Studi Komparasi Naive Bayes, K-Nearest Neighbor, dan Random Forest untuk Prediksi Calon Mahasiswa yang Diterima atau Mundur

Sejati, Puteri; Munawar, Munawar; Pilliang, Marzuki; Akbar, Habibullah

doi:10.25126/jtiik.2022976737

Cited by 4 publications

(4 citation statements)

References 1 publication

(1 reference statement)

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“…In this method, the assumption is that the presence or absence of an attribute is not related to other attributes. It calculates the probability of each previously determined data feature, calculates the likelihood value by multiplying the value of each probability, and then predicts the data based on the previously determined label [4]. In addition, Support Vector Machine (SVM) can also be used as a classification method.…”

Section: Sentiment Analysis Of Content Permenkominfo No5 Of 2020 Usin...mentioning

confidence: 99%

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Amada

Munawar

Pilliang

2023

jitk

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This study aims to evaluate the impact of the policy issued by the Minister of Communication and Information Technology (PERMENKOMINFO No.5 of 2020) on the public's ability to access content through Private Scope Electronic System Providers (PSE). The study uses sentiment analysis and data classification methods to analyze the content of PERMENKOMINFO No.5 of 2020 and provides results on the accuracy of sentiment prediction. The results of the study show that the data classification method in sentiment analysis can provide accurate results in predicting the sentiment towards the content of PERMENKOMINFO No.5 of 2020. The study also highlights the need for improvement and better policy to ensure the interests of the public in accessing online information. The negative sentiment of 80.34% obtained through sentiment analysis provides important contributions for policy evaluation and feedback for improvement. This study provides valuable insights into the public's sentiment towards the PERMENKOMINFO No.5 of 2020 policy and its impact on their ability to access content. It also contributes to understanding the legal uncertainty in accessing content and reinforces the case for better policy to ensure the interests of the public.

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Section: Sentiment Analysis Of Content Permenkominfo No5 Of 2020 Usin...mentioning

confidence: 99%

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Amada

Munawar

Pilliang

2023

jitk

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“…𝑃(𝑥|𝑐) adalah probabilitas dari prediktor yang diberikan oleh kelas. 𝑃(𝑥) adalah probabilitas sebelumnya dari prediktor (Sejati et al, 2022).…”

