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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Multitek Indonesia : Jurnal Ilmiah
Mulyati, Mulyati Mulyati
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Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering

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FACTORS AFFECTING THE INTEREST OF INFORMATION SYSTEM UTILIZATION (Case Study: STMIK ABC) Mulyati Mulyati
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 1 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i1.709

Abstract

Application of information systems has been widely used in various fields including in the field of education. This study aims to examine the factors that influence interest in the use of information systems, performance expectations on the use of information systems at STMIK ABC. The method used by the author in collecting this data is a questionnaire, data processing and analysis is carried out using the Structural Equation Modeling (SEM) method with the help of SmartPLS version 3.2.8 application software. The results of this study indicate that the results of hypothesis testing on the Performance Expectations variable have a significant positive effect on the Interest in Information Systems Utilization (H1), thus H1 is accepted, while the Social Factors Variable Hypothesis test has a significant positive effect on the Interest in Information Systems Utilization (H2) in this way the results of the hypothesis variable Condition Facilitating the User has a positive effect on the Interest in Using Information Systems (H3) so that H3 is accepted. The results of this test identify the variable performance expectations, social factors and conditions that facilitate the user to influence the user's interest in the use of information systems so that it can help improve performance in improving the quality of work output and further improve the quality of teaching and learning at STMIK ABC.
PERBANDINGAN METODE NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN RECURRENT NEURAL NETWORK PADA ANALISIS SENTIMEN ULASAN PRODUK E-COMMERCE Zuraiyah, Tjut Awaliyah; Mulyati, Mulyati Mulyati; Harahap, Gilang Haikal Fikri
MULTITEK INDONESIA Vol 17, No 1 (2023): Juli
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v17i1.7092

Abstract

Abstrak Analisis sentimen digunakan sebagai alat bantu untuk mendapatkan pendapat dari konsumen atau masyarakat luas. Ulasan produk pada e-commerce memberikan pengaruh pada penjualan produk. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan produk pada platform e-commerce menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Recurrent Neural Network (RNN). Penelitian juga melibatkan tahapan seleksi data, preprocessing, transformasi, data mining, dan evaluasi/interpretasi. Selain itu, penelitian ini juga bertujuan untuk mengatasi masalah imbalanced data yang terjadi antara sentimen positif dan negatif dengan menerapkan teknik oversampling menggunakan library SMOTE. Dengan melakukan penelitian ini, diharapkan dapat memberikan wawasan dan pemahaman yang lebih baik tentang analisis sentimen dan kontribusinya dalam memahami pendapat konsumen serta meningkatkan keputusan pembelian produk. Dalam penelitian ini, dilakukan analisis sentimen terhadap ulasan produk e-commerce menggunakan algoritma Naïve Bayes, SVM, dan RNN. Data opini diklasifikasikan menjadi positif, negatif, atau netral. Terdapat perbedaan jumlah data antara sentimen positif dan negatif (imbalanced data), yang diperlakukan secara berbeda dalam model. Hasil penelitian menunjukkan bahwa Naïve Bayes memiliki akurasi 86%, SVM memiliki akurasi 88%, dan RNN memiliki akurasi 96% dengan epoch 100. Abstract Sentiment analysis serves as a valuable tool for capturing consumer opinions and broader public sentiment. Product reviews posted on e-commerce platforms significantly influence product sales. The objective of this research is to perform sentiment analysis on e-commerce product reviews utilizing Naïve Bayes, Support Vector Machine (SVM), and Recurrent Neural Network (RNN) algorithms. The study encompasses data selection, preprocessing, transformation, data mining, and evaluation/interpretation as crucial stages. Moreover, addressing the issue of imbalanced data, particularly the disparity between positive and negative sentiments, is achieved through the application of oversampling techniques utilizing the SMOTE library. This research aims to enhance the understanding of sentiment analysis, its significance in comprehending consumer opinions, and its role in improving product purchase decisions. The sentiment analysis of e-commerce product reviews was conducted using Naïve Bayes, SVM, and RNN algorithms. The opinions were classified as positive, negative, or neutral. Notably, there is a distinction in the data distribution between positive and negative sentiments (imbalanced data), which necessitates distinct treatment within the models. The findings revealed an accuracy of 86% for Naïve Bayes, 88% for SVM, and 96% for RNN with an epoch of 100. 
PERBANDINGAN METODE NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN RECURRENT NEURAL NETWORK PADA ANALISIS SENTIMEN ULASAN PRODUK E-COMMERCE Zuraiyah, Tjut Awaliyah; Mulyati, Mulyati Mulyati; Harahap, Gilang Haikal Fikri
MULTITEK INDONESIA Vol 17 No 1 (2023): Juli
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v17i1.7092

Abstract

Abstrak Analisis sentimen digunakan sebagai alat bantu untuk mendapatkan pendapat dari konsumen atau masyarakat luas. Ulasan produk pada e-commerce memberikan pengaruh pada penjualan produk. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan produk pada platform e-commerce menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Recurrent Neural Network (RNN). Penelitian juga melibatkan tahapan seleksi data, preprocessing, transformasi, data mining, dan evaluasi/interpretasi. Selain itu, penelitian ini juga bertujuan untuk mengatasi masalah imbalanced data yang terjadi antara sentimen positif dan negatif dengan menerapkan teknik oversampling menggunakan library SMOTE. Dengan melakukan penelitian ini, diharapkan dapat memberikan wawasan dan pemahaman yang lebih baik tentang analisis sentimen dan kontribusinya dalam memahami pendapat konsumen serta meningkatkan keputusan pembelian produk. Dalam penelitian ini, dilakukan analisis sentimen terhadap ulasan produk e-commerce menggunakan algoritma Naïve Bayes, SVM, dan RNN. Data opini diklasifikasikan menjadi positif, negatif, atau netral. Terdapat perbedaan jumlah data antara sentimen positif dan negatif (imbalanced data), yang diperlakukan secara berbeda dalam model. Hasil penelitian menunjukkan bahwa Naïve Bayes memiliki akurasi 86%, SVM memiliki akurasi 88%, dan RNN memiliki akurasi 96% dengan epoch 100. Abstract Sentiment analysis serves as a valuable tool for capturing consumer opinions and broader public sentiment. Product reviews posted on e-commerce platforms significantly influence product sales. The objective of this research is to perform sentiment analysis on e-commerce product reviews utilizing Naïve Bayes, Support Vector Machine (SVM), and Recurrent Neural Network (RNN) algorithms. The study encompasses data selection, preprocessing, transformation, data mining, and evaluation/interpretation as crucial stages. Moreover, addressing the issue of imbalanced data, particularly the disparity between positive and negative sentiments, is achieved through the application of oversampling techniques utilizing the SMOTE library. This research aims to enhance the understanding of sentiment analysis, its significance in comprehending consumer opinions, and its role in improving product purchase decisions. The sentiment analysis of e-commerce product reviews was conducted using Naïve Bayes, SVM, and RNN algorithms. The opinions were classified as positive, negative, or neutral. Notably, there is a distinction in the data distribution between positive and negative sentiments (imbalanced data), which necessitates distinct treatment within the models. The findings revealed an accuracy of 86% for Naïve Bayes, 88% for SVM, and 96% for RNN with an epoch of 100. 
Co-Authors Harahap, Gilang Haikal Fikri Tjut Awaliyah Zuraiyah