1,721,005 research outputs found
PERBANDINGAN METODE WEIGHTED PRODUCT DAN WEIGHTED SUM MODEL DALAM PEMILIHAN PERGURUAN SWASTA TERBAIK JURUSAN KOMPUTER
Full text link<p>Sistem Pendukung Keputusan (SPK) merupakan suatu sistem yang dapat membantu seseorang dalam mengambil keputusan yang dengan lebih <em>efektif</em> dan <em>efisien</em>. Dengan adanya sistem ini, permasalahan yang di hadapi dapat di selesaikan, seperti penentuan perguruan tinggi swasta terbaik. Ada beberpa metode yang dapat di gunakan dalam membangun suatu SPK seperti <em>Metode Weighted Product</em> dan <em>Weighted Sum Model. Metode Weighted Product (WP)</em> menggunakan perkalian untuk menghubungkan rating atribut, dimana rating setiap harus di pangkatkan dulu dengan bobot atribut yang bersangkutan. <em>Metode Weighted Sum Model</em> (WSM) merupakan penjumlahan dari perkalian rating atribut dengan bobot atribut. WP dan WSM merupakan metode yang sederhana, dimana penggunaannya mudah untuk di pahami, seperti dalam SPK penentuan perguruan tinggi swasta terbaik di kota pematangsiantar. Penelitian ini menggunakan metode WP dan WSM. Dalam penentuan kualitas perguruan tinggi, ada beberapa kriteria yang menjadi dasar pengambilan keputusan antara lain jumlah jurusan komputer, biaya kuliah, lingkungan kampus, jumlah program beasiswa, dan akreditasi BAN PT. Adapun hasil dalam penelitian ini adalah hasil pilihan pengguna sistem dengan nilai kriteria yang di tentukan sendiri oleh pengguna, dan hasilnya akan diurutkan dari nilai yang tertinggi hingga terendah, sehingga pengguna lebih mudah mengambil keputusan dengan melihat hasil tersebut.</p><p><strong>Kata kunci :</strong> Sistem Pendukung Keputusan, <em>Weighted Product</em>, <em>Weighted Sum Model</em>, Perguruan Tinggi.</p></jats:p
RETRACTED : Decision support system in Predicting the Best teacher with Multi Atribute Decesion Making Weighted Product (MADMWP) Method
No full textFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Artificial Intelligence Research to article entitled “Decision support system in Predicting the Best teacher with Multi-Attribute Decision Making Weighted Product (MADMWP) Method” Vol 1, No 1, pp. 47-53, June 2017, DOI: https://doi.org/10.29099/ijair.v1i1.1This paper has been found to be in violation of the International Journal of Artificial Intelligence Research Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in JURASIK(Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol. 1, No. 1, pp. 56-63, 2016, http://ejurnal.tunasbangsa.ac.id/index.php/jurasik/article/view/9The document and its content have been removed from International Journal of Artificial Intelligence Research, and reasonable effort should be made to remove all references to this article.</jats:p
Relevansi Konsepsi Rahmatan Lil Alamin dengan Keragaman Umat Beragama
Full text linkThe impression of western world to the eastern has not shown a positive proportion. It always considers the latter as a minor. It is more so after the incident of WTC 9 September 2001. The eastern or as an assertive known as a terrorist, unhuman, and far from humane proportion. While in Islam itself, there is rahmatan li al-‘alamin. These things make the researcher interested in examining deeper what it means to be a rahmatan li al-‘alamin in Koran as a source of that word. Apart from the phrase rahmatan li al-‘alamin, a rahmat word is not connected to the li al-‘alamin word (universe) in the Koran. What also precisely is the difference between grace (rahmat) and rahmatan li al-‘alamin. Indonesia is a plural nation. That is not only ethnic, language, and custom tradition, but also religion. How is the relationship between faith in diversity frame? Is there relevance of li al-‘alamin in the diversity frame of religious people? Some questions need to search for answer in various reverence. The nation's founders have been proud not to obtrude by doing sharia for the adherents is a space for tolerance and religious people
Relevansi Konsepsi Rahmatan Lil Alamin dengan Keragaman Umat Beragama
