This study aimed to understand the determinant factors of information technology (IT) strategic management to individual (lecturer) performance using data samples from selected higher education institutions in Indonesia. Since the use of IT innovation in (HEI) is often considered a lens representing the strength of strategy, competitiveness, and quality within a corporate view, it is vague on its impact on individual performance. The investigation included data collection based on an online survey conducted on 325 respondents to investigate the relationship between strategic factors, elaborated into several relevant criteria. The results of statistical data processing showed that of all the strategic factors involved, the business model and strategic alignment categorized in high determinations in influencing academicians' performance at HEI.
{"title":"Determinant Factors in the Implementation of Information Technology Strategic Management to Academicians' Performance in Higher Education Institution","authors":"C. Slamet, Aedah Binti Abdul Rahman, M. Ramdhani","doi":"10.15575/join.v6i2.829","DOIUrl":"https://doi.org/10.15575/join.v6i2.829","url":null,"abstract":"This study aimed to understand the determinant factors of information technology (IT) strategic management to individual (lecturer) performance using data samples from selected higher education institutions in Indonesia. Since the use of IT innovation in (HEI) is often considered a lens representing the strength of strategy, competitiveness, and quality within a corporate view, it is vague on its impact on individual performance. The investigation included data collection based on an online survey conducted on 325 respondents to investigate the relationship between strategic factors, elaborated into several relevant criteria. The results of statistical data processing showed that of all the strategic factors involved, the business model and strategic alignment categorized in high determinations in influencing academicians' performance at HEI.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84830385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Medical image processing has developed rapidly in the last decade. The autodetection and classification of white blood cells (WBC) is one of the medical image processing applications. The analysis of WBC images has engaged researchers from medical also technology fields. Since WBC detection plays an essential role in the medical field, this paper presents a system for distinguishing and classifying WBC types: eosinophils, neutrophils, lymphocytes, and monocytes, using K-Nearest Neighbor (K-NN) and Logistic Regression (LR). This study aims to find the best accuracy of pre-processing images using original grayscale, shock filtering, and thresholding grayscale. The highest average accuracy in classifying WBC images in the conducting research is 43.54% using the LR algorithm from 2103 images. It is obtained from the combination of thresholding grayscale image and shock filtering equation to enhance the quality of an image. Overall, using two algorithms, KNN and LR, the classification accuracy can increase up to 12%.
{"title":"Enhancement of White Blood Cells Images using Shock Filtering Equation for Classification Problem","authors":"Gregorius Vito, P. H. Gunawan","doi":"10.15575/join.v6i2.739","DOIUrl":"https://doi.org/10.15575/join.v6i2.739","url":null,"abstract":"Medical image processing has developed rapidly in the last decade. The autodetection and classification of white blood cells (WBC) is one of the medical image processing applications. The analysis of WBC images has engaged researchers from medical also technology fields. Since WBC detection plays an essential role in the medical field, this paper presents a system for distinguishing and classifying WBC types: eosinophils, neutrophils, lymphocytes, and monocytes, using K-Nearest Neighbor (K-NN) and Logistic Regression (LR). This study aims to find the best accuracy of pre-processing images using original grayscale, shock filtering, and thresholding grayscale. The highest average accuracy in classifying WBC images in the conducting research is 43.54% using the LR algorithm from 2103 images. It is obtained from the combination of thresholding grayscale image and shock filtering equation to enhance the quality of an image. Overall, using two algorithms, KNN and LR, the classification accuracy can increase up to 12%.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87537873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stemming is a technique to return the word derivation to the root or base word. Stemming is widely used for data processing such as searching word indexes, translating, and information retrieval from a document in the database. In general, stemming uses a morphological pattern from a derived word to produce the original word or root word. In the previous research, this technique faced over-stemming and under-stemming problems. In this study, the stemming process will be improved by the syllable pattern (canonical) based on the phonological rule in Sundanese. The stemming result for syllable patterns gets an accuracy of 89% and the execution of the test data resulted in 95% from all the basic words. This simple algorithm has the advantage of being able to adjust the position of the syllable pattern with the word to be stemmed. Due to some data shortage constraints (typo, loan-word, non-deterministic word with syllable pattern), we can improve to increase the accuracy such as adjusting words and adding reference dictionaries. In addition, this algorithm has a drawback that causes the execution to be over-stemming.
{"title":"Sundanese Stemming using Syllable Pattern","authors":"A. Sutedi, Rickard Elsen, M. Nasrulloh","doi":"10.15575/join.v6i2.812","DOIUrl":"https://doi.org/10.15575/join.v6i2.812","url":null,"abstract":"Stemming is a technique to return the word derivation to the root or base word. Stemming is widely used for data processing such as searching word indexes, translating, and information retrieval from a document in the database. In general, stemming uses a morphological pattern from a derived word to produce the original word or root word. In the previous research, this technique faced over-stemming and under-stemming problems. In this study, the stemming process will be improved by the syllable pattern (canonical) based on the phonological rule in Sundanese. The stemming result for syllable patterns gets an accuracy of 89% and the execution of the test data resulted in 95% from all the basic words. This simple algorithm has the advantage of being able to adjust the position of the syllable pattern with the word to be stemmed. Due to some data shortage constraints (typo, loan-word, non-deterministic word with syllable pattern), we can improve to increase the accuracy such as adjusting words and adding reference dictionaries. In addition, this algorithm has a drawback that causes the execution to be over-stemming.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89214449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.
