cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yopi Andry Lesnussa
Contact Email
pijmath.journal@mail.unpatti.ac.id
Phone
+6285243358669
Journal Mail Official
pijmathunpatti@gmail.com
Editorial Address
Pattimura University, Jln. Ir. M. Putuhena, Kampus Unpatti, Poka-Ambon City, 97124, Maluku Province, Indonesia
Location
Kota ambon,
Maluku
INDONESIA
Pattimura International Journal of Mathematics (PIJMath)
Published by Universitas Pattimura
ISSN : -     EISSN : 28306791     DOI : https://doi.org/10.30598/pijmathvol1iss2year2022
Core Subject : Education,
Mathematics
Pattimura International Journal of Mathematics (PIJMath) is provided for writers, teachers, students, professors, and researchers, who will publish their research reports about mathematics and its is applications. Start from June 2022, this journal publishes two times a year, in May and November
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
 1 
Search results for , issue "Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)" : 5 Documents clear
Application of Classification Data Mining Technique for Pattern Analysis of Student Graduation Data with Emerging Pattern Method Handayani, Aditya; Satyahadewi, Neva; Perdana, Hendra
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp01-06

Abstract

Data mining has been applied in various fields of life because it is very helpful in extracting information from large data sets. Student graduation data is one example of data that can be extracted for information and become a recommendation. This study used a classification data mining technique to extract information from the student graduation data. The classification technique used was the Emerging Pattern method to search for patterns in the student graduation data. The data in this study were graduation data for students of the Statistics Study Program, Faculty of Mathematics and Natural Sciences, Tanjungpura University, from 2013-2018. The sample data used amounted to 186 records. Attributes used in this study include as many as four attributes, including gender, batch, GPA, and TUTEP scores. This research began by finding the class and frequency values obtained. It was continued by calculating each item set's support, growth rate, and confidence values. This study obtained the highest confidence value among all the attributes owned, namely 91% in the 2013 batch itemized list and the 2018 batch. Female students dominated the class attribute. TUTEP dominated the TUTEP value attribute with a score of 425, and the GPA attribute of 3.51-4.00 dominated the class with a confidence value of 60%.
Comparison of Adaboost Application to C4.5 and C5.0 Algorithms in Student Graduation Classification Crismayella, Yuveinsiana; Satyahadewi, Neva; Perdana, Hendra
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp07-16

Abstract

Students become a benchmark used to assess quality and evaluate college learning plans. Therefore, students who graduate not on time can have an effect on accreditation assessment. The characteristics of students who graduate on time or not on time in determining student graduation can be analyzed using classification techniques in data mining, namely the C4.5 and C5.0 algorithms. The purpose of this study is to compare the application of the Adaboost Algorithm to the C4.5 and C5.0 Algorithms in the classification of student graduation. The data used is the graduation data of students of the Statistics Study Program at Tanjungpura University Period I of the 2017/2018 Academic Year to Period II of the 2022/2023 Academic Year. The analysis begins by calculating the entropy, gain and gain ratio values. After that, each data was given the same initial weight and iterated 100 times. Based on the classification results using the C5.0 Algorithm, the attribute that has the highest gain ratio value is school accreditation, meaning that the school accreditation attribute has the most influence in the classification of student graduation. The application of the Adaboost Algorithm to the C5.0 Algorithm is better than the C4.5 Algorithm in classifying the graduation of students of the Untan Statistics Study Program. The Adaboost algorithm was able to increase the accuracy of the C5.0 Algorithm by 12.14%. While in the C4.5 Algorithm, the Adaboost Algorithm increases accuracy by 10.71%.
Implementation Fuzzy and Extended Kalman Filter for Estimation of High and Low Stock Price Travel Company Santoso, Ismanto Hadi; Katias, Puspandam; Herlambang, Teguh; Anshori, Mohamad Yusak; Adinugroho, Mukhtar
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp17-24

