Effects of Distance Measurement Methods in K-Nearest Neighbor Algorithm to Select Indonesia Smart Card Recipient

Karo, Ichwanul Muslim Karo; Khosuri, Ananda; Setiawan, Robi

doi:10.1109/icodsa53588.2021.9617476

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…The confusion matrix was used to calculate all metrics (Table 2). Precision (P) is defined as the proportion of correctly predicted positive observations to all positively predicted observations [19]. The metric was calculated using formula in equation (5).…”

Section: Discussionmentioning

confidence: 99%

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Karo Karo,

Romia,

Dewi

et al. 2023

josh

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Twitter is one of the most popular social media platforms in the world nowadays. Twitter users in Indonesia are the fifth largest in the world and are always active in expressing themselves and getting information through tweets. A hoax is a lie created as if it were true. Hoaxes are also often spread via tweets. The spread of hoaxes is extremely dangerous because it can cause social discord and even misunderstanding. Therefore, hoaxes must be resisted. This study aims to build a system to detect hoaxes on Indonesian tweets. The objective of this research is to identify hoax Indonesian tweets by using the Naïve Bayes classifier with Term Frequency Inverse Document Frequency (TF-IDF). This study collects and annotates tweets from hoax tweets post which sent by a user account. This study also applied several text preprocessing techniques to provide datasets. To provide the best hoax prediction model, this work splits datasets into training and testing datasets. There are four experimental scenarios that refer to splitting the dataset. The experimental results showed that the hoax prediction model using Naïve Bayes with TF-IDF had 64% accuracy and recall, 69% and 67% precision, and a F1-score respectively. This result is also superior to the hoax prediction model when using the Naïve Bayes classifier without the TF-IDF. It means that TF-IDF has made a positive contribution to improving model performance. Finally, this research contributes by detecting news with a proclivity for hoaxes and filtering what is classified as hoaxes or not.

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Section: Discussionmentioning

confidence: 99%

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Karo Karo,

Romia,

Dewi

et al. 2023

josh

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“…Minkowski distance is generalized Euclidean and Manhattan distance [24]. The formula of Minkowski distance is shown in Equation ( 7).…”

Section: Similarity Functionmentioning

confidence: 99%

Analyzing the Effect of Similarity Functions on Partitioning Around Medoids Algorithm for Mapping DHF Disease in North Sumatra

Fadillah,

Rangkuti,

Karo Karo

2024

BAREKENG: J. Math. & App.

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Dengue hemorrhagic fever (DHF) is an acute febrile illness caused by a virus through the Aedes mosquito. North Sumatra is among the three provinces with the highest incidence and mortality rates in Indonesia. Mapping of DHF cases is very important in efforts to control and prevent the disease. The Partitioning Around Medoid (PAM) algorithm is commonly used to cluster DHF cases. The idea of PAM is a clustering algorithm with a similarity-based approach to grouping objects in one cluster. There are two main focuses in the research: mapping regencies/cities based on dengue case information and analyzing the performance of several similarity functions. The dataset includes variables of incidence rate (IR), case fatality rate (CFR), larva-free rate (ABJ), and population, obtained from the North Sumatra Provincial Health Office and the Central Statistics Agency (BPS). The analysis showed that three clusters were formed in North Sumatra Province. The first cluster includes regencies/cities such as Langkat, Deli Serdang, Karo, Simalungun, Dairi, Samosir, Humbahas, North Labuhan Batu, North Padang Lawas, South Labuhan Batu, Padang Sidempuan, Nias, South Nias, North Nias, and Sibolga. The second cluster consists of regencies/cities such as Medan, Binjai, Sedang Berdagai, Tebing Tinggi, Batubara, Asahan, Tanjung Balai, Labuhan Batu, Toba, North Tapanuli, Central Tapanuli, Gunungsitoli, and West Nias. The third cluster includes the regencies of South Tapanuli and Mandailing Natal. In addition, an evaluation was conducted using the Silhouette Index to measure the quality of the clustering. Based on the comparison using distance methods (Euclidean distance, Manhattan distance, Minkowski distance, and Chebyshev distance), the highest Silhouette Index value was obtained using Chebyshev distance, which amounted to 0.527554. This value indicates reasonable cluster quality. Thus, this study contributes to the mapping of DHF cases in North Sumatra Province and can be the basis for decision-making in overcoming the disease.

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“…Akan tetapi, pada penelitian tersebut, hasil tingkat akurasi data algoritma Naive Bayes angka diperoleh kurang maksimal [6]. Sedangkan penelitian yang membahas tentang klasifikasi siswa yang berhak menerima beasiswa di UN PGRI kediri diperoleh hasil bahwa algoritma K-Means Clustering dapat membantu dalam mengklasifikasi siswa yang berhak menerima beasiswa, siswa yang dipertimbangkan menerima, dan siswa yang tidak berhak menerima dengan kriteria yang telah ditentukan [7]. Algoritma K-Means Clustering mampu membantu proses menentukan siswa yang mendapatkan beasiswa secara otomatis, sehingga proses pelaporan menjadi lebih efektif dan efisien [8].…”

Section: Beasiswa Pip K-means Clustering Euclidean Distanceunclassified

Algoritma K-Means Clustering Sebagai Penentuan Beasiswa PIP pada SMPN 9 Kota Blitar

Romadhona,

Rofiq

2023

JASIEK

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The structure in this study involves taking data on obtaining PIP scholarships with various predetermined indicators (parental income, parents' occupation, vehicle ownership, KIP, KKS, place of residence, number of siblings, PKH) as scholarship recipients. Held in one of the state schools SMPN 9 Blitar City. Where the data is processed with an unsupervised learning algorithm, namely K-Means Clustering with the calculation of the Euclidean distance with the closest value distance from the predetermined centroid which is then used to determine the recipient of the PIP scholarship. The sample test was conducted on 289 students which could be implemented properly and the cluster was running according to the provisions set as indicators of 10 attributes (X1-X10).

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Effects of Distance Measurement Methods in K-Nearest Neighbor Algorithm to Select Indonesia Smart Card Recipient

Cited by 6 publications

References 12 publications

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF

Analyzing the Effect of Similarity Functions on Partitioning Around Medoids Algorithm for Mapping DHF Disease in North Sumatra

Algoritma K-Means Clustering Sebagai Penentuan Beasiswa PIP pada SMPN 9 Kota Blitar

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