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All Journal Sistemasi: Jurnal Sistem Informasi JURNAL TEKNOLOGI DAN OPEN SOURCE JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Jurnal Tunas Jurnal Teknik Informatika (JUTIF) Jutsi: Jurnal Teknologi dan Sistem Informasi J-Com (Journal of Computer) Jurnal Ipteks Terapan : research of applied science and education Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat
Herman Saputra
Manajemen Informatika, Sekolah Tinggi Manajemen Informatika Dan Komputer Royal, Indonesia
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
THE USE OF THE WMA METHOD PREDICTS THE INVENTORY OF TOFU RAW MATERIALS CASE STUDY INDUSTRY TAHU IYUS Desy Julika Sari; Herman Saputra; Akmal Nasution
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.2.224

Abstract

Industry Tahu Iyus is a business engaged in the industrial sector, this business makes white tofu and fried tofu. The problem that occurs in the Industry Tahu Iyus is that it is still difficult to determine the amount of tofu raw material inventory for the next month and experiencing an excess of tofu raw materials if this continues, the Industry Tahu Iyus will experience losses, one of the raw materials will be damaged and will experience a bad smell to the raw materials. The purpose of this research is to be able to build a forecasting system for the inventory of tofu raw materials in the Industry Tahu Iyus using previous data. The method that can be used in this study to predict the inventory of tofu raw materials is the Weighted Moving Average method, because this method is able to provide predictions by utilizing previous data and each data is given a different weight. This study applies the Weighted Moving Average method to obtain accurate forecasting results so as to minimize errors between forecasting and inventory reality, and is applied to a forecasting system for tofu raw material inventory per month with forecasting results for the following month, namely 4.150 Kg with a MAPE value of 6.54%.
IMPLEMENTATION OF WEB-BASED NAIVE BAYES ALGORITHM FOR DETERMINING DEPARTMENTS AT SMK 10 MUHAMMADIYAH KISARAN Nurlaili Sabila; Herman Saputra; Muthia Dewi
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.605

Abstract

Determination of majors is very important for the convenience of prospective students in the process and continuation of education so that they do not experience difficulties in the teaching and learning process in the future. SMK 10 Muhammadiyah Kisaran is one of the private vocational schools in Asahan that provides 3 majors including Audio Video Engineering (TAV), Computer and Network Engineering (TKJ), and Motorcycle Engineering and Business (TBSM). SMK 10 Muhammadiyah Kisaran does not yet have a special system for selecting majors so that prospective students are welcome to choose majors according to their own wishes, not a few students find it difficult because the students themselves do not understand their abilities.so that it’s not uncommon for students to choose majors in a random way or follow their friends' choices. Therefore we need a system that can help prospective students in selecting majors that match their interests and talents and reduce mistakes in choosing majors. The technique used for the classification data mining model in this study is the Naïve Bayes Algorithm. The dataset that will be used as training data and test data is data for new students for the 2021/2022 school year, to be precise, for class X SMK 10 Muhammadiyah Kisaran obtained from the results of documentation and questionnaires. The criteria used were school origin, gender, interests, major, influence of friends, parental suggestions, math scores, English grades, and science grades. The results of the classification modeling with the Naïve Bayes Algorithm produce an accuracy value of 89%.
Co-Authors Adi Prijuna Adi Prijuna Lubis Andri Nata Ari Dermawan Arip Muhridan Bachtiar Efendi Desy Julika Sari Dian Yudha Kurnia Sirni Fauzy Maulana Sinaga Habibullah . Ilham Ramanda Iqbal Kamil Siregar Jhonson Efendi Hutagalung Maulana Dwi Sena Muhammad basirun Muthia Dewi Nasrun Marpaung Nasution, Akmal Nofriadi Nofriadi Nofriadi Nofriadi Novi Sentia Putri Nur Aini Nurlaili Sabila Pristiyanilicia Putri Puspita Deri Syahfitri Rahayu, Elly Ruri Ashari Dalimunthe Sahren Sri Rezki Maulina Azmi Suparmadi S Wan Mariatul Kifti Yunita Afril Tisa