Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country

Rahman, Fathur; Ridho, Ihda Innar; Muflih, Muhammad; Pratama, Sefto; Raharjo, Mokhamad Ramdhani; Windarto, Agus Perdana

doi:10.1088/1757-899x/835/1/012058

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…The procedure of k-medoids is same to that of k-means. But it different from k-means in the step of updatd and replacd medoid by comparing the sum of distances between a point and other points and the sum Because the use of medoids to represent the centers inhibits early convergence, the findings are relatively successful [26], [27].…”

Section: K-means and K-medoids Clustering Algorithmsmentioning

confidence: 99%

Improving prediction of plant disease using k-efficient clustering and classification algorithms

Ahmed

Obeas

Alhade

et al. 2022

IJ-AI

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<span lang="EN-US">Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence (AI) model based on k-nearest neighbors (k-NN) classifier and k-efficient clustering that integrates k-means with k-medoids to take advantage of both k-means and k-medoids to improve plant disease prediction strategies. Objectives of this article are to determine performance of k-mean, k-medoids and k-efficient also we compare k-NN before clustering and with clustering in prediction of soybean disease for selecting best one for plant disease forecasting. These objectives enable us to analysis data of plant that help to understand nature of plant. Results indicate that k-NN with k-efficient is more efficient than other in terms of inter-class, intra-class, normal mutual information (NMI), accuracy, precision, recall, F-measure, and running time.</span>

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Section: K-means and K-medoids Clustering Algorithmsmentioning

confidence: 99%

Improving prediction of plant disease using k-efficient clustering and classification algorithms

Ahmed

Obeas

Alhade

et al. 2022

IJ-AI

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show abstract

“…Data mining adalah proses yang menggunakan teknik statistic, matematika, kecerdasan buatan, dan machine learning untuk mengekstraksi dan mengident ifikasi informasi yang bermanfaat dan pengetahuan yang terkait dari berbagai database besar. Data mining, sering juga disebut sebagai Knowledge Discovery in Database (KDD) [6], [13]. KDD adalah kegiatan yang meliputi pengumpulan,pemakaian data, historis untuk menemukan keteraturan,pola atau hubungan dalam set data berukuran besar [14].…”

Section: Data Miningunclassified

Analisa Klasifikasi C4.5 Terhadap Faktor Penyebab Menurunnya Prestasi Belajar Mahasiswa Pada Masa Pandemi

Irnanda

Hartama

Windarto

2021

mib

Self Cite

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The purpose of the study was to classify the factors causing the decline in student achievement during the pandemic using the C4.5 datamining method. Sources of research data were obtained by conducting interviews and distributing questionnaires to 7th semester students of the 2020-2021 school year information system study program. Attributes that used in the classification of the factors causing the decline in student achievement include: Learning Method (C1), Study Time (C2), Material Understanding (C3), Giving Assignments (C4) and Environment (C5). The results of the calculation show that the Material Understanding (C3) attribute is the attribute that most influences the decline in student learning achievement. Testing was also carried out using the help of Rapidminer software and obtained an accuracy of 97.5%.

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“…Memiliki banyak informasi saja tidak cukup jika tidak dapat mengelolanya menjadi alternatif terbaik dalam pengambilan keputusan [1]. Data mining adalah [2]- [6] bagian dari machine learning dimana bertugas mencari pola atau informasi menarik dalam data terpilih dengan menggunakan teknik atau metode tertentu [7]. Salah satu metode yang merupakan bagian dari data mining adalah Metode Rough Set (RS) [8].…”

Section: Pendahuluanunclassified

Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah)

Raharjo

Windarto

2021

mib

Self Cite

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The Rough Set (RS) method is part of machine learning that analyzes the uncertainty of the dataset used to determine the attributes of important objects (classification). The purpose of this study was to extract information from the rough set using the Rosetta application in predicting cases of students' level of understanding of the course. The attributes used are communication (F1), learning atmosphere (F2), learning media (F3), appearance (F4), and teaching methods (F5). Sources of data obtained from the output of the Journal of Physics: Conference Series, 1255 (1). https://doi.org/10.1088/1742-6596/1255/1/012005. The results of the application of the Rough Set method in determining the prediction of the level of student understanding of the course, produce new knowledge, namely learning outcomes based on the subject. There are 15 Reductions with 90 Generate Rules. But overall, the attributes that affect the level of student understanding of the subject are communication (F1) and learning media (F3)

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Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country

Cited by 21 publications

References 12 publications

Improving prediction of plant disease using k-efficient clustering and classification algorithms

Improving prediction of plant disease using k-efficient clustering and classification algorithms

Analisa Klasifikasi C4.5 Terhadap Faktor Penyebab Menurunnya Prestasi Belajar Mahasiswa Pada Masa Pandemi

Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah)

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