{"title":"The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence","authors":"Helmy Dzulfikar, S. Adinandra, E. Ramadhani","doi":"10.15575/join.v6i2.702","DOIUrl":"https://doi.org/10.15575/join.v6i2.702","url":null,"abstract":"Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82649546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aolia Ikhwanudin, Kusrini Kusrini, Agung Budi Prasetio
The South Tangerang City Government launched a digital financial service called TangselPay. This payment instrument will function as a means of paying levies and other transactions paid by taxpayers, TangselPay is basically a service from the South Tangerang City Government which is accessed via cellular phones (cell phones/smartphones) with the main aim of providing convenience to taxpayers in making levy payments. . . , so that taxpayers do not need to pay cash to the officer. This study aims to determine what factors influence people's interest in using TangselPay services in South Tangerang. The research model used is a modified model of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). Data collection using purposive sampling method with the number of respondents in this study as many as 116 people in our market Pamulang. The data analysis technique in this study used Structural Equation Modeling (SEM) with SmartPLS version 3.3.3 software. The results of the analysis illustrate that the variables of Performance Expectations (PE) and Facilitation Condition (FC) have a positive effect on Use behavior and interest in use have a positive effect on usage behavior. While the variables of business expectations, social influence and hedonic motivation do not have a direct effect.
{"title":"Model TangselPay Receipts Using the UTAUT 2 Method","authors":"Aolia Ikhwanudin, Kusrini Kusrini, Agung Budi Prasetio","doi":"10.15575/join.v6i2.803","DOIUrl":"https://doi.org/10.15575/join.v6i2.803","url":null,"abstract":"The South Tangerang City Government launched a digital financial service called TangselPay. This payment instrument will function as a means of paying levies and other transactions paid by taxpayers, TangselPay is basically a service from the South Tangerang City Government which is accessed via cellular phones (cell phones/smartphones) with the main aim of providing convenience to taxpayers in making levy payments. . . , so that taxpayers do not need to pay cash to the officer. This study aims to determine what factors influence people's interest in using TangselPay services in South Tangerang. The research model used is a modified model of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). Data collection using purposive sampling method with the number of respondents in this study as many as 116 people in our market Pamulang. The data analysis technique in this study used Structural Equation Modeling (SEM) with SmartPLS version 3.3.3 software. The results of the analysis illustrate that the variables of Performance Expectations (PE) and Facilitation Condition (FC) have a positive effect on Use behavior and interest in use have a positive effect on usage behavior. While the variables of business expectations, social influence and hedonic motivation do not have a direct effect.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84443178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soybean is one of the protein main sources that can be used for consumption in tempeh, tofu, milk, etc. Based on projection results, soybean production and consumption balance in Indonesia, in 2018-2022, it is estimated that deficit will increase by 6.18% per year. So, it's necessary to guide soybean land suitability, which can be carried out by evaluating existing land suitability to support soybean farming expansion and production. This study conducted an analytical study to evaluate soybean land suitability using C5.0 algorithm based on land and weather characteristics. The C5.0 algorithm is an extension of spatial decision tree, an ID3 decision tree extension. Dataset is divided into two categories: explanatory factors representing seven land characteristics (drainage, land slope, base saturation, cation exchange capacity, soil texture, soil pH, and soil mineral depth) and two weather data (rainfall and temperature), and a target class represent soybean land suitability in two study areas, namely Bogor and Grobogan Regency. The result generated two land suitability models with the best model obtained accuracy for training data 98.58%, while testing data was 97.17%. The best model rules are 69 rules that do not involve three attributes: cation exchange capacity, soil mineral depth, and rainfall.
