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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Journal of Community Development
Emigawaty, Emigawaty
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Computer Science & IT Nursing Public Health Social Sciences

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Pelatihan Akuntansi Bagi Karang Taruna Sebagai Upaya Peningkatan SDM Dusun Bolu Kecamatan Seyegan Hartanti, Ninik Tri; Maemunah, Mei; Emigawaty, Emigawaty; Saptyawati, Laksmindra; Nur’aini, Nur’aini; Mulyatun, Sri
Journal of Community Development Vol. 3 No. 3 (2023): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v3i3.115

Abstract

Yogyakarta is a province that has many features, both cultural and natural. One of the factors driving the economic growth of a region is the agricultural sector, which can be a promising opportunity, apart from being a producer of economic growth it can also have the opportunity to be a driver of growth in other development sectors in an area. One of them is the expansion of the agro-tourism business from Bolu hamlet, Seyegan sub-district, Yogyakarta, which will empower the younger generation, especially youth organizations, as managerial actors in tourism villages. Improving financial management skills for the community, especially in agro-tourism villages, is one of the solutions to existing problems. Through this community service activity, it is hoped that the community, especially youth organizations, will understand the concept and meaning of a cash book, namely a financial journal that contains notes on cash receipts and payments, and understand the benefits of a cash book, namely being able to know the financial condition of a business based on detailed and consistent recording. , as well as a reference to see which part of the business can be the biggest sales driver or discover what causes the biggest expenses. The result of this community service activity is to add insight to the community, especially youth organizations about the bookkeeping system, recording daily transactions so that the business being carried out can provide the expected benefits.
Data Quality Analysis on Open Government Data Portals: A Qualitative Study Using ISO/IEC 25012:2008 Standards Emigawaty, Emigawaty; Syafrianto, Andri
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.862

Abstract

This study evaluates the data quality on Open Government Data (OGD) portals using the ISO/IEC 25012:2008 standard, which categorizes data quality into two main groups: inherent data quality and system-dependent data quality. This standard encompasses dimensions such as accuracy, completeness, consistency, and relevance. Using a qualitative approach, interviews were conducted with data providers and users from the government, industry, and academia. The findings indicate that while some datasets are adequate, there are issues with semantic consistency, completeness, timeliness, and currency of the data. These findings highlight the importance of strict and continuous application of data quality standards in OGD management. Recommendations for improvement include training for data managers and enhancing validation mechanisms before data is published. This study supports government efforts to improve transparency and accountability by providing high-quality data that can be reliably used by various stakeholders.
A Qualitative Study of Researchers Perspective on the Use and Risks of Open Government Data Emigawaty, Emigawaty; Sukmaningrum, Dinda; Nurastuti, Wiji
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1122

Abstract

Open government data has the potential to improve transparency, accountability, public participation, business innovation, and research quality. However, this openness also poses various opportunities for losses or even risks, especially related to low data quality, personal data security issues, data translation errors, and misuse of information. This study aims to review the potential risks of data openness on government data portals from the perspective of researchers as one of the important actors who use data. Using qualitative method with structured interviews, this study involved five potential researchers who actively used open data between May and August 2023. The results of the interviews showed that high data quality, such as accuracy, completeness, and currency, can increase researchers' trust in the data. At the same time, obstacles in accessibility and bureaucracy or data administration requirements can slow down the research process or stages. Security and privacy issues are also important parameters, with strict security policies and good audit processes can reduce the risk of data misuse. Data openness and transparency play a major role in increasing the use of data for public policy and evidence-based research. In addition, data standardization is essential to ensure the efficiency of data use by researchers. This study concludes that to optimize the benefits of data openness, there needs to be proper and measurable management in order to consider data quality, accessibility, security, and standardization.
Stock Price Prediction Using Deep Learning (LSTM) with a Recursive Approach Zakka, Muhamad Syukron; Emigawaty, Emigawaty
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10514

Abstract

Stock price prediction is a critical topic in financial technology research, as accurate forecasts support better decision-making in volatile markets. Numerous studies have applied statistical and machine learning models; however, most focus on one-step-ahead predictions and lack evaluation of recursive strategies in multi-day horizons. This study investigates the application of Long Short-Term Memory (LSTM) with a recursive forecasting approach to enhance stock price prediction accuracy. The dataset was enriched with multiple technical indicators and processed through a systematic Knowledge Discovery in Databases (KDD) pipeline, including preprocessing, transformation, modelling, and evaluation. Experimental results show that the recursive LSTM model achieves superior performance compared to baseline machine learning methods, with high accuracy in short-term horizons and stable performance up to nine days ahead, although accuracy gradually declines due to error accumulation. This work highlights the importance of integrating recursive forecasting with technical indicators to improve predictive capability in emerging markets and provides a foundation for developing adaptive financial forecasting frameworks.
Lightweight BiLSTM-Attention Model Using GloVe for Multi-Class Mental Health Classification on Reddit Branwen, Devin; Emigawaty, Emigawaty
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10157

Abstract

Mental health issues such as depression, stress, anxiety, and personality disorders are increasingly prevalent, particularly within online communities. This study proposes a lightweight and efficient multi-class classification framework to identify five mental health conditions using Reddit user-generated posts. While previous studies predominantly rely on conventional CNNs or standard machine learning techniques for binary classification, our work introduces a novel Bidirectional Long Short-Term Memory (BiLSTM) model integrated with an attention mechanism. The architecture is further enhanced by synonym-based data augmentation using the WordNet lexical database, which improves semantic diversity and enhances model robustness, particularly for underrepresented classes. Unlike prior works that focus narrowly on binary classification or employ transformer-based models with high computational demands, our model offers a lightweight, high-performance architecture optimized for multi-class detection and real-world deployment. Experimental results demonstrate that the proposed model achieves a peak validation accuracy of 95.02%, along with precision 95.08%, recall 95.02%, and F1-scores of 95.03%. These findings support the advancement of efficient AI-driven diagnostic systems in mental health analytics and lay the groundwork for future integration into mobile or resource-constrained platforms.
Co-Authors Branwen, Devin Maemunah, Mei Mulyatun, Sri NINIK TRI HARTANTI Nur’aini, Nur’aini Saptyawati, Laksmindra Sukmaningrum, Dinda Syafrianto, Syafrianto Wiji Nurastuti, Wiji Zakka, Muhamad Syukron