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All Journal GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Enthusiastic : International Journal of Applied Statistics and Data Science
Gumelar, Satya Fajar
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Social Sciences

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The Influence of Self Efficacy on Learning Outcomes in E-learning Activities with Learning Motivation as Moderator Gumelar, Satya Fajar; Sary, Fetty Poerwita
GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Vol 11, No 1 (2021)
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/gdn.v11i1.3583

Abstract

Who conducted this research on students of the Faculty of Business Economics, Telkom University Class of 2017 to know how the influence of self-efficacy (X) on learning outcomes (Y) during the e-learning period with learning motivation (Z) as the moderator variable. In this study, the authors collected data using a questionnaire with a total sample of 285 respondents and used probability sampling with a simple random sampling method. The data analysis technique used is quantitative analysis with linear regression and multiple linear regression methods with moderating variables using the IBM SPSS Statistics program. The results showed that self-efficacy (X) partially affected learning outcomes (Y), and simultaneously learning motivation (Z) and self-efficacy (X) influenced learning outcomes (Z) during the e-learning period. We can conclude that self-efficacy (X) can improve learning outcomes (Y) during the e-learning period. Still, when coupled with high learning motivation (Z), it will increase the relationship of self-efficacy (X) to learning outcomes. (Y) so that it can further improve learning outcomes during the e-learning period for students of the 2017 Faculty of Business Economics, Telkom University.
Recommending E-Commerce Platforms for MSMEs: A Sentiment Analysis Approach Adiyana, Imam; Kurniawan, Angga; Rahmatika, Alfilia Hilda; Setiono, Nisrina Hanifa; Gumelar, Satya Fajar
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art8

Abstract

The rapid growth of e-commerce in Indonesia presents significant opportunities for micro, small, and medium enterprises (MSMEs), yet the diversity of marketplace platforms complicates the selection of an optimal sales channel. This study addressed this challenge by developing a data-driven recommendation system based on sentiment analysis of user reviews. Utilizing a dataset of 80,000 reviews scraped from four major platforms on the Google Play Store (Shopee, Tokopedia, Lazada, and Blibli), two classification approaches were implemented and compared: support vector machine (SVM) and long short-term memory (LSTM). Both models demonstrated a competitive performance, enabling effective sentiment categorization. Furthermore, multinomial logistic regression was employed to analyze the influence of key variables rating, number of likes, and marketplace brand on sentiment outcomes. The analysis revealed that Shopee yielded the highest probability of receiving positive reviews (97.82%) and showed no significant association with negative sentiment. Consequently, this study recommends Shopee as the primary platform for MSMEs to enhance their digital presence and sales performance. The primary contribution lies in integrating machine learning-based sentiment analysis with statistical modelling to generate actionable, evidence-based marketplace recommendations for MSMEs.
Co-Authors Adiyana, Imam Angga Kurniawan Fetty Poerwita Sary Rahmatika, Alfilia Hilda Setiono, Nisrina Hanifa