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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model

Procedia Computer Science 2023年 227卷 298-306页

作者： Merinda Lestandy Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang 65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta 55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach.

关键词： Social Media Depression Word Embedding BiLSTM

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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Biomedical Instrumentation and Technology Conference (IBITeC), International

作者： Arief Purnama Muharram Farhan Hilmi Taufikulhakim Ayu Purwarianti Faculty of Medicine Universitas Indonesia Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Master Program of Informatics Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.

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Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory

Sentiment Analysis of Cooking Oil Price Policy in Indonesia ...

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Dedy Sugiarto Ema Utami Ainul Yaqin Suwanto Raharjo Faculty of Industrial Technology Universitas Trisakti DKI Jakarta Indonesia Master Program of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Yogyakarta Indonesia

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as ‘mafia’, ‘mahal’, ‘langka’, ‘tangkap’, ‘bongkar’, and ‘mendag’ could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions.

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Topic Relation Graph Construction to Identify Challenges in Introducing programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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Robot and Human Interaction using Natural Language Understanding in Indonesian based on Mental Image Based Understanding

Robot and Human Interaction using Natural Language Understan...

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International Conference of Information and Communication Technology (ICoICT)

作者： Muhammad Giriarda Abrari Hilwadi Hindersah Pranoto Hidaya Rusmin Master Program in Electrical Engineering Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

Robots, in order to understand human commands, could be possible if there is an interaction between them. Possible interaction including the Natural Language, that humans have used to communicate with each other. The method used in this research for robots to understand Natural Language is Mental Image Directed Semantic Theory (MIDST) which uses unique Lmd formula as a basis for computing the Natural Language Understanding (NLU) processes. Equipped with support modules such as Speech to Text, Text to Speech, and Synonym Search Algorithm the robot successfully to receive, understand, and issue responses according to the Natural Language expressions given in Indonesian Language. The movement of the robot is also simulated in a two-dimensional plane to demonstrate its ability to serve humans.

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Decision Support System in Determining the Achievement of Widyaiswara Value Using WASPAS and TOPSIS Algorithm 1

Decision Support System in Determining the Achievement of Wi...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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Predicting Potential Fires in Indonesia by Analyzing VIIRS Night Data during 2018 – 2022

Predicting Potential Fires in Indonesia by Analyzing VIIRS N...

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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case Study UCI Machine Learning Dataset)

Finding the Best Techniques for Predicting Term Deposit Subs...

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International Conference on Sustainable Engineering and Creative Computing (ICSECC)

作者： Lila Setiyani Ayu Indahsari Rosalina Tjong Wansen Master of Science Information Technology study program President University Bekasi Indonesia Informatics Study Program President University Bekasi Indonesia

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%).

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Low-Power Current-Starved Digitally-Controlled Oscillator for Phase-Locked Loop in Battery- Powered Devices 8

Low-Power Current-Starved Digitally-Controlled Oscillator fo...

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8th International Conference on Electrical Engineering and informatics, ICEEI 2021

作者： Madya Putra, Tengku Ahmad Adiono, Trio Master's Program in Electrical Engineering Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781665405560

The demand for IoT SoCs with low power and high precision internal oscillators is greatly increasing. This paper presents a design of digitally controlled oscillator which has low power consumption suitable for use in phase-locked loop inside battery-powered devices. The design incorporates current-starved logic buffers in ring configuration to produce oscillating logic signal as clock source. The circuit has been implemented and fabricated in 180-nm CMOS technology. The measured fabricated chip achieves frequency range from 1.1 MHz to 200 MHz. The power consumption is very low that only consumes 872 μW at maximum frequency and 59.4 μW at minimum frequency. © 2021 IEEE.

关键词： Phase locked loops

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations

An Examination of the Possible Application of Artificial Int...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Wahyu Sardjono Dewi Sagita Pranata Muhammad Isamesal Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster England Master of Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. This study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. The findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. These developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation.

关键词： Productivity Ethics Data analysis Reviews Data integrity Transforms Natural language processing Teamwork Stakeholders Artificial intelligence

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