Abstract

Competition in the business world is getting tougher from year to year both within a country and abroad. There are a large number of companies competing with one another, especially entering the free market share in Asia, namely the Asean Economic Community (AEC). In the current development of modern economy, Indonesia is making efforts to increase its economic growth. For this, developments in any fields are made. Among others is the service industry such as accommodation, travel, and transportation services. Considering that Indonesia is a country comprised of many islands with a variety of natural beauty, it has the very potential for tourist resort attraction. This kind of thing leads to the growth of the Travel, tourism and hotel industry to support development of tourism. With such rapid service industry development, supported by promising business opportunities, investors for such sector are encouraged. The right way to reduce risk for investors interested is to develop a system for estimating the stock prices. Therefore, in this study, the high and low stock price estimation method applied for travel companies adopted developed Kalman Filter, a comparison of two Kalman Filter development methods, namely Extended Kalman Filter (EKF) and Fuzzy Kalman Filter (FKF) as a chart for investors to take into consideration in their investment decision making. The simulation results showed that the EKF method had higher accuracy than the FKF method with an error by the EKF of 3.5% and that by the FKF of 8.9%.
Application of Fuzzy Logic Mamdani Method to Determine the Amount of Ayudes Production (Case Study: CV. Abadi Tiga Mandiri Ambon) Rumalowak, Diana; Lesnussa, Yopi Andry; Rumlawang, Francis Yunito
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp25-32

Abstract

A company does not see a problem, namely level competition. This competition is for companies to be able to provide a marketing or productivity strategy in order to survive and even have to increase their production volume. Because the estimated number of products produced is less than the number of requests, the company will lose the opportunity to get maximum and somewhat profit. Therefore, what needs to be considered in deciding the total of production is the total of demand and supply date. Writing and discussion in this study is about the application of the Logic Fuzzy Method Mamdani (Min-Max) to determine the total of production based on the total of demand and supply where the data is taken from CV. Abadi Tiga Mandiri Ambon and by applying the fuzzy logic method mamdani and Matlab assistance obtained results with a truth level of 93,238%. So that the application of Mamdani's Fuzzy Logic Method can help companies determine the number of items that must be made.
Ordinal Logistic Regression Analysis of Factors that Affecting the Blood Sugar Levels Diabetes Mellitus Patients Mayawi, Mayawi; Nurhayati, Nurhayati; Talib, Taufan; Bustan, Ariestha W; Laamena, Novita S
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp33-42

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh faktor-faktor risiko terhadap kadar gula darah pada penderita diabetes mellitus menggunakan analisis regresi logistik ordinal. Faktor-faktor risiko yang dijadikan variabel bebas adalah usia, jenis kelamin, Indeks Massa Tubuh (IMT), tekanan darah, Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL), Thyrocalcitonin Hormone (TCH) dan Loss Trigliserida(LTG). Data yang digunakan dalam penelitian ini diperoleh dari https://hastie.su.domains/Papers/LARS/diabetes.data. Jumlah sampel yang diambil sebanyak 100 responden yang telah terdiagnosis diabetes mellitus. Hasil penelitian menunjukkan bahwa faktor-faktor risiko seperti usia, Indeks Massa Tubuh (IMT), Tingkat Kolesterol (TC), Low Density Lipoprotein (LDL), High Density Lipoprotein (HDL) dan jenis serum Thyrocalcitonin Hormone (TCH) berpengaruh signifikan terhadap kadar gula darah pada penderita diabetes mellitus. Model logit terbaik untuk regresi logistic ordinal adalah Logit 1 yaitu g(x_1 )= -2.721-0.079 X_1+2.813〖 X〗_3+〖0.100 X〗_5-0.099 X_6-0.119 X_7-0.989 X_8 dan Logit 2 yaitu g(x_2 )= -8.571-0.079 X_1+2.813〖 X〗_3+〖0.100 X〗_5-0.099 X_6-0.119 X_7-0.989 X_8. Disimpulkan bahwa analisis regresi logistik ordinal dapat digunakan untuk mengidentifikasi faktor-faktor yang mempengaruhi kadar gula darah pada penderita diabetes mellitus dan membantu pengembangan strategi pengelolaan dan intervensi yang lebih efektif

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol 4 No 2 (2025): Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 2 (2024): Pattimura International Journal of Mathematics (PIJMath) Vol 3 No 1 (2024): Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 2 (2022): Pattimura International Journal of Mathematics (PIJMath) Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)