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All Journal Infotech Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics
Kusuma, Irnayanti Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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BLACK BOX TESTING IN THE ACCESS BY KAI APPLICATION USING BOUNDARY VALUE ANALYSIS AND GRAPH-BASED TESTING Kusuma, Irnayanti Dwi; Pradipta, I Gusti Lanang Agung Oka Cahyadi; Yuhana, Umi Laili
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.9577

Abstract

The Access by KAI application is an application that focuses on the process of ordering train tickets via internet technology. The aim of making this application is to help make it easier for people to order train tickets. By creating this application, it is hoped that it can increase train ticket sales and make it easier for people to order train tickets online. Each feature of this application was subject to testing to ensure that it was running according to the expected functionality. The testing of a Black Box Testing using Boundaries Value Analysis and Graph-Based Test on the Access by KAI has success percentage 78%.
Deep Learning‑Based Sentiment Classification on Category Service and Resolution of Consumer Complaints in Digital Banking Kusuma, Irnayanti Dwi; Fatichah, Chastine
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.825

Abstract

The growth of digital banking in Indonesia has transformed customer interactions. It emphasizes the need to understand user sentiment and feedback. This study aims to analyze public perceptions of the Jenius digital banking application through sentiment analysis using deep learning methods enhanced by easy data augmented (EDA). The dataset written in Indonesian related to Jenius from Twitter. Data collected between August 2016 and August 2024 were manually annotated for sentiment polarity (positif, netral, negatif) and complaint handling categories (edukasi, konsultasi, fasilitasi, none). The EDA technique was used to enhance linguistic diversity and reduce class imbalance before training two deep learning models, Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN). The results show that EDA + BiLSTM achieved an accuracy of 0.68, whereas EDA + CNN obtained 0.66. BiLSTM slightly outperforms CNN across precision, recall, and F1-score. These findings indicate that both models effectively handle augmented data, with the BiLSTM model demonstrating a better contextual understanding of Bahasa Indonesia. The integration of EDA significantly improves the robustness and performance of the model in sentiment and aspect-based classification. This study highlights the potential of EDA as a simple yet effective method for enhancing deep learning models.
Co-Authors Chastine Fatichah Pradipta, I Gusti Lanang Agung Oka Cahyadi Yuhana, Umi Laili