Garuda - Garba Rujukan Digital

Interpretasi model Stacking Ensemble untuk analisis sentimen ulasan aplikasi pinjaman online menggunakan LIME Munna, Aliyatul; Zuliarso, Eri
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.183-196

Local Interpretable Model-agnostic Explanations (LIME) can be used to overcome black box problems in the results of sentiment analysis classification models. This research uses reviews of online loan applications on the Play Store as a dataset. Each classification model has weaknesses and its performance can be improved by using stacking ensembles, especially to overcome the problem of imbalanced data classes. The dataset that has been obtained will be cleaned, pre-processed and converted into a numerical vector using TF-IDF. Classification is carried out using three basic models, namely random forest, naïve Bayes and support vector machine (SVM). The output of the basic classification model is used as an input for stacking ensemble logistic regression. Based on the comparison of the four models, stacking ensemble has the best performance with an accuracy of 87.05%. The application of LIME for interpreting classification models with sample data succeeded in explaining the factors that influence model decisions with a prediction probability of 95% and in accordance with manual observations. The results of this research can be used as insight and education to the public about the ease of online loan and its dangers, which are reflected in the positive and negative sentiments in a review.

PENERAPAN TECHNOLOGY READINESS ACCEPTANCE MODEL (TRAM) DALAM MENGUKUR KESIAPAN DAN PENERIMAAN TEKNOLOGI CASHLESS Priambodo, Wisnu; Munna, Aliyatul; Pratama, Dicky Yudha; Supriyanto, Aji
SOSCIED Vol 7 No 2 (2024): SOSCIED - November 2024
Publisher : LPPM Politeknik Saint Paul Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jsoscied.v7i2.750

In the era of globalization and industrial revolution 4.0, information and communication technology (ICT) has created major changes, especially in financial transactions. This phenomenon has led to the emergence of cashless payment systems as the dominant trend, including in the education sector. Schools in Semarang, as part of their efforts to adapt to such developments, need to shift from traditional payment methods to cashless payment technologies to improve efficiency and convenience. This study aims to understand the acceptance of Cashless technology among school teachers and employees in Semarang using the Technology Readiness Acceptance Model (TRAM) approach. Through Structural Equation Modeling (SEM) analysis, the results show that insecurity plays a major role in influencing perceived ease of use. Although the insecurity factor did not have a significant effect, security also had a positive effect on users' assessment of the usability of Cashless technology.

Improved playstore review sentiment classification accuracy with stacking ensemble Santoso, Dwi Budi; Munna, Aliyatul; Untari Ningsih, Dewi Handayani
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.247

In today's digital era, user reviews on the Playstore platform are an invaluable source of information for developers, offering insights that are critical for service improvement. Previous research has explored the application of stacking ensemble methods, such as in the context of predicting depression among university students, to enhance prediction accuracy. However, these studies often do not explicitly detail the data acquisition process, leaving a gap in understanding the applicability of these methods to different domains. This research aims to bridge this gap by applying the stacking ensemble approach to improve the accuracy of sentiment classification in Playstore reviews, with a clear exposition of the data collection method. Utilizing Logistic Regression as the meta classifier, this methodology is executed in several stages. Initially, data was collected from user reviews of online loan applications on Google Playstore, ensuring transparency in the data acquisition process. The data is then classified using three basic models: Random Forest, Naive Bayes, and SVM. The outputs of these models serve as inputs to the Logistic Regression meta model. A comparison of each base model output with the meta model was subsequently carried out. The test results on the Playstore review dataset demonstrated an increase in accuracy, precision, recall, and F1 score compared to using a single model, achieving an accuracy of 87.05%, which surpasses Random Forest (85.6%), Naive Bayes (85.55%), and SVM (86.5%). This indicates the effectiveness of the stacking ensemble method in providing deeper and more accurate insights into user sentiment, overcoming the limitations of single models and previous research by explicitly addressing data acquisition methods.

Analisis Penerimaan Teknologi Aplikasi Pemesanan Makanan Gofood dengan Technology Acceptance Model dan Pearson Correlation Munna, Aliyatul; Nugroho, Kristiawan; Hadiono, Kristophorus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.682

Technology has proven itself as a powerful tool to ease human work in many ways, including food ordering technology. GoFood is a popular and innovative food ordering application that has brought convenience and comfort to users in Indonesia. This research aims to analyze the technology acceptance of the Gofood food ordering application using the Technology Acceptance Model (TAM). TAM is a framework used to understand the factors that influence the acceptance and use of technology. In the context of food ordering apps, user acceptance of the app is critical to the success and growth of the business. This research method involves collecting data through online surveys among Gofood application users. Respondents were asked to assess relevant factors in the TAM, including perceived usefulness, perceived ease of use, as well as attitudes toward use and behavioral intention to use. ), and test the correlation between constructs using Pearson correlation. The results of the analysis show that these findings indicate that perceived usefulness and perceived ease of use of the GoFood application contribute to attitudes toward use and interest in utilizing and using the application. .

Analisis Penerimaan Teknologi Aplikasi Pemesanan Makanan Gofood dengan Technology Acceptance Model dan Pearson Correlation Munna, Aliyatul; Nugroho, Kristiawan; Hadiono, Kristophorus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.682

Technology has proven itself as a powerful tool to ease human work in many ways, including food ordering technology. GoFood is a popular and innovative food ordering application that has brought convenience and comfort to users in Indonesia. This research aims to analyze the technology acceptance of the Gofood food ordering application using the Technology Acceptance Model (TAM). TAM is a framework used to understand the factors that influence the acceptance and use of technology. In the context of food ordering apps, user acceptance of the app is critical to the success and growth of the business. This research method involves collecting data through online surveys among Gofood application users. Respondents were asked to assess relevant factors in the TAM, including perceived usefulness, perceived ease of use, as well as attitudes toward use and behavioral intention to use. ), and test the correlation between constructs using Pearson correlation. The results of the analysis show that these findings indicate that perceived usefulness and perceived ease of use of the GoFood application contribute to attitudes toward use and interest in utilizing and using the application. .

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