The manuscript selection process is the process of assessing manuscripts worthy of publication. The Editor's job is to provide an evaluation of each manuscript based on the assessment criteria and sub-criteria. By using a decision support system, it can make it easier for policymakers to determine the suitability of a manuscript. In this research, a decision support system is applied to select papers that are worthy of publication, namely the Fuzzy Analytical Hierarchy Process (F-AHP) method for selecting the suitability of manuscripts using subjective criteria and the Naïve Bayes method for classifying books based on their genre. The test results using the F-AHP method produced an accuracy rate of 83.33% using 30 books out of 150 books and using the Naïve Bayes method produced an accuracy rate of 80% using 30 books from the internet. This system uses the Visual Studi Code IDE, Firebase, and Pythonanywhere as its database with an Android display.
{"title":"Application of the Naive Bayes Classifier Method and Fuzzy Analytical Hierarchy Process in Determining Books Eligible for Publishing","authors":"Mochamad Denny Irwansyah, Teguh Puja Negara, Erniyati Erniyati, Puspa Citra","doi":"10.33751/komputasi.v21i1.6677","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.6677","url":null,"abstract":"The manuscript selection process is the process of assessing manuscripts worthy of publication. The Editor's job is to provide an evaluation of each manuscript based on the assessment criteria and sub-criteria. By using a decision support system, it can make it easier for policymakers to determine the suitability of a manuscript. In this research, a decision support system is applied to select papers that are worthy of publication, namely the Fuzzy Analytical Hierarchy Process (F-AHP) method for selecting the suitability of manuscripts using subjective criteria and the Naïve Bayes method for classifying books based on their genre. The test results using the F-AHP method produced an accuracy rate of 83.33% using 30 books out of 150 books and using the Naïve Bayes method produced an accuracy rate of 80% using 30 books from the internet. This system uses the Visual Studi Code IDE, Firebase, and Pythonanywhere as its database with an Android display.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"30 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488710","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}
Pub Date : 2024-01-29DOI: 10.33751/komputasi.v21i1.8991
Salsa Nadira Putri, Tjut Awaliyah Zuraiyah, Dinar Munggaran Akhmad
Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give recommendations based on each product's IMDB Weighted score. Content-based Filtering uses Cosine Distance calculations which generate personal recommendations and give recommendations based on the score cosine distance of each product in the form of a presentation of the similarity of products that have been purchased with other products. This research uses 107 UMKM fashion and craft product data that was obtained from Bogor City Regional Craft Center which sells various kinds of UMKM products from Bogor City and Regency. Data preprocessing is then carried out on the raw data, with the Data Cleaning, Data Transforming and Data Splitting stages which divide the data in a ratio of 80:20. The accuracy of Demographic Filtering Recommendation System reaches 82.7% and Content-based Filtering Recommendation System reaches 100%.
{"title":"Recommender Systems using Hybrid Demographic and Content-Based Filtering methods for UMKM Products","authors":"Salsa Nadira Putri, Tjut Awaliyah Zuraiyah, Dinar Munggaran Akhmad","doi":"10.33751/komputasi.v21i1.8991","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.8991","url":null,"abstract":"Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give recommendations based on each product's IMDB Weighted score. Content-based Filtering uses Cosine Distance calculations which generate personal recommendations and give recommendations based on the score cosine distance of each product in the form of a presentation of the similarity of products that have been purchased with other products. This research uses 107 UMKM fashion and craft product data that was obtained from Bogor City Regional Craft Center which sells various kinds of UMKM products from Bogor City and Regency. Data preprocessing is then carried out on the raw data, with the Data Cleaning, Data Transforming and Data Splitting stages which divide the data in a ratio of 80:20. The accuracy of Demographic Filtering Recommendation System reaches 82.7% and Content-based Filtering Recommendation System reaches 100%.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"30 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140489536","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}
Pub Date : 2024-01-29DOI: 10.33751/komputasi.v21i1.9268
Ahdan Darul Mutaqin, S. Rahardiantoro, Mohammad Masjkur
Equitable development from a gender perspective needs attention. Based on data from the World Economic Forum (WEF), gender equality in Indonesia has increased. Even so, the island of Papua is still very low on gender equality. It can be seen from the Gender Development Index (IPG) from the Central Bureau of Statistics (BPS), there is a considerable gap between the Papua Island IPG and the National. IPG is a comparison between the Human Development Index (IPM) for Men and Women. Based on these conditions, this study aims to classify GPI, Male IPM, and Female IPM by region using the spatial clustering method in 2022. One of the analytical methods that can overcome these conditions is Generalized LASSO. Generalized LASSO can be used on data that only has a response variable (y) for clustering. Generalized LASSO clustering uses a penalty matrix D. The formation of the D matrix is formed by giving values -1 and 1 for areas that intersect or are adjacent and a value of 0 for other areas. The best clustering for IPG uses KNN with K = 3 and the number of clusters formed is 2 clusters. The best clustering for male HDI uses KNN with K = 2 and the number of clusters formed is 8. The best clustering for female HDI uses KNN with K = 2 and the number of clusters formed is 10 clusters.
