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All Journal Proceeding International Conference on Information Technology and Business Jurnal Informasi dan Komputer
Chairani Fauzi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Mobile Blockchain in Digital Supply Chain Riko Herwanto; Nisar Nisar; Chairani Fauzi; Fitria -; Amnah Amnah; Faurani I Santi Singagerda; Hary Sabita
Prosiding International conference on Information Technology and Business (ICITB) 2022: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 8
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

Mobile device users are estimated to reach 4 billion in 2018 and 6 billions users in 2023. These mobile devices have changed how individuals and organizations carry out their activities. Increasing smartphone efficiency is changing how companies collaborate with their suppliers and distributors. Blockchain technology, which was first created for Bitcoin, is generating disruption in other industries. Even if the long-term forecasts for Bitcoin and the economy remain uncertain, it is increasingly evident that Blockchain technology can cause enormous disruption. However, because it poses a threat to the existing order of the modern world's prominent organizations, institutions, and power structures, its potential may never be fully realized. This article will introduce you to Blockchain technology and its potential use in the logistics and supply chain. If maintained on the Blockchain, data about supply chains and logistics can be sent across the network in a reliable, secure, and auditable manner. In some supply chain implementations, Blockchain is being replaced by distributed ledger technology, which offers several benefits. This article discusses the possible use of blockchain technology and mobile devices in the supply chain and logistics industryKeywords— Blockchain, Distributed Ledger, Mobile Device,  Supply Chain, and Digital Supply Chain
Naïve Bayes Classifier Algorithm for Predicting Non-Participation of Elections in Lampung Province Fitria -; Rifad Sobah; Chairani Fauzi; Septilia Arfida; Suci Mutiara; Siti Nurlaila
Prosiding International conference on Information Technology and Business (ICITB) 2022: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 8
Publisher : Proceeding International Conference on Information Technology and Business

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Abstract

Several problems related to the DPT (Permanent Voter List) including the KPU (General Election Commission) it is difficult to get the NIK (Population Identification Number) of people who are in correctional institutions or prisoners, beginner voters who do not have an ID card (Kartu Tanda Sipil) who are currently in prison. study in student dormitories, Islamic boarding schools, and others who are outside the city, the number of which is 3-5% of invalid NIK, voters who do not have a resident identity, voters with KTP (Kartu Identity Card)/old Family Card and NIK (Population Identification Number) ) invalid around 7-19% and voters are difficult to find around 5-8% so that the KPU must-visit houses as regulated in the legislation. This could allow not all DPT (Permanent Voters List) to be registered.Naïve Bayes Classifier is one of the classification methods used in Data Mining which is based on the Bayes theorem. Bayes is a simple probability-based prediction technique based on the application of Bayes' theorem (or Bayes' rule) with strong (naive) independent (independence) assumptions. Naive Bayes is only a method for analyzing, it takes other media to display information that is easy to understand the results of the Naïve Bayes Classifier calculations. Pentaho data integration is a tool that integrates large amounts of data, calls from Excel, MySQL, and provides instructions for existing data. Tableau is an application that will improve, tableau can call data that has been integrated and display the data in the form of diagrams, text, spatial data, and points from locations.Keywords: Naïve Bayes Classifier, Algorithm, Election Commission
PREDIKSI PENDAFTARAN PESERTA DIDIK BARU DENGAN METODE POLYNOMIAL REGRESSION, DAN K-MEDOIDS noviana .; Chairani Fauzi; Sriyanto .
Jurnal informasi dan komputer Vol 11 No 02 (2023): Jurnal Informasi dan Komputer yang terbit pada tahun 2023 pada bulan 10 (Oktobe
Publisher : STMIK Dian Cipta Cendikia Kotabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v11i02.528

Abstract

One school attempted to predict the acceptance of new students based on data from the previous year, but the results were inaccurate. The fluctuation in the number of new student admissions is a problem for SMK Negeri 2 Kotabumi in preparing class facilities, uniforms, books to support learning activities and determining steps and policies related to school promotion and targets for new student admissions in the following years. Predicting new student enrollment using the polynomial regression method, and K-Medoids, in processing student enrollment prediction data. The results obtained are Y values that are in accordance with the implementation results using python. For example, in 2018 the value Y = 0.0034x6 - 0.6194x5 + 46.754x4 - 1864.6x3 + 41412x2 - 485358x + 2E+06 = 1744.01 with R = 0.8779 accompanied by the same for each year, whereas for the K- Medoids method obtained in 2018 clustering 0 obtained 73 prospective students in the non-passing category and 19 in the pass category, while for 2019 to 2022 the number of cluster 0 is worth 0 and cluster 1 is worth 92 which means that all participants have passed
Co-Authors Amnah Amnah Faurani I Santi Singagerda Fitria - Fitria - Hary Sabita Nisar Nisar Noviana . Rifad Sobah Riko Herwanto septilia arfida Siti Nurlaila Sriyanto . Suci Mutiara