Pub Date : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.4664
I. Sudipa, I. N. T. A. Putra, Dwi Putra Asana, Revan Dwi Hanza
Purpose: CV. Harmoni Permata has several employees, and each employee has a bonus salary. However, in determining who is eligible for the employee salary bonus at CV. Harmoni Permata is still done manually assessment. This causes an error in the calculation because the decision-maker must look at previous historical data to decide.Design/methodology/approach: System design includes systems that can manage user data, position data, criteria data, criteria description data, absences data, task data, and assessment data, which will produce an assessment report. The MOORA method approach is used because it has calculations with minimum and simple math and has a good level of selectivity. Findings/result: The normalization comparison test of the manual calculation of the MOORA method with the system calculation results is the same, with the best five alternative employees who deserve a salary bonus.Originality/value/state of the art: Based on previous research reviews, this study uses the criteria for performance, honesty, attendance, and accuracy by determining the weight based on the type of benefit or cost and the MOORA method in calculating the final value of alternative ranking.
{"title":"Implementation of Fuzzy Multi-Objective Optimization On The Basic Of Ratio Analysis (Fuzzy-MOORA) In Determining The Eligibility Of Employee Salary","authors":"I. Sudipa, I. N. T. A. Putra, Dwi Putra Asana, Revan Dwi Hanza","doi":"10.31315/TELEMATIKA.V18I2.4664","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.4664","url":null,"abstract":"Purpose: CV. Harmoni Permata has several employees, and each employee has a bonus salary. However, in determining who is eligible for the employee salary bonus at CV. Harmoni Permata is still done manually assessment. This causes an error in the calculation because the decision-maker must look at previous historical data to decide.Design/methodology/approach: System design includes systems that can manage user data, position data, criteria data, criteria description data, absences data, task data, and assessment data, which will produce an assessment report. The MOORA method approach is used because it has calculations with minimum and simple math and has a good level of selectivity. Findings/result: The normalization comparison test of the manual calculation of the MOORA method with the system calculation results is the same, with the best five alternative employees who deserve a salary bonus.Originality/value/state of the art: Based on previous research reviews, this study uses the criteria for performance, honesty, attendance, and accuracy by determining the weight based on the type of benefit or cost and the MOORA method in calculating the final value of alternative ranking.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81913328","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 : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.5316
Angelica Amartya Putri, H. Jayadianti, B. Yuwono
Purpose: This study aims to measure success and determine the factors that support or hinder the success of the Jogja Istimewa application.Methodology: This study uses a modified DeLone and McLean Model 2003. The data used are primary data obtained from interviews with the DISKOMINFO and answers to 125 users of the Jogja Istimewa application as respondents in a distributed questionnaire. The results of the questionnaire were processed using SPSS to test the validity, reliability and normality of the data. After that, the data is processed using Structural Equation Modeling (SEM) to test the inner model and outer model which includes hypothesis testing.Result There are nine hypotheses tested using the SEM model. Nine hypotheses were proposed, it was stated that five hypotheses were accepted and four other hypotheses were rejected. the Jogja Istimewa application has a high success rate. The factors that are stated to influence the success of the Jogja Istimewa application are Information Quality, Service Quality, System Quality and User Satisfaction. The factors that are stated to hinder the success of the Jogja Istimewa application are Format of Output and Reliability in the Information Quality variable, the System Quality variable in the Language indicator, and the Charges for System Use indicator on the Intention to Use variable.Value: Based on previous research, this study has a fairly similar reference but different case studies, indicators, and conceptual models to test hypotheses in addition to knowing the factors that hinder and support the success of the Jogja Istimewa application.