{"title":"Spatial Clustering Using Generalized LASSO on the Gender and Human Development Index in Papua Island in 2022","authors":"Ahdan Darul Mutaqin, S. Rahardiantoro, Mohammad Masjkur","doi":"10.33751/komputasi.v21i1.9268","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.9268","url":null,"abstract":"Equitable development from a gender perspective needs attention. Based on data from the World Economic Forum (WEF), gender equality in Indonesia has increased. Even so, the island of Papua is still very low on gender equality. It can be seen from the Gender Development Index (IPG) from the Central Bureau of Statistics (BPS), there is a considerable gap between the Papua Island IPG and the National. IPG is a comparison between the Human Development Index (IPM) for Men and Women. Based on these conditions, this study aims to classify GPI, Male IPM, and Female IPM by region using the spatial clustering method in 2022. One of the analytical methods that can overcome these conditions is Generalized LASSO. Generalized LASSO can be used on data that only has a response variable (y) for clustering. Generalized LASSO clustering uses a penalty matrix D. The formation of the D matrix is formed by giving values -1 and 1 for areas that intersect or are adjacent and a value of 0 for other areas. The best clustering for IPG uses KNN with K = 3 and the number of clusters formed is 2 clusters. The best clustering for male HDI uses KNN with K = 2 and the number of clusters formed is 8. The best clustering for female HDI uses KNN with K = 2 and the number of clusters formed is 10 clusters.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"18 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487799","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}
Pub Date : 2024-01-29DOI: 10.33751/komputasi.v21i1.9265
Hizkia Nathanael, Alz Danny Wowor
This research uses the logarithm function as a key component in generating random numbers in the Chaos CSPRNG framework. The main problem addressed here is the generation of keys for cryptography, recognizing the important role of cryptographic keys in safeguarding sensitive information. By using mathematical functions, specifically logarithmic functions, as a key generation method, this research explores the potential for increasing the uncertainty and strength of cryptographic keys. The proposed approach involves the systematic utilization of various mathematical functions to generate diverse and unpredictable data sets. This data set, derived from the application of logarithmic functions, serves as the basis for generating random numbers. Through a series of tests such as Randomness Test and Cryptography Test, this research shows that the data generated from these functions can be utilized effectively as a reliable source for generating random numbers, and has a low correlation value, thereby contributing to the overall security of a symmetric cryptographic system.
{"title":"Chaos CSPRNG Design As a Key in Symmetric Cryptography Using Logarithmic Functions","authors":"Hizkia Nathanael, Alz Danny Wowor","doi":"10.33751/komputasi.v21i1.9265","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.9265","url":null,"abstract":"This research uses the logarithm function as a key component in generating random numbers in the Chaos CSPRNG framework. The main problem addressed here is the generation of keys for cryptography, recognizing the important role of cryptographic keys in safeguarding sensitive information. By using mathematical functions, specifically logarithmic functions, as a key generation method, this research explores the potential for increasing the uncertainty and strength of cryptographic keys. The proposed approach involves the systematic utilization of various mathematical functions to generate diverse and unpredictable data sets. This data set, derived from the application of logarithmic functions, serves as the basis for generating random numbers. Through a series of tests such as Randomness Test and Cryptography Test, this research shows that the data generated from these functions can be utilized effectively as a reliable source for generating random numbers, and has a low correlation value, thereby contributing to the overall security of a symmetric cryptographic system.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"10 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140489134","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}
Pub Date : 2024-01-29DOI: 10.33751/komputasi.v21i1.9260
B. H. Situmorang, Ali Isra, Dhatu Paragya, David Aulia Akbar Adhieputra
The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved.
