Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE
Dwipa Junika Putra
Unknown Affiliation
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Student Identification Based on Patterns of Association For Student Misbehaviour Using Frequent Pattern Growth Algorithms Erlinda Erlinda; Dwipa Junika Putra; Mourend Devegi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.3071

Abstract

Student infractions are incidents often committed by students who break the rules at school. This naturally worries school authorities and overwhelms them with student misbehavior. Student rule-breaking is a common problem that can interfere with a safe and orderly learning environment. The more students break the rules, the greater the impact on several aspects, including student achievement, discipline, suboptimal teaching and learning activities, and students' social lives outside of school. Identifying students who are prone to rule violations can help school officials implement more effective prevention programs. Data mining is a process of extracting information from large data sets to discover patterns and relationships hidden within them. This study aims to identify frequent student infractions using the Frequent Pattern Growth algorithm. The Frequent Pattern Growth (FP -growth) algorithm is used to generate frequent itemsets that are then used in the association rules process. The association rules process aims to find rules or relationships between violations based on the discovered Frequent Itemsets. This process is influenced by predefined minimum support and minimum confidence values. A Minimum Support value of 30% and a Minimum Confidence value of 50% are used to obtain rules with a sufficiently high confidence level. It is expected that the identification results from this study will provide a better understanding of the types of violations commonly committed by students in school. This information can be used by school officials to develop more effective prevention strategies and focus on.
Implementation of Fuzzy Logic Using Mamdani Method to Determine The Quantity of Bag Production (Case Study In Roman Indah Padang Bag Factory) Dwipa Junika Putra; Nofriadi Nofriadi; Erlinda Erlinda
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 5 No. 1 (2022): Jurnal Teknologi dan Open Source, June 2022
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v5i1.2220

Abstract

Roman Indah bag Factory is a bag manufacturing company that receives orders from customers every month. Fuzzy Logic is used to define the production known so far, so that the preferred result can be used as a basis for manager's decision. In the calculation process, Fuzzy Logic Mamdani requires maximum and minimum production data, maximum and minimum demand data, and maximum and minimum inventory data. Fuzzy logic is able to map an input into an output without factor factors. Fuzzy logic is used to create a model of a system that is able to determine the quantity of production. the factors that affect the quantity of production. Fuzzy logic is called the old new logic because the science of modern fuzzy logic and methodological was discovered only a few years ago, in fact the concept of fuzzy logic itself has been in us for a long time. Mamdani method is the most common method when it comes to fuzzy methodology. Mamdani method uses a set of IF-THEN rules derived from experienced operators/experts. The Mamdani model is often known as the Max-Min model. By using Mamdani method in Roman Indah handbag factory can assist in the efficiency of time and labour, because using Mamdani method can calculate the amount of production in the next month, so from the results can be derived consideration material decision by the manager, whether in determining raw materials, promotion, bag model, consumer, HR, etc. so that more company profits.
Student Identification Based on Patterns of Association For Student Misbehaviour Using Frequent Pattern Growth Algorithms Erlinda Erlinda; Dwipa Junika Putra; Mourend Devegi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.3071

Abstract

Student infractions are incidents often committed by students who break the rules at school. This naturally worries school authorities and overwhelms them with student misbehavior. Student rule-breaking is a common problem that can interfere with a safe and orderly learning environment. The more students break the rules, the greater the impact on several aspects, including student achievement, discipline, suboptimal teaching and learning activities, and students' social lives outside of school. Identifying students who are prone to rule violations can help school officials implement more effective prevention programs. Data mining is a process of extracting information from large data sets to discover patterns and relationships hidden within them. This study aims to identify frequent student infractions using the Frequent Pattern Growth algorithm. The Frequent Pattern Growth (FP -growth) algorithm is used to generate frequent itemsets that are then used in the association rules process. The association rules process aims to find rules or relationships between violations based on the discovered Frequent Itemsets. This process is influenced by predefined minimum support and minimum confidence values. A Minimum Support value of 30% and a Minimum Confidence value of 50% are used to obtain rules with a sufficiently high confidence level. It is expected that the identification results from this study will provide a better understanding of the types of violations commonly committed by students in school. This information can be used by school officials to develop more effective prevention strategies and focus on.
Co-Authors Erlinda Erlinda Mourend Devegi Nofriadi Nofriadi