Priority Analysis of Community Complaints through E-Government Based on Social Media

Madyatmadja, Evaristus Didik; Olivia, Jennifer; Sunaryo, Rizky Febriyanto

doi:10.35940/ijrte.c5011.098319

Cited by 4 publications

(3 citation statements)

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“…In its back-end operations, LAKSA also aggregates complaints crowdsourced through other digital channels. Currently, the city government pulls complaints submitted initially to its official Facebook and Instagram accounts and complaintrelated comments from its online news sites [91]. However, multi-channel crowdsourcing In the super app, the city government includes LAKSA (Figure 2, right side).…”

Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

“…In its back-end operations, LAKSA also aggregates complaints crowdsourced through other digital channels. Currently, the city government pulls complaints submitted initially to its official Facebook and Instagram accounts and complaint-related comments from its online news sites [91]. However, multi-channel crowdsourcing has made it difficult for the government to manage the massive datasets, thereby requiring an accurate classification algorithm to ensure an agile and correct routing of the complaints to relevant governmental bodies.…”

Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

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Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Madyatmadja,

Sianipar,

Wijaya

et al. 2023

Informatics

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Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.

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Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

“…In its back-end operations, LAKSA also aggregates complaints crowdsourced through other digital channels. Currently, the city government pulls complaints submitted initially to its official Facebook and Instagram accounts and complaint-related comments from its online news sites [91]. However, multi-channel crowdsourcing has made it difficult for the government to manage the massive datasets, thereby requiring an accurate classification algorithm to ensure an agile and correct routing of the complaints to relevant governmental bodies.…”

Section: Case Study: Crowdsourced Citizen Complaints In Tangerang Cit...mentioning

confidence: 99%

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Madyatmadja,

Sianipar,

Wijaya

et al. 2023

Informatics

View full text Add to dashboard Cite

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“…Naive Bayes classifies documents into certain classes using a probabilistic model with an independent assumption of the term. The advantage of using naïve Bayes is that it only requires a small amount of training data to determine the estimated parameters needed in the classification process (Madyatmadja et al, 2019).…”

Section: Naive Bayesmentioning

confidence: 99%

Sentiment Analysis on User Reviews of Mutual Fund Applications

Madyatmadja¹,

Shinta²,

Susanti³

et al. 2022

Journal of Computer Science

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The primary goal of this study is to compare the accuracy of the results of sentiment analysis using the Naive Bayes, Support Vector Machine (SVM), and Random Forest methods on one of the mutual fund application's user reviews. The second goal is to identify user reviews of the mutual fund app to gain insight into the topics covered by each sentiment. The user reviews have been collected through a web scraping method on the google play store, then cleaned through several processes of data pre-processing. Feature extraction was performed using TF-IDF along with vectorization using ngrams. The model performance was measured using a confusion matrix. Using a ratio of 80:20 on training and testing data, resulting in an accuracy of 92.7, 93.7 and 94.2% for Naive Bayes, SVM, and Random Forest methods, respectively. Identify the topics covered by each sentiment in user reviews using visualizations. In the positive sentiment of users, the majority discusses the application which is easy and good, especially for novice investors. In negative sentiment, the majority discussed the slow sales process to disbursement of funds and long loading times when opening the application.

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Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

2022

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Citizen science has emerged in many countries to contribute to the prompt resolution of individual field problems and has been shifted toward Information System (IS) research. In the domain of IS, a citizen report mechanism has been introduced in many local governments to understand regional problems based on the public participation. The rising of social media enforces many organizations including the local governments to utilize any information from the citizens including texts. Text mining has been utilized in various types of analyses such as sentiment analysis. However, it shows many challenges when it comes to the local context. The local context of words could cause various conflation errors that highly affect the learning task such as classification methods. This study aims to propose a context-based text processing and feed the proposed approach into a machine learning framework to classify the data of citizen reports-. The context-based text preprocessing utilized statistical-and semantic-based measurements to extract the local context and elaborate domain expertise to verify the misinterpretation for further text processing such as feature extractions. Subsequently, the n-gram language models together with the Term Frequency and Inverse Document Frequency schemes were performed to build the features. The result showed that the context-based text preprocessing improved the classification performance in majority classifiers in about 3% with the combinations of n-gram features.

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Priority Analysis of Community Complaints through E-Government Based on Social Media

Cited by 4 publications

References 1 publication

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

Sentiment Analysis on User Reviews of Mutual Fund Applications

Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

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