Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Random Forest dan Naive Bayes

Sari, Veronica Retno; Firdausi, Feranandah; Azhar, Yufis

doi:10.29408/edumatic.v4i2.2202

Cited by 20 publications

(20 citation statements)

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“…Serta pengujian algoritma klasifikasi dapa dilakukan menggunakan pengujian Precision, Recall, dan F1-SCORE, serta Accuracy (Harianto, Sunyoto, & Sudarmawan, 2020). Model pengujian tersebut dapat dibandingkan dengan hasil pada beberapa skenario yang dilakukan (Sari, Firdausi, & Azhar, 2020). Presisi adalah rasio prediksi true positif dibandingkan dengan hasil prediksi positif secara keseluruhan.…”

Section: Pengujianunclassified

Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

Hidayat¹,

Ardiansyah²,

Setyanto³

2021

Edumatic

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Traveling activities are increasingly being carried out by people in the world. Some tourist attractions are difficult to reach hotels because some tourist attractions are far from the city center, Airbnb is a platform that provides home or apartment-based rentals. In lodging offers, there are two types of hosts, namely non-super host and super host. The super-host badge is obtained if the innkeeper has a good reputation and meets the requirements. There are advantages to being a super host such as having more visibility, increased earning potential and exclusive rewards. Support Vector Machine (SVM) algorithm classification process by these criteria data. Data set is unbalanced. The super host population is smaller than the non-super host. Overcoming the imbalance, this over sampling technique is carried out using ADASYN and SMOTE. Research goal was to decide the performance of ADASYN and sampling technique, SVM algorithm. Data analyses used over sampling which aims to handle unbalanced data sets, and confusion matrix used for testing Precision, Recall, and F1-SCORE, and Accuracy. Research shows that SMOTE SVM increases the accuracy rate by 1 percent from 80% to 81%, which is influenced by the increase in the True (minority) label test results and a decrease in the False label test results (majority), the SMOTE SVM is better than ADASYN SVM, and SVM without over sampling.

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Section: Pengujianunclassified

Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

Hidayat¹,

Ardiansyah²,

Setyanto³

2021

Edumatic

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“…Pada penelitian ini juga melakukan pengolahan data dengan menggunakan aplikasi RapidMiner. Rapid miner adalah sebuah tool yang digunakan untuk mengolah data yang telah didapatkan dengan menggunakan metode data mining dan menganalisis kuantitas data secara kualitatif (Pamulang, Aini, & Enri, 2021;Sari, Firdausi, & Azhar, 2020). Rapid miner ini merupakan sebuah tools open source atau tool yang gratis.…”

Section: Metodeunclassified

Penentuan Paket Promosi Pakaian PT. D&C Production dengan Menggunakan Algoritma FP-Growth

2021

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PT. D&C Production is a center for selling, foam mattresses, mattresses of all sizes and models, may types of car accessories and various kinds of women's and men's underwear. Problems regarding the decline in sales resulted in the accumulation of goods so that it became a loss. Data mining can be a solution to overcome these problems. This study will use the fp-growth algorithm to form an association model with the aim of helping companies increase their sales by creating underwear promotional packages. The data set that will be used to support this research is the sales transaction data set for the period April 2020 to December 2020. The results show that known rules have been obtained using the fp-growth algorithm, where the rules of this association can create strategies to increase clothing sales. In the form of five association rules that are ready to be used for making clothing promotional packages by meeting the support values and confidence values that have been set at the beginning, namely having a confidence value above 80% and a support value above 25%.

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“…Data Mining merupakan suatu proses penggalian informasi melalui sekumpulan data dengan tujuan memperoleh pengetahuan untuk diterapkan pada bidang-bidang tertentu. Dalam praktiknya, kita dapat memanfaatkan data mining untuk melakukan klasifikasi (Sari, Firdausi, & Azhar, 2020), prediksi, estimasi, klastering maupun asosiasi. Subash pada penelitiannya di tahun 2016 menyebutkan beberapa hal yang dapat dilakukan data mining pada dunia medis diantaranya ; mendeteksi penipuan asuransi kesehatan, memberikan solusi medis yang lebih baik untuk pasien yang kekurangan biaya, membantu mendeteksi penyakit tertentu, dan mengidentifikasi metode perawatan medis yang efisien (Pandey & Jain, 2017).…”

Section: Pendahuluanunclassified

Komparasi Distance Measure Pada K-Medoids Clustering untuk Pengelompokkan Penyakit ISPA

Pamulang¹,

Aini

Enri

2021

Edumatic

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K-Medoids is an unsupervised algorithm that uses a distance measure to classify data. The distance measure is a method that can help an algorithm classify data based on the similarity of the variables. Several studies have shown that using the right distance measure can improve the performance of the algorithm in clustering. Euclidean and Chebyshev is two of some distance measures that can be used. In 2016, Karawang Health Office stated that 175.891 Karawang citizens were suffering from ISPA. This figure continued to increase in the following year until 2019. The total of Karawang citizens who suffering from ISPA reached 181.945 people. To assist the government in overcoming this problem, a clustering process will be carried out to group the areas where the ISPA is spreading in Karawang District. The area will be divided into three clusters, namely low, medium and high. Comparison of distance measures is carried out to find the best model based on the evaluation of the Davies Bouldin Index (DBI). The use of Euclidean-distance produces a DBI score of 0,088 meanwhile the use of Chebyshev distance resulted in a DBI score of 0,116. The performance of the K-Medoids algorithm with Euclidean-distance is considered to be better than Chebyshev distance because it produces a DBI score that is near to 0.

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Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Random Forest dan Naive Bayes

Cited by 20 publications

References 0 publications

Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

Pengaruh Algoritma ADASYN dan SMOTE terhadap Performa Support Vector Machine pada Ketidakseimbangan Dataset Airbnb

Penentuan Paket Promosi Pakaian PT. D&C Production dengan Menggunakan Algoritma FP-Growth

Komparasi Distance Measure Pada K-Medoids Clustering untuk Pengelompokkan Penyakit ISPA

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