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All Journal Jurnal Media Infotama Journal of Information Systems and Informatics JISA (Jurnal Informatika dan Sains) IT For Society : Journal of Information Technology Eduvest - Journal of Universal Studies
Hasanul Fahmi
President University
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Health Professions Neuroscience Social Sciences

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Implementation of AI Chatbot Application for Social Anxiety Problem Melisa Dewi; Hasanul Fahmi
IT for Society Vol 8, No 1 (2023)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/itfs.v8i1.4662

Abstract

The past decade has witnessed the rapid development of Artificial Intelligence (AI)[1]. AI nowadays, are becoming more popular and integrated in every aspect of the world. Using Ai chatbot as a companion partner has been a thing since a few years ago, but with the development of AI, it is possible to inject personality into those chatbots now. With the increasing growth of social anxiety issues, it is possible to implement AI chatbot as a way to train social skills into people. Past researches have already created a chatbot that is solely for the purpose of depression in a one-on-one chatting environment. In order to create a working training regime, it is necessary to give the user the ability to change from a fully AI environment to slowly integrate with humans. The present paper considers the ability of personality trained AI chatbots and environment as a way to help people learn to socialize. This research aims to help provide a cheaper and reliable alternative for therapists by using AI chatbots as a guide for socially anxious people.
Handling Long Sequences in BERT for Question Answering Systems Andreyanto Pratama; Hasanul Fahmi
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.51006

Abstract

The Question Answering System is an important component of Natural Language Processing applications, allowing for efficient information processing and improving user experience. Despite the fact that BERT has provided additional work in QA tasks, the fact that it only supports up to 512 tokens has reduced its effectiveness in large-scale scenarios. This study addresses the problem by introducing a new algorithm that integrates hierarchical and dynamic memory networks with BERT. The method used to collect broad contexts into chunks that may be used for independent research, ensuring that no important information is missing. The dynamic memory module integrates and stores information in real time throughout the system, allowing for comprehensive context understanding. Depending on the SQuAD v2.0 dataset, the model achieved an Exact Match score of 78.10% and an F1 score of 87.27%. The F1-score value of standard BERT with a value of 81.9% increased to 87.27% with this approach. This research investigated the potential of structured and memory networks to overcome the weaknesses of BERT, provide solutions, and adapt to QA tasks.
Deblurring Photos with Lucy-Richardson and Wiener Filter Algorithm in RGBA Color Michiavelly Rustam; Agung Brotokuncoro; Wiranto Herry Utomo; Hasanul Fahmi
Jurnal Media Infotama Vol 21 No 1 (2025): April 2025
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v21i1.6709

Abstract

Photographers and social media influencers encounter challenges with hand tremors during photo capture, leading to unintended blurriness in their posts, reducing visual impact and audience engagement. To mitigate this problem, the authors aim to effectively reduce the blurring caused by instability in handling, producing sharper and noise-free photos. The methodology involves implementing the Lucy-Richardson and Wiener Filter algorithms into a Python-based web application optimized for RGBA photo processing. Data requirements include sample photos affected by hand tremors to validate the efficacy of the solution. The outcome successfully eliminates blur in captured photos affected by hand tremors in RGBA color format.
Analysis of Customer Satisfaction with Marketing Services Using Fuzzy Logic Nurli Alifah; Hasanul Fahmi
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.752

Abstract

In sales companies, the acquisition of turnover every month is very influential on the assessment of sales quality. The problem faced by the company is a decrease in turnover, this may be caused by the performance of marketing services, therefore the purpose of this study is to evaluate the service performance of the marketing team at Jayaindo Abadi Makmur. To improve customer satisfaction, the team should consider components such as reliability, responsiveness, assurance, and empathy. To get the results, mamdani fuzzy logic is used with the stages of fuzzy set, implication function, rule composition, and affirmation (deffuzzy). The results showed that customer satisfaction with manual calculations amounted to 84.12, while the results with mamdani fuzzy logic using matlab software amounted to 81.3. The company's customer satisfaction is classified as very satisfied. Sales quality shows a decrease in turnover several times, but this is not caused by the marketing team. Recommendations for improvements that can be made include improving product management, pricing policies, and overcoming market competition. The data presented shows that the company's ability to manage products, pricing policies, and the competitive market atmosphere can contribute to higher levels of customer satisfaction.
Hybrid Feature Combination of TF-IDF and BERT for Enhanced Information Retrieval Accuracy Pajri Aprilio; Michael Felix; Putu Surya Nugraha; Hasanul Fahmi
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2179

Abstract

Text representation is a critical component in Natural Language Processing tasks such as information retrieval and text classification. Traditional approaches like Term Frequency-Inverse Document Frequency (TF-IDF) provide a simple and efficient way to represent term importance but lack the ability to capture semantic meaning. On the other hand, deep learning models such as Bidirectional Encoder Representations from Transformers (BERT) produce context-aware embeddings that enhance semantic understanding but may overlook exact term relevance. This study proposes a hybrid approach that combines TF-IDF and BERT through a weighted feature-level fusion strategy. The TF-IDF vectors are reduced in dimension using Truncated Singular Value Decomposition and aligned with BERT vectors. The combined representation is used to train a fully connected neural network for binary classification of document relevance. The model was evaluated using the CISI benchmark dataset and compared with standalone TF-IDF and BERT models. Experimental results show that the hybrid model achieved a training accuracy of 97.43 percent and the highest test accuracy of 80.02 percent, outperforming individual methods. These findings confirm that combining lexical and contextual features can enhance classification accuracy and generalization. This approach provides a more robust solution for improving real-world information retrieval systems where both term specificity and contextual relevance are important.
Co-Authors Agung Brotokuncoro Andreyanto Pratama Melisa Dewi Michael Felix Michiavelly Rustam Nurli Alifah Pajri Aprilio Putu Surya Nugraha Wiranto Herry Utomo