{"title":"Apriori Algorithm Application for Consumer Purchase Patterns Analysis","authors":"B. H. Situmorang, Ali Isra, Dhatu Paragya, David Aulia Akbar Adhieputra","doi":"10.33751/komputasi.v21i1.9260","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.9260","url":null,"abstract":"The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"10 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140489775","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}
Pub Date : 2024-01-29DOI: 10.33751/komputasi.v21i1.9266
Fiki Andrianto, A. Fadlil, Imam Riadi
Twitter is a short message platform commonly used as a means of news information, commentary, and social interaction. One of the utilization of twitter is to analyze the sentiment of the online marketplace which can be used to determine the service, quality of goods, and delivery of goods on a product, service or application. This research aims to categorize the reviews or responses of the Indonesian people, especially to the online marketplace using the linear Support Vector Machine (SVM) algorithm. In order to make continuous improvements to the role of the Indonesian online marketplace in the future, sentiment analysis is needed. The analysis research tweets used were 4165 datasets using the python programming language. Sentiment analysis research stages include data collection, preprocessing, labeling, tf-idf weighting, split data, SVM model analysis and result evaluation. The data is then divided into 80% training data and 20% testing data, 50% training data and 50% testing data, 20% training data and 80% testing data. The results of the svm algorithm testing scenario obtained the highest optimization with an accuracy value of 97%, F1-score value on positive labels 88% and negative 98%, also obtained a positive recall value of 80% and negative 100% precision value on positive labels 98% and negative 97%, on 80% training data and 20% testing. It can be concluded that in this case, the linear svm algorithm is able to work to recognize models with a high level of accuracy so that in the future it can be used in similar cases.
{"title":"Linear Kernel Optimization of Support Vector Machine Algorithm on Online Marketplace Sentiment Analysis","authors":"Fiki Andrianto, A. Fadlil, Imam Riadi","doi":"10.33751/komputasi.v21i1.9266","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.9266","url":null,"abstract":"Twitter is a short message platform commonly used as a means of news information, commentary, and social interaction. One of the utilization of twitter is to analyze the sentiment of the online marketplace which can be used to determine the service, quality of goods, and delivery of goods on a product, service or application. This research aims to categorize the reviews or responses of the Indonesian people, especially to the online marketplace using the linear Support Vector Machine (SVM) algorithm. In order to make continuous improvements to the role of the Indonesian online marketplace in the future, sentiment analysis is needed. The analysis research tweets used were 4165 datasets using the python programming language. Sentiment analysis research stages include data collection, preprocessing, labeling, tf-idf weighting, split data, SVM model analysis and result evaluation. The data is then divided into 80% training data and 20% testing data, 50% training data and 50% testing data, 20% training data and 80% testing data. The results of the svm algorithm testing scenario obtained the highest optimization with an accuracy value of 97%, F1-score value on positive labels 88% and negative 98%, also obtained a positive recall value of 80% and negative 100% precision value on positive labels 98% and negative 97%, on 80% training data and 20% testing. It can be concluded that in this case, the linear svm algorithm is able to work to recognize models with a high level of accuracy so that in the future it can be used in similar cases.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"47 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487566","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}
Pub Date : 2024-01-29DOI: 10.33751/komputasi.v21i1.9314
Asep Saepulrohman, Agus Ismangil, L. Heliawati
Message encryption in digital images using the Zhang LSB Image method is a steganography technique that utilizes the Least Significant Bit (LSB) method to hide secret messages in the last bit of the image pixel. This method allows the use of images as a medium to convey hidden messages. The encryption process involves two main stages, namely message encryption and message hiding in an image. Message encryption is carried out using strong cryptographic algorithms to secure the authenticity and confidentiality of messages. Then, the encrypted message is inserted into the last bit of the image pixel using the LSB method. This is done by modifying the last bit value of the pixel so that the change is not visually visible to the human eye. To recover the original message, the message recovery process involves extracting the last bit of the modified image pixel and decrypting the message using the appropriate key. The Zhang LSB Image method is a steganography technique that is relatively simple but effective in hiding messages in digital images.
