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p-Index From 2021 - 2026
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All Journal Journal of Applied Data Sciences Moestopo International Review on Social, Humanities, and Sciences (MIRSHuS)
Mauritsius, Tuga
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Social Sciences

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Anomaly Detection in Sales Transactions for FMCG (Fast Moving Consumer Goods) Distribution Tanuwijaya, Eggy; Mauritsius, Tuga
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.228

Abstract

In today’s era, companies operating in the FMCG industry played an important role in society, especially regarding the distribution of goods used in daily life, which were distributed directly from factories or principals. Despite rapid technological advancements, many distribution companies in Indonesia still relied on human labor and conducted distribution processes manually. Concerns about inaccuracies in employee actions and other detrimental activities such as embezzlement, fraud, and so on, drove companies to undertake digital transformation processes. To reduce these risks, some FMCG companies had already implemented systems to monitor distribution activities and customer payment processes. However, another issue arose due to the limited number of employees available to conduct professional audits, resulting in suboptimal monitoring processes and increased risks of integrity issues or fraud committed by employees. To address this, the implementation of an Autoencoder system was utilized to help companies detect fraudulent activities, particularly in the sales domain. Referring to this study, it showed that the implementation of machine learning technology, such as Autoencoders, yielded positive results and was considered effective in detecting suspicious activities, especially in large transaction datasets. The Autoencoder system utilized in this research was developed using TensorFlow, showing promising results in detecting fraudulent transactions in the company. Additionally, the model was able to train on 80% of the data and was tested on the remaining 20%. According to the outcome, approximately 6.664% of transactions were predicted to be fraudulent. Based on the results, this research showed that the implementation of the AutoEncoder system had proven to be effective in helping the organization prevent and protect against potential non-compliant activities. This proof could be used as a learning opportunity for other organizations facing similar challenges. 
Transformer-Based Sentiment Analysis of DOKU E-Wallet User Reviews Situmorang, Wenny TY; Mauritsius, Tuga
Moestopo International Review on Social, Humanities, and Sciences Vol. 6 No. 1 (2026)
Publisher : Universitas prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/mirshus.v6i1.174

Abstract

The rapid advancement of digital payment technologies has accelerated the widespread adoption of mobile wallet applications, making it increasingly important for service providers to understand user perceptions and experiences. User reviews published on mobile application platforms represent valuable sources of feedback that reflect satisfaction, complaints, and expectations regarding service performance. However, the large volume of textual reviews makes manual analysis inefficient and difficult to manage. This study aims to analyze user sentiment toward the DOKU e-wallet application by applying transformer-based natural language processing techniques. A total of 11,685 user reviews collected from mobile application platforms were analyzed using two transformer-based models. The analytical process followed a structured data mining approach, including data collection, preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the IndoBERT model achieved an accuracy of 93.1%, while the GPT-3.5 Turbo model achieved 93.2%, indicating strong performance in sentiment classification tasks. In addition, the analysis identified several recurring issues reported by users, including account access problems, verification difficulties, transaction errors, and customer service responsiveness. This study contributes to the literature by providing a comparative evaluation of transformer-based models in the context of digital payment platforms, particularly within the Indonesian ecosystem.
Public Perceptions of HPV Vaccination Through Transformer-Based Social Media Sentiment Analysis Silaban, Desi Elfrida; Mauritsius, Tuga
Moestopo International Review on Social, Humanities, and Sciences Vol. 6 No. 1 (2026)
Publisher : Universitas prof. Dr. Moestopo (Beragama)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32509/mirshus.v6i1.175

Abstract

Public perception plays a crucial role in determining the success of vaccination programs, particularly for the human papillomavirus vaccine aimed at preventing cervical cancer. Despite the increasing implementation of vaccination initiatives, public opinions expressed in digital environments may influence the acceptance and effectiveness of such programs. This study aims to examine public sentiment toward the human papillomavirus vaccine by analyzing discussions on a social media platform widely used for public communication. A data mining framework was employed to guide the analytical process, including data collection, preprocessing, sentiment classification, and thematic exploration. Transformer-based language models were utilized to classify public sentiment expressed in social media posts, followed by topic modeling to identify key issues discussed by users. The findings reveal that public discourse is largely characterized by supportive attitudes toward vaccination, reflecting a growing awareness of its role in cervical cancer prevention. Nevertheless, several concerns related to vaccine cost, accessibility, and post-vaccination experiences continue to emerge in online discussions. These results highlight the importance of integrating digital discourse analysis into public health communication strategies in order to better understand societal perspectives and improve the effectiveness of vaccination programs.
Co-Authors Silaban, Desi Elfrida Situmorang, Wenny TY Tanuwijaya, Eggy