Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE

Pribadi, Muhammad Rizky; Manongga, Danny; Purnomo, Hindriyanto Dwi; Setyawan, Iwan; Hendry, .

doi:10.1109/isitia56226.2022.9855372

Cited by 13 publications

(10 citation statements)

References 10 publications

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“…One way to solve this problem is to apply the SMOTE method [17]- [19]. SMOTE can optimize the performance of the classification model algorithm when performing Sentiment analysis [19], [20]. This approach creates new minority class samples by combining neighboring models in a convex manner to balance the dataset [21], [30].…”

Section: Synthetic Minority Oversampling Techniquementioning

confidence: 99%

“…Thus, it is necessary to have case folding so that there is no more redundancy in calculating the words. In this step, all data letters will be transformed into lowercase [11], [13], [20], [33]. Thus, no data use capital letters or a mixture of uppercase and lowercase letters.…”

Section: Pre-processing Datamentioning

confidence: 99%

“…The data that has been broken down can be called a token [13]. Tokenization is helpful in the process of calculating words that appear during the analysis process [20]. The process uses Python's basic syntax, the split() function.…”

Section: Pre-processing Datamentioning

confidence: 99%

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Factor analysis influencing Mobile JKN user experience using sentiment analysis

Al Qahar,

Ruldeviyani,

Mukharomah

et al. 2024

IJ-AI

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Social security administration for health or Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS Kesehatan), as a public legal entity, has a critical role in the health of the Indonesian population. BPJS Kesehatan introduced the Mobile national health insurance or jaminan kesehatan nasional (JKN) application to enhance its services, enabling Indonesians to access it directly. Nevertheless, the rating of the Mobile JKN application on the Google Play Store has shown a gradual decline over time. Therefore, this study was conducted to analyze the factors influencing the user experience of the Mobile JKN application, utilizing the review data obtained from the Google Play Store. Sentiment analysis using the Naïve Bayes (NB) classification model and support vector machine (SVM) combined with synthetic minority oversampling technique (SMOTE) and slang word replacement. The results obtained an accuracy value of 93.33%, precision of 93.76%, recall of 93.33%, and F1-score of 93.43%. A further analysis was conducted using online service quality factors to obtain the main factors influencing the experience of Mobile JKN application users. The evaluation findings revealed that factors of security, ease of use, and timeliness are three fundamental aspects that should be given immediate attention by BPJS Kesehatan while improving the Mobile JKN application in the future.

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Section: Synthetic Minority Oversampling Techniquementioning

confidence: 99%

Section: Pre-processing Datamentioning

confidence: 99%

See 1 more Smart Citation

Factor analysis influencing Mobile JKN user experience using sentiment analysis

Al Qahar,

Ruldeviyani,

Mukharomah

et al. 2024

IJ-AI

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“…In order to match the operational forecasting schedule of NIER, Created a recurrent neural network (RNN) model that forecasts PM2.5 concentrations up to 2 days at 6-h intervals [6], [7], [8]. The RNN model is quick enough for real-time operational forecasting, and depending on the forecast lead time [9], [10], [11], the RNN-based prediction accuracy ranges from 74 to 80% (11% to 18% better than the CMAQ-based forecast). The CMAQ-based PM2.5 estimations are improved by the RNN model, but it is impossible to determine the exact steps that each input variable took to influence the prediction or the relative weight that each input variable had [12], [13].…”

Section: Levels In Recognition Of the Severely Harmful Impactsmentioning

confidence: 99%

The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Rahardja¹,

Aini²,

Sunarya³

et al. 2022

att

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Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables.

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“…Pada hasil penelitian [3] mengenai sentimen publik terhadap aplikasi PeduliLindungi di google play cenderung negatif, menggunakan algoritma Random Forest dan SMOTE. Implementasi membuktikan Random Forest menghasilkan akurasi 60%, recall 57%, dan presisi 55% dan sedangkan implementasi Random Forest SMOTE menghasilkan akurasi 71%, recall 70%, dan presisi 70%.…”

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Analisis Sentimen Masyarakat terhadap Layanan Provider Internet di Indonesia menggunakan SVM

Saputra

Pribadi

2023

MDP-SC

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Analisis sentimen masyarakat mengenai layanan provider internet merupakan salah satu upaya yang dapat dilakukan untuk mempermudah pengembang aplikasi untuk mengambil keputusan kedepannya, dengan tujuan agar dapat bersaing dengan kompetitor lainnya. Pada penelitian ini dilakukan analisis sentimen masyarakat pada review aplikasi MyTelkomsel di google play menggunakan algoritma Support Vector Machine disertai seleksi fitur Recursive Feature Elimination (n=7) dan menerapkan metode SMOTE untuk mengatasi masalah imbalance class pada dataset. Dataset yang digunakan berupa dataset review layanan provider internet aplikasi MyTelkomsel sebanyak 1000 data, yang terbagi menjadi 80% data training dan 20% data testing. Pembagian dataset menjadi data latih dan data uji menggunakan K-Fold Cross Validation dengan K=10 fold. Dari hasil pengujian dataset review MyTelkomsel pada kernel RBF dengan metode SMOTE dan RFE, diperoleh hasil akurasi rata – rata tertinggi dicapai classifier pada dataset review MyTelkomsel, yaitu sebesar 99,7%, dengan nilai precision 100%, recall 100%, dan f1-score sebesar 99,68%.

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Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE

Cited by 13 publications

References 10 publications

Factor analysis influencing Mobile JKN user experience using sentiment analysis

Factor analysis influencing Mobile JKN user experience using sentiment analysis

The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Analisis Sentimen Masyarakat terhadap Layanan Provider Internet di Indonesia menggunakan SVM

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