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All Journal JAIS (Journal of Applied Intelligent System) Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Pengabdian Mandiri Proceeding International Pelita Bangsa
Edy Widodo
Universitas Pelita Bangsa
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Perbandingan Dalam Memprediksi Penyakit Liver Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor al fiyan; Muhamad Fatchan; Nanang Tedi Kurniadi; Edy Widodo
Jurnal Pelita Teknologi Vol 16 No 1 (2021): Maret 2021
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.728 KB) | DOI: 10.37366/pelitatekno.v16i1.309

Abstract

Along with the rapid development of information technology, and also the increasing need for information in various fields including health sector. Based on data from the World Health Organization (WHO), chronic hepatitis B attacks 300 million people in the world including Southeast Asia and Africa which causes the death of more than 1 million people each year. So far, a lot of data in the hospital has not been used, even though this data can be used to predict liver disease if used. The purpose of this study was to determine the comparison of the accuracy value of the Naïve Bayes algorithm and K-Nearest Neighbor. One of the classifications is to use the Naïve Bayes and K-Nearest Neighbor algorithms and use the Rapid Miner tools in the tests used. The results of this study indicate that the Naïve Bayes algorithm has a higher accuracy rate of 84.00% in diagnosing liver disease compared to the K-Nearest Neighbor algorithm which only gets a value of 80.57%. From this research it can be concluded that the Naïve Bayes algorithm is 3.43% greater than K-Nearest Neighbor.
PREDIKSI PENJURUSAN IPA, IPS DAN BAHASA DENGAN MENGGUNAKAN MACHINE LEARNING Edy Widodo
Jurnal Pelita Teknologi Vol 15 No 1 (2020): Maret 2020
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (440.871 KB) | DOI: 10.37366/pelitatekno.v15i1.262

Abstract

ABSTRACT Data Mining is an artificial intelligence (Artificial Intelegent) that is very useful and useful for research scientists who include machine learning, statistics and databases. Thus data mining is a research method with the aim of uncovering hidden patterns. It can also be interpreted that data mining is a very useful and useful research pattern for turning data into information. With this application, it will be easier for teachers, parents, guardians of students and students to be more practical and accurate in determining the direction of Natural Sciences, Social Sciences and Languages. Which will be the weight of the calculation of the majors determination are the SMP (National Examination) UN scores, TKD scores (Basic Ability Tests) in high school, IQ scores, students 'interest values ​​and parents' desire values ​​(being another alternative). Thus the results of these predictions will result in the appropriate and accurate major with the ability of students and girls related. Keywords: Naive Bayes, DecisionTtree, Support Vector Machine and Machine Learning
Analisis Sentimen Terhadap Masyarakat Indonesia Di Masa PPKM Menggunakan Algoritma Naïve Bayes Ahmad Turmudi Zy; Aswan S Sunge; Riani Riani; Edy Widodo
Jurnal SIGMA Vol 13 No 2 (2022): Juni 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Abstract Coronavirus is a group of viruses that can cause disease in animals or humans. Several types of coronavirus are known to cause respiratory tract infections in humans ranging from coughs and colds to more serious ones such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). One of the topics currently being discussed by the public, including on social media Twitter, is the government's policy regarding the Enforcement of Restrictions on Community Activities (PPKM). PPKM is a policy of the Government of Indonesia to deal with COVID-19 that has been made since early 2021. The implementation of PPKM raises pros and cons from the community. Based on the results of the SRMC survey reported through the saifulmunjani.com page, it was stated that nationally, 44% chose to strictly implement PPKM even though on the other hand income decreased, and 40% chose to stop PPKM with an increased risk of COVID-19 transmission. Based on the problems that occurred, it became the basis of this research which aims to find out how the public sentiment towards the implementation of PPKM policies in Indonesia through tweets and comments on the Twitter social media platform using sentiment analysis Keywords: PPKM, Naïve bayes, Covid19
Penerapan Algoritma Naive Bayes Pada Analisa Penyebab Kurang Dan Lebihnya Penggunaan Cutting Tool (Study Kasus Di PT. Sumiden Sintered Component Indonesia (SSI) Edy Widodo
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