目的:本研究的目的是衡量成功，并确定支持或阻碍慢跑运动应用成功的因素。方法:本研究采用改良的DeLone and McLean模型2003。所使用的数据是从对DISKOMINFO的访谈和对Jogja Istimewa应用程序的125名用户的回答中获得的主要数据。问卷结果采用SPSS软件进行处理，检验数据的效度、信度和正态性。然后，利用结构方程模型(SEM)对数据进行内部模型和外部模型的检验，其中包括假设检验。结果用SEM模型检验了9个假设。提出了9个假设，其中5个假设被接受，另外4个假设被拒绝。Jogja Istimewa应用程序有很高的成功率。影响Jogja Istimewa应用程序成功的因素包括信息质量、服务质量、系统质量和用户满意度。阻碍Jogja Istimewa应用程序成功的因素是信息质量变量中的输出格式和可靠性，语言指标中的系统质量变量，以及使用意向变量中的系统使用费用指标。价值:在前人研究的基础上，本研究具有相当相似的参考，但案例研究、指标和概念模型不同，可以检验假设，并了解阻碍和支持Jogja Istimewa应用成功的因素。
{"title":"Evaluation Of Jogja Application Success From User's Perspective Using Development of Delone And Mclean Models To Support The Realization Of The Smart Province","authors":"Angelica Amartya Putri, H. Jayadianti, B. Yuwono","doi":"10.31315/TELEMATIKA.V18I2.5316","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.5316","url":null,"abstract":"Purpose: This study aims to measure success and determine the factors that support or hinder the success of the Jogja Istimewa application.Methodology: This study uses a modified DeLone and McLean Model 2003. The data used are primary data obtained from interviews with the DISKOMINFO and answers to 125 users of the Jogja Istimewa application as respondents in a distributed questionnaire. The results of the questionnaire were processed using SPSS to test the validity, reliability and normality of the data. After that, the data is processed using Structural Equation Modeling (SEM) to test the inner model and outer model which includes hypothesis testing.Result There are nine hypotheses tested using the SEM model. Nine hypotheses were proposed, it was stated that five hypotheses were accepted and four other hypotheses were rejected. the Jogja Istimewa application has a high success rate. The factors that are stated to influence the success of the Jogja Istimewa application are Information Quality, Service Quality, System Quality and User Satisfaction. The factors that are stated to hinder the success of the Jogja Istimewa application are Format of Output and Reliability in the Information Quality variable, the System Quality variable in the Language indicator, and the Charges for System Use indicator on the Intention to Use variable.Value: Based on previous research, this study has a fairly similar reference but different case studies, indicators, and conceptual models to test hypotheses in addition to knowing the factors that hinder and support the success of the Jogja Istimewa application.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90856985","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 : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.4823
Augyeris Lioga Seandrio, A. H. Pratomo, Mangaras Yanu Florestiyanto
Tujuan: Membantu pengajar melakukan monitoring emosi siswa dengan menerapkan metode Convolutional Neural Network pada aplikasi, serta mengetahui akurasi dalam melakukan pengenalan ekspresi wajah.Perancangan/metode/pendekatan: Menggunakan Convolutional Neural Network untuk mengklasifikasi pengolahan berupa citra. Pengembangan sistem menggunakan metode prototype.Hasil: Berdasarkan hasil pengujian yang dilakukan dengan menggunakan 3589 data ekspresi dasar manusia mendapatkan nilai akurasi sebesar 70,46%, nilai presisi sebesar 71% dan nilai recall sebesar 70%.Keaslian/ state of the art: Berdasarkan penelitian sebelumnya, penelitian ini mempunyai karakteristik yang relatif serupa dalam tema penelitian. Namun memiliki perbedaan pada metode penelitan, perangkat yang digunakan, dan hasil keluaran penelitian.Pada penelitian sebelumnya, dengan objek yang sama yaitu wajah dan emosi wajah, pada metode yang digunakan, perangkat dalam pengambilan citra emosi dan wajah, serta langkah-langkah dalam prosesnya pun berbeda. Pada penelitian ini emosi pada wajah diidentifikasi melalui citra yang diambil secara real-time menggunakan kamera dan dengan menerapkan metode Convolutional Neural Network dengan arsitektur visual group geometry (VGG) dengan 11, 13, 16 dan 19 lapisan yang akan menghasilkan probabilitas ekspresi dalam 7 ekspresi dasar manusia beserta kategorinya.
