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All Journal Kharisma Tech Journal of Technology Research in Information System and Engineering
S, Abdul Munir
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Computer Science & IT Engineering

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IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI PADA TOKO RUMAH SKINCARE 88 Sugianto, Michael Christian; S, Abdul Munir; Musdar, Izmy Alwiah
JTRISTE Vol 11 No 1 (2024): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/jtriste.v11i1.511

Abstract

The purpose of this research is to implement the apriori algorithm on sales transaction data of cosmetic products at Rumah Skincare 88 store so that it can help develop marketing strategies and increase sales at the store. The results of this study are that the apriori algorithm was successfully implemented on the sale of cosmetic products at the RumahSkincare88 store. The support values used are 3% and 10% and the minimum confidence values are 30% and 50%. The number of association rules generated is 17 with the highest support value of 14% and the highest confidence of 53.4%. The resulting association rule with the highest support and confidence value is if consumers buy Powder/Foundation then they will also buy Lips. The recommended marketing strategies are Cross-Selling and Bundling. For Cross-Selling strategy that can be applied is if consumers buy Powder/Foundation, then when they want to pay the cashier can offer to buy Lips as well. As for Bundling, the strategy that can be applied is to sell Face Serum and Sunscreen in one package at a special price.
ANALISIS PENGARUH MODEL-VIEW-VIEWMODEL TERHADAP KINERJA APLIKASI COMFY LEARN Raintung, Juan Kevin; Rahman, Syaiful; S, Abdul Munir
KHARISMA Tech Vol 21 No 1 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i1.581

Abstract

Interactive learning applications for early childhood, such as Comfy Learn, play an important role in the digital age. However, poor technical performance can hinder their pedagogical effectiveness. This study aims to analyze and improve the performance of Comfy Learn by implementing the Model-View-ViewModel (MVVM) architecture pattern, recommended by Google for modern Android applications. A quantitative approach was used by comparing performance metrics (CPU usage, memory consumption, and janky frames) and code maintainability metrics (Source Lines of Code, Cyclomatic Complexity, Cognitive Complexity) before and after migrating to MVVM. Testing was conducted with Android Studio Profiler, Firebase Test Lab, and SonarQube under standardized procedures. Results show that MVVM implementation reduced janky frames by 46.3% (p < 0.05), lowered code complexity by 37.5%, and improved application stability. This study provides empirical evidence that MVVM not only improves technical performance but also enhances long-term maintainability of Android-based educational applications.
Co-Authors izmy alwiah musdar Raintung, Juan Kevin Sugianto, Michael Christian Syaiful Rahman