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The UI/UX Design of a Mobile-Based Catering Application Using the Activity Centered Design Method Faizal Rahmadani; Yeremia Alfa Susetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3396

Catering services represent a practical option to meet individual food requirements. Having catering services available will help people find solutions for their food requirements more easily. The catering industry stands out as one of the most profitable business sectors. The support of this factor leads many individuals to choose catering services or to open their catering businesses. The process of placing catering orders by users and managing catering businesses by owners remains conventional. This study intends to establish UI UX elements for a mobile-based catering app through Activity Centered Design methodology. The Activity Centered Design method targets the specific activities performed by future users when they engage tasks. The product feasibility for potential users requires the execution of the Requirements stage followed by Design phase then Implementation phase and finally the Evaluation stage. The completed stages lead to the acquisition of feasibility testing results by measuring prospective users' satisfaction levels. The assessment of adjectives showed that the test achieved an 83.5 score which falls under the "Good" category and corresponds to an "A" grade

Performance Analysis of NoSQL Databases: MongoDB Document Store and Redis Key-Value Store in Microservices-Based Applications Using Flask Dava Ataya Shafi Andali; Yeremia Alfa Susetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4206

The research examines performance characteristics between two NoSQL database architectures: MongoDB as a document-oriented system and Redis as an in-memory key-value store, implemented within microservices applications developed using Flask framework. Growing enterprise requirements for scalable, high-performance systems drive increased adoption of NoSQL databases paired with microservices architectures. The investigation assesses database performance through systematic CRUD and aggregation operations executed on nested data structures that mirror real-world public transportation datasets. Redis demonstrates superior operational efficiency in real-time scenarios, attributed to its memory-resident architecture. Empirical findings reveal Redis maintains consistently reduced response latencies compared to MongoDB across virtually all tested operations. Read operations show Redis achieving 0.00037-second average execution times, representing a 60.22% performance improvement over MongoDB's 0.00093-second baseline. Read-by-ID queries exhibit more pronounced differences, with Redis completing operations in 0.00105 seconds against MongoDB's 0.00873 seconds—an 87.96% performance differential. Update and delete operations demonstrate Redis execution times of 0.00026 and 0.00028 seconds respectively, compared to MongoDB's 0.00088 and 0.00087 seconds, yielding approximately 70% and 68% performance advantages. Delete-all operations reveal substantial disparities: Redis completes bulk deletions in 0.083 seconds while MongoDB requires 0.27 seconds, representing a 69.26% performance penalty. Aggregation functions including summation, minimum, and maximum value calculations follow similar performance patterns, with Redis executing operations more efficiently across all test scenarios. Performance evaluations were conducted on Windows 10 Pro 64-bit (Build 19045) equipped with 15.8 GB memory and an 11th Generation Intel® Core™ i5-11400H processor featuring 6 cores and 12 threads. Testing utilized MongoDB version 1.45.4 and Redis version 2.66, with hardware specifications directly influencing benchmark outcomes. Results indicate Redis optimization for applications demanding high-performance real-time data access, while MongoDB serves applications requiring flexible document storage capabilities and complex data structure management.

ETL Pipeline with DTO Normalization for IPOS Data Integration in Spring Boot Adhi Septian Nugroho; Yeremia Alfa Susetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6850