{"title":"Implementation of Convolutional Neural Network (CNN) in Facial Expression Recognition","authors":"Augyeris Lioga Seandrio, A. H. Pratomo, Mangaras Yanu Florestiyanto","doi":"10.31315/TELEMATIKA.V18I2.4823","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.4823","url":null,"abstract":"Tujuan: Membantu pengajar melakukan monitoring emosi siswa dengan menerapkan metode Convolutional Neural Network pada aplikasi, serta mengetahui akurasi dalam melakukan pengenalan ekspresi wajah.Perancangan/metode/pendekatan: Menggunakan Convolutional Neural Network untuk mengklasifikasi pengolahan berupa citra. Pengembangan sistem menggunakan metode prototype.Hasil: Berdasarkan hasil pengujian yang dilakukan dengan menggunakan 3589 data ekspresi dasar manusia mendapatkan nilai akurasi sebesar 70,46%, nilai presisi sebesar 71% dan nilai recall sebesar 70%.Keaslian/ state of the art: Berdasarkan penelitian sebelumnya, penelitian ini mempunyai karakteristik yang relatif serupa dalam tema penelitian. Namun memiliki perbedaan pada metode penelitan, perangkat yang digunakan, dan hasil keluaran penelitian.Pada penelitian sebelumnya, dengan objek yang sama yaitu wajah dan emosi wajah, pada metode yang digunakan, perangkat dalam pengambilan citra emosi dan wajah, serta langkah-langkah dalam prosesnya pun berbeda. Pada penelitian ini emosi pada wajah diidentifikasi melalui citra yang diambil secara real-time menggunakan kamera dan dengan menerapkan metode Convolutional Neural Network dengan arsitektur visual group geometry (VGG) dengan 11, 13, 16 dan 19 lapisan yang akan menghasilkan probabilitas ekspresi dalam 7 ekspresi dasar manusia beserta kategorinya.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87502074","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 : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.5508
Wilis Kaswidjanti, B. Yuwono, N. Azizah, Nurheri Cahyana
Purpose:This study aims to detect the presence of pneumonia or not in thorax x-ray images using the Gray Level Co-Occurence Matrix (GLCM) method as well as find out the accuracy of the accuracy of pneumonia detection accuracy.Design/methodology/approach:The process of detecting pneumonia in thorax x-ray images can use Content Based Image Retriveal (CBIR). CBIR is an image search method by comparing the input image feature with the image feature in the database. Extraction features x-ray texture of thorax in pneumonia detection using Color Histogram, Discrete Cosine Transform and Gray Level Cooccurence Matrix (GLCM). From the day of extraction the feature will be carried out similarity measurements with database images using Euclidean Distance..Findings/result: The test results showed that the GLCM extraction feature with euclidean distance similarity measurements gained 95% accuracy on 100 training data and 20 test data, with the number of images displayed 6. Whereas when testing using data that has been trained produces 100% accuracy.Originality/value/state of the art:The difference between this study and previous research is in the pre-processing method section of imagery. This pre-processing process, x-ray image of thorax is carried out color histogram and discrete cosine transform process. Then continued the extraction of features using GLCM. The output of this system is the result of detection whether normal or pneumonia. Tujuan:Penelitian ini bertujuan untuk mendeteksi adanya Pneumonia atau tidak pada citra x-ray thorax menggunakan metode Gray Level Co-Occurence Matrix (GLCM) serta mengetahui akurasi tingkat akurasi deteksi pneumonia.Perancangan/metode/pendekatan:Proses deteksi penyakit Pneumonia pada citra x-ray thorax dapat menggunakan Content Based Image Retriveal (CBIR). CBIR adalah suatu metode pencarian citra dengan melakukan perbandingan antara fitur citra input dengan fitur citra yang ada didalam database. Ekstraksi fitur tekstur x-ray thorax dalam deteksi pneumonia menggunakan Color Histogram, Discrete Cosine Transform dan Gray Level Cooccurence Matrix (GLCM). Dari hari ekstraksi fitur tersebut akan dilakukan pengukuran kemiripan dengan citra database menggunakan jarak Euclidean Distance.Hasil:Hasil pengujian menunjukkan bahwa fitur ekstraksi GLCM dengan pengukuran kemiripan Euclidean Distance diperoleh akurasi sebesar 95% pada data latih 100 dan data uji 20, dengan jumlah citra yang ditampilkan 6. Sedangkan bila pengujian menggunakan data yang sudah dilatihkan menghasilkan akurasi 100%.State of the art:Perbedaan penelitian ini dengan penelitian sebelumnya adalah pada bagian metode pre processing citra. Proses pre processing ini, citra x-ray thorax di lakukan proses Color Histogram dan Discrete Cosine Transform. Kemudian dilanjutkan ekstraksi fitur menggunakan GLCM. Output dari sistem ini berupa hasil deteksi apakah normal atau pneumonia.
