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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 35 Documents
 1   2   3   4 
Application of Classification Data Mining Technique for Pattern Analysis of Student Graduation Data with Emerging Pattern Method Aditya Handayani; Neva Satyahadewi; Hendra Perdana
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 Yuveinsiana Crismayella; Neva Satyahadewi; Hendra Perdana
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 Ismanto Hadi Santoso; Puspandam Katias; Teguh Herlambang; Mohamad Yusak Anshori; Mukhtar Adinugroho
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) Diana Rumalowak; Yopi Andry Lesnussa; Francis Yunito Rumlawang
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; Taufan Talib; Ariestha W Bustan; Novita S Laamena
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
Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia Vera Maya Santi; Yuliana Rahayuningsih
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp43-52

Abstract

Indonesia's extreme poverty status in 2021 was recorded to be high at 4% or 10.86 million people. One of the efforts in poverty alleviation is to analyze the factors influencing extreme poverty. Although the number of studies on poverty in Indonesia continues to grow, the findings are inconclusive because they are often discussed qualitatively. This study aimed to analyze the factors that influence extreme poverty in Indonesia using negative binomial regression. The data used was the amount of extreme poverty in 34 provinces of Indonesia as the response variable. Then, the explanatory variables used consist of 8 from the Central Bureau of Statistics. The analysis stage sought data exploration, the correlation between variables, Poisson regression model specification and assumption test, handling overdispersion with negative binomial regression, and model feasibility test. Based on the AIC value and dispersion ratio, the negative binomial model obtained an AIC value of 920.03 with a dispersion ratio 1.372. It shows that the negative binomial regression model is good enough to model extreme poverty in Indonesia. Furthermore, the factors significantly influencing extreme poverty in Indonesia are households with proper drinking water, housing status, and families with access to appropriate sanitation.
Application of Neural Machine Translation with Attention Mechanism for Translation of Indonesian to Seram Language (Geser) Abdul Wahid Rukua; Yopi Andry Lesnussa; Dorteus Lodewyik Rahakbauw; Berni Pebo Tomasouw
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp53-62

Abstract

The Seram language (Geser) is one of the regional languages in Kabupaten Seram Bagian Timur of Maluku Province which has been classified by the Language Office as an endangered language. This study uses the Neural Machine Translation (NMT) method in an effort to preserve the Seram (Geser) language. The NMT method has proven to be effective compared to SMT in overcoming the challenges of language translation by using the attention mechanism to improve translation accuracy. The data used in this study were obtained through interviews of 3538 parallel corpus, 255 Indonesian vocabularies and 269 Seram (Geser) vocabularies. The result showed that using 708 test data without Out-of Vocabulary (OOV) the BLUE Score was 0.90518992895191 or 90.518%.
Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province M. Y Matdoan; Nur A. Purnamasari; Novita S. Laamena
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp63-70

Abstract

Maluku Province has large natural resources with various potentials, from the ocean floor to the mainland. Capture fishery products are one of the leading sectors that contribute greatly to the GRDP of Maluku Province. The K-Means clustering algorithm is a suitable algorithm for grouping data objects that have the same identity. The purpose of this study is to cluster districts/cities in Maluku Province based on capture fishery products. The type of data in this study is secondary data sourced from the Maluku Province Central Bureau of Statistics (BPS) Publication in 2022. The result is that there are 3 districts/cities clusters in Maluku Province based on capture fishery products. Cluster 1 with the category of sufficient capture fisheries products, namely the Districts of Tanimbar Islands, Buru, East Seram, West Seram, South Buru, Southwest Maluku, Ambon City and Tual City. Furthermore, Cluster 2 with the category of many capture fishery products, namely the Aru Islands Regency and Southeast Maluku Regency. Furthermore, for Cluster 3, the category of capture fishery products is very large, namely Central Maluku Regency.
Determination of the Annual Pension Fund Premium for Joint-Life Status Using the Aggregate Cost Method Syuradi syuradi; Neva Satyahadewi; Hendra Perdana
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp71-78

Abstract

A pension fund is one of the responsibilities of an institution or company for all employees during their working life. In pension fund insurance, several agreements must be agreed upon by the insured and the insurer for the agreement, namely the premium. The premium to be paid by the insured (employee) of the pension fund insurance must adjust to the income earned, so that the premium to pay does not burden the insured. This study aims to determine the annual pension fund premium amount that must pay use the Aggregate Cost method in the joint-life case. The case study uses information from a husband and wife as civil servants with a husband class III B and wife III A participating in a pension program with a retirement age limit of 58 years (r = 58). The husband (insured x) was 28 years old, and the wife (insured y) was 24 when they started working and joined the pension program. The result of calculating the value of the annual pension fund insurance premium that must pay use the Aggregate Cost method is Rp.41,440,163. If the husband's age is lower than the wife's (x=24, y=28), then the value of the premium paid is more significant than when the husband's age is higher than the wife's (x=28, y=24), which is IDR 41,594,217. That is because the husband's working period is more extended than the wife's, while the chance of death for men is higher than for women. Meanwhile, premiums producing if the husband and wife are of the same age, which is cheaper than when the husband and wife are of different ages
The Influence of Macroeconomic Factors on Credit Risk of Banks in Indonesia using ARDL Model Lexy Janzen Sinay; Esther Kembauw
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp79-88

Abstract

One of the efforts to maintain economic stability during the Covid-19 pandemic is to reduce the risk of in the banking sector. One of the risks in the banking sector that must be anticipated is credit risk. Non-Performing Loan (NPL) is one of the indicators used to detect credit risk. There are various factors that can affect credit risk, both from internal and external banking. One of the external factors that can affect NPL is macroeconomic conditions. This study aims to identify macroeconomic factors that affect banking NPLs in Indonesia using the autoregressive distributed lag (ARDL) model. The data used is time series data from January 2015 – August 2020, which period describes the condition of the Indonesian economy before and during the Covid-19 pandemic. The data consists of six variables, namely the NPL ratio of commercial banks and macroeconomic factors in Indonesia such as gross domestic product (GDP), inflation rate, USD-IDR exchange rate, benchmark interest rates [BI 7-Day (Reverse) Repo Rate], and credit growth. The results of the data analysis show that the NPL ratio and macroeconomic variables are experiencing shocks due to the COVID-19 pandemic. The results of the ARDL model analysis show that these macroeconomic variables are able to explain the NPL of 66.61%

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