Jurnal Sisfokom最新文献

Determining Scholarship Recipients at STIT Prabumulih Using the AHP Method 使用AHP方法确定Prabumulih STIT的奖学金获得者

Jurnal Sisfokom Pub Date : 2023-11-08 DOI: 10.32736/sisfokom.v12i3.1717

Andi Christian, Ariansyah Ariansyah, Anggie Sri Wahyuni

{"title":"Determining Scholarship Recipients at STIT Prabumulih Using the AHP Method","authors":"Andi Christian, Ariansyah Ariansyah, Anggie Sri Wahyuni","doi":"10.32736/sisfokom.v12i3.1717","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1717","url":null,"abstract":"In every educational institution, especially universities, there are lots of scholarships offered to students. Likewise with the Prabumulih College of Engineering (STIT Prabumulih) which has a scholarship program for its students by applying predetermined rules or criteria, for example, parents' income, parents' dependents, student achievement index scores, etc. Due to this, not all scholarship recipients who apply for scholarships will receive a scholarship. The problem faced by the campus today is in the process of winning scholarships. therefore a decision support system is needed that can assist in providing scholarship recipient recommendations. In this study the authors used the AHP method and the Expert Choice application. From the calculation results obtained by the specified criteria, the GPA of 0.389 is the highest priority weight compared to other criteria. Then, from the results of calculating student data or all alternatives, the total value of each student is obtained. It can be concluded that the one who can be recommended to get a UKT scholarship is Student A because it has the highest score, namely 16.6% of the total calculated.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135430784","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}

引用次数: 0

Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms 利用随机森林和决策树算法识别非法交易中的信用卡欺诈

Jurnal Sisfokom Pub Date : 2023-11-08 DOI: 10.32736/sisfokom.v12i3.1730

Indah Werdiningsih, Endah Purwanti, Gede Rangga Wira Aditya, Auliya Rakhman Hidayat, R. Sulthan Rafi Athallah, Virda Adisty Sahar, Tio Satrio Wibisono, Darren Febriand Nura Somba

{"title":"Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms","authors":"Indah Werdiningsih, Endah Purwanti, Gede Rangga Wira Aditya, Auliya Rakhman Hidayat, R. Sulthan Rafi Athallah, Virda Adisty Sahar, Tio Satrio Wibisono, Darren Febriand Nura Somba","doi":"10.32736/sisfokom.v12i3.1730","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1730","url":null,"abstract":"The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429847","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}

引用次数: 0

Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture 基于U-Net结构的CNN心室分割方法

Jurnal Sisfokom Pub Date : 2023-11-07 DOI: 10.32736/sisfokom.v12i3.1976

Tommy Saputra, Siti Nurmaini, Muhammad Taufik Roseno, Hadi Syaputra

引用次数: 0

Comparison of Gabor Filter Parameter Characteristics for Dorsal Hand Vein Authentication Using Artificial Neural Networks 人工神经网络手背静脉认证Gabor滤波器参数特性比较

Jurnal Sisfokom Pub Date : 2023-11-07 DOI: 10.32736/sisfokom.v12i3.1819

Wahyu Irwan Putra, Muchtar Ali Setyo Yudono, Alun Sujjada

引用次数: 0

Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data 系统文献综述:文本数据情感分类中的机器学习方法

Jurnal Sisfokom Pub Date : 2023-11-07 DOI: 10.32736/sisfokom.v12i3.1787

Putu Widyantara Artanta Wibawa, Cokorda Pramartha

{"title":"Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data","authors":"Putu Widyantara Artanta Wibawa, Cokorda Pramartha","doi":"10.32736/sisfokom.v12i3.1787","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1787","url":null,"abstract":"Emotions are a person's response to an event. Emotions can be expressed verbally or nonverbally. Over time people can express their emotions through social media. Considering that emotion is a reflection of society's response, it is important to classify emotions in society to find out the community's response as information for consideration in decision-making. This study is aimed to identify and analyze the datasets, methods, and evaluation metrics that are being used in the classification of emotional texts in textual data from research data from 2013 to 2022. Based on the inclusion and exclusion design in selecting literature, a total of 50 kinds of literature were used in extracting and synthesizing data. Analysis of the data shows that out of 50 pieces of literature, there are 36 works of literature that use public datasets while 14 kinds of literature use private datasets. In the method of developing models for classifying, the SVM and Naive Bayes models are the most popular among the other models. In evaluating the model, the F-measure or F1-score metric is the most widely used metric compared to other metrics. There are three main contributions identified in this study, namely methods, models, and evaluation","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545450","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}