目的:本研究旨在利用灰度共现矩阵(GLCM)方法检测胸腔x线图像是否存在肺炎，并找出肺炎检测准确率的准确性。设计/方法/方法:在胸部x线图像中检测肺炎的过程可以使用基于内容的图像检索(CBIR)。CBIR是一种将输入的图像特征与数据库中的图像特征进行比较的图像搜索方法。利用颜色直方图、离散余弦变换和灰度共生矩阵(GLCM)提取肺炎检测中胸部x射线特征纹理。从提取特征之日起，将使用欧几里得距离与数据库图像进行相似性测量。发现/结果:测试结果表明，基于欧氏距离相似度量的GLCM提取特征在100个训练数据和20个测试数据上获得了95%的准确率，显示的图像数量为6。然而，当使用经过训练的数据进行测试时，会产生100%的准确性。原创性/价值/艺术水平:本研究与以往研究的不同之处在于图像预处理方法部分。本预处理过程中，对胸部x射线图像进行了颜色直方图和离散余弦变换处理。然后继续使用GLCM进行特征提取。该系统的输出是检测正常或肺炎的结果。图juan:Penelitian ini bertujuan untuk mendeteksi adanya肺炎，x线胸透，蒙古纳坎方法，灰度共现矩阵(GLCM)，显示蒙古纳坎与阿古纳坎肺炎。Perancangan/ method /pendekatan:研究基于内容的图像检索(Content Based Image retrieval, CBIR)的肺炎x线胸片检测方法。CBIR adalah suatu方法，彭安柑橘，登根，melakukan, perbandbandan和antara fitcitra输入登根fitcitra yang ada didalam数据库。彩色直方图，离散余弦变换和灰度共生矩阵(GLCM)。欧几里得距离(欧几里得距离)哈西尔:哈西尔企鹅menunjukkan bahwa fitur ekstraksi GLCM登干企鹅kmiripan欧几里得距离diperoleh akurasi sebesar 95%帕达数据latih 100丹数据uji 20，登干jumlah citra yang ditampilkan 6。Sedangkan bila penguin menggunakan数据yang sudah dilatihkan menghasilkan akurasi 100%。现有技术现状:Perbedaan penelitian ini dengan penelitian sebelumnya adalah padbagian预处理柑橘的方法。过程预处理ini，柠檬酸x射线胸片迪拉坎处理颜色直方图和离散余弦变换。Kemudian dilanjutkan ekstraksi fitur menggunakan GLCM。输出达里系统异常，可诊断为正常肺炎。
{"title":"Content Based Image Retrieval Using Gray Level Co-Occurrence Matrix to Detect Pneumonia in X-Ray Thorax Image","authors":"Wilis Kaswidjanti, B. Yuwono, N. Azizah, Nurheri Cahyana","doi":"10.31315/TELEMATIKA.V18I2.5508","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.5508","url":null,"abstract":"Purpose:This study aims to detect the presence of pneumonia or not in thorax x-ray images using the Gray Level Co-Occurence Matrix (GLCM) method as well as find out the accuracy of the accuracy of pneumonia detection accuracy.Design/methodology/approach:The process of detecting pneumonia in thorax x-ray images can use Content Based Image Retriveal (CBIR). CBIR is an image search method by comparing the input image feature with the image feature in the database. Extraction features x-ray texture of thorax in pneumonia detection using Color Histogram, Discrete Cosine Transform and Gray Level Cooccurence Matrix (GLCM). From the day of extraction the feature will be carried out similarity measurements with database images using Euclidean Distance..Findings/result: The test results showed that the GLCM extraction feature with euclidean distance similarity measurements gained 95% accuracy on 100 training data and 20 test data, with the number of images displayed 6. Whereas when testing using data that has been trained produces 100% accuracy.Originality/value/state of the art:The difference between this study and previous research is in the pre-processing method section of imagery. This pre-processing process, x-ray image of thorax is carried out color histogram and discrete cosine transform process. Then continued the extraction of features using GLCM. The output of this system is the result of detection whether normal or pneumonia. Tujuan:Penelitian