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INDONESIA
Telematika : Jurnal Informatika dan Teknologi Informasi
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 1829667X     EISSN : 24609021     DOI : 10.31315
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 361 Documents
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Backpropagation with BFGS Optimizer for Covid-19 Prediction Cases in Surabaya Zuraidah Fitriah; Mohamad Handri Tuloli; Syaiful Anam; Noor Hidayat; Indah Yanti; Dwi Mifta Mahanani
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5454

Abstract

Covid-19 is a new type of corona virus called SARS-CoV-2. One of the cities that has contributed the most to infected Covid-19 cases in Indonesia is Surabaya, East Java. Predicting the Covid-19 is the important thing to do. One of the prediction methods is Artificial Neural Network (ANN). The backpropagation algorithm is one of the ANN methods that has been successfully used in various fields. However, the performance of backpropagation is depended on the architecture and optimization method. The standard backpropagation algorithm is optimized by gradient descent method. The Broyden - Fletcher - Goldfarb - Shanno (BFGS) algorithm works faster then gradient descent. This paper was predicting the Covid-19 cases in Surabaya using backpropagation with BFGS. Several scenarios of backpropagation parameters were also tested to produce optimal performance. The proposed method gives better results with a faster convergence then the standard backpropagation algorithm for predicting the Covid-19 cases in Surabaya.
Automated Website Monitoring System Using Web Scraping and Raspberry Pi Putra Prima Arhandi; Irsyad Arief Mashudi; Fuad Adi Nugroho
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5506

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. 
Implementation of Fuzzy Multi-Objective Optimization On The Basic Of Ratio Analysis (Fuzzy-MOORA) In Determining The Eligibility Of Employee Salary I Gede Iwan Sudipa; I Nyoman Tri Anindia Putra; Dwi Putra Asana; Revan Dwi Hanza
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.4664

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.
Development of a Group Decision Support System with the Multi-Stage Multi-Attribute Group Decision Making (MS-MAGDM) Method on the Intelligent Warehouse Management System Simon Pulung Nugroho
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5507

Abstract

Purpose: to find a solution with MS-DAGDM for the problem of different criteria used by decision maker at each stage.Design/methodology/approach: This research was conducted using literature review with a study of the theory of decision-making methods, group decisions, suplier selection processes, and factors that influence decisions in the context of warehousing and MS-MAGDM to solve the problems.Findings/result: This research find that GDSS prototypes which have four methods in making decisions. First, Analytical Hierarchy Process for weighting the division head level. Second, TOPSIS for divison head level decisions. Third, Hybrid Weight Averaging (HWA) manager level. Fourth, Time Weight Averaging (TWA) for manager level decisions.Originality/value/state of the art:The decision-making model of the GDSS system in this study combines four methods at each level of management. The section head level uses AHP for the level weighting and TOPSIS for decision making. Level managers use Hybrid Weight Averaging (HWA) weighting and Time Weight Averaging (TWA) for decisions. The combination of these methods is carried out using a Poisson distribution, for HWA and TWA operators to combine individual decisions into group decisions. Tujuan: Fokus penelitian ini adalah mencari solusi dengan MS-MAGDM untuk permasalahan perbedaan kriteria yang dipergunakan pembuat keputusan dalam setiap stage.Perancangan/metode/pendekatan: Metode yang digunakan yaitu kajian kepustakaan dengan kajian terhadap teori metode pembuatan keputusan, keputusan kelompok, proses pemilihan supplier, dan faktor yang berpengaruh pada keputusan dalam konteks pergudangan serta MS-MAGDM untuk menyelesaikan permasalahan tersebut.Hasil: Hasil penelitian ini berupa purwarupa GDSS yang memiliki 4 metode dalam pembuatan keputusan yaitu Analytical Hierarchi Process (AHP) untuk pembobotan level kepala bagian, TOPSIS untuk keputusan level kepala bagian, Hybrid Weight Averaging (HWA) pembobotan pada level manager dan Time Weight Averaging (TWA) untuk keputusan level managerKeaslian/ state of the art:Model pengambilan keputusan sistem GDSS penelitian ini menggabungkan 4 metode pada setiap tingkatan manajemen. Level kepala bagian menggunakan AHP untuk pembobotan level dan TOPSIS untuk pembuatan keputusan. Level manager menggunakan Hybrid Weight Averaging (HWA) pembobotan dan Time Weight Averaging (TWA) untuk keputusan. Penggabungan metode dilakukan menggunakan distribusi Poisson, untuk operator HWA dan TWA guna memadukan keputusan individu mejadi keputusan kelompok.
Development Of Executive Information Systems Of Cirebon City Government (Case Study: Department Of Communication, Informatics And Statistics) Muhammad Nur Hendra Alvianto; Herry Sofyan; Juwairiah Juwairiah
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.4844