{"title":"Message Encryption in Digital Images using the Zhang LSB Imange Method","authors":"Asep Saepulrohman, Agus Ismangil, L. Heliawati","doi":"10.33751/komputasi.v21i1.9314","DOIUrl":"https://doi.org/10.33751/komputasi.v21i1.9314","url":null,"abstract":"Message encryption in digital images using the Zhang LSB Image method is a steganography technique that utilizes the Least Significant Bit (LSB) method to hide secret messages in the last bit of the image pixel. This method allows the use of images as a medium to convey hidden messages. The encryption process involves two main stages, namely message encryption and message hiding in an image. Message encryption is carried out using strong cryptographic algorithms to secure the authenticity and confidentiality of messages. Then, the encrypted message is inserted into the last bit of the image pixel using the LSB method. This is done by modifying the last bit value of the pixel so that the change is not visually visible to the human eye. To recover the original message, the message recovery process involves extracting the last bit of the modified image pixel and decrypting the message using the appropriate key. The Zhang LSB Image method is a steganography technique that is relatively simple but effective in hiding messages in digital images.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"54 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140487146","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}
Pub Date : 2023-07-26DOI: 10.33751/komputasi.v20i2.8284
Juan Rizky Mannuel Ledoh, Ferdinandus Elfanto Andreas, Emerensye Sofia Yublina Pandie, Clarissa Elfira Amos Pah
Implementation of education during the emergency period of Covid-19 in Higher Education was carried out at home through online/distance learning. The lecturer is one of the key holders of success in the learning process. Lecturer performance is a main factor needed to improve education and service quality in online learning. In this study, the authors implemented the C4.5 algorithm using RapidMiner 9.10 app to predict student satisfaction with lecturer performance during the Covid-19 pandemic. The data in this study were obtained from a questionnaire distributed to active students in the Computer Science Study Program (class of 2016 - 2021) at the University of Nusa Cendana with 942 records. The attributes used in this study were the lecturer's age, gender, suitability of learning media (SLM), and the competencies of Pedagogic Competence (PeC), Professional Competence (PrC), Personal Competence (PsC), and social competence (SC), with the level of student satisfaction as the target class divided into two, namely Satisfied and Dissatisfied. The dataset is processed using RapidMiner and produces 11 decision rules which show that the attribute PeC has the most significant influence on the level of student satisfaction with lecturer performance during the Covid-19 pandemic and the test results of the decision tree model using cross-validation. The test results show that the C4.5 algorithm has a good performance in predicting levels of student satisfaction with an accuracy rate of 94.8%, precision for the prediction class Dissatisfied and Satisfied of 92.23 % and 95.52%, and recall of the actual Dissatisfied and Satisfied classes of 85.2% and 97.77%.