ini bertujuan untuk mendeteksi adanya Pneumonia atau tidak pada citra x-ray thorax menggunakan metode Gray Level Co-Occurence Matrix (GLCM) serta mengetahui akurasi tingkat akurasi deteksi pneumonia.Perancangan/metode/pendekatan:Proses deteksi penyakit Pneumonia pada citra x-ray thorax dapat menggunakan Content Based Image Retriveal (CBIR). CBIR adalah suatu metode pencarian citra dengan melakukan perbandingan antara fitur citra input dengan fitur citra yang ada didalam database. Ekstraksi fitur tekstur x-ray thorax dalam deteksi pneumonia menggunakan Color Histogram, Discrete Cosine Transform dan Gray Level Cooccurence Matrix (GLCM). Dari hari ekstraksi fitur tersebut akan dilakukan pengukuran kemiripan dengan citra database menggunakan jarak Euclidean Distance.Hasil:Hasil pengujian menunjukkan bahwa fitur ekstraksi GLCM dengan pengukuran kemiripan Euclidean Distance diperoleh akurasi sebesar 95% pada data latih 100 dan data uji 20, dengan jumlah citra yang ditampilkan 6. Sedangkan bila pengujian menggunakan data yang sudah dilatihkan menghasilkan akurasi 100%.State of the art:Perbedaan penelitian ini dengan penelitian sebelumnya adalah pada bagian metode pre processing citra. Proses pre processing ini, citra x-ray thorax di lakukan proses Color Histogram dan Discrete Cosine Transform. Kemudian dilanjutkan ekstraksi fitur menggunakan GLCM. Output dari sistem ini berupa hasil deteksi apakah normal atau pneumonia.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79952444","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 : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.5506
P. P. Arhandi, Irsyad Arief Mashudi, Fuad Adi Nugroho
Purpose: Create a system to monitor website availability automatically using web scraping and raspberry piDesign/methodology/approach: This system successfully checks website availability using various ISPs with an accuracy of more than 90%.Findings/result: This system successfully checks website availability using various ISPs with an accuracy of more than 90%.Originality/value/state of the art: The contribution of this research is to create systems and agents that collaborate automatically to check website availability. Tujuan: Membuat sebuah sistem untuk melakukan pemantauan ketersediaan situs web secara otomatis menggunakan web scraping dan raspberyy piPerancangan/metode/pendekatan: Pada penelitian ini dibuat sebuah sistem utama sebagai pusat data dan beberapa agent menggunakan raspberry pi. Sistem utama dibangun menggunakan codeigniter dan web scraping di raspberry pi dilakukan menggunakan node js serta REST API untuk komunikasi antara agent dan sistem utama.Hasil: Sistem ini berhasil melakukan pengecekan ketersediaan situs web menggunakan berbagai ISP dengan keakuratan lebih dari 90%.Keaslian/ state of the art: Kontribusi penelitian ini adalah membuat sistem dan agen yang berkolaborasi secara otomatis untuk mengecek ketersediaan situs web.
