CoreID Journal

Implementation of Model-View-ViewModel and Clean Architecture in Android Mobile App Development

2026-05-13T11:04:14+00:00

Android applications are capable of assisting users in managing activities effectively, including the management of school tasks. However, in the development process, several issues often arise, such as code complexity, low maintainability, and difficulties in further development. These problems are generally caused by the lack of a clear separation between business logic and the user interface. Therefore, the implementation of an appropriate software architecture is necessary. One widely used approach is Model-View-ViewModel (MVVM). The MVVM concept is able to separate the user interface from the business logic. In addition to MVVM, Clean Architecture is another approach that provides a solution for building well-structured systems. Clean Architecture divides an application into several layers, such as presentation, domain, and data. Previous studies have implemented either the MVVM or Clean Architecture independently. In addition, these architectures have been applied using different development frameworks and programming languages. Although several studies have successfully implemented both MVVM and Clean Architecture in mobile application development, their implementation has generally not been extended to unit and instrumentation testing. In this study, a mobile application was developed by implementing the MVVM concept and Clean Architecture using the Kotlin programming language and Room as the local data storage solution. The development of an application using Extreme Programming (XP) Software Development Method. The developed application was tested using unit testing and instrumentation testing with the JUnit, Mockito, Espresso, and Room Testing frameworks. The testing process was conducted across three application layers, namely the presentation, domain, and data layers, with a total of nine test files evaluated modularly. The test results showed that all executed scenarios were successfully passed. The test results demonstrate that the implementation of MVVM and Clean Architecture improves application modularity, allowing each layer to be tested independently. It shows that by implementing MVVM and Clean Architecture, the program code becomes more organized, structured, easier to understand, and easier to be tested. In addition, due to the low coupling between components, the code is more maintainable and easier to extend in the future.

Optimizing Subscription Package Selection Using a Business Intelligence-Based Decision Support System on Customer Payment Data

2026-05-07T01:00:35+00:00

Subscription-based business models generate large amounts of customer payment data that has the potential to be used to support strategic decision-making. However, in many companies, the use of this data is still limited to presenting descriptive information through Business Intelligence dashboards, thus failing to produce objective decision recommendations. This condition often results in the selection of superior subscription packages being based on subjective assessments. This study aims to design a Decision Support System (DSS) integrated with Business Intelligence to determine the optimal subscription package. The Analytic Hierarchy Process method is used to determine the criteria weights, Simple Additive Weighting is used to rank alternatives, and the Technique for Order Preference by Similarity to Ideal Solution is used to validate the ranking results. The research data comes from customer payment data including the number of customers, number of transactions, total revenue, and average transaction value. The results show that the New Hit package consistently ranks highest in the SAW and TOPSIS methods. These findings demonstrate that the integration of Business Intelligence and Decision Support System (DSS) can produce objective, stable and reliable data-driven recommendations to support a company's strategic decisions.

Analysis of Feature Engineering on LSTM and GRU Forecasting Performance Across Food Commodities with Different Volatility Levels

2026-07-13T03:59:05+00:00

Food commodity price prediction plays a crucial role in monitoring food price stability and inflation, particularly for commodities with varying volatility characteristics. This study compares the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in forecasting daily food commodity prices in Bandung City, using chili and rice dataset. Daily price data were collected and modeled in two experimental scenarios: forecasting using only historical price data and forecasting with temporal feature engineering. The results showed that GRU consistently outperformed LSTM across all experiments. Without feature engineering, GRU achieved a MAPE score 0.49% and 3.26% for rice and chili, respectively. The incorporation of temporal features improved forecasting performance, reducing forecasting errors by up to 31.30% for rice and 29.55% for chili. The best overall performance was achieved by the GRU model with temporal feature engineering (MAPE rice = 0.48% and MAPE chili = 2.48%). These findings indicate that incorporating feature engineering remains beneficial for enhancing deep learning models performance. This study contributes to the development of deep learning methods for food commodity price forecasting and provides an understanding of the role of temporal feature engineering in handling commodities with different levels of volatility.

PhishTect: A Hybrid SMOTEN–FT-Transformer–PSO Framework for Enhanced Phishing Website Detection on Imbalanced Data

2026-07-09T00:15:00+00:00

Phishing websites remain a major cybersecurity threat, while conventional blacklist-based detection systems often fail to identify newly emerging and zero-day attacks. In addition, phishing datasets are frequently imbalanced, causing machine learning models to exhibit poor minority-class detection performance. To address these challenges, this study proposes PhishTect, a hybrid phishing detection framework that integrates Synthetic Minority Oversampling for Nominal Data (SMOTEN), Feature Tokenizer Transformer (FT-Transformer), and Particle Swarm Optimization (PSO). Unlike existing approaches that typically focus on balancing, deep learning, or optimization techniques separately, the proposed framework combines these components within a unified architecture. SMOTEN is employed to balance categorical phishing data, FT-Transformer learns contextual representations from tabular features, and PSO optimizes model hyperparameters to improve predictive capability. Experiments conducted on the PhishTank dataset evaluated three scenarios: baseline FT-Transformer, FT-Transformer with SMOTEN, and the proposed SMOTEN–FT-Transformer–PSO framework. The proposed model achieved the best performance, obtaining 96.62% accuracy, 0.9696 F1-score, 0.9963 ROC-AUC, and 0.997 PR-AUC. The results demonstrate that integrating oversampling, Transformer-based feature learning, and swarm intelligence optimization significantly improves phishing detection effectiveness, robustness, and generalization on imbalanced cybersecurity datasets.

Strategic Planning for Information Systems at CV. Fancy Using the Ward and Peppard Method

2026-05-30T02:22:40+00:00

Food distributors serving large retail chains face a dilemma that is easy to overlook: the reporting standards and system integration requirements imposed by corporate clients are essentially the same whether the supplier is a large logistics company or a two-person warehouse operation. CV. Fancy, a registered food distributor (NIB 0702250134384) based in Bandung, West Java, occupies exactly this position. The company supplies Alfamidi retail outlets and PT. Primafood manufacturing under long-standing contracts, yet its day-to-day operations remain entirely dependent on spreadsheets and mobile messaging. This paper reports a strategic information systems (IS) planning study carried out at CV. Fancy using the Ward and Peppard framework, with the aim of producing a practical IS/IT roadmap for the period 2026 to 2030. Data were gathered through in-depth interviews and direct observation during the first quarter of 2026. The analysis drew on Porter's Value Chain, SWOT, PEST, Porter's Five Forces, and the McFarlan Strategic Grid. The principal finding is that Cloud Warehouse Management System (WMS) and Order Management System (OMS) warrant immediate investment, as both fall in the Key Operational quadrant of the McFarlan Grid and directly address the operational gaps most likely to put existing contracts at risk. API integration with Alfamidi is identified as the medium-term priority that will determine the company's ability to retain and grow its enterprise client base. The phased roadmap developed from these findings offers a financially realistic path to digital capability for small distributors whose IT budgets are modest but whose clients expect enterprise-level performance