PT.Sumiden Sintered Component Indonesia (SSI) Is a company engaged in automotive component parts, and this company is also one of a group of companies from Japan namely Sumitomo Corporation in collaboration with local companies Santini Group. PT.SSI was founded in 2012 in the manufacture of component parts with metallurgical technology. Metallurgical technology with this synthesis is a new technology that has existed in Indonesia. PT.SSI has difficulty in processing data using cutting tools which often results in excess and underuse due to inaccurate data. To support this problem, the authors apply the Naive Bayes method to provide a solution in analyzing the problem of the lack and excess use of cuting tools at PT SSI. The data taken in this study is based on data in 2017 and 2018. This research is expected to help SSI companies in analyzing the problem of less and more use of cutting tools. That way, the application of this method is expected to help the user in doing his work. Naive Bayes Method Is a simple probabilistic classification that calculates a set of probabilities by adding up the frequency and combination of given dataset values. The algorithm uses the Bayes theorem and assumes all the attributes are independent or not interdependent given by the value of the class variable. In the above problem, the choice of using the Naive Bayes algorithm is due to the amount of data used in this study. Because the calculation of Naive Bayes algorithm only requires a small amount of training data to estimate parameters. Keyword : Naive Bayes, Prediction, Cutting Tool.
Penerapan Data Mining Untuk Prediksi Penerima Bantuan Pangan Non Tunai (BPNT) Di Desa Wanacala Menggunakan Metode Naïve Bayes Edy Widodo; Ahmad Jaelani
Jurnal SIGMA Vol 13 No 3 (2022): September 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The Non-Cash Food Assistance Program held by the government is often not on target due to many factors, one of which is the number of criteria that must be considered to be a decision of beneficiaries. Of the eleven criteria set requires the right algorithm to perform calculations so that the results given are more accurate. Naïve Bayes algorithm is a method for classification using probability theory that has a high degree of accuracy. Naïve Bayes algorithm testing uses Rapid Miner tools that produce an accuracy rate of 96% of the 50 data provided. This algorithm is right for the selection of recipients of non-cash food assistance. There are 2 classes that are needed, namely Worthy and Not Eligible. Keywords: Classification, Naïve Bayes, Rapid Miner, Non-Cash Food Aid
Decision Support System Recommendation Housing Using AHP And Saw Method Palangka Raya City Gatot Tri Pranoto; Ismasari Nawangsih; Edy Widodo
Journal of Applied Intelligent System Vol 7, No 3 (2022): 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.v7i3.7038

Abstract

Palangka Raya City, as one of the provincial capitals in Indonesia, which has an area of around 2,400,000 km2, is a strategic city as a service and distribution hub for the industrial, trade, government and education sectors. Regional Policy of the city government with the existence of a development plan in the City of Palangka Raya as an implementation of the city space with all the disadvantages of its designation resulting in a distribution pattern of urban land types which in fact is not evenly distributed throughout the city. This research was conducted based on the results of observations made in several Marketing Agents in the Palangkaraya Region, which included 5 districts where the survey results obtained several marketing agents for KPR housing with the aim of facilitating the purchase of KPR housing. The purpose of this study is to design a decision support system that is used to support the decision to purchase housing loans in the Palangkaraya area. Based on the research that has been done, it is expected that the results of the purchase decision support system for the KPR recommendation with the best value can be a recommendation for the purchase. This system is designed with the AHP and SAW methods to help prospective residents to determine the house based on the desired criteria.
Klasifikasi Barang Paling Laku (Pareto) Indomaret Untung Suropati 35 (T3m1) Menggunakan Rapidminer Dengan Metode Naive Bayes Edy Widodo; Ananto Tri Sasongko; Antika Zahrotul Kamalia
Jurnal SIGMA Vol 13 No 4 (2022): Desember 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Penelitian ini dilatarbelakangi oleh banyaknya barang paling laku. Salah satunya pengiriman barang dari pusat distribusi ke store. Masalah utama dalam penelitian ini, adalah: banyaknya kiriman barang yang kurang laku membuat area gudang tidak bisa menahan barang yang membuat barang tersebut menjadi over stok. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan 2 jenis data yaitu kualitatif dan kuantitatif dengan metode klasifikasi naive bayes. Teknik pengambilan sampel menggunakan teknik data penjualan dengan total sampel 1.173 item. Teknik pengumpulan data dengan obervasi, wawancara, dan dokumentasi. Teknik analisis penelitian data yang digunakan adalah klasifikasi. Dari dokumen yang diperoleh hasilnya bahwa klasifikasi barang paling laku (pareto) Indomaret Untung Suropati 35 (T3M1) menggunakan Tools Rapidminer dengan Metode Naive Bayes. Adapun yang diperoleh dapat memprediksi barang yang benar-benar dibutuhkan dan dahulukan dalam pengiriman dari pusat distribusi barang. Tujuan penelitian menggunakan Tools Rapidminer untuk menghasilkan data-data yang lebih akurat dalam proses penjualan barang retail dengan konsumen itu sendiri seperti pedagang retail, grosir, Pareto dan supermarket. Penelitian berbentuk studi kasus dengan metode penelitian Neive Bayes. Penelitian Klasifikasi Penjualan Barang Paling Laku (Pareto) di Indomaret Untung Suropati 35 (T3M1) menggunakan Data Mining ini memperlihatkan proses penjualan barang yang paling laku memiliki verifikasi yang akurat mengenai sistem pendataan barang, stok barang, ketersediaan barang, FIFO (First In First Out),FEFO (First End First Out) dengan tujuan mempermudah karyawan dalam melakukan transaksi proses dan penerimaan barang dari supplier dan dari Pusat DC (Distribution Center) ke toko. Hasil penelitian klasifikasi barang paling laku (pareto) Indomaret Untung Suropati 35 (T3M1) menggunakan Tools Rapidminer dengan Metode Naive Bayes memiliki nilai akurasi 88,50%, precision 97,92%, recall 81,74%. Dari hasil validasi penghitungan metode klasifikasi Naive Bayes dengan Tools Rapidminer mampu memberikan penjabaran secara signifikan dengan nilai akurasi yang baik dan berpengaruh pada prediksi penerimaan barang yang sesuai dengan permintaan dan kebutuhan konsumen. Kata kunci : Tools Rapidminer, Retail, Konsumen, Distributor, Klasifikasi, Metode Neive Bayes, Pareto
Smart Fishfeed Untuk Budi Daya Ikan Air Tawar Berbasis Internet Of Things Edy Widodo; Agus Sulistiawan
Jurnal SIGMA Vol 10 No 3 (2019): September 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Internet Of Things (IoT) merupakan suatu teknologi yang bertujuan untuk melakukan kontrol jarak jauh dengan memanfaatkan koneksi internet. Internet Of Things (IoT) dapat dimanfaatkan untuk pemberian pakan ikan secara otomatis dari jarak jauh menggunakan ponsel android dengan sistem smart fishfeed. Metode yang digunakan dalam pembuatan sistem pemberian pakan ikan berbasis Internet Of Things (IoT) adalah metode prototipe. Sistem pemberi pakan ikan otomatis ini terdiri dari prototipe smart fishfeed dan aplikasi android serta web server smart fishfeed. Hasil penelitian menunjukan bahwa ponsel android dapat digunakan untuk mengontrol pemberian pakan ikan dan memantau stok pakan ikan. Sehingga dengan hasil tersebut pembudidaya dapat melakukan pemberian pakan ikan tanpa harus turun langsung di lapangan. Kata kunci: Internet of Things (IoT), Smart Fishfeed, Pakan Ikan.
Analisa Prediksi Hasil Produksi Popok Bayi Metode Naïve Bayes Edy Widodo; Sifa Fauziah; Asep Arwan Sulaeman
Bulletin of Information Technology (BIT) Vol 4 No 1: Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i1.504