{"title":"Automated Website Monitoring System Using Web Scraping and Raspberry Pi","authors":"P. P. Arhandi, Irsyad Arief Mashudi, Fuad Adi Nugroho","doi":"10.31315/TELEMATIKA.V18I2.5506","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.5506","url":null,"abstract":"Purpose: Create a system to monitor website availability automatically using web scraping and raspberry piDesign/methodology/approach: This system successfully checks website availability using various ISPs with an accuracy of more than 90%.Findings/result: This system successfully checks website availability using various ISPs with an accuracy of more than 90%.Originality/value/state of the art: The contribution of this research is to create systems and agents that collaborate automatically to check website availability. Tujuan: Membuat sebuah sistem untuk melakukan pemantauan ketersediaan situs web secara otomatis menggunakan web scraping dan raspberyy piPerancangan/metode/pendekatan: Pada penelitian ini dibuat sebuah sistem utama sebagai pusat data dan beberapa agent menggunakan raspberry pi. Sistem utama dibangun menggunakan codeigniter dan web scraping di raspberry pi dilakukan menggunakan node js serta REST API untuk komunikasi antara agent dan sistem utama.Hasil: Sistem ini berhasil melakukan pengecekan ketersediaan situs web menggunakan berbagai ISP dengan keakuratan lebih dari 90%.Keaslian/ state of the art: Kontribusi penelitian ini adalah membuat sistem dan agen yang berkolaborasi secara otomatis untuk mengecek ketersediaan situs web. ","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79909372","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 : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.4786
Tresna Maulana Fahrudin, P. Riyantoko, K. M. Hindrayani, M. H. P. Swari
Purpose: The research proposed an approach for grouping hospital inpatient service efficiency that have the same characteristics into certain clusters based on BOR, BTO, TOI, and AvLOS indicators using Agglomerative Hierarchical Clustering.Design/methodology/approach: Applying Agglomerative Hierarchical Clustering with dissimilarity measures such as single linkage, complete linkage, average linkage, and ward linkage.Findings/result: The experiment result has shown that ward linkage was given a quite good score of silhouette coefficient reached 0.4454 for the evaluation of cluster quality. The cluster formed using ward linkage was more proportional than the other dissimilarity measures. Ward linkage has generated cluster 0 consists of 23 members, cluster 1 consists of 34 members, while both of cluster 2 and 3 consists of only 1 member respectively. The experiment reported that each cluster had problems with inpatient indicators that were not ideal and even exceeded the ideal limit, but cluster 0 generated the ideal BOR and TOI parameters, both reached 52.17% (12 of 23 hospital inpatient) and 78.36% (18 of 23 hospital inpatient) respectively.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce more proportional, representative and quality clusters in mapping hospital inpatient service efficiency that have the same characteristics into certain clusters using Agglomerative Hierarchical Clustering Method compared to the K-means Clustering Method which is often trapped in local optima.
