Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP

Rahayu, Sri; Mz, Yumarlin; Bororing, Jemmy Edwin; Hadiyat, Rahmat

doi:10.29408/edumatic.v6i1.5433

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…Metode TF-IDF merupakan metode untuk menentukan frekuensi relatif dari setiap kata-kata atau token-token yang mana setiap kata tersebut akan diberikan pembobotan berupa nilai berdasarkan penting atau tidaknya suatu kata dalam dokumen berdasarkan jumlah kemunculan kata dalam suatu dokumen dan mengukur kata-kata tersebut terhadap keseluruhan dokumen yang ada [14].…”

Section: Transformationunclassified

Analisis Sentimen Pada Ulasan Access by Kereta Api Indonesia Dengan K-Nearest Neighbor

Rhamadanti,

Rifa'i,

Dikananda

et al. 2024

JITET

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Abstrak. Access by KAI adalah suatu aplikasi pembelian tiket kereta api indonesiaKAI Access diluncurkan pada 4 September 2014, aplikasi ini mengalami pembaruan pada 10 Agustus 2023 dan diubah namanya menjadi Access by KAI, menjadi versi ke-5 dari PT. Kereta Api Indonesia (PERSERO). Metodologi penelitian melibatkan studi literatur, pengumpulan data melalui teknik web scraping, dan analisis terhadap 3715 ulasan pengguna dari 10 Agustus hingga 10 November 2023. Data tersebut mencatat 555 ulasan positif dan 3160 ulasan negatif, dengan penilaian 1 dan 2 dianggap sebagai negatif, sementara 3, 4, dan 5 dianggap positif. Setelah proses preprocessing, algoritma K-NN diimplementasikan untuk analisis sentimen. Dengan parameter optimal K=17 pada rasio pembagian data uji 90:10, model mencapai tingkat akurasi tinggi dengan presisi 87%, recall 100%, dan f1-score 93%. Pada rasio 80:20 dengan K=9, presisi meningkat menjadi 88%, recall mencapai 99%, namun f1-score tetap 93%. Pada rasio 70:30 dengan K=9, model menunjukkan presisi 87%, recall 99%, dan f1-score 93. Temuan ini diharapkan memberikan wawasan berharga bagi pengembang aplikasi untuk meningkatkan kualitas dan kepuasan pengguna pada platform "Access by KAI." Keywords: analisis sentimen, K-Nearest Neighbors (K-NN), ulasan pengguna, Access by KAI, web scraping

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Section: Transformationunclassified

Analisis Sentimen Pada Ulasan Access by Kereta Api Indonesia Dengan K-Nearest Neighbor

Rhamadanti,

Rifa'i,

Dikananda

et al. 2024

JITET

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“…Nilai recall sebesar 86,92 persen menunjukkan bahwa proporsi data yang diprediksi berisi review positif juga tinggi dibandingkan dengan jumlah total data yang berisi review positif. [8] Perbandingan Naive Bayes, SVM, dan KNN untuk sentimen gadget berdasarkan aspek dilakukan oleh Jessica pada tahun 2021. Menurut temuan penelitian dan pengujian yang dilakukan pada komentar Gadget flip Samsung Galaxy Z di YouTube, banyak pengguna memiliki pendapat positif tentang desain sementara yang lain memiliki pendapat negatif tentang harga, spesifikasi, dan citra merek.…”

Section: Pendahuluanunclassified

Analisis Sentimen Ulasan Aplikasi WeTV Untuk Peningkatan Layanan Menggunakan Metode K-Nearst Neighbor

Faridhotun,

Haerani,

Candra

2023

josh

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Online streaming applications are activities for watching movies that are very popular with the public, one of which is WeTV. WeTV is an online streaming that is used by the public as a medium of entertainment. The WeTV application has a rating of 4 out of 256 thousand reviews written by its users. The collection of reviews in the form of text can be collected and classified into negative responses, neutral responses, and negative responses. Positive responses are comments that are optimistic or supportive. Negative responses are comments on phrases or cases that do not support statements about related cases. Neutral responses are comments that are difficult to understand between negative or positive in nature to provide suggestions, sentences that have reviews from application users can be positive, negative and neutral, the data will go through a classification process using the K-Nearst Neighbor method. In this study, 12,000 reviews were used from the playstore. The research used the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, stopword removal and steaming then to the TF-IDF stage and the final results will be tested with a confusion matrix using the Python programming language. The highest accuracy results from the testing process with a value of K = 3 in the dataset model 90% training data and 10% test data obtain an accuracy of 0.70%, precision 0.76%, recall 0.69%, f1-score 0.72% . Based on the results of the research that the K-Nearest Neighbor method is good in the process of identifying negative responses on WeTV.

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“…Another study on titled "Sentiment Analysis on User Reviews of Bibit and Bareksa Applications using KNN Algorithm" yielded accuracies, precision, and recall for Bibit at 85.14%, 91.91%, and 76.44% respectively, and for Bareksa at 81.70%, 87.15%, and 75.73% respectively [8].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors

Kurniawan,

Wijaya,

Aprisusanti

2024

SISFOKOM

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The Digital Population Identity Application provides convenience for users to access and manage their population data digitally. Based on the increasing usage of the Digital Population Identity Application on the Google Play Store, various user reviews of the application have emerged. Therefore, sentiment analysis is needed to provide a deeper understanding of user perceptions and to classify user reviews of the Digital Population Identity Application. Sentiment analysis is a computational study of opinions, feelings, and emotions expressed in text, using the K-Nearest Neighbors method, which is a classification method based on the closest distance or similarity to objects in the training data. Using 5000 relevant review data from September 2022 to December 2023, after labeling them into positive, negative, and neutral sentiment classes, the results show 3581 negative sentiments, 1031 positive sentiments, and 388 neutral sentiments. Testing was conducted by applying the K-Nearest Neighbors method in the classification stage, testing this method by varying K values from 1 to 10. The best results were obtained with a training data ratio of 90% to testing data ratio of 10%. The best results were achieved at K values of 8, 9, and 10, with an accuracy of 81%, precision of 82%, recall of 95%, and an F1-Score of 88%. With a training data ratio of 70% to testing data ratio of 30%, the best results were obtained at K values of 6, 7, 8, 9, and 10, with an accuracy of 80%, precision of 81%, recall of 95%, and an F1-Score of 88%. Based on the results of this research, the K-Nearest Neighbors method can be used for sentiment classification of user reviews with good results.

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Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP

Cited by 6 publications

References 11 publications

Analisis Sentimen Pada Ulasan Access by Kereta Api Indonesia Dengan K-Nearest Neighbor

Analisis Sentimen Pada Ulasan Access by Kereta Api Indonesia Dengan K-Nearest Neighbor

Analisis Sentimen Ulasan Aplikasi WeTV Untuk Peningkatan Layanan Menggunakan Metode K-Nearst Neighbor

Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors

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