Abstract

Purpose: Developing an executive information system to meet the information needs of the Mayor, Deputy Mayor, Regional Secretary and the heads of SKPD within the Cirebon City Government.Design / method / approach: Using the drill down method for solving information on executive information systems and the GRAPPLE system development methodResult: The development of an executive information system in Cirebon city government has assisted the executive, consisting of mayors, deputy mayors and regional secretaries and middle executives consisting of skpd within the Cirebon city government. Cirebon city government executive information system consists of five sectors in the city of Cirebon, namely economy, health, population, education and government. The results of the validation testing are 100% and the average user acceptance testing results are 85.29%.Authenticity / state of the art: Based on previous research, this study has the same characteristics but in the development of executive information systems it has differences in objects and methods of software development.
Content Based Image Retrieval Using Gray Level Co-Occurrence Matrix to Detect Pneumonia in X-Ray Thorax Image Wilis Kaswidjanti; Bambang Yuwono; Nisa’ul Azizah; Nur Heri Cahyana
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5508

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.
Evaluation Of Jogja Application Success From User's Perspective Using Development of Delone And Mclean Models To Support The Realization Of The Smart Province Angelica Amartya Putri; Herlina Jayadianti; Bambang Yuwono
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5316

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.
Data Mining for Determining The Best Cluster Of Student Instagram Account As New Student Admission Influencer Ahmad Irfan Abdullah; Adri Priadana; Muhajir Muhajir; Syahrir Nawir Nur
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.5067

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.
Cluster Analysis of Hospital Inpatient Service Efficiency Based on BOR, BTO, TOI, AvLOS Indicators using Agglomerative Hierarchical Clustering Tresna Maulana Fahrudin; Prismahardi Aji Riyantoko; Kartika Maulida Hindrayani; Made Hanindia Prami Swari
Telematika Vol 18, No 2 (2021): Edisi Juni 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i2.4786

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. 
Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network Irvan Denata; Tedy Rismawan; Ikhwan Ruslianto
Telematika Vol 18, No 3 (2021): Edisi Oktober 2021
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v18i3.5541

Abstract

Orange is a type of fruit that is easily found in Sambas Regency. The types that are widely sold are Siam oranges, madu susu and susu. Each type of orange has a different quality and a different price. The price difference often results in fraud committed by traders against buyers to the detriment of the buyer. This is because differentiating types of oranges based on the appearance of the fruit does not have a standard. Therefore, in this study, a citrus fruit classification system was created based on images by implementing deep learning. The method of deep learning used in this research is Convolutional Neural Network (CNN) with AlexNet architecture. The types of oranges that will be observed are madu oranges, madu susu, and siam. The data used are 2250 images of oranges with each class totaling 750 images with a size of 227x227 pixels. The training data is 1575 images and the test data is 675 images. The training is carried out with a total of 10 epochs and each epoch will produce a model. System testing is carried out based on the model generated in the training process. Each model will be observed results in the form of accuracy which is calculated using a confusion matrix. The most optimal model was generated from training in epoch the 9th which resulted in an accuracy of 94.81%.

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