{"title":"C4.5 Algorithm Implementation to Predict Student Satisfaction Level of Lecturer’s Performance in the Covid-19 Pandemic","authors":"Juan Rizky Mannuel Ledoh, Ferdinandus Elfanto Andreas, Emerensye Sofia Yublina Pandie, Clarissa Elfira Amos Pah","doi":"10.33751/komputasi.v20i2.8284","DOIUrl":"https://doi.org/10.33751/komputasi.v20i2.8284","url":null,"abstract":"Implementation of education during the emergency period of Covid-19 in Higher Education was carried out at home through online/distance learning. The lecturer is one of the key holders of success in the learning process. Lecturer performance is a main factor needed to improve education and service quality in online learning. In this study, the authors implemented the C4.5 algorithm using RapidMiner 9.10 app to predict student satisfaction with lecturer performance during the Covid-19 pandemic. The data in this study were obtained from a questionnaire distributed to active students in the Computer Science Study Program (class of 2016 - 2021) at the University of Nusa Cendana with 942 records. The attributes used in this study were the lecturer's age, gender, suitability of learning media (SLM), and the competencies of Pedagogic Competence (PeC), Professional Competence (PrC), Personal Competence (PsC), and social competence (SC), with the level of student satisfaction as the target class divided into two, namely Satisfied and Dissatisfied. The dataset is processed using RapidMiner and produces 11 decision rules which show that the attribute PeC has the most significant influence on the level of student satisfaction with lecturer performance during the Covid-19 pandemic and the test results of the decision tree model using cross-validation. The test results show that the C4.5 algorithm has a good performance in predicting levels of student satisfaction with an accuracy rate of 94.8%, precision for the prediction class Dissatisfied and Satisfied of 92.23 % and 95.52%, and recall of the actual Dissatisfied and Satisfied classes of 85.2% and 97.77%.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139354542","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}
This study aims to provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The EDAS method requires a lot of input, and preference must be precise in the determination of the weight of the criteria. To fix the problem of weighting criteria in the EDAS method, the Centroid Rank Order (ROC) method is used. ROC is a simple method used to assign weight values to each criterion used. The results of this study provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The application of the EDAS method in the selection of exemplary student candidates resulted in exemplary prospective students obtained on behalf of Hadi Santoso with a final score of 0.70885 and obtained 1st rank. The results of these recommendations can help the school determine the selection of the best students by applying the EDAS method and ROC weighting.
{"title":"Implementation of EDAS Method in the Selection of the Best Students with ROC Weighting","authors":"Dedi Darwis, H. Sulistiani, Dyah Ayu Megawaty, Setiawansyah Setiawansyah, Intan Agustina","doi":"10.33751/komputasi.v20i2.7904","DOIUrl":"https://doi.org/10.33751/komputasi.v20i2.7904","url":null,"abstract":"This study aims to provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The EDAS method requires a lot of input, and preference must be precise in the determination of the weight of the criteria. To fix the problem of weighting criteria in the EDAS method, the Centroid Rank Order (ROC) method is used. ROC is a simple method used to assign weight values to each criterion used. The results of this study provide recommendations for the best students to be selected using the EDAS method and ROC weighting, so as to help schools in decision making. The application of the EDAS method in the selection of exemplary student candidates resulted in exemplary prospective students obtained on behalf of Hadi Santoso with a final score of 0.70885 and obtained 1st rank. The results of these recommendations can help the school determine the selection of the best students by applying the EDAS method and ROC weighting.","PeriodicalId":339673,"journal":{"name":"Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139356238","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}
Pub Date : 2023-07-22DOI: 10.33751/komputasi.v20i2.8281
Arlan Joliansa Ndruru, Muhammad Fikry, Yusra
Chatbot research is a unique innovation in the development of Artificial Intelli- gence and has promising prospects in the field of Education. One form of information service available at the university is the Customer Care Center (C3) PTIPD UIN Suska Riau, which is responsible for handling problems submitted by students. However, with so many questions or problems submitted to the PTIPD Customer Care Center, it is difficult for the PTIPD Cus- tomer Care Center to respond to student questions submitted, the service becomes ineffective and the response to the answers to the problems submitted becomes late. To overcome this problem, chatbot development was carried out for PTIPD UIN Suska Riau Customer Care Center Services using Dialogflow to improve services and overcome existing problems. Di- alogflow as conversation development platform that uses natural language processing (NLP) to understand and interpret user intent in conversations. Through User Acceptance Test (UAT) testing, the chatbot managed to achieve an acceptance rate of 84% overall. This shows that users, in this case, students respond positively to the use of chatbots in PTIPD Customer Care Center services. In addition, Usability Testing was also conducted to evaluate the level of usability of the chatbot. Based on this test, the chatbot achieved a score of 76, which indicates a good level of usability in interaction with users. The test results illustrate that the chatbot at the Customer Care Center PTIPD UIN Suska Riau has provided a positive user experience.
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