Implementation of Argon2i Algorithm in A Gamification-Based Habit Tracker Application for Account Security on Android Platform

Abstract

The rapid penetration of smartphones in Indonesia has driven the growth of the productivity application ecosystem, one of which is the gamification-based habit tracker application. However, this type of application indirectly collects users' sensitive daily routine data, while the majority of local applications still store passwords in plaintext form or use traditional hashing algorithms such as MD5 and SHA-256, which are vulnerable to GPU- and ASIC-based brute-force attacks. This research aims to design and implement the Argon2 algorithm (Argon2i variant) as an account security system for a gamification-based Android habit tracker application. The system was built using the Kotlin language, the Jetpack Compose framework, MVVM architecture, and Jetpack DataStore as local storage, with a layered security scheme (Dual-Layer Security) integrating Firebase Authentication as the first layer and an Argon2i-based App Lock as the second layer. The Argon2i algorithm was configured with a memory cost parameter of 64 MB (m=65536), a time cost of 2 iterations (t=2), and 1 parallel lane (p=1), producing a strong hash value resistant to specialized hardware exploitation. Functional testing using the Black Box Testing method across two iterations showed that all test scenarios (100%) passed after the debugging process, proving that the cryptographic system successfully secured user credentials without disrupting the application's stability and speed. This research produces a technical blueprint for integrating Argon2i password hashing into Android applications as a reference standard for account security on mobile devices.