cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Jaka Wijaya Kusuma
Contact Email
jakawijayak@gmail.com
Phone
+6285718831118
Journal Mail Official
lebesguejournal@gmail.com
Editorial Address
Universitas Bina Bangsa Jl. Raya Serang – Jakarta KM.3 No.1B (Pakupatan) Kota Serang Provinsi Banten
Location
Kota serang,
Banten
INDONESIA
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Published by Universitas Bina Bangsa
ISSN : 27218929     EISSN : 27218937     DOI : 10.46306/lb
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Education Mathematics
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Lebesgue Adalah Jurnal Ilmiah yang terbit secara daring pada bulan April, Agustus dan Desember. untuk menyebarluaskan hasil-hasil penelitian dalam bidang matematika, statistika, aktuaria, matematika terapan, matematika komputasi, Model Pembelajaran Matematika dan pendidikan matematika.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 554 Documents
 23   24   25   26   27 
UPAYA MENINGKATKAN HASIL BELAJAR MATEMATIKA MELALUI MODEL PROBLEM BASED LEARNING DI KELAS XI SMA METHODIST 2 PALEMBANG Amirotul Qudsiy; Lusiana Lusiana; Tina Mora Silalahi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i3.398

Abstract

This study aims to improve the learning outcomes of mathematics subject of class XI SMA Methodist 2 Palembang. This research incorporates Classroom Action Research on class XI 4 SMA Methodist 2 Palembang as the subject. The data was collected through observation, documentation, and tests and analyzed using a descriptive quantitative approach. This research found: (1) The student's cognitive learning outcomes in the first cycle were 65% in the first meeting and 71% in the second. Then, in cycle II, it increased to 81% in the first meeting and 88% in the second. (2) The student's affective learning outcomes in cycle I acquired an average of 86.32 in the first meeting and 88.89 in the second. Then, in cycle II, it increased with an average of 91.45 in the first meeting and 93.59 in the second, which was in the "Very Good" category. (3) The student's psychomotor learning outcomes in cycle I acquired an average of 80.45 in the first meeting and 83.04 in the second. Then, in cycle II, it increased with an average of 85.26 in the first meeting and 88.14 in the second, which was in the "Good" category. The problem-based learning model can be concluded to improve the mathematics learning results of class XI 4 SMA Methodist 2 Palembang
PEMODELAN DATA HARGA CABAI DENGAN PENDEKATAN DERET WAKTU FRAKSIONAL ARFIMA Elsa Wahyuni; Dodi Devianto; Maiyastri Maiyastri
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.399

Abstract

Long-memory is a type of time series data that has a high correlation between long observation times. This can be seen from the autocorrelation function where the lag falls slowly over a long period. Such long-memory data can be modeled in the form of an Autoregressive Fractionally Integrated Moving Average (ARFIMA). One of the data that meets the long-memory criteria is the monthly chili price from March 2017 to April 2023 as much as 73 data. ARFIMA model selection is done by comparing the AIC and BIC values of each candidate model, so that the best model is ARFIMA (1;0.22785;0), this means that the movement of chili prices is influenced by previous prices in the long term
PERBANDINGAN METODE MLE BAYESIAN LOSS FUNCTION DALAM PENDUGAAN PARAMETER DISTRIBUSI INVERS RAYLEIGH Muhammad Iqbal; Ferra Yanuar; Dodi Devianto
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.400

Abstract

The purposes of this study are to estimate the scale parameter  of invers Rayleigh distribution under MLE, Bayesian Generalized square error loss function (SELF), and Bayesian LINEX loss function . The posterior distribution is considered to use two types of prior, namely Jeffrey’s prior and exponential distribution. The proposed methods are then employed in the real data. Several criteria AIC, AICc, and BIC for the selection model are considered in order to identify the method which results in a suitable value of parameter estimated. This study found that Bayesian Generalized SELF at first polynomial  and Bayesian LINEX loss function under exponential distribution  yielded better estimation values than MLE based on AIC, AICc, and BIC
ALGORITMA K-NEAREST NEIGHBOR TERHADAP PELUANG MAHASISWA MENJADI AKTIVIS KAMPUS PADA JURUSAN MATEMATIKA UNIVERSITAS NEGERI PADANG Muhammad Fadhli Gusvino; Defri Ahmad
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.401

