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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika SOSCIED Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Anjis Sapto Nugroho
STMIK AKI Pati
Computer Science & IT Education Social Sciences

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Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform Nugroho, Anjis Sapto; Nugroho, Kristiawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14296

Abstract

As of 2024, the Merdeka Mengajar Platform has been used by more than 3.5 million teachers across Indonesia. This number represents an increase of more than 3.85% compared to the previous academic year, which was 3.37 million. However, the utilization of this application has not yet reached the expected target number of users, so an analysis is needed to identify the factors causing this. This research uses Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to perform sentiment analysis on reviews of the Merdeka Mengajar platform. RNN and LSTM are chosen for their advantages in handling sequential data, particularly in text processing for sentiment analysis. This research aims to address the challenges in understanding the positive or negative sentiments of users on the platform. The research methodology includes important stages such as data cleaning, preprocessing, and transforming text into numerical vectors using FastText embedding. Next, RNN and LSTM models are applied to predict sentiment based on patterns in the text data. The research results show that the LSTM model is capable of capturing long-term relationships in sequential data with an expected accuracy of 93.58%. Meanwhile, the RNN model yields a lower accuracy of 91.70%. The LSTM model is more effective in classifying sentiment with high accuracy, especially in text data with complex temporal contexts. This research contributes to understanding user perceptions and feedback regarding the Merdeka Mengajar platform, which is expected to provide insights for platform developers to enhance service quality.
Analysist of User Satisfaction of the High School Student Admissions Website using the User Experience Questionnaire Method Prabowo, Ardian Adi; Fathoni, Ahmad; Nugroho, Anjis Sapto; Nugroho, Kristiawan; Farooq, Omar
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 1 (2025): March 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i1.4841

Abstract

In the era of digitalization of public services, web-based student registration systems have become an important instrument in the education sector. The Website for New Student Admission (PPDB) of Senior High Schools (SMA) and Vocational High Schools (SMK) in Central Java Province has been implemented as a single platform for new student registration, but the main problem identified is the lack of a comprehensive evaluation of the level of user satisfaction with the quality of interaction experience with this platform, especially after the emergence of several complaints on the official PPDB social media regarding the system flow, services, and website appearance. The purpose of this study is to measure and analyze the level of user satisfaction with the PPDB website of SMA/SMK in Central Java Province using the User Experience Questionnaire (UEQ) approach which covers six aspects of user experience. This research method is descriptive quantitative with a survey approach using a standardized UEQ instrument consisting of 26 question items, involving 30 respondents of class X students of SMA Negeri 1 Karanganyar Demak selected using a 10% sampling technique from the population. The results of this study are indicate that the efficiency criteria obtained the highest score of 1.125, while the novelty criteria received the lowest score of 0.792, with the benchmark comparison diagram indicating a position below average (poor) in the criteria of attractiveness (1.061), clarity (1.092), accuracy (0.983), and stimulation (0.992), while in the criteria of efficiency and novelty, they are in a position above average (quite good). The implication of these findings underlines the need for further development in the aspects of visual appeal, clarity of information, accuracy of functions, and interaction stimulation to improve the overall quality of the user experience of the PPDB website.
Implementasi Algoritma Deep Learning untuk Analisis Sentimen Pengguna Platform Pendidikan Nugroho, Anjis Sapto; Prasetyo, Eko; Priyanto, Adhi; Puryono, Daniel Alfa
SOSCIED Vol 8 No 2 (2025): SOSCIED - November 2025
Publisher : LPPM Politeknik Saint Paul Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jsoscied.v8i2.999

Abstract

More than 3.5 million Indonesian educators had used the Merdeka Mengajar Platform by 2024. Comparing this figure to the 3.37 million from the previous academic year, there has been an increase of almost 3.85%. However, an investigation is required to determine the reasons why the application's use has not yet achieved the anticipated goal number of users. This study does sentiment analysis on evaluations of the Merdeka Mengajar platform using Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). The benefits of RNN and LSTM in processing sequential data, especially in text processing for sentiment analysis, led to their selection. The purpose of this study is to solve the difficulties in determining if users' sentiments on the platform are favorable or negative. Important steps in the research technique include preprocessing, data cleaning, and employing FastText embedding to convert text into numerical vectors. Then, using patterns in the text data, RNN and LSTM models are used to forecast sentiment. The study's findings demonstrate that the LSTM model can, with an estimated accuracy of 93.58%, identify long-term associations in sequential data. The RNN model, on the other hand, produces a lesser accuracy of 91.70%. Particularly in text data with intricate temporal circumstances, the LSTM model performs better at accurately classifying sentiment. By better understanding customer opinions and input on the Merdeka Mengajar platform, this study helps platform developers improve the quality of their services.
Co-Authors Adhi Priyanto Ahmad Fathoni Daniel Alfa Puryono Dian Kristiawan Nugroho Eko Prasetyo Farooq, Omar Prabowo, Ardian Adi