引用次数: 0

Determining Promotional Package Recommendations Using the Frequent Pattern Growth Algorithm at The Java Cafe 在Java咖啡馆使用频繁模式增长算法确定促销包推荐

Jurnal Sisfokom Pub Date : 2023-11-07 DOI: 10.32736/sisfokom.v12i3.1904

Samsinar Samsinar, Dwi Astuti

引用次数: 0

Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study 基于标题评估新闻标签的机器学习方法:在线媒体案例研究

Jurnal Sisfokom Pub Date : 2023-11-07 DOI: 10.32736/sisfokom.v12i3.1808

Rezky Yuranda, Tata Sutabri, Delpiah Wahyuningsih

{"title":"Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study","authors":"Rezky Yuranda, Tata Sutabri, Delpiah Wahyuningsih","doi":"10.32736/sisfokom.v12i3.1808","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1808","url":null,"abstract":"In the current digital era, information availability is abundant, and news serves as a primary source of up-to-date and reliable information for the public. However, with the increasing volume of information, a robust evaluation method is necessary to ensure accurate and dependable news labeling. This research employs a machine learning approach, utilizing three common classification algorithms: Naive Bayes, SVM, and Random Forest, to evaluate news labels based on their titles. The dataset utilized in this study is obtained from Jakarta AI Research and consists of 10,000 samples covering various news topics. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics to gain a comprehensive understanding of the classification algorithm's performance. The results of this research demonstrate that the SVM algorithm exhibits the best performance, achieving an accuracy rate of 92.92%. Random Forest follows with an accuracy rate of 91.21%, and Naive Bayes with an accuracy rate of 89.61%. These findings provide deep insights into the effectiveness of the machine learning approach in evaluating news labels based on their titles. Furthermore, the study highlights the importance of considering other evaluation metrics such as precision, recall, and F1-Score to obtain a more holistic understanding of the algorithm's performance. Further research is encouraged to involve additional classification algorithms and more diverse and extensive datasets to enhance the comprehension of news label evaluation comprehensively. Such endeavors can significantly contribute to the development of automated systems for classifying news with higher accuracy and reliability in the future","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545458","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}

引用次数: 0

Analysis of Behavioral Use of Academic Information Systems with the Implementation of UTAUT 2 Integration at the Muhammadi-Palembang Institute of Health Science and Technology 穆罕默迪-巨港卫生科学与技术研究所实施UTAUT 2集成的学术信息系统行为使用分析

Jurnal Sisfokom Pub Date : 2023-11-07 DOI: 10.32736/sisfokom.v12i3.1978

Hendri Donan, Edi Surya Negara Surya Negara, Tata Sutabri, Firdaus Firdaus

{"title":"Analysis of Behavioral Use of Academic Information Systems with the Implementation of UTAUT 2 Integration at the Muhammadi-Palembang Institute of Health Science and Technology","authors":"Hendri Donan, Edi Surya Negara Surya Negara, Tata Sutabri, Firdaus Firdaus","doi":"10.32736/sisfokom.v12i3.1978","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1978","url":null,"abstract":"The utilization of Information Technology (IT) in higher education setting aims to enhance the quality of education, and this initiative is realized through the implementation of Information Technology at the Institute of Health Sciences and Technology Muhammadiyah Palembang (IKesT MP) in the form of an Academic Information System (SIMAKAD). SIMAKAD is a vital role as a tool to manage internal data and serves as an information hub for students. This research is conducted to evaluate the acceptance level of the UTAUT2 model and the impact of both the main and target variables within the UTAUT2 model. This research utilizes a quantitative method with 150 respondents, analyzed using SMART PLS 3.0 software.\" software. The research findings indicate that the acceptance level of the UTAUT2 model reaches 74%, signifying a high adoption rate. Variables like Perceived Value (p-Value: 0.019) and Habit (p-Value: 0.009) significantly influence Behavioral Intention, with a p-Value 0.05, indicating that their hypotheses are accepted. On the other hand, variables such as Performance Expectancy (p-Value: 0.660), Effort Expectancy (p-Value: 0.417), Social Influence (p-Value: 0.652), and Facilitating Conditions (p-Value: 0.292) There is no substantial influence on Behavioral Intention as a result of using Information Technology (IT), indicating that their hypotheses have not been endorsed.. Additionally, the variable Hedonic Motivation (p-Value: 0.978) also does not can significantly impact one's inclination toward a behavior Intention. However, variables Facilitating Conditions (p-Value: 0.000) and Behavioral Intention (p-Value: 0.000) have a positive impact on Use Behavior, indicating that their hypotheses are accepted. Conversely, the variable Habit (p-Value: 0.915) Does not exert a significant impact on Uss Behavior, resulting in the rejection of its hypothesis.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135545749","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}