{"title":"Cluster Analysis of Hospital Inpatient Service Efficiency Based on BOR, BTO, TOI, AvLOS Indicators using Agglomerative Hierarchical Clustering","authors":"Tresna Maulana Fahrudin, P. Riyantoko, K. M. Hindrayani, M. H. P. Swari","doi":"10.31315/TELEMATIKA.V18I2.4786","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.4786","url":null,"abstract":"Purpose: The research proposed an approach for grouping hospital inpatient service efficiency that have the same characteristics into certain clusters based on BOR, BTO, TOI, and AvLOS indicators using Agglomerative Hierarchical Clustering.Design/methodology/approach: Applying Agglomerative Hierarchical Clustering with dissimilarity measures such as single linkage, complete linkage, average linkage, and ward linkage.Findings/result: The experiment result has shown that ward linkage was given a quite good score of silhouette coefficient reached 0.4454 for the evaluation of cluster quality. The cluster formed using ward linkage was more proportional than the other dissimilarity measures. Ward linkage has generated cluster 0 consists of 23 members, cluster 1 consists of 34 members, while both of cluster 2 and 3 consists of only 1 member respectively. The experiment reported that each cluster had problems with inpatient indicators that were not ideal and even exceeded the ideal limit, but cluster 0 generated the ideal BOR and TOI parameters, both reached 52.17% (12 of 23 hospital inpatient) and 78.36% (18 of 23 hospital inpatient) respectively.Originality/value/state of the art: Based on previous research, this study provides an alternative to produce more proportional, representative and quality clusters in mapping hospital inpatient service efficiency that have the same characteristics into certain clusters using Agglomerative Hierarchical Clustering Method compared to the K-means Clustering Method which is often trapped in local optima. ","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74734109","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 : 2021-10-04DOI: 10.31315/TELEMATIKA.V18I2.5067
Ahmad Irfan Abdullah, A. Priadana, M. Muhajir, Syahrir Nawir Nur
Purpose: This study aims to apply the web data extraction method to extract student Instagram account data and the K-Means data mining method to perform clustering automatically to determine the best cluster of students' Instagram accounts as influencers for new student admissions.Design/methodology/approach: This study implemented the web data extraction method to extract student Instagram account data. This study also implemented a data mining method called K-Means to cluster data and the Silhouette Coefficient method to determine the best number of clusters.Findings/result: This study has succeeded in determining the seven best student accounts from 100 accounts that can be used as influencers for new student admissions with the highest Silhouette Score for the number of influencers selected between 5-10, which is 0.608 of the 22 clusters.Originality/value/state of the art: Research related to the determination of the best cluster of students' Instagram accounts as new student admissions influencers using web data extraction and K-Means has never been done in previous studies.
{"title":"Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer","authors":"Ahmad Irfan Abdullah, A. Priadana, M. Muhajir, Syahrir Nawir Nur","doi":"10.31315/TELEMATIKA.V18I2.5067","DOIUrl":"https://doi.org/10.31315/TELEMATIKA.V18I2.5067","url":null,"abstract":"Purpose: This study aims to apply the web data extraction method to extract student Instagram account data and the K-Means data mining method to perform clustering automatically to determine the best cluster of students' Instagram accounts as influencers for new student admissions.Design/methodology/approach: This study implemented the web data extraction method to extract student Instagram account data. This study also implemented a data mining method called K-Means to cluster data and the Silhouette Coefficient method to determine the best number of clusters.Findings/result: This study has succeeded in determining the seven best student accounts from 100 accounts that can be used as influencers for new student admissions with the highest Silhouette Score for the number of influencers selected between 5-10, which is 0.608 of the 22 clusters.Originality/value/state of the art: Research related to the determination of the best cluster of students' Instagram accounts as new student admissions influencers using web data extraction and K-Means has never been done in previous studies.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88920037","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 : 2021-08-26DOI: 10.35671/telematika.v14i2.1262
Nur Nafi’iyah, Jauharul Maknun
In such conditions, it is necessary to have a system that can automatically classify plant species or identify types of plant diseases using either machine learning or deep learning. The plant classification system for ordinary people who are not familiar with the field of crops is not an easy job, it requires in-depth knowledge of the field from the experts. This study proposes a system for identifying mango plant species based on leaves using the CNN method. The reason for proposing the CNN method from previous research is that the CNN method produces good accuracy. Most previous studies to classify plant species use the leaves of the plant. The purpose of this study is to propose a CNN architectural model in classifying mango species based on leaf imagery. The input image of colored mango tree leaves measuring 224x224 is trained based on the CNN architectural model that was built. There are 4 CNN architectural models proposed in the study and 1 transfer learning InceptionV4. Based on the evaluation test results of the proposed CNN architectural model, that the best architectural model is the third. The number of parameters of the third CNN architecture is 1,245,989 with loss values and accuracy during evaluation are 1,431 and 0.55. The largest number of parameters is transfer learning InceptionV3 21,802,784, but transfer learning shows the lowest accuracy value and the highest loss, namely 0.2, and 1.61.