Full text linkThe impression of western world to the eastern has not shown a positive proportion. It always considers the latter as a minor. It is more so after the incident of WTC 9 September 2001. The eastern or as an assertive known as a terrorist, unhuman, and far from humane proportion. While in Islam itself, there is rahmatan li al-‘alamin. These things make the researcher interested in examining deeper what it means to be a rahmatan li al-‘alamin in Koran as a source of that word. Apart from the phrase rahmatan li al-‘alamin, a rahmat word is not connected to the li al-‘alamin word (universe) in the Koran. What also precisely is the difference between grace (rahmat) and rahmatan li al-‘alamin. Indonesia is a plural nation. That is not only ethnic, language, and custom tradition, but also religion. How is the relationship between faith in diversity frame? Is there relevance of li al-‘alamin in the diversity frame of religious people? Some questions need to search for answer in various reverence. The nation\u27s founders have been proud not to obtrude by doing sharia for the adherents is a space for tolerance and religious people
Analisis Quantum Perceptron Untuk Memprediksi Jumlah Pengunjung Ucok Kopi Pematangsiantar Pada Masa Pandemi Covid-19
Full text linkQuantum perceptron adalah merupakan metode jaringan saraf tiruan yang memadukan antara algoritma perceptron dengan komputasi quantum. Pada penelitian ini, peneliti melakukan analisis quantum perceptron untuk memprediksi jumlah pengunjung pada ucok kopi Pematangsiantar pada masa pandemi Covid-19. Dalam memprediksi jumlah pengunjung pada Ucok Kopi Pematangsiantar, peneliti menggunakan data pengunjung sebelumnya pada masa panedmi Covid-19. Variabel yang digunakan adalah 10 varibel dimulai dari x1 sampai dengan x10. Hasil dari penelitian ini adalah analisis quantum perceptron untuk memprediksi jumlah pengunjung ucok kopi Pematangsiantar
Q-Madaline: Madaline Based On Qubit
Full text linkThis research focuses on developing the MADALINE algorithm using quantum computing. Quantum computing uses binary numbers 0 or 1 or a combination of 0 and 1. The main problem in this research is how to find other alternatives to the MADALINE algorithm to solve pattern recognition problems with a quantum computing approach. The data used in this study are heart failure data to predict whether a patient is at risk of death. The data source comes from KAGGLE, consisting of 299 data with 12 symptoms and one target, alive or dead. The result of this study is an alternative to the MADALINE algorithm that uses quantum computing. The precision of the test results with MADALINE with a learning rate of 0.1 = 100% with 2 epochs. The accuracy of the test results using a quantum approach with a learning rate of 0.1 is 85.71%. The results of this study can be an alternative to the MADALINE algorithm with a quantum computing approach, although it has not shown better accuracy than the classical MADALINE algorithm. More research is needed to produce better accuracy with larger data
Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data
Full text linkThe creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in this study's computation. The primary proposed learning method was not ideal, which is the context of this study. The percentage of qubits measurement value is still 90.7 percent. It is essential to raise the value of the qubit rate. Using the IBM Quantum Experience quantum computer, researchers measured, trained, and tested the quantum circuit architecture. Bank marketing data from the UCI Machine Learning Repository was used. A quantum circuit architecture model results from this research the quantum circuit measurement results.The creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in this study's computation. The primary proposed learning method was not ideal, which is the context of this study. The percentage of qubits measurement value is still 90.7 percent. It is essential to raise the value of the qubit rate. Using the IBM Quantum Experience quantum computer, researchers measured, trained, and tested the quantum circuit architecture. Bank marketing data from the UCI Machine Learning Repository was used. A quantum circuit architecture model results from this research the quantum circuit measurement results
A COMPARATIVE EVALUATING NUMERICAL MEASURE VARIATIONS IN K-MEDOIDS CLUSTERING FOR EFFECTIVE DATA GROUPING
