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All Journal Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Sistem Informasi MDP Student Conference
Aditya, Putra
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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ANALISIS SENTIMEN PEMAIN SUBWAY SURF MELALUI METODE NAIVE BAYES MENURUT ULASAN PLAY STORE Aditya, Putra; Azzahra, Afifah; Wijaya, Andri
Jurnal Ilmiah Sistem Informasi Vol. 3 No. 2 (2023): Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/sm.v3i2.51

Abstract

Subway Surf, an endless run arcade game, offers a dynamic experience and online competition that successfully attracts players. Although the majority of reviews on the Play Store were positive, some users expressed dissatisfaction with some aspects. This study uses the Naïve Bayes method to analyze the sentiment of reviews, with structured steps such as data collection, preprocessing, data sharing, model application, evaluation, and result interpretation. The predominance of positive sentiment in Figure 3 reflects player satisfaction, while negative sentiment highlights game progress issues and technical constraints. The Naïve Bayes classification model gave good results, with 86% and 79% precision for negative and positive classification, 78% and 86% recall, and an F1-score of 82%. The total accuracy reached 82%, indicating good predictive ability on the test dataset. Recommendations for future research include more in-depth analysis for user experience improvements, ensuring Subway Surf continues to maintain its appeal amidst growing player growth
LITERATUR REVIEW: EVALUASI PEMANFAATAN DATA WAREHOUSE DALAM TRANSFORMASI WAKTU Azzahra, Afifah; Aditya, Putra; Wijaya, Andri
Jurnal Ilmiah Sistem Informasi Vol. 3 No. 2 (2023): Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/sm.v3i2.52

Abstract

In today's digital era, data has a very important role for various organizations and industrial sectors. One of the technological solutions used to manage and analyze large amounts of data is a data warehouse. Covers the time period from 2018 to 2022. The research results show that the role of data warehouses is increasingly vital in various domains, including television editorial, sales, administration, education, health, agriculture and other sectors. There is potential for further research involving more Case studies in less explored areas, such as the environment, energy or transport
Analisis Sentimen Film Squid Game Melalui Platform X Menggunakan Metode Lexicon Based Anggoro, Deo; Alessandro, Andreas; Aditya, Putra; Wijaya, Andri
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11197

Abstract

This study discusses the role of movies as a medium in conveying social and emotional messages, with a focus on the Squid Game series. This research applies Lexicon-Based sentiment analysis to evaluate audience reactions to the series using 10,000 tweets from Twitter (X). The results of the analysis showed that 36.3% of the comments had a positive sentiment, 46.4% were neutral, and 17.2% were negative. The majority of neutral sentiment indicates that many viewers were interested but did not have strong opinions, while the significant positive sentiment indicates a favorable reception to the movie. These results provide insight into how audiences responded to the themes of economic inequality and social oppression in the movie. This analysis highlights the relevance of sentiment analysis in understanding audience responses to social issues, as well as how films can reflect deeper cultural and social issues.
Prediksi Performa Akademik Siswa Berdasarkan Kehadiran dan Aktivitas E-Learning Menggunakan Algoritma Decision Tree Simbolon, Ibran; Aditya, Putra; Br Purba, Estetika
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1352

Abstract

The advancement of digital technology has driven the widespread adoption of e-learning systems in the field of education. However, a key challenge lies in effectively utilizing e-learning data to improve students' academic performance. This study aims to predict students' academic performance based on their attendance and activity data within an e-learning platform using the Decision Tree algorithm. The dataset used was obtained from the public platform Kaggle, titled “Student’s Academic Performance Dataset”, which includes demographic attributes, attendance records, and student engagement in online learning. The analysis process involved data preprocessing, model training, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and cross-validation. The results show that the combination of attendance and e-learning activity has a significant correlation with academic performance, with the model achieving an accuracy of 78.12% and an F1-score of 0.77. These findings highlight the potential of utilizing learning analytics to support data-driven academic decision-making and provide early interventions for at-risk students.
Co-Authors Alessandro, Andreas Andri Wijaya Anggoro, Deo Azzahra, Afifah Br Purba, Estetika Simbolon, Ibran