Garuda - Garba Rujukan Digital

APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT Saputri, Daniati Uki Eka; Aziz, Faruq; Khasanah, Nurul; Hidayat, Taopik; Septian, Rendi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5532

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.

Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms Muhammad Iqbal; L, Lisnawanty; Steven Dharmawan, Weiskhy; Septian, Rendi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4053

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.

Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms Iqbal, Muhammad; L, Lisnawanty; Steven Dharmawan, Weiskhy; Septian, Rendi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4053

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.

SENTISTRENGTH-BASED SENTIMENT ANALYSIS TO UNDERSTAND THE LOYALTY AND SHOPPING INTERESTS OF DIGITAL BUSINESS MARKETPLACE Astuti, Widi; Firasari, Elly; Cahyani, F. Lia Dwi; Sarasati, Fajar; Septian, Rendi
Jurnal Techno Nusa Mandiri Vol. 23 No. 1 (2026): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/z9qneg62

In Indonesia's dynamic digital economy, customer reviews on marketplace platforms like TikTok Shop, Shopee, and Tokopedia are strategic assets for understanding consumer loyalty and online shopping interest. However, extracting information from thousands of informal reviews presents a significant challenge for rapid business decision-making. This study aims to implement an automated sentiment analysis system by comparing three major machine learning algorithms: Logistic Regression (LR), Naive Bayes (NB), and K-Nearest Neighbors (KNN), utilizing the sentiment strength feature of the Indonesian SentiStrength method. The research dataset consists of 881 reviews collected through crawling techniques and subjected to text preprocessing stages including case folding, cleaning, tokenization, stemming, and stop word removal. Automatic labeling using SentiStrength resulted in a sentiment distribution consisting of Neutral (41.9%), Positive (40.2%), and Negative (17.9%). The data was then divided into training and test data to evaluate the performance of the three algorithms. Experimental results show that all three models performed very reliably in classifying customer opinions. Based on an evaluation using the Classification Report, K-Nearest Neighbors (KNN) provided the most optimal results with an accuracy rate of 99%, followed by Naive Bayes with 96% accuracy, and Logistic Regression with 94%. The high performance of these three models demonstrates that using SentiStrength sentiment scores as input features is highly effective in minimizing language ambiguity. Managerially, this research contributes to digital business practitioners' ability to monitor public perception in real-time to formulate more responsive marketing strategies and maintain customer retention in the marketplace ecosystem

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