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All Journal Semantik Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information System Journal of Development Research Abdimasku : Jurnal Pengabdian Masyarakat OISAA Journal of Indonesia Emas Jurnal Indonesia Sosial Teknologi
Affandy Affandy
Dian Nuswantoro University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Improving C4.5 Algorithm Accuracy With Adaptive Boosting Method For Predicting Students in Obtaining Education Funding Mohammad Ahmad Maidanul Abrori; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Journal of Development Research Vol. 6 No. 2 (2022): Volume 6, Number 2, November 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v6i2.205

Abstract

The level of accuracy in determining the prediction of the provision of educational funding assistance is very important for the education agency. The large number of data on prospective beneficiaries can be processed into information that can be used as decision support in determining eligibility for education funding assistance. The data processing is included in the field of data mining. One method that can be applied in predicting the feasibility of receiving aid funds is classification. There are several classification algorithms, one of which is a decision tree. The famous decision tree algorithm is C4.5. The C4.5 algorithm can be applied in classifying prospective recipients of educational aid funds. This study uses datasets from student data of SMK Al Fattah Kertosono. The purpose of this study is to increase the accuracy of the C4.5 algorithm by applying adaboost in classifying students who deserve education funding and not, by comparing the results before and after applying adaboost. Validation in this study uses cross validation. While the measurement of accuracy is measured by the confusion matrix. The experimental results show that there is an increase in accuracy of 7.2%. The accuracy of the application of the C4.5 algorithm reaches 91.32%. While the accuracy of the application of the C4.5 algorithm with adaboost reached 98.55%.
Deteksi Dini Covid-19 Melalui Citra CT-Scan Paru-Paru Menggunakan K-Nearest Neighbor dengan Komparasi Jarak Lu'luul Maknun; Abdul Syukur; Affandy Affandy; Moch Arief Soeleman
Jurnal Indonesia Sosial Teknologi Vol. 3 No. 03 (2022): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1049.395 KB) | DOI: 10.59141/jist.v3i03.397

Abstract

Covid -19 yang telah mewabah dan menjadi pandemik secara global yang merupakan masalah utama yang perlu di perhatikan dan di tangani, beberapa cara yang harus di lakukan adalah dengan memutus mata rantai penyebaran virus salah satunya dengan melakukan deteksi dini dan melakukan karantina, dengan CT scan paru-paru. CT scan paru-paru dapat dijadikan jalan alternatif. Berdasarkan permasalahan di atas maka peneliti mengetahui kondisi paru-paru secara detail dan dalam mendiagnosis virus secara dini. Pada penelitian ini pendekatan yang di ajukan menggunakan metode K-NN dengan perhitungan jarak euclidean distance, manhattan distance, miskowski distance untuk deteksi dini Covid -19 melalui citra CT scan paru-paru yang di duga terinfeksi Covid -19 . dalam mendeteksi secara dini evaluasi yang di gunakan untuk mengetahui pervorma yang di usulkan menggunakan coufusion matrix dengan hasil eksperimen menunjukkan hasil dari tiga perhitungan jarak menunjukkan hasil akurasi yang baik dan menggunakan dataset secara publik yaitu euclidean distance berjumlah 83%, Manhattan distance berjumlah 87%, Minkowski berjumlah 76%, di harapkan metode ini dapat di gunakan dan di kembangkan untuk melengkapi dioglosa medis.
Co-Authors Abdul Syukur Abdul Syukur Adhe Irham Thoriq Ayu Pertiwi Ayu Pertiwi Desi Hanindya Ujianti Erika Devi Udayanti Erwin Yudi Hidayat Erwin Yudi Hidayat Lu'luul Maknun Moch Arief Soeleman Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muslih Muslih Nurhindarto, Aris Oktania Nandiyati Raindy Wicaksana Hardiansyah S. Kholidah Rahayu Maharani Syafaatun Khasanah Titien Suhartini