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All Journal JAIS (Journal of Applied Intelligent System) Jurnal DISPROTEK
Bijanto Bijanto
Sekolah Tinggi Teknik Pati
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Improvement of Accuracy and Handling of Missing Value Data in the Naive Bayes Kernel Algorithm Bijanto Bijanto; Ryan Yunus
Journal of Applied Intelligent System Vol 6, No 2 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i2.5288

Abstract

The lost impact on the research process, can be serious in classifying results leading to biased parameter estimates, statistical information, decreased quality, increased standard error, and weak generalization of the findings. In this paper, we discuss the problems that exist in one of the algorithms, namely the Naive Bayes Kernel algorithm. The Naive Bayes kernel algorithm has the disadvantage of not being able to process data with the mission value. Therefore, in order to process missing value data, there is one method that we propose to overcome, namely using the mean imputation method. The data we use is public data from UCI, namely the HCV (Hepatisis C Virus) dataset. The input method used to correct the missing data so that it can be filled with the average value of the existing data. Before the imputation process means, the dataset uses yahoo bootstrap first. The data that has been corrected using the mean imputation method has just been processed using the Naive Bayes Kernel Algorithm. From the results of the research tests that have been carried out, it can be obtained an accuracy value of 96.05% and the speed of the data computing process with 1 second.
PEMBELAJARAN ALGORITMA K-NN UNTUK BIG DATASET MENGGUNAKAN METODE SAMPLE BOOTSTRAP DAN WEIGHTED GINI INDEX Bijanto Bijanto; Zainal Abidin; Teguh Tamrin
JURNAL DISPROTEK Vol 12, No 2 (2021)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v12i2.2091

Abstract

Dataset yang mempunyai jumlah record atau atribut dalam jumlah besar bisa disebut juga dengan dataset besar. Ukuran dataset besar memiliki jumlah dalam besaran dari terabyte sampai petabyte. Pengolahan dataset besar tersebut membutuhkan komputer yang memiliki spesifikasi tinggi. Untuk mengklasifikasikan objek baru berdasarkan atribut data training sample tersebut bisa menggunakan algoritma k-NN. Salah satu kelebihan algorotma kNN adalah efektif dan sering digunakan untuk mengatur permasalahan mengenai klasifikasi. Cukup lamanya waktu komputasi menjadi salah satu kelemahan algoritma kNN. Hal ini diakibatkan oleh proses kalkulasi algoritma kNN terhadap dataset yang besar. Dari masalah-masalah yang muncul tersebut, maka peneliti mengusulkan sistem pembelajaran kNN menggunakan boostraping dan Weighted Gini Index sebagai solusi untuk penanganan masalah pengolahan dataset besar. Pembelajaran kNN menggunakan Bootstrap-Weighted Gini Index dipakai untuk memangkas atribut maupun record berlandaskan hasil penyaringan atribut dan record yang mempunyai kuwalitas error sedikit. Penelitian ini membuktikan bahwa, hasil penambahan akurasi yang didapat dari pengolahan pada dataset Landsat (akurasi semula sebesar 91,40% menjadi 94,95%), Thyroid (akurasi semula 89,31% menjadi 96,61%), HTRU (akurasi semula 96,01% menjadi 98,18%) dan EEG Eye (akurasi semula 97,40% menjadi 97,80%).
PERANCANGAN DAN IMPLEMENTASI SISTEM SMARTFARMING MENGGUNAKAN ARDUINO UNO DAN MODUL ESP8266 Bijanto Bijanto; Sigit Prakosa Adi Nugroho; Yayang Fredyatama; Dini Fahrani; Ellen Proborini
JURNAL DISPROTEK Vol 14, No 1 (2023)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v14i1.3924

Abstract

The agricultural sector is a promising sector in the future, but in Indonesia the agricultural sector has not developed properly due to many factors such as individual land ownership which tends to be narrow, young people who do not want to continue their parents' business in farming, then 60% of farmers in Indonesia still relies on traditional farming systems, regardless of water needs, seasons, climate, sales prices, types of plants and others. Traditional farming systems also require high costs, so the income is less than optimal. According to BPS, in the second quarter of 2022 the highest economic sector was found in the business fields of agriculture, forestry and fisheries at 13.15%. This potential should be developed in the industrial era 4.0, as an example of technology that can be applied in the agricultural sector, namely the Internet of Think, a system is needed to control water demand, temperature and light intensity needed to help plant photosynthesis. The proposed system will use four sensors including a light sensor (LDR), a temperature sensor (dht-11), an ultrasonic sensor, and a soil moisture sensor. In this proposed system monitoring can also be carried out using the Blynk application which can be monitored via a smartphone. The system development process uses the Rnd method, because the Rnd method is suitable for making products that can be applied to support agriculture.
Co-Authors Dini Fahrani Ellen Proborini Ryan Yunus Sigit Prakosa Adi Nugroho Teguh Tamrin Yayang Fredyatama Zainal Abidin