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Journal : Journal of Applied Data Sciences

Long short-term memory-based chatbot for vocational registration information services Langgeng, Yudo Sembodo Hastoro; Setiawan, Esther Irawati; Imron, Syaiful; Santoso, Joan
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.128

Abstract

The development of chatbots can communicate fluently like humans thanks to the Natural Language Processing (NLP) technology. Using this technology, chatbots can provide more accurate and natural responses, providing an almost the same experience as human interaction. Therefore, chatbot technology is in great demand by companies and government agencies as a cost-effective solution for information and administrative services that require little human effort and can operate 24/7. The registration information service at BLK Surabaya still uses an operator who serves prospective trainees and answers questions via social media or chat. However, these operators have limitations in terms of time and effort. The purpose of this study is to examine how to use chatbots to answer questions about registration information training at BLK Surabaya using the Long Short Term Memory (LSTM) algorithm with a dataset of questions collected in the form of Frequently Asked Questions (FAQ) in Indonesian. The dataset consists of 2,636 labeled samples of questions, which were divided into three sets: 60% for training (1,581 pieces), 20% for validation (527 samples), and 20% for testing (528 samples) to evaluate the model's performance. Several steps were taken in implementing this research, including changing the list of questions and answers into the JSON data format, preprocessing, creating LSTM modeling, data training, and data testing. The test results show that Chatbot can provide accurate solutions related to training registration questions with Precision of 88.4%, Accuracy of 87.6%, and Recall of 87.3%.
Aspect-Based Sentiment Analysis of Healthcare Reviews from Indonesian Hospitals based on Weighted Average Ensemble Setiawan, Esther Irawati; Tjendika, Patrick; Santoso, Joan; Ferdinandus, FX; Gunawan, Gunawan; Fujisawa, Kimiya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.328

Abstract

Public assessments are essential for evaluating hospital quality and meeting patient demand for superior medical treatment. This study offers a novel approach to aspect-based sentiment analysis (ABSA), which consists of aspect extraction, emotion categorization, and aspect classification. The goal is to examine patient reviews (6,711 reviews) from Google assessments of 20 Indonesian hospitals, broken down by categories including cost, doctor, nurse, and other categories. For example, there are 469 good, 66 negative, and 7 neutral ratings for cleanliness and 93 positive, 125 negative, and 19 neutral reviews for pricing in the sample, which covers a range of attitudes. Using the Conditional Random Field (CRF) approach, aspect phrase extraction was refined and word characteristics and positional tags were adjusted, resulting in an improvement in the F1-score from 0.9447 to 0.9578. The Support Vector Machine (SVM) model had the greatest F1-score of 0.8424 out of two strategies used for aspect categorization. With the addition of sentiment words, sentiment classification improved and led by SVM to an ideal F1-score of 0.7913. For aspect and sentiment classification, a Weighted Average Ensemble approach incorporating SVM, Naïve Bayes, and K-Nearest Neighbors was employed, yielding F1-scores of 0.7881 and 0.8413, respectively. The use of an ensemble technique for sentiment and aspect classification and the incorporation of hyperparameter optimization in CRF for aspect term extraction, which led to notable performance gains, are the innovative aspects of this work.
Co-Authors Aditya Dwi Aryanto Adriel Ferdianto Agung Dewa Bagus Soetiono Ahmad Syaifuddin Ali Djamhuri Ananta Tio Putra Andik Jatmiko Anita Guterres Bayu Anggara Putra Budi Irawan Chandra, Francisca H. Christian Nathaniel Purwanto Devi Dwi Purwanto Dewi, Nindian Puspa Dipa, Sasra Edwin Pramana Eka Rahayu Setyaningsih Eko Mulyanto Yuniarno Elizabeth Shirley, Stephanie Endang Setyati Ernest Lim Esther Irawati S. Esther Irawati Setiawan Esther Irawati Setiawan Eunike Kardinata F.X. Ferdinandus Fachrul Kurniawan Febriantoro, Erfan Francisca Chandra Fujisawa, Kimiya Gunawan Gunawan Gunawan Gunawan Gunawan Gunawan Hans Juwiantho Hans Keven Budi Prakoso Hartarto Junaedi Hendrawan Armanto Heppi Siswanto Herman Budianto Imron, Syaiful Indra Maryati Jatmiko, Andik Kristian Indradiarta Gunawan Kristina, Natalia Kurniawan S, Putu Widiarsa Langgeng, Yudo Sembodo Hastoro Leonel Hernandez Luhfita Tirta Lukman Zaman Machfudin, Mohammad Farid Mauridhi Hery Purnomo Miftah Farid Mochamad Hariadi Muhammad Amfahtori Wijarnoko Mustaqin, Farhan Faisal Zainul Nagari, Widean Nikko Riestian Putra Wardoyo Nindian Puspa Dewi Ong, Hansel Santoso Patrick Sutanto Reddy Alexandro Harianto Ricky Sutanto Rossy P. C. Rully Widiastutik Samuel Budi Wardhana Kusuma Saputra, Daniel Gamaliel Setya Ardhi Soetiono, Agung Dewa Bagus Stefanie Hilda Kusumahadi Stella Vania Surya Sumpeno Syabith Umar Ahdan Syaiful Huda Syaiful Imron Tjendika, Patrick Tjwanda Putera Gunawan Tri Septianto Tuesday saka gustaf Ubaidi Ubaidi Ubaidi, Ubaidi Yosi Kristian