引用次数: 0

Performance Analysis of Classification Models in Multiclass Facial Expression Recognition Based on Eigenface Features 基于特征脸特征的多类面部表情识别分类模型性能分析

Jurnal Sisfokom Pub Date : 2023-11-06 DOI: 10.32736/sisfokom.v12i3.1742

Syefrida Yulina, Heni Rachmawati

{"title":"Performance Analysis of Classification Models in Multiclass Facial Expression Recognition Based on Eigenface Features","authors":"Syefrida Yulina, Heni Rachmawati","doi":"10.32736/sisfokom.v12i3.1742","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1742","url":null,"abstract":"Facial Expression Recognition (FER) is currently widely explored by researchers in the field of Computer Vision. The application of Machine Learning and Deep Learning methods is useful in developing an intelligent system that is accurate in recognizing facial expressions such as emotions. This is inseparable from the type of dataset and classification method used which certainly affects the desired results. To choose the right method, it is necessary to compare the performance of these methods. This study focuses on comparing the performance results of four classification methods namely, Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes Classifier (NBC) on a multiclass dataset for seven classes of facial emotion labels based on Eigenface feature selection uses the Personal Component Analysis (PCA) algorithm. The test parameters used to perform method comparisons are accuracy, recall, precision, f1-score, as well as the Receiving Operating Characteristic (ROC) and Area Under Curve (AUC) curves. The results of the analysis state that the SVM method has the highest accuracy value, while other methods show varying performance based on recall, precision, f1-score, and ROC and AUC analysis. This research was conducted on the FER 2013 dataset which showed that the classification method tested had quite good performance according to the test parameters.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135723748","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}

引用次数: 0

Comparison Analysis of Graph Theory Algorithms for Shortest Path Problem 最短路径问题图论算法的比较分析

Jurnal Sisfokom Pub Date : 2023-11-06 DOI: 10.32736/sisfokom.v12i3.1756

Yosefina Finsensia Riti, Jonathan Steven Iskandar, Hendra Hendra

{"title":"Comparison Analysis of Graph Theory Algorithms for Shortest Path Problem","authors":"Yosefina Finsensia Riti, Jonathan Steven Iskandar, Hendra Hendra","doi":"10.32736/sisfokom.v12i3.1756","DOIUrl":"https://doi.org/10.32736/sisfokom.v12i3.1756","url":null,"abstract":"The Sumba region, Indonesia, is known for its extraordinary natural beauty and unique cultural richness. There are 19 interesting tourist attractions spread throughout the area, but tourists often face difficulties in planning efficient visiting routes. From this case, it can be solved by applying graph theory in terms of searching for the shortest distance which is completed using the shortest path search algorithm. Then these 19 tourist objects are used to build a weighted graph, where the nodes represent the tourist objects and the edges of the graph describe the distance or travel time between these objects. Therefore, this research aims to compare the shortest path search algorithm with parameters to compare the shortest distance results, algorithm complexity and execution time for tourism in the Sumba area. The results of this research involve a comparison of several shortest path search algorithms, with the aim of finding the shortest distance results, algorithm complexity, and execution time for tourism in the Sumba area. Based on the test results of the five algorithms with the parameters that have been prepared, and the findings show that each algorithm has its own characteristics, the results are as follows: Dijkstra's algorithm can be used to calculate the shortest route for single-source and single-destination types. This resembles the Bellman-Ford algorithm, only the Bellman-Ford algorithm can be used simultaneously on graphs that have negative weight values. Meanwhile, the Floyd-Warshall algorithm is suitable for use on the all-pairs type. Then, the Johnson Algorithm can be used to determine the shortest path from all pairs of paths where the destination node is located in the graph. Finally, the Ant Colony algorithm to compute from a node to each pair of destination nodes.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135723762","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}

引用次数: 0