Comparison of Machine Learning Methods in Classifying Poverty in Indonesia in 2018

Sihombing, Pardomuan Robinson; Arsani, Ade Marsinta

doi:10.20884/1.jutif.2021.2.1.52

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…Pada tahapan ini, dilakukan implementasi perhitungan algoritma Naive Bayes yang merupakan salah satu metode data mining yang termasuk algoritma klasifikasi paling popular [12]. Teorema Bayes memiliki bentuk umum [17]:…”

Section: Pemodelan Algoritmaunclassified

“…Berdasarkan penelitian rujukan dan latar belakang yang telah diuraikan sebelumnya, maka tujuan penelitian ini adalah membahas khusus prediksi penerimaan beasiswa PPA dan beasiswa KIP-K di Sekolah Tinggi Teknologi Garut dengan menggunakan Algoritma Naive Bayes. Pemilihan Naive Bayes karena menghasilkan tingkat akurasi cukup baik berdasarkan dari penelitian sebelumnya yang telah diuraikan, serta mampu mempelajari data kasus sebelumnya yang digunakan sebagai data uji [12]. Kontribusi dari penelitian ini diharapkan dapat membantu pengelola Beasiswa di STT-Garut dalam menentukan mahasiswa yang diterima dan ditolak khususnya untuk Beasiswa Jenis PPA dan KIP-K.…”

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The Prediction of Ppa and Kip-Kuliah Scholarship Recipients Using Naive Bayes Algorithm

Mulyani

Kurniadi

Nashrulloh

et al. 2022

J. Tek. Inform. (JUTIF)

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The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.

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Section: Pemodelan Algoritmaunclassified

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The Prediction of Ppa and Kip-Kuliah Scholarship Recipients Using Naive Bayes Algorithm

Mulyani

Kurniadi

Nashrulloh

et al. 2022

J. Tek. Inform. (JUTIF)

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“…Dari waktu ke waktu metode ini juga dikembangkan sehingga kemampuannya jauh lebih baik dari sebelumnya. Seiring dengan berkembangnya deep learning, muncul lah berbagai model baru pada ranah pengenalan dan pendeteksian objek pada computer vision, contohnya seperti convolutional Neural Network (CNN) terus Region based Convolutional Network (R-CNN) [3][4], Spatial Pyramid Pooling Network (SPP-Net), faster R-CNN [5][6] [7][8] [9] dan You Only Look Once (YOLO) [10] [11].…”

Section: Pendahuluanunclassified

Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

Sanubari

Puriyanto²

2022

Buletin Ilmiah Sarjana Teknik Elektro

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This research is a form of development of object detection capabilities on wheeled soccer robots using an omnidirectional camera with the You Only Look Once (YOLO) method where the results show that the robot can detect more than one object, namely the ball and the goal on the green field. This study uses the KRSBI-Wheeled UAD robot using an omnidirectional camera as a tool to carry out the detection process and then uses OpenCV 4.0, Deep Learning, and a laptop as a place to create a detection model, as well as balls and goals as objects to be detected. The results obtained from this study are that the two types of YOLO models tested, namely YOLOv3 and YOLOv3-Tiny can detect ball and goal objects in two different types of frame sizes, namely 320x320 and 416x416 which can be seen from the performance of the YOLOv3 model which has an mAP value of 76%. on the 320x320 frame and an mAP value of 87.5% in the 416x416 frame then the YOLOv3-Tiny model has an mAP value of 68.1% in the 320x320 frame and an mAP value of 75.5% in the 416x416 frame where the YOLOv3 model can detect both object class is much more stable compared to YOLOv3-Tiny. Penelitian ini merupakan bentuk pengembangan dari kemampuan deteksi objek pada robot sepak bola beroda dengan menggunakan kamera omnidirectional dengan metode You Only Look Once (YOLO) dimana hasil penelitian menunjukkan bahwa robot dapat mendeteksi lebih dari satu objek yaitu bola dan gawang di atas lapangan hijau. Penelitian ini menggunakan robot KRSBI-Beroda UAD dengan memakai kamera omnidirectional sebagai alat untuk melakukan proses pendeteksian lalu menggunakan OpenCV 4.0, Deep Learning, dan laptop sebagai tempat membuat model pendeteksian, serta bola dan gawang sebagai objek yang akan dideteksi. Hasil yang didapatkan dari penelitian ini yaitu kedua jenis model YOLO yang diuji yaitu YOLOv3 dan YOLOv3-Tiny dapat mendeteksi objek bola dan gawang pada dua jenis ukuran frame yang berbeda yaitu 320x320 dan 416x416 yang dapat dilihat dari performa pada model YOLOv3 memiliki nilai mAP sebesar 76% pada frame 320x320 dan serta nilai mAP sebesar 87,5% pada frame 416x416 lalu pada model YOLOv3-Tiny memiliki nilai mAP sebesar 68,1% pada frame 320x320 serta nilai mAP sebesar 75,5% pada frame 416x416 yang dimana model YOLOv3 dapat mendeteksi kedua kelas objek jauh lebih stabil dibandingkan dengan model YOLOv3-Tiny.

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“…Penggunaan data masa lampau untuk menganalisis pola yang terjadi pada masa depan Merupakan konsep dari aplikasi prediksi maupun aplikasi rekomendasi [3]. Aplikasi untuk memprediksi suatu kejadian telah banyak diimplementasikan pada banyak kasus dengan menggunakan konsep klasifikasi pada data mining, dengan berbagai macam metode maupun algoritma, seperti algoritma Naïve Bayes [4]- [6], ID3 [7], Rotation Forest [8], Neural Network [9], [10], maupun algoritma yang lain [8], [11], [12].…”

Section: Pendahuluanunclassified

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

Kurniawan

Fatikasari

Hidayat

et al. 2021

J. Tek. Inform. (JUTIF)

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BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

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Comparison of Machine Learning Methods in Classifying Poverty in Indonesia in 2018

Cited by 12 publications

References 8 publications

The Prediction of Ppa and Kip-Kuliah Scholarship Recipients Using Naive Bayes Algorithm

The Prediction of Ppa and Kip-Kuliah Scholarship Recipients Using Naive Bayes Algorithm

Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

Prediction for Cooperative Credit Eligibility Using Data Mining Classification With C4.5 Algorithm

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