Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace

Sebastián, Danny

doi:10.28932/jutisi.v5i1.1581

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…K-Nearest Neighbor merupakan suatu metode klasifikasi terhadap data objek yang didasarkan pada data pembelajaran yang jaraknya dekat dengan perhitungan menggunakan jarak euclidean [16]. Tujuannya yaitu untuk mengklasifikasi data baru berdasarkan atribut dan data training dengan klasifikasi berdasarkan mayoritas kategorinya, beberapa penelitian menggunakan Algoritma telah dilakukan dalam beberapa bidang kesehatan, e-commerce, transportasi dan fisika [17][18][19][20][21]. Berdasarkan penelitian yang telah dilakukan algoritma K-Nearest Neighbor menghasilkan tingkat akurasi yang baik dalam berbagai kasus tersebut.…”

Section: Pendahuluanunclassified

Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis

Dinanti

Purwadi

2023

Jambura J. Math

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This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reduction using PCA produces 3 main components of the 8 variables in the original data, namely PC1, PC2, and PC3. Then classification result using K-Nearest Neighbor shows that by choosing 3 closest neighbor parameters (K), for K = 3, K = 5, and K = 7. The result for K = 3 has an accuracy of 67,53%, for K = 5 had an accuracy is 72,72%, and for K=7 had an accuracy of 77,92%. Thus, it was concluded that the best accuracy performance for the classification of diabetes was achieved at K=7 with an accuracy of 77.92%.

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Section: Pendahuluanunclassified

Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis

Dinanti

Purwadi

2023

Jambura J. Math

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“…Metode K-NN mengasumsikan bahwa data yang memiliki jarak dekat memiliki kemiripan atau keterkaitan yang tinggi. Metode ini juga termasuk dalam metode pembelajaran berbasis contoh (instance-based learning) yang tidak memerlukan proses pembelajaran sebelumnya, tetapi langsung menggunakan data yang ada sebagai dasar klasifikasi [20]. Cara kerja metode K-Nearest Neighbors (K-NN) adalah sebagai berikut [21]:…”

Section: K-nearest Neighborsunclassified

Eksplorasi dan Klasifikasi K-NN Terhadap Kejadian Luar Biasa Diare di Jawa Barat

Fulazzaky,

Waode,

Fitrianto

et al. 2023

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Tujuan dari penelitian ini adalah untuk mengkaji bagaimana kualitas air dan sanitasi mempengaruhi Kejadian Luar Biasa (KLB) Diare di Provinsi Jawa Barat, Indonesia, menggunakan data Pendataan Potensi Desa (PODES) tahun 2021. Diare merupakan permasalahan serius dalam kesehatan masyarakat Indonesia, terutama pada kelompok anak balita, dan salah satu faktor penyebab utamanya adalah rendahnya kualitas air dan sanitasi. Dalam konteks penelitian ini, kami menerapkan metode algoritma K-Nearest Neighbors (K-NN) untuk mengklasifikasikan wilayah-wilayah yang mengalami KLB Diare. Hasil eksplorasi data menunjukkan variasi yang signifikan dalam jumlah kasus diare di sejumlah kabupaten dan kota yang tersebar di wilayah Jawa Barat. Untuk menangani masalah ketidakseimbangan data, kami menerapkan teknik Pengurangan Acak (Random Under Sampling), Penambahan Acak (Random Over Sampling), dan Synthetic Minority Oversampling Technique (SMOTE).Hasil analisis menunjukkan bahwa model K-NN dengan penggunaan metode SMOTE menghasilkan tingkat akurasi tertinggi, yaitu sebesar 71.28%. Meskipun demikian, nilai F1 score untuk semua model cenderung rendah, yang mengindikasikan adanya tantangan dalam mengklasifikasikan wilayah-wilayah dengan KLB Diare. Penelitian ini memberikan wawasan yang penting mengenai korelasi antara kualitas air, sanitasi, dan KLB Diare di Jawa Barat, serta mengidentifikasi wilayah-wilayah yang memerlukan perhatian lebih dalam upaya pencegahan dan pengendalian penyakit diare. Hasil ini dapat digunakan sebagai dasar untuk merancang program-program kesehatan yang lebih efektif di daerah-daerah dengan tingkat insiden diare yang tinggi. Kata kunci: Algoritma K-Nearest Neighbors (K-NN), SMOTE, Ketidakseimbangan data dan teknik pengambilan sampel ulang, Kualitas air dan sanitasi, Program pencegahan dan pengendalian diare.

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“…The third stage is stemming, a process that eliminates affixes in order to find the root words. This stage aims to reduce the number of words processed in text mining with the purpose of reducing the processing time and minimizing the memory used [16]. The results of the stemming process can be seen in Table IV.…”

Section: ) Stemmingmentioning

confidence: 99%

Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine

Dionisia Bhisetya Rarasati,

Angelina Pramana Thenata,

Afiyah Salsabila Arief

2023

Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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The COVID-19 pandemic is still occurring in various countries, including Indonesia. This pandemic is caused by the coronavirus, which has mutated into multiple virus variants, such as Delta and Omicron. As of 9 February 2022, 4,626,936 people were confirmed positive for COVID-19 in Indonesia. This number continues to rise. The Indonesian government has prevented the spread of these virus variants by introducing booster vaccines to the public. However, this vaccination program has caused various sentiments among Indonesians. To optimize efforts to combat COVID-19, the government needs to know these sentiments immediately. Based on these problems, the researcher proposes the application of machine learning technology to develop a system that can analyze the sentiments of the Indonesians toward the booster vaccine. This research has several stages: data collection, data labeling, text preprocessing, feature extraction, and application of the support vector machine (SVM) algorithm using various kernels, namely the linear kernel, Gaussian radial basis function (RBF) kernel, and polynomial kernel. Furthermore, the results of the system were tested for accuracy using a 10-fold cross validation and confusion matrix. The dataset used was 681 tweets with the hashtag “vaksinbooster.” The dataset consists of two classes: negative (0) and positive (1). The results showed that the data were positive for the booster vaccine, as evidenced by the higher number of positive tweets, with 554 data, compared to 127 negative tweets. In addition, the dataset was divided into training data of 545 and testing data of 136. In addition, the test results of this study revealed that the SVM algorithm with the polynomial kernel, which was evaluated with 10-fold cross validation, yielded the highest level of accuracy, namely 79.22%.

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Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace

Cited by 10 publications

References 11 publications

Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis

Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis

Eksplorasi dan Klasifikasi K-NN Terhadap Kejadian Luar Biasa Diare di Jawa Barat

Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine

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