey complaints through various communication media. However, the submitted complaints are sometimes not given a response and there is a given response, but the response is given in a long time. This happens because the institution is quite difficult to determine the appropriate department in accordance with the content of the complaint, because the relevant departments should be determined manually and sometimes complaints
given to the departments which are not appropriate. This causes responses given in a long time.
Based on the explanation, in this research, we propose a system that can be used to handle these problems. A classification algorithm in machine learning will be implemented in this system. Arthur Samuel, who is a pioneer in the field of machine learning, in 1959, defines that machine learning as the field of study that gives computers the ability to learn without being explicitly programmed [1]. One of the categories of machine learning is a classification. In this research, classification is conducted on the text, so it is called as text classification or text categorization. The goal of the text classification is to classify documents into a number of predefined categories. There are many algorithms have been developed for text classification, such as K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, Support Vector Machine (SVM), etc [2].
Among all these algorithms, KNN is a widely used text classifier because of its simplicity and efficiency [3]. Many researches have shown that the KNN algorithm produces very good performance in the experiments. In the research of short text classification, KNN algorithm shows a better performance compared with the Naïve Bayes and SVM algorithm [4]. Moreover, the other research, that uses Reuters corpus as a dataset, shows that the KNN algorithm also produces very good performance, when compared with the Naïve Bayes, Rocchio, and C4.5 algorithm [5]. Based on that, we propose a system that implements KNN algorithm to classify the text of the complaint into a number of the government departments.
In this section, we explain the background and purpose of this research. In the second section, we explain the proposed system along with the input and output of the system. A detailed explanation of KNN algorithm to determine the appropriate government departments in accordance with the contents of the complaint will be outlined in the third section. The fourth and fifth sections are test section of KNN algorithm implementation and conclusion of the proposed system.
II. SYSTEM OVERVIEW
