Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal International Journal Software Engineering and Computer Science (IJSECS) Journal of Computing Theories and Applications Jurnal Informatika
Suyud Widiono
Universitas Teknologi Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Performance Evaluation of RESTful API in Sales Target Monitoring System for Direct Sales and Sales Canvassers Suyud Widiono; Restian Dwi Friwaldi; Afwan Anggara
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

In an increasingly competitive digital era, manual sales target monitoring often leads to delayed information and inefficiency in decision-making. This research aims to develop a web and mobile-based sales target monitoring system integrated with RESTful API to enhance the efficiency of monitoring the performance of direct sales and sales canvassers. The system is developed using the Laravel framework for the back-end and Flutter for the mobile application, with Agile methodology applied in the development process. Testing is conducted using the Black Box Testing method to ensure the accuracy of system functionalities, including user authentication, sales data management, and sales target monitoring. Additionally, load testing is performed using Apache JMeter with scenarios of 500, 750, and 1000 users. The test results show that the system has stable performance with an average response time of 758 ms for 500 users, 762 ms for 750 users, and 880 ms for 1000 users, all below the threshold of 900 ms. The error rate is recorded at 0.00%, and the system throughput exceeds the set target, indicating the system's reliability in handling simultaneous user requests. The conclusion of this research shows that the implementation of RESTful API in the sales monitoring system can improve operational efficiency, enable real-time data exchange, and support faster, data-driven decision-making. As a recommendation, further development could include broader integration with mobile applications and the implementation of AI-based analytics for sales strategy optimization.   
Implementation of Flutter and Firebase in Bamboo Craft Digitalization Application Fajar Jati; Suyud Widiono
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5936

Abstract

: The bamboo craft industry in Brajan Hamlet, Sleman faces significant operational constraints due to continued reliance on manual recording systems, resulting in inefficiency, data duplication, and reporting difficulties. This study develops a web-based digitalization application using Flutter and Firebase to enhance business management efficiency in bamboo craft enterprises. The Research and Development (R&D) method was employed through the Waterfall model across four stages: requirements analysis, system design, implementation, and testing. Data collection involved structured interviews with 6 bamboo craft business operators, 4-week field observations, and literature review. The developed application integrates product management, inventory, transactions, customer relations, and sales reporting features through Backend-as-a-Service (BaaS) architecture utilizing Firebase Authentication, Cloud Firestore, and Firebase Storage. Black Box Testing results demonstrated a 96% functional success rate with an average response time of 1.2 seconds for CRUD operations. User Acceptance Testing with 6 respondents yielded a satisfaction score of 4.3/5 and revealed a 65% reduction in transaction recording time compared to manual methods. However, evaluation identified critical weaknesses in automatic stock synchronization post-transaction, necessitating Firebase Cloud Functions or Firestore Triggers implementation to ensure real-time data consistency. This study offers practical solutions through integrated digitalization for local craft MSMEs while academically demonstrating the effectiveness of Flutter-Firebase integration in developing web-based business management applications, with recognized limitations in business process automation requiring further development.
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection De Rosal Ignatius Moses Setiadi; Arnold Adimabua Ojugo; Octara Pribadi; Etika Kartikadarma; Bimo Haryo Setyoko; Suyud Widiono; Robet Robet; Tabitha Chukwudi Aghaunor; Eferhire Valentine Ugbotu
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
Co-Authors Afwan Anggara Arnold Adimabua Ojugo Bimo Haryo Setyoko De Rosal Ignatius Moses Setiadi Eferhire Valentine Ugbotu Etika Kartikadarma Fajar Jati Octara Pribadi Restian Dwi Friwaldi Robet Robet Tabitha Chukwudi Aghaunor