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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Information Systems and Informatics Decode: Jurnal Pendidikan Teknologi Informasi Melek IT: Information Technology Journal Jurnal Informatika: Jurnal Pengembangan IT
Khadija, Mutiara Auliya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Aspect Based Sentiment Analysis Menggunakan Indobert Model Terhadap Review Pengunjung Objek Wisata Baturraden Febrianto, Dany Candra; Fitriani, Maulida Ayu; Afrad, Mahazam; Khadija, Mutiara Auliya
Melek IT : Information Technology Journal Vol. 10 No. 2 (2024): Melek IT: Information Technology Journal
Publisher : Informatics Department-Universitas Wijaya Kusuma Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30742/melekitjournal.v10i2.358

Abstract

Tourism significantly contributes to regional economic growth and enhances public welfare. Baturraden tourist attraction, located in Banyumas Regency, Central Java, is one of the destinations whose main attraction is nature tourism. Data on visitors to Baturraden tourist attraction over the past few years shows a good trend. To ensure long-term sustainability and enhance service quality, understanding visitor perceptions and experiences is crucial. This study employs Aspect-Based Sentiment Analysis (ABSA) to analyze visitor reviews of Baturraden. Utilizing the IndoBERT model, a deep learning architecture based on Bidirectional Encoder Representations from Transformers (BERT) specifically tailored for the Indonesian language, the research focuses on four key aspects: Attraction, Accessibility, Amenities, and Ancillary Services. Next stage, a pre-processing process is carried out which includes Case Folding, Cleansing, Tokenizing, Normalization, Stemming and Stopword. Model evaluation is conducted using a confusion matrix, assessing accuracy (94.61%), precision (83.22%), recall (96%), and F1-score (88.11). These results demonstrate the model's can classif reviews into the required aspects.A primary challenge encountered in this research involves analyzing reviews exhibiting diverse linguistic styles, including variations in language and dialect, as well as addressing the issue of imbalanced data distribution across the different aspects.
Implementation Of Dynamic Role-Based Access Control and Record Log-In Dependency Injection Based VRMS System Paradhita, Astrid Noviana; Khadija, Mutiara Auliya; Purbayu, Agus; Bawono, Sahirul Alim Tri; Aziz, Abdul; Haryati, Sri; Daaniis Raditya Pramoda Wardani; Rafael Kurniawan Albyseptra Pratama
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.752

Abstract

Human resource management is one of the critical factors for a company in order to realize the company's vision, mission, and goals. This requires effective and efficient human resource management by integrating the entire management process well. This is to manage the company's internal and external human resources. VRMS is an integrated application that can manage the company's external resources. VRMS is built based on dependency injection. VRMS has two superior features: role-based access control (RBCA) and record log. RBCA is a feature that manages all resources involved in a company project. At the same time, the record log allows super admins to monitor and evaluate the performance of all external company resources. The VRMS system is built using hybrid methodoly in software development life cycles from initiation phase, planning, execution, and testing. The testing process used in this system is the user acceptance test (UAT) method. The test results show that all features on VRMS can run 80% well.
PDF-Document Chatbot Responses using Large Language Models to Enable Smart City Engagement Khadija, Mutiara Auliya; Nurharjadmo, Wahyu; Aziz, Abdul; Primasari, Ina
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8262

Abstract

Traditional documents, including Rencana Pembangunan Jangka Menengah Daerah (RPJMD), Strategic Plans (Renstra), and e-masterplans, have undergone a remarkable transformation, evolving from their conventional printed formats to the dynamic realm of electronic versions. While this shift holds the promise of enhanced accessibility and convenience for the public, the full potential of these resources remains unrealized due to inherent challenges. On the other hand, a Generative AI approach is employed for the creation of an intelligent chatbot. Our primary contribution lies in the PDF-Document Chatbot Response utilizing Large Language Models (LLMs) GPT 3.5 Turbo from OpenAI, aimed at fostering engagement within Smart City. The dataset consists of Masterplan documents for Smart City development in Yogyakarta City, presented in PDF format and employing the Indonesian language. This research leverages the Large Language Models (LLMs) GPT-3.5 Turbo from OpenAI, in conjunction with user input and prompts. The development process for crafting this chatbot utilizes the LangChain Framework and Pinecone for storing vector embeddings. The results underscore the chatbot's capability to generate coherent responses closely aligned with the context found within the PDF document.
Generative Indonesian chatbot for university major selection using transformers embedding Khadija, Mutiara Auliya; Harjito, Bambang; Saberi, Morteza; Paradhita, Astrid Noviana; Nurharjadmo, Wahyu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3474-3482

Abstract

Selecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings.
Improving IT Governance Maturity at Universitas Sebelas Maret Using COBIT 2019 Setyawan, Haidar Hendri; Khadija, Mutiara Auliya; Budianto, Aris
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1200

Abstract

This study evaluates and improves the IT governance maturity of the Directorate of ICT at Universitas Sebelas Maret using the COBIT 2019 framework. The evaluation was driven by increasing IT complexity, resource inefficiencies, and low risk management capability. A case study approach applied COBIT 2019 domains to assess practices and identify gaps, with data gathered through interviews, observations, and document analysis. Significant deficiencies were found in six key processes. The highest gap score is APO12 (Managed Risk) at 1.89, followed by DSS04 (Managed Continuity) at 1.88, DSS01 (Managed Operations) at 1.75, APO14 (Managed Data) at 1.74, DSS05 (Managed Security) at 1.57, and the lowest is APO01 (Managed I&T Framework) at 1.27, with all domains targeting a maturity level of 3. Results indicate current maturity scores fall below expectations, highlighting the need for systematic improvement. A phased strategic plan was developed for short, medium, and long-term priorities, aligned with resources and organizational needs. The study demonstrates that structured implementation of COBIT 2019 can enhance governance alignment, improve risk control, and ensure sustainable ICT performance, providing a roadmap for future IT governance at the university.
Co-Authors Abdul Aziz Agus Purbayu, Agus Aris Budianto, Aris Bambang Harjito, Bambang Bawono, Sahirul Alim Tri Daaniis Raditya Pramoda Wardani Dany Candra Febrianto Fitriani, Maulida Ayu Mahazam Afrad Nurharjadmo, Wahyu Paradhita, Astrid Noviana Primasari, Ina Rafael Kurniawan Albyseptra Pratama Saberi, Morteza Setyawan, Haidar Hendri Sri Haryati