{"title":"CNN Architecture for Classifying Types of Mango Based on Leaf Images","authors":"Nur Nafi’iyah, Jauharul Maknun","doi":"10.35671/telematika.v14i2.1262","DOIUrl":"https://doi.org/10.35671/telematika.v14i2.1262","url":null,"abstract":"In such conditions, it is necessary to have a system that can automatically classify plant species or identify types of plant diseases using either machine learning or deep learning. The plant classification system for ordinary people who are not familiar with the field of crops is not an easy job, it requires in-depth knowledge of the field from the experts. This study proposes a system for identifying mango plant species based on leaves using the CNN method. The reason for proposing the CNN method from previous research is that the CNN method produces good accuracy. Most previous studies to classify plant species use the leaves of the plant. The purpose of this study is to propose a CNN architectural model in classifying mango species based on leaf imagery. The input image of colored mango tree leaves measuring 224x224 is trained based on the CNN architectural model that was built. There are 4 CNN architectural models proposed in the study and 1 transfer learning InceptionV4. Based on the evaluation test results of the proposed CNN architectural model, that the best architectural model is the third. The number of parameters of the third CNN architecture is 1,245,989 with loss values and accuracy during evaluation are 1,431 and 0.55. The largest number of parameters is transfer learning InceptionV3 21,802,784, but transfer learning shows the lowest accuracy value and the highest loss, namely 0.2, and 1.61.","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76205081","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 : 2021-08-26DOI: 10.35671/telematika.v14i2.1221
I. Riadi, R. Umar, Tri Lestari
The digital era is an era everyone has used technology and they are connected to each other very easily. The Smart Payment application is one of the applications that is developing in the digital era. This application is not equipped with security, so there is a concern that hackers will try to change user or even change user data. One of the possible attacks on this application is a cross-site attack (XSS). It is a code injection attack on the user side. Security in the Smart Payment application needs to be improved so that data integrity is maintained. In this research, security optimization is carried out by implementing blockchain. Blockchain has the advantage in terms of security with the concept of decentralization by utilizing a consensus algorithm that can eliminate and make improvements to data changes made by hackers. The result obtained from this study is the implementation of blockchain to maintain the security of payment transaction data on the Smart Payment application from XSS attacks. It is proven by the results of the vulnerability before and after blockchain implementation. Before the implementation of the vulnerability is found, 1 XSS vulnerability had a high level of overall risk. Meanwhile, the result of the vulnerability after blockchain implementation was not found from XSS attacks (the XSS vulnerability was 0 or not found).
{"title":"Smart Payment Application Security Optimization from Cross-Site Scripting (XSS) Attacks Based on Blockchain Technology","authors":"I. Riadi, R. Umar, Tri Lestari","doi":"10.35671/telematika.v14i2.1221","DOIUrl":"https://doi.org/10.35671/telematika.v14i2.1221","url":null,"abstract":"The digital era is an era everyone has used technology and they are connected to each other very easily. The Smart Payment application is one of the applications that is developing in the digital era. This application is not equipped with security, so there is a concern that hackers will try to change user or even change user data. One of the possible attacks on this application is a cross-site attack (XSS). It is a code injection attack on the user side. Security in the Smart Payment application needs to be improved so that data integrity is maintained. In this research, security optimization is carried out by implementing blockchain. Blockchain has the advantage in terms of security with the concept of decentralization by utilizing a consensus algorithm that can eliminate and make improvements to data changes made by hackers. The result obtained from this study is the implementation of blockchain to maintain the security of payment transaction data on the Smart Payment application from XSS attacks. It is proven by the results of the vulnerability before and after blockchain implementation. Before the implementation of the vulnerability is found, 1 XSS vulnerability had a high level of overall risk. Meanwhile, the result of the vulnerability after blockchain implementation was not found from XSS attacks (the XSS vulnerability was 0 or not found).","PeriodicalId":31716,"journal":{"name":"Telematika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76065664","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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