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Christina Juliane
STMIK LIKMI, Jawa Barat, Indonesia
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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Application of Data Mining to Measure the Effectiveness of the Islamic Boarding School’s Independent Curriculum based on Learning Achievement using the Clustering Method Iim Imron Rosyadi; Fitri Nurhadits; Christina Juliane
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.595

Abstract

The evolution of educational curricula has been a focal point for institutions aiming to enhance learning outcomes and adapt to students' diverse needs. In this context, Islamic boarding schools, or pesantren, are increasingly exploring independent curricula to better serve their students. This research aims to measure the effectiveness of the independent curriculum at the Al Binaa Bekasi Islamic Boarding School, especially regarding learning achievements in general and Islamic subjects. The method used is data mining clustering to analyze student learning achievement data. In the initial stage, the data collected includes student scores in general subjects (such as Islamic Religious Education, Pancasila Education, Indonesian, English, Mathematics, Science, Social Sciences, Arts, Sports, ICT, Sundanese) and Syar'i (Quran tajwid, hadith, aqidah, fiqh, Hadassah, short). Then, data mining clustering techniques are used to group students based on their achievements in the two subjects. The results of the analysis show that the independent curriculum at Al Binaa Islamic Boarding School effectively increases student learning achievement. The groups formed from data mining clustering show patterns consistent with curriculum objectives, where students in the same group have similar levels of achievement in general and star subjects. This indicates that the independent curriculum has succeeded in leveling student learning achievement. This research contributes to understanding the effectiveness of the independent curriculum in Islamic boarding schools. It can be a basis for further development in designing Islamic boarding school education curricula that are more adaptive and responsive to student needs.
Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining Haryanto Haryanto; Hadi Winarto; Christina Juliane
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.597

Abstract

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.
Optimizing Marketing Strategies Using FP-Growth and Association Rule Mining Algorithms in the Textile Industry Wijaya NG; Robby Sukma; Christina Juliane
Journal of World Science Vol. 3 No. 5 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i5.599

Abstract

This study leverages association rule mining to analyze transaction data from PT. Labda Anugerah Tekstil, a prominent player in the textile industry, to uncover significant purchasing patterns and associations between different fabric types. Utilizing data from January 1, 2022, to December 31, 2023, which includes 7,143 transaction entries, the research applies the FP-Growth algorithm followed by Association Rule Mining to identify and evaluate frequent itemsets and strong association rules within the dataset. The analysis revealed robust associations among fabrics such as Cotton, Linen, Rayon, and Viscose, suggesting substantial opportunities for targeted marketing strategies and inventory management enhancements. The findings indicate that strategically bundling and promoting associated fabrics can drive higher sales volumes and improve customer purchasing experiences. The insights from this study provide actionable strategies for optimizing marketing efforts and inventory management, aiming to enhance sales performance and customer satisfaction in the competitive textile market.
Co-Authors Fitri Nurhadits Hadi Winarto Haryanto Haryanto Iim Imron Rosyadi Robby Sukma Wijaya NG