cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Satrya Fajri Pratama
Contact Email
genintelektualdigital@gmail.com
Phone
+6285171553440
Journal Mail Official
coreidjournal@gmail.com
Editorial Address
Jl. Mig Raya No. 1 Melong Green Garden Kota Cimahi
Location
Kota cimahi,
Jawa barat
INDONESIA
Coreid Journal
Published by Generasi Intelektual Digital
ISSN : -     EISSN : 29876990     DOI : https://doi.org/10.60005/coreid.v1i2.14
Core Subject : Science,
Computer Science & IT
CoreID is a scientific journal that contains scientific papers from Academics, Researchers, and Practitioners about research on informatics and Computer. CoreID is published 3 times a year in March, July, and November. The paper is an original script and has a research base on Informatics. The scope of the paper includes several studies but is not limited to the following study. 1. Computer Sciences 2. Software Engineering 3. Information Technology 4. Digital Innovation
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 53 Documents
 2   3   4   5   6 
Implementation of Model-View-ViewModel and Clean Architecture in Android Mobile App Development Meriska Defriani; Irsan Jaelani; Leonard Putra Sanjaya; Rezha Shahidzinda
CoreID Journal Vol. 4 No. 2 (2026): July 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i2.163

Abstract

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 Moch Mukhsin Nauval; Kamaludin Kamaludin; Yogi Saputra
CoreID Journal Vol. 4 No. 2 (2026): July 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i2.164

Abstract

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 Zalid Qomalita Hijriana; Muhammad Mulyawan; Lina Alfaridah ZH; Widya Puteri Aulia
CoreID Journal Vol. 4 No. 2 (2026): July 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i2.182

Abstract

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.

Page 6 of 6 | Total Record : 53

 2   3   4   5   6 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2026): July 2026 Vol. 4 No. 1 (2026): March 2026 Vol. 3 No. 3 (2025): November 2025 Vol. 3 No. 2 (2025): July 2025 Vol. 3 No. 1 (2025): March 2025 Vol. 2 No. 3 (2024): November 2024 Vol. 2 No. 2 (2024): July 2024 Vol. 2 No. 1 (2024): March 2024 Vol. 1 No. 3 (2023): November 2023 Vol. 1 No. 2 (2023): July 2023 Vol. 1 No. 1 (2023): March 2023 More Issue