IPOS point-of-sale software, widely used by Indonesian small and medium retail enterprises (UMKM), exports transaction data as Excel files with no enforced schema—producing format-variable, multi-row receipt blocks with heterogeneous date representations, locale-dependent numeric formats, and embedded unit strings that resist conventional relational import. Transforming these unstructured exports into a relational database requires a structured architectural approach capable of handling format variability, type inconsistency, and record duplication. This study designs and implements a Spring Boot-based ETL (Extract, Transform, Load) service that applies the Data Transfer Object (DTO) pattern through ten purpose-specific DTO classes covering each pipeline phase, structured within a four-layer Model-View-Controller (MVC) architecture (Controller-Service-Repository-Entity). The Extractor employs a streaming Excel reader with dynamic column-layout detection based on header keywords, producing raw String-typed ExtractedReceipt and ExtractedItem DTOs. The Transformer applies six normalization steps via four utility classes—StringNormalizer, DateParser (seven date-format patterns), NumberParser (Indonesian and Western currency formats), and a HashSet-based duplicate detector—converting raw strings into typed ValidatedReceipt and ValidatedItem DTOs with explicit error logging. The Loader performs batch inserts per 1,000 records using pre-loaded duplicate sets for O(1) lookup. The pipeline operates asynchronously, returning a jobId immediately while processing continues on a background thread. Functional evaluation across ten scenarios yielded a 100% pass rate, covering valid files, invalid file types, date-format heterogeneity, embedded-unit quantity strings, Indonesian numeric formats, cross-file and intra-file duplicate detection, grand-total reconciliation tolerance, and product-variation tracking. Performance observation shows that files of 200–500 receipts complete within 5–15 seconds. These results indicate that a DTO-centric, explicitly mapped ETL pipeline over Spring Boot MVC provides a maintainable, auditable, and production-ready solution for UMKM retail data integration.

Implementation of a Mental Health Care Website for Psychological Counseling and Education Based on Spring Boot and Tailwind CSS Yosua Christian Prasetio; Yeremia Alfa Susetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7132

The increasing demand for mental health services among young people in Indonesia is not matched by adequate accessibility due to constraints related to time, cost, and social stigma. This study aims to implement a "Mental Health Care" website as an online counseling and psychological education platform by integrating the Spring Boot framework on the backend and Tailwind CSS on the frontend. The development method used is the Waterfall model, which includes the stages of problem identification, system analysis and design, implementation, and testing. The system is designed using a three-layer architecture consisting of a frontend layer based on Vanilla JavaScript and Tailwind CSS, a backend layer based on Spring Boot with Spring Security for authentication and authorization, and a database layer using MongoDB. The main features implemented include real-time online counseling using WebSocket (SockJS & StompJS), an artificial intelligence assistant powered by Gemini AI through Spring AI, MBTI psychological tests, access to psychological educational e-books, a per-session payment system, and authentication via Google OAuth2. Testing is conducted using the Black-box Testing method and interface responsiveness testing to validate system functionality and compatibility across various devices. The results of this study indicate that all 12 functional test scenarios passed with no failures detected, and interface responsiveness testing confirmed full compatibility across Desktop (1920×1080), Tablet (768×1024), and Mobile (375×667) devices. These findings demonstrate that the integration of Spring Boot and Tailwind CSS can produce a functional, secure, responsive, and accessible platform, making it a potential technological solution to improve the accessibility of mental health services and psychological literacy in the digital era.

Predict Airline Customer Satisfaction using a Machine Learning Model Yoel Dinata Suwito; Yeremia Alfa Susetyo
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5868

Customer satisfaction is a strategic factor for the sustainability of airline businesses amid increasingly intense competition in the aviation industry. This study aims to predict airline customer satisfaction using an Artificial Neural Network (ANN) approach by leveraging a publicly available Kaggle dataset containing 22 airline service features. Two ANN architectures were developed, differing primarily in the number of hidden layers, the number of neurons, and the application of Batch Normalization and LeakyReLU in the second model. The experimental results show that the first ANN model achieves an accuracy of 92.31%, while the second model attains significantly higher performance, with an accuracy of 95.75% on the test dataset. The second model also demonstrates a strong balance between precision and recall (0.94–0.97), with an average F1-score of 0.95–0.96 and a minimal number of misclassifications. These results confirm that employing a more complex ANN architecture can deliver highly accurate predictions of customer satisfaction. The implementation of ANN-based predictive models not only enhances passenger experience quality but also strengthens customer loyalty and helps airlines maintain long-term competitiveness.

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