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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 15 Documents
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Search results for , issue "Vol 4, No 4: DECEMBER 2023" : 15 Documents clear
Image Classifier based on Histogram Matching and Outlier Detection using Hellinger distance Anamika Gupta; Sarabjeet Kaur Kochchar; Anurag Joshi
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.v4i4.114

Abstract

In this paper, we developed a prediction model based on histogram matching of Chest X-ray images. Hellinger distance metric is used to match two histograms. The chest x-ray images are pre-processed and converted to histograms. A benchmark histogram is obtained by finding the average of all pixel intensity values. Then outlier images are detected by comparing the histogram of an image with the benchmark histogram using the hellinger metric. Finally, a prediction method is proposed which matches the histogram of unseen images to histograms of nearest neighbor images.  Hypertuning of input parameters to the proposed prediction method is performed to get the best set of parameters. The proposed model gives an accuracy of 92.3 % and F1 score of 94.6 % on the training set, accuracy of 86.2% and F1 score of 89.6% on the test set.
Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method Latifah Rahayu; Novi Reandy Sasmita; Wulan Farisa Adila; Zurnila Marli Kesuma; Rumaisa Kruba
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.v4i4.141

Abstract

Dengue Fever (DHF) is a dangerous infectious disease that can cause death in an infected person. DHF is a disease transmitted by the Aedes Aegypti mosquito. Dengue cases have been reported in 449 districts/cities spread across 34 provinces with deaths spread across 162 districts/cities in 31 provinces, one of which is in Aceh Province. However, there are districts and cities in Aceh Province with a large number of cases and population at risk, and there are also districts and cities with fewer cases and population at risk. As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to do planning to see which districts and cities have a high chance of DHF. In this study, the type of data used is secondary data sourced from the Aceh Provincial Health Profile from 2016 to 2022. The approach used is the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). The results of this study showed that mortality in dengue cases in Aceh Province from 2016 to 2022 had the highest mortality values in 2016 and 2022. The results of estimating the relative risk of DHF cases using the Bayesian Conditional Autoregressive (CAR) approach of the Besag-York-Mollie (BYM) Model in Aceh Province fulfill all categories with their relative risk values. Some districts/cities have relative risk values. Some districts/cities have high relative risk values of DHF cases and low relative risk values of DHF cases. Sabang city had the highest relative risk value of 3.54 and Bener Meriah district had the lowest relative risk of 0.2.
Acceptance of Information Technology Security Among Universities: A Model Development Study Ahmad Sulhi; Nor Adnan Bin Yahaya; Aang Subiyakto
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.v4i4.142

Abstract

This study aims to investigate the acceptance model of information technology security among religious higher education institutions in Indonesia, especially focusing on lecturers or lecturers. This study adopts the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model with the addition of additional variables, namely security, privacy, and trust. As reflected in various studies of information systems (IS), many IS models are developed by adopting, combining, and adapting previous models. The researcher in this study developed his model based on input-process-output logic as well as processional and causal models of the information systems (IS) success model. The resulting model has a structure with ten variables and 43 indicators. The relationship between variables is explained through 19 influence links. In addition, in the implementation of the study, the authors break down the model into more detailed assessment instrument levels. Although this model development study may have limitations related to the assumptions used and the researcher's understanding, it has the potential to make a theoretical contribution in terms of the proposition of the new model. In addition, it is important to consider transparency in the development of proposed models and data collection instruments presented as practical points for further research in the context of religious higher education institutions in Indonesia.
Long short-term memory-based chatbot for vocational registration information services Yudo Sembodo Hastoro Langgeng; Esther Irawati Setiawan; Syaiful Imron; Joan Santoso
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%.
Analysis of Real Time Twitter Sentiments using Deep Learning Models Raed Alsini
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.v4i4.146

Abstract

Understanding attitudes regarding distinct topics and public opinions on the sentimental analysis of social media data is important. This research analyses the real-time twitter sentiments using deep learning. The major objective of the study is to create an efficient sentiment analysis algorithm to accurately ensure the sentiment polarity (positive, neutral or negative) of tweets. This study proposed a deep learning approach to capture the contextual information and complex patterns in social media data which leverages the power of neutral networks. To assess the performance of the algorithm the study relies on the evaluation of F1 score, accuracy, precision, and recall through rigorous evaluation metrics. The efficiency of the proposed approach is demonstrated by the numerical outcomes of the study. A novel contribution is provided with a specific emphasis on real-time Twitter sentiments by the study to enhance the sentiment analysis techniques for social media data. The significant implication from accurate and timely analysis of Twitter sentiments for several applications includes public opinion tracking, brand management, customer feedback analysis, and reputation monitoring. The potential to provide significant insights to researchers, organisations and business can be made from promptly addressing the sentiments expressed on real time data of twitter.
Gold Prices Time-Series Forecasting: Comparison of Statistical Techniques Indra Maryati; Christian Christian; Adi Suryaputra Paramita
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.v4i4.135