Section: 𝑃(𝑐|𝑥) = 𝑃(𝑥|𝑐)unclassified

Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes

Samir,

Purnawansyah,

Darwis

et al. 2023

JTIIK

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Daun herbal bermanfaat sebagai obat alternatif karena kandungan alaminya dapat menyembuhkan berbagai penyakit dan menjaga kesehatan tubuh. Klasifikasi citra daun herbal digunakan untuk membedakan jenis tanaman herbal berdasarkan bentuk daun. Penelitian ini Penelitian menggunakan Fourier Descriptor (FD) untuk mengekstraksi fitur pada daun herbal dan mengklasifikasikannya menggunakan metode Support Vector Machine (SVM) dan Naive Bayes (NB). SVM diimplementasikan dengan empat kernel yaitu Linear, polynomial, Radial Basis Function (RBF), dan sigmoid sementara Naive bayes diaplikasikan dengan tiga jenis kernel yaitu Gaussian, Multinomial, Bernoulli. Evaluasi kinerja menggunakan Precision, accuracy F1-Score dan Recall. Citra daun herbal terdiri dari daun katuk (Sauropus Androgynus) dan daun kelor (Moringa) dengan total 480 citra. Data tersebut dibagi menjadi 80% untuk training dan 20% untuk testing. Terdapat dua skenario pencahayaan yaitu kondisi gelap dan terang. Hasil penelitian menunjukkan bahwa perbandingan metode SVM dengan ekstraksi FD dimana kernel Linear mencapai akurasi sebesar 98% pada skenario gelap, sementara kernel Sigmoid memberikan akurasi terendah sebesar 44% pada scenario gelap maupun terang. Adapun hasil dari metode Naive bayes dengan ekstraksi FD pada kernel multinomial menghasilkan akurasi tertinggi sebesar 83% pada terang, sedangkan kernel Bernoulli memberikan akurasi terendah sebesar 46% pada skenario gelap dan terang. Berdasarkan perbandingan hasil klasifikasi dari kedua metode, disarankan bahwa metode SVM pada ekstraksi FD lebih direkomendasikan dalam proses klasifikasi daun herbal. Penelitian ini dapat memberikan rekomendasu pengembang sistem untuk menetapkan metode yang tepat dalam klasifikasi citra daun herbal. Abstract Herbal leaves are beneficial as alternative medicine because their natural content can cure various diseases and maintain a healthy body. The classification of herbal leaf images is used to differentiate types of herbal plants based on leaf shapes. This study utilizes Fourier Descriptor (FD) to extract features from herbal leaves and classify them using the Support Vector Machine (SVM) and Naive Bayes (NB) methods. SVM is implemented with four kernels namely linear, polynomial, Radial Basis Function (RBF), and Sigmoid while Naive bayes is applied with three types of kernels namely Gaussian, multinomial, Bernoulli. Performance evaluation includes precision, accuracy, F1- score and recall. Herbal leaf images consist of leaves (Sauropus Androgynus) and moringa leaves with a total of 480 images. The data is divided into 80% for training and 20 % for testing. There are two lighting scenarios, namely dark and light conditions. The result of this study shows a comparison of the SVM method with FD extraction where the Linear kernel achieves the highest accuracy of 98% in dark scenarios, while the Sigmoid kernel provides the lowest accuracy of 44% in both dark and light scenarios. The result of the naïve bayes method with FD extraction on the Multinomial kernel yield the highest accuracy of 83% in light scenarios while the Bernoulli kernel provides the lowest accuracy 46% in both dark and light scenarios. Based on the comparison of the classification result of the two methods, it is suggested that the SVM method for FD extraction is more recommended in the herbal leaf classification process. This research can provide recommendation for system developers to determine the appropriate method for classifying herbal leaf images.

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“…Penelitian tersebut menghasilkan nilai akurasi Random Forest lebih baik dari Naïve Bayes. Hasil yang sama diperoleh dari penelitian yang dilakukan oleh [8], [9], [7], [10], [11], [12], [13], [14], dan [15]. Random Forest merupakan algoritma berbasis esemble yang dibangun berdasarkan algoritma Decision Tree dan dikenal memiliki performa yang baik.…”

Section: Pendahuluanunclassified

Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest

Sari¹,

Romadloni²,

Listyaningrum³

2023

infotekmesin

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Cancer is the second highest cause of death in the world. In Indonesia, it is a disease with a high mortality rate. Most patients do not realize that they have lung cancer thus the treatment is sometimes too late. A prediction method with a high degree of accuracy is needed to detect lung cancer earlier. Previous research used data mining calcification methods with the Naïve Bayes algorithm to predict lung cancer. This research resulted in high recall values for the positive class (Yes class) but low for the negative class (No class). This research was made using the Random Forest algorithm which is known to have good performance. The modeling is optimized by applying the K-fold Cross Validation technique. The Random Forest algorithm produces a higher Accuracy value than the Naïve Bayes algorithm, which is 98.4%. This algorithm produces 100% Recall for the positive class, 80% for the negative class and provides a 100% correct prediction as can be seen from the AUC value of 1. Although a statistical test with a significance level of 5% shows the results of the two algorithms are not significantly different.

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Studi Komparasi Naive Bayes, K-Nearest Neighbor, dan Random Forest untuk Prediksi Calon Mahasiswa yang Diterima atau Mundur

Cited by 4 publications

References 1 publication

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Sentiment Analysis of Content Permenkominfo NO.5 of 2020 Using a Classification Algorithm

Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes

Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest

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