Abstract

PT. Elleair Interantional Manufacturing Indonesia is a company engaged in the field of manufacturing baby diapers. With the increasing market demand causing an increase in the production process, what is often experienced is that there is often a lack of finish good product to meet consumer de mand due to delays in the production process. To make it easier for companies to look for factors that can increase production result, the authors coduct research with data mining using the naïve bayes method. In this study the training data and testing data were tested using the RapidMiner application with the naïve bayes algorithm where the tested data were 500 data. Testing is done by calculating the value of precision, recall, AUC dan accuracy using the RapidMiner Application and using Microsoft Excel and calculating the final probability of each class to calculate predictions of product result. With the naïve bayes method we can calculate predictions of production result based on data from the previus year as training data to anticipate shortages in production due to factors that can hider the production process. From the results of the analysis obtained factors that affect production result, namely, the number of materia used for the production of 318 data. The human error factor with the category of “No” as much as 305 data also influences because the less the occurrence of human error the production results are also high. Stop delivery factor with the category “No” as many as 299 data, with fewer cases of stop delivery, the more finish good product that can be sold
KLARIFIKASI DALAM MENENTUKAN PESERTA PEMILIH UNTUK MEMBANTU PETUGAS PANTARLIH DALAM MENENTUKAN HAK PEMILIH DALM PEMILU 2024 Edy Widodo
JURNAL PENGABDIAN MANDIRI Vol. 2 No. 7: Juli 2023
Publisher : Bajang Institute

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Abstract

In the current digital era, the general public does not yet understand the term digitization, therefore the government, in seeking correct and valid data, collects citizen data according to KTPs and residences manually. Then valid data will later be used as a potential voter in the 2024 PEMILI. Therefore the government formed a committee to record all local residents according to their KTPs and residences called PANTARLIH officers (Voters Data Updating Officers) who are the spearhead of the KPU in updating and registering voters. The data collection includes age, having an ID card, family card, all of this is recorded according to the local address, so that the voters' data will be truly local residents. The results of this data will become valid voter data in the 2024 election era.
Co-Authors Agus Sulistiawan Ahmad Jaelani Ahmad Turmudi Zy al fiyan Ananto Tri Sasongko Antika Zahrotul Kamalia Asep Arwan Sulaeman Aswan S Sunge Aswan Supriyadi Sunge Gatot Tri Pranoto Muhamad Fatchan Nanang Tedi Kurniadi Nawangsih, Ismasari Riani Riani Sifa Fauziah