Abstract

The fluctuation of gold prices throughout the year makes it difficult for both investors and regular individuals to predict the future value. The goal of this research is to utilize various statistical techniques, such as linear regression, naive bayes, and various types of smoothing algorithms, to predict the price of gold. The data used in this study was obtained from Kaggle and is from a 70-year time period. The results showed that using a single exponential smoothing method had the highest accuracy and precision, with a good MAPE score of 7.12%. This study is unique in that it compares multiple algorithms using data over a long time period, and it can be useful for investors and traders in making decisions related to gold prices. Additionally, it can also serve as a reference for future research studies.
Automated Class Attendance Management System using Face Recognition: An Application of Viola-Jones Method Andree E Widjaja; Nathanael Joshua Harjono; Hery Hery; Aditya Rama Mitra; Calandra Alencia Haryani
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.v4i4.133

Abstract

Over the past few years, face recognition has been widely used to help human activities in various sectors, including the education sector. By using facial recognition, the class attendance system at universities can be significantly improved. For example, students are no longer asked to sign attendance sheets manually, but attendance can be taken, recorded, and managed automatically through an integrated class attendance management system using facial recognition. The recorded data can then be further analysed to produce useful information for instructors and administrators. In turn, this arrangement will assist them in making decisions about matters relating to student attendance. The main objective of this research is to develop an automatic class attendance management system using facial recognition. In particular, the system we propose was developed using a prototyping software development approach and was modelled using UML version 2.0. As a choice of methods and tools, we used the Viola-Jones method as a face detection algorithm, Python and PHP as programming languages, OpenCV as the computer vision library, and MySQL as the DBMS. The results obtained from a number of black box tests carried out were a fully functional automatic class attendance system prototype using facial recognition.
User Interface Design for DIVAYANA Evaluation Application Based on Positive-Negative Discrepancy Dewa Gede Hendra Divayana; P. Wayan Arta Suyasa; I Putu Wisna Ariawan; Ni Wayan Rena Mariani; Gusti Ayu Dessy Sugiharni; Adie Wahyudi Oktavia Gama
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.v4i4.136

Abstract

This study aims to show the user interface design form of the DIVAYANA evaluation application based on Positive-Negative Discrepancy. The method in this research is a development method that uses the Borg and Gall model. The development refers to the design stage, initial design trials, and revisions to initial design trials. Tests on user interface design involved 104 respondents. The instrument was a questionnaire consisting of 15 questions. Analysis of the trial data used a quantitative descriptive technique. The results of the study show that the quality of the user interface design is quite good. The impact of the results of this research on educational evaluators is that there is new knowledge about the existence of a user interface design that is important to know to support the realization of physical quality evaluation applications.
Studying Electricity Load Forecasting and Optimizing User Benefits with Smart Metering Shereen Sadeq Jumaa
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.v4i4.147

Abstract

Accurate energy projections and optimal utilization of resources require the consideration of real-time variations in demand-side response components. Innovative ultra-short-term power load forecasting approaches such as CNN-BiLSTM-Attention, CNN-LSTM, and GRU models are used to assess the load level and predict daily raw load curve. The study shows that by incorporating predicted raw loads and two types of customer reactions influenced by average reduction rate under different energy efficient classes, wholesale market price fluctuations can be minimized through retail-to-wholesale market connection using demand-side responses. This helps diminish both frequency and amplitude of sudden changes in prices for wholesalers while taking into account an average overall usage pattern based on user class resource consumption rates.
Implementation of PageRank Algorithm for Visualization and Weighting of Keyword Networks in Scientific Papers Adyanata Lubis; Elyandri Prasiwiningrum
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.v4i4.138

Abstract

Papers are written works that contain thoughts about a particular problem or topic that are written systematically accompanied by logical analysis. Scientific papers are often found on the internet or in libraries for various titles of scientific papers, citations or references can be found in every scientific paper and can be obtained easily, but to display all citations in scientific papers in the form of visualization cannot be done easily. Visualizing the citation network of scientific papers in the form of a graph, with nodes representing research papers and edges representing the relationship between researchers' scientific papers and other scientific papers based on scientific paper citations. This research uses the pagerank algorithm to create a keyword network that has a high relationship and potential relevance in a data library. This research is the first research in using the pagerank algorithm and testing its accuracy by comparing with KNN and linear clustering. The presentation displays the citation of scientific papers based on the size of the node by showing the number of citations of the scientific paper. It can be concluded that all processes in the system have run according to design, and functionally the visualization system and weighting of the scientific paper citation network are in accordance with the design. The results obtained from 51 articles, this algorithm produces a visual user interest of 81.60%, compared to the accuracy of the data suitability produced by the linear clustering and KNN algorithms in the form of 71.22% and 61.34%, helping to facilitate the search for scientific papers in large quantities.

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