The Similarity of Essay Examination Results using Preprocessing Text Mining with Cosine Similarity and Nazief-Adriani Algorithms

et.al, Rika Rosnelly

doi:10.17762/turcomat.v12i3.938

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Namun pada penelitian ini, algoritma similaritas digunakan untuk menentukan nilai mahasiswa dengan melihat kemiripan jawaban soal dengan jawaban kunci. Algoritma yang terbaik adalah algoritma yang menghasilkan nilai jawaban yang terdekat dengan jawaban yang dilakukan oleh manusia [18].…”

Section: Pendahuluanunclassified

Performance of Text Similarity Algorithms for Essay Answer Scoring in Online Examinations

Susanto,

Husni Thamrin,

Naufal Azmi Verdikha

2023

J. Tek. Inform. (JUTIF)

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The purpose of assessment is to determine learning success. Exams with question descriptions have several advantages, including ease of preparation and the ability to reveal student comprehension and originality. The problem with space is that it takes time to fix. Therefore, it is important to develop algorithms and software that automatically evaluate space. With the help of this algorithm and this software, you can solve some exam and assessment problems. This study aims to investigate similarity algorithms that approximate human patterns in evaluating ambiguous answers. This study examines his five similarity algorithms, including TF-IDF and LSA. The data was a collection of correct answers with a total of 371 texts. The similarity algorithm's performance was compared with human correction results. Evaluation was performed using Root Mean Square Error (RMSE). This study shows that his TF-IDF algorithm like Jaccard has the lowest his RMSE compared to human judgement. However, the LSA algorithm tended better to follow human rating patterns for descriptive tests..

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Section: Pendahuluanunclassified

Performance of Text Similarity Algorithms for Essay Answer Scoring in Online Examinations

Susanto,

Husni Thamrin,

Naufal Azmi Verdikha

2023

J. Tek. Inform. (JUTIF)

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“…It helps subsequent stages focus solely on words with semantic weight within the text. Furthermore, stemming identifies root forms from the filtered words [48]. It helps reduce dimensionality by grouping words with the same root, even if they appear in different grammatical forms in the text.…”

Section: Data and Text Mining: An Overviewmentioning

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

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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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A code error correction system for PDF documents using regex and similarity matching

Pan

Hao

et al. 2022

2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

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The Similarity of Essay Examination Results using Preprocessing Text Mining with Cosine Similarity and Nazief-Adriani Algorithms

Cited by 4 publications

References 3 publications

Performance of Text Similarity Algorithms for Essay Answer Scoring in Online Examinations

Performance of Text Similarity Algorithms for Essay Answer Scoring in Online Examinations

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

A code error correction system for PDF documents using regex and similarity matching

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