No full textThe K-Medoids Clustering algorithm is a frequently employed technique among researchers for data categorization. The primary difficulty addressed in this investigation pertains to the extent of optimality achieved when varying distance computation methodologies are applied within the framework of K-Medoids Clustering. This study is primarily concerned with the application of K-Medoids Clustering, employing a multitude of distance calculation methods, specifically those involving numerical metrics. The aim is to undertake a comparative analysis of Davies-Bouldin Index (DBI) values in order to ascertain the most productive distance calculation technique. In this research, the distance calculation methodologies include Manhattan Distance, Jaccard Similarity, Dynamic Time Warping Distance, Cosine Similarity, Chebyshev Distance, Canberra Distance and Euclidean Distance. The dataset consists of sales data from Devi Cosmetics, covering the period between January and April 2022 and comprising 56 distinct sales items. The research provides an exhaustive evaluation of numerical metrics concerning the K-Medoids Clustering algorithm. The findings indicate that the optimal clustering is achieved using the Chebyshev distance, resulting in 9 clusters with a DBI value of 166.632. The study's contribution is that it can improve more optimal data grouping to help make decisions correctly
Application of Numerical Measure Variations in K-Means Clustering for Grouping Data
Full text linkThe K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clustering. The purpose of this research was to compare distance calculation methods with K-Means such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Similarity, Dynamic TimeWarping Distance, Jaccard Similarity, and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. The best distancecalculation was determined from the smallest Davies Bouldin Index value. This research aimed to find optimal clusters using the K-Means Clustering algorithm with seven distance calculations based on types of numerical measures. This research method compared distance calculation methods in the K-Means algorithm, such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Smilirity, Dynamic Time Warping Distance, Jaccard Smilirity and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. Determining the best distance calculation can be seen from the smallest Davies Bouldin Index value. The data used in this study was on cosmetic sales at Devi Cosmetics, consisting of cosmetics sales from January to April 2022 with 56 product items. The result of this study was a comparison of numerical measures in the K-Means Clustering algorithm. The optimal cluster was calculating the Euclidean distance with a total of 9 clusters with a DBI value of 0.224. In comparison, the best average DBI value was the calculation of the Euclidean Distance with an average DBI value of 0.265
POLAK-RIBIERE CONJUGATE GRADIENT ALGORITHM IN PREDICTING THE PERCENTAGE OF OPEN UNEMPLOYMENT IN NORTH SUMATRA PROVINCE
Full text linkThe economic problem that has a direct impact on human life and welfare is unemployment. One of the cities in Indonesia with the highest unemployment rate is North Sumatra Province. For example, Tebing Tinggi City had the highest unemployment rate of 9.73% in 2017, while Nias Selatan had the lowest percentage of 0.31%. This research is important to do in order to anticipate the unemployment rate in North Sumatra for any party, be it the government or the private sector, so that they can work together to overcome the problem of unemployment in the future which is the main problem in the economy. For example, the government creates programs to help reduce the number of unemployed, provide preparation or do other things, helping people to become more imaginative and have skills so they can compete in the world market. Predicting unemployment has been the subject of many studies, one of which is by utilizing artificial neural networks. This study aims to predict the percentage of unemployed in North Sumatra from 2022 to 2026, using the Backpropagation Neural Network Algorithm, the Conjugate Gradient Polak-Ribiere method and Matlab version 2011 for research and data analysis. This research utilizes open action rate stimulation data for the population of North Sumatra based on residents aged over 15 years from 2017 to 2021. Using five architectural models, namely: 4-50-1, 4-55-1, 4-70- 1, 4-75-1, and 4-77-1. The final results were obtained using the most accurate architectural model, namely model 4-75-1 which has a Mean Squared Error (MSE) of 0.0000004288 and an accuracy rate of 100% with a time of 00.09 at epoch 452
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