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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Inferensi Enthusiastic : International Journal of Applied Statistics and Data Science
Anindya Apriliyanti Pravitasari
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Social Sciences

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Message from the Chair of the Committee Anindya Apriliyanti Pravitasari
Inferensi Special Issue: Seminar Nasional Statistika XI 2022
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v1i1.19116

Abstract

Atas nama panitia penyelenggara, kami merasa terhormat dan senang menyambut seluruh peserta, pembicara utama (keynote speaker), dan invited speaker, serta peserta dalam Seminar Nasional Statistika XI (SNS XI). Acara ini adalah seminar nasional tahunan yang diselenggarakan oleh Departemen Statistika Universitas Padjadjaran, dengan dukungan dari Forstat dan Jurnal Inferensi ITS. Secara khusus, tema dari SNS XI ini adalah "Machine Learning: Statistics and Lifestyle" yang merupakan penelitian mengenai kemajuan statistika di era machine learning dan kecerdasan buatan. Kami berharap acara ini dapat memfasilitasi semua peserta untuk berinteraksi secara intensif guna memperluas jaringan ilmiah di masa depan.
NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs Najma Rafifah Putri Syallya; Anindya Apriliyanti Pravitasari; Afrida Helen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6276

Abstract

Chatbots are increasingly prevalent in various fields, including academic fields. Universities often rely on lecturers and staff for information access, which can lead to delays, limited availability outside working hours, and the risk of missed questions. This study aims to develop a chatbot model capable of addressing questions about the curriculum through intent classification, reducing reliance on manual responses, and providing a solution that ensures quick, accurate information retrieval. The research focuses on optimizing the IndoBERT model for intent classification and addresses challenges that arose due to imbalance data, which could have impacted model performance. Data was collected through an open poll on common curriculum-related questions asked by students. To address data imbalance, we tried oversampling techniques, such as SMOTE, B-SMOTE, ADASYN, and Data Augmentation. Data augmentation was chosen and successfully addressed the imbalance problem while maintaining data semantics effectively. We achieved the best model with hyperparameters batch size of 8, learning rate of 0.00001, 15 epochs, and 64 neurons in the hidden layer, resulting in 98.7% accuracy on the test data. Evaluation metrics further demonstrate the model's robustness across multiple intents. This research demonstrates the advantages of the IndoBERT model in intent classification for academic chatbots, achieving excellent performance.
MRI-Based Brain Tumor Classification Using Inception Resnet V2 Azzahra, Thalita Safa; Jessica Jesslyn Cerelia; Farid Azhar Lutfi Nugraha; Anindya Apriliyanti Pravitasari
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 3 Issue 2, October 2023
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol3.iss2.art4

Abstract

Brain tumors are one of the most fatal disorders owing to the uncontrolled proliferation of abnormal cells inside the brain. Digital images are obtained using Magnetic Resonance Imaging (MRI), which is a medical instrument that can assist doctors and other medical personnel in assessing and diagnosing the presence and type of brain tumors. However, manual and subjective classification is time-consuming and error prone. Hence, an objective, automatic, and more reliable method is needed to classify MRI images of brain tumors. Artificial intelligence is considered appropriate to determine the type of brain tumor via MRI images to overcome the constraints of conventional testing methods. One method for performing automatic classification is the Convolutional Neural Network (CNN). This work demonstrates how the Inception Resnet v2 architecture in CNN is utilized to classify MRI brain tumors into four categories via transfer learning, namely glioma tumors, meningioma tumors, no tumors, and pituitary tumors. The accuracy value of the generated model reached 93.4% after running for 20 epochs. It infers that artificial intelligence is beneficial in identifying a brain tumor objectively to help doctors and radiologists in the medical field.
Co-Authors Afrida Helen Azzahra, Thalita Safa Farid Azhar Lutfi Nugraha Jessica Jesslyn Cerelia Najma Rafifah Putri Syallya