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All Journal PROSIDING SEMINAR NASIONAL Journal of Applied Information, Communication and Technology (JAICT) Jurnal Litbang Kota Pekalongan
M. Adib Al Karomi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Education Engineering Public Health

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SISTEM PENDUKUNG KEPUTUSAN DETEKSI PENYAKIT KANKER PAYUDARA MENGGUNAKAN ALGORITMA NAIVE BAYES Ivandari Ivandari; Erni Rahmawatie; M. Adib Al Karomi
PROSIDING SEMINAR NASIONAL & INTERNASIONAL 2018: SEMINAR NASIONAL PENDIDIKAN SAINS DAN TEKNOLOGI
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (141.738 KB)

Abstract

Cancer is one of the biggest causes of death in the world. Data from the International Agency for Research of Cancer (IARC) states that in 2012 more than 8.2 million people died from cancer. From these data it is known that breast cancer is the most common type of cancer suffered by 19.2% of all cancer cases. The amount of data and records related to cancer patient cases can  be  useful  if  from  this  data  an  information  or  new  knowledge  can  be retrieved. Data mining is a field of knowledge that processes past data to be used as new information and knowledge. From the comparative research of data mining algorithms for detection of breast cancer in 2017 naive bayes is the best algorithm. Naive Bayes is proven to have a higher level of accuracy than other algorithms. In this study a decision support system for the detection of breast cancer was made. The system created using this Excel application can be one of the recommendations. The method used for calculation is the probability of naive bayes..Keywords: Naive bayes, Microsoft excell, decission support system
Improved Decission Tree Performance using Information Gain for Classification of Covid-19 Survillance Datasets Ivandari Ivandari; Much. Rifqi Maulana; M. Adib Al Karomi
JAICT Vol 7, No 1 (2022)
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jaict.v7i1.3501

Abstract

One of the most feared infectious diseases today is COVID-19. The transmission of this disease is quite fast. Patients also sometimes do not have the same symptoms. Overcoming the spread of the pandemic has been widely carried out throughout the world. Apart from the medical method, there are also many other methods, including computerization. Data mining is a discipline that can project data into new knowledge. One of the main functions of data mining is classification. Decision tree is one of the best models to solve classification problems. The number of data attributes can affect the performance of an algorithm. This study uses information gain to select the attribute features of the Covid-19 surveillance dataset. This study proves that there is an increase in the accuracy of the decision tree algorithm by adding information gain feature selection. Previously, the decision tree only had an accuracy rate of 65% for the classification of the Covid-19 surveillance dataset. After pre-processing using information gain, the accuracy rate increased to 75%.
INFORMATION GAIN UNTUK MENGETAHUI PENGARUH ATRIBUT TERHADAP KLASIFIKASI PERSETUJUAN KREDIT Much. Rifqi Maulana; M. Adib Al Karomi
JURNAL LITBANG KOTA PEKALONGAN Vol. 9 (2015)
Publisher : Badan Perencanaan Pembangunan, Penelitian dan Pengembangan Daerah (Bappeda) Kota Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54911/litbang.v9i0.28

Abstract

KREDIT MERUPAKAN SEBUAH PERILAKU EKONOMI YANG BANYAK DILAKUKAN OLEH MASYARAKAT MODERN. PERSETUJUAN KREDIT DAPAT MEMBANTU MASYARAKAT UNTUK MENDAPATKAN PINJAMAN SEJUMLAH UANG GUNA MEMBELI SUATU BARANG YANG UMUMNYA TIDAK DAPAT TERJANGKAU DENGAN PEMBAYARAN KONTAN. BANYAKNYA KRITERIA YANG DIAJUKAN OLEH PIHAK BANK ATAU PIHAK PENYEDIA DANA PADA UMUMNYA BERFUNGSI UNTUK MENYARING DATA NASABAH YANG NANTINYA DINYATAKAN LAYAK DIBERIKAN KREDIT ATAUPUN TIDAK. SEMAKIN BANYAK KRITERIA ATAU ATRIBUT YANG DIGUNAKAN TIDAK AKAN MENJAMIN KEAKURATAN KLASIFIKASI PERSETUJUAN KREDIT. BEBERAPA ATRIBUT YANG TIDAK BERPENGARUH JUSTRU AKAN MEMBUAT HASIL KLASIFIKASI MENJADI KURANG AKURAT. USAHA UNTUK MENGETAHUI BERAPA BESAR PENGARUH SEBUAH ATRIBUT DALAM KLASIFIKASI BANYAK DILAKUKAN OLEH PENELITI. SALAH SATUNYA MENGGUNAKAN TEKNIK DATA MINING. DALAM TAHAP PRE PROCESSING DATA MINING SALAH SATU YANG BANYAK DIGUNAKAN ADALAH SELEKSI FITUR. BEBERPA TEKNIK SELEKSI FITUR BERHASIL UNTUK MENINGKATKAN AKURASI KLASIFIKASI DATA MINING. DALAM PENELITIAN INI AKAN DILAKUKAN SELEKSI FITUR MENGGUNAKAN ALGORITMA INFORMATION GAIN UNTUK MENGETAHUI PENGARUH ATRIBUT TERHADAP KLASIFIKASI PERSETUJUAN KREDIT.
Co-Authors Erni Rahmawatie Ivandari Ivandari Ivandari Ivandari Much. Rifqi Maulana Much. Rifqi Maulana