{"title":"Prediction Model for Soybean Land Suitability Using C5.0 Algorithm","authors":"Andi Nurkholis, Styawati Styawati","doi":"10.15575/join.v6i2.711","DOIUrl":"https://doi.org/10.15575/join.v6i2.711","url":null,"abstract":"Soybean is one of the protein main sources that can be used for consumption in tempeh, tofu, milk, etc. Based on projection results, soybean production and consumption balance in Indonesia, in 2018-2022, it is estimated that deficit will increase by 6.18% per year. So, it's necessary to guide soybean land suitability, which can be carried out by evaluating existing land suitability to support soybean farming expansion and production. This study conducted an analytical study to evaluate soybean land suitability using C5.0 algorithm based on land and weather characteristics. The C5.0 algorithm is an extension of spatial decision tree, an ID3 decision tree extension. Dataset is divided into two categories: explanatory factors representing seven land characteristics (drainage, land slope, base saturation, cation exchange capacity, soil texture, soil pH, and soil mineral depth) and two weather data (rainfall and temperature), and a target class represent soybean land suitability in two study areas, namely Bogor and Grobogan Regency. The result generated two land suitability models with the best model obtained accuracy for training data 98.58%, while testing data was 97.17%. The best model rules are 69 rules that do not involve three attributes: cation exchange capacity, soil mineral depth, and rainfall.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74459245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Korean culture began to spread widely throughout the world, ranging from lifestyle, music, food, and drinks, and there are still many exciting things from this Korean culture. One of the interesting things to learn is to know Korean letters (Hangul), which are non-Latin characters. If the Hangul letters have been learned, the next thing that lay people must learn is the Korean syllables, which are different from the Indonesian syllables. Because of the difficulty of learning Korean syllables, understanding a sentence needed a system to recognize Korean syllables. Therefore, in this study designing a system to acknowledge Korean syllables, the method used is Convolutional Neural Network with VGG architecture. The system performs the process of detecting Korean syllables based on models that have been trained using 72 syllable classes. The tests on 72 Korean syllable classes obtain an average accuracy of 96%, an average precision value of 96%, an average recall value of 100%, and an average F1 score of 98%.
{"title":"Application of VGG Architecture to Detect Korean Syllables Based on Image Text","authors":"Irma Amelia Dewi, Amelia Shaneva","doi":"10.15575/join.v6i2.653","DOIUrl":"https://doi.org/10.15575/join.v6i2.653","url":null,"abstract":"Korean culture began to spread widely throughout the world, ranging from lifestyle, music, food, and drinks, and there are still many exciting things from this Korean culture. One of the interesting things to learn is to know Korean letters (Hangul), which are non-Latin characters. If the Hangul letters have been learned, the next thing that lay people must learn is the Korean syllables, which are different from the Indonesian syllables. Because of the difficulty of learning Korean syllables, understanding a sentence needed a system to recognize Korean syllables. Therefore, in this study designing a system to acknowledge Korean syllables, the method used is Convolutional Neural Network with VGG architecture. The system performs the process of detecting Korean syllables based on models that have been trained using 72 syllable classes. The tests on 72 Korean syllable classes obtain an average accuracy of 96%, an average precision value of 96%, an average recall value of 100%, and an average F1 score of 98%.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77354037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Syafaah, Z. Zulfatman, I. Pakaya, Merinda Lestandy
The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.
{"title":"Comparison of Machine Learning Classification Methods in Hepatitis C Virus","authors":"L. Syafaah, Z. Zulfatman, I. Pakaya, Merinda Lestandy","doi":"10.15575/JOIN.V6I1.719","DOIUrl":"https://doi.org/10.15575/JOIN.V6I1.719","url":null,"abstract":"The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79137208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Google Maps is one of the popular location selection systems. One of the popular features of Google Maps is nearby search. For example, someone who wants to find the closest restaurants to his location can use the nearby search feature. This feature only considers one specific location in providing the desired place choice. In a real-world situation, there may be a need to consider more than one location in selecting the desired place. Assume someone would like to choose a hotel close to the conference hall, the museum, beach, and souvenir store. In this situation, nearby search feature in Google Maps may not be able to suggest a list of hotels that are interesting for him based on the distance from each destination places. In this paper, we have successfully developed a web-based application of Google Maps search using Voronoi-based Spatial Skyline (VS2) algorithm to choose some Point Of Interest (POI) from Google Maps as their considered locations to select desired place. We used Google Maps API to provide POI information for our web-based application. The experiment result showed that the execution time increases while the number of considered location increases.
{"title":"Location Selection Query in Google Maps using Voronoi-based Spatial Skyline (VS2) Algorithm","authors":"A. Annisa, Leni Angraeni","doi":"10.15575/join.v6i1.667","DOIUrl":"https://doi.org/10.15575/join.v6i1.667","url":null,"abstract":"Google Maps is one of the popular location selection systems. One of the popular features of Google Maps is nearby search. For example, someone who wants to find the closest restaurants to his location can use the nearby search feature. This feature only considers one specific location in providing the desired place choice. In a real-world situation, there may be a need to consider more than one location in selecting the desired place. Assume someone would like to choose a hotel close to the conference hall, the museum, beach, and souvenir store. In this situation, nearby search feature in Google Maps may not be able to suggest a list of hotels that are interesting for him based on the distance from each destination places. In this paper, we have successfully developed a web-based application of Google Maps search using Voronoi-based Spatial Skyline (VS2) algorithm to choose some Point Of Interest (POI) from Google Maps as their considered locations to select desired place. We used Google Maps API to provide POI information for our web-based application. The experiment result showed that the execution time increases while the number of considered location increases.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86776011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kartika Rizqi Nastiti, A. Hidayatullah, A. R. Pratama
Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.
{"title":"Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation","authors":"Kartika Rizqi Nastiti, A. Hidayatullah, A. R. Pratama","doi":"10.15575/join.v6i1.636","DOIUrl":"https://doi.org/10.15575/join.v6i1.636","url":null,"abstract":"Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.","PeriodicalId":32019,"journal":{"name":"JOIN Jurnal Online Informatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76629874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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