Abstract

The purpose of this study was to predict whether mathematics students at Padang State University have the opportunity to become campus activists using the K-Nearest Neighbor Algorithm (KNN). This research will be used as a benchmark to calculate how many mathematics students can become activists at Padang State University. The K-Nearest Neighbor Algorithm (KNN) is a machine learning algorithm that has resistance to training data where there is a lot of noise and is more effective for large data. The K-Nearest Neighbor Algorithm itself is a distance-based data classification process for determining the closeness between different data. become the closest neighbor data and choose a class or category based on the K category of nearest neighbors. In this research, for the first step, data was collected on mathematics students based on several factors that influenced them to become activists, and an analysis of finding distance using the Euclidean distance was carried out. 46% are not activists
MODEL VOLATILITAS SAHAM LQ45 DENGAN PENDEKATAN MARKOV-SWITCHING GARCH Ermanely Ermanely; Dodi Devianto; Ferra Yanuar
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.402

Abstract

Financial markets have an important role in the economy of a country including Indonesia. One of the activities chosen by investors in the financial market is investing. In the world of investment, especially in stocks, there is a phenomenon of volatility, which is a situation where a stock price value increases and decreases. Volatility in this financial market is something that is very interesting for investors because of its impact on the existence of global financial markets. The purpose of this study is to model the LQ45 index data using a model that can overcome the problem of heteroscedasticity and changes in data structure. The commonly used model for heteroscedasticity problem is ARCH/GARCH. Furthermore, a model that can account for structural changes is the Markov Switching model. The model that can overcome the problem of heteroscedasticity as well as structural changes is the MS GARCH model. The financial data used in this study are daily data for the LQ45 Index from 10 June 2019 to 28 May 2020. Based on the results of data analysis conducted using the MS GARCH model is the best model in modelling the volatility of the LQ45 index. The best model selection uses the criteria for the AIC and BIC values with the smallest value
PREDIKSI JUMLAH PEMAKAIAN AIR BERSIH MENGGUNAKAN METODE HYBRID SINGULAR SPECTRUM ANALYSIS (SSA) DAN SARIMA DI PDAM TIRTANADI SIBOLANGIT Sophia Salsalina; Rina Widyasari
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.405

Abstract

Clean water is one of the basic human needs that is needed on an ongoing basis. Water has an important role in human life. As the population increases, the use of clean water also increases, resulting in the need for demand for the availability of clean water to continue to increase, this is explained by data from the Central Statistics Agency (2022) that the population growth rate in Sibolangit District from 2021-2022 is 0.25% and that This is also explained by data on the amount of clean water consumption by PDAM Tirtanadi Sibolangit which has increased by 6.5%. The purpose of this study is to apply the SSA-SARIMA hybrid model to predict the amount of clean water consumption in the coming period at PDAM Tirtandi Sibolangit so that there is no shortage and wastage of clean water. Hybrid SSA-SARIMA is a combination of two data analysis methods that take advantage of the advantages of each method, namely Singular Spectrum Analysis (SSA) and Seasonal Autoregressive Moving Average (SARIMA). SSA is a technique used to separate signals into periodic and non-periodic components while SARIMA is used to model time series data with trend and seasonal patterns and make predictions for future periods. SARIMA cannot separate periodic and non-periodic signals in data like SSA did. The data used in this study is monthly data on clean water usage from January 2018 to December 2022. The prediction results for the amount of clean water consumption in PDAM Tirtanadi Sibolangit in 2024 use the SSA-SARIMA(1,1,0)(1,0,0) hybrid model. )12 experienced a decrease in the use of clean water with a level of forecasting accuracy having a MAPE value of 6.920446%.
FAKTOR-FAKTOR YANG MEMENGARUHI INDEKS ARTIFICIAL INTELLIGENCE GLOBAL Yanuar Ichwan Satria Nugroho; Triyani Hendrawati; Kennedy Marthendra; Brian Riski Jayama Simanjuntak
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i3.406

Abstract

The Global AI (Artificial Intelligence) Index is a value that aims to measure the progress of artificial intelligence (AI) around the world. Currently, technology is increasingly sophisticated and of course makes humans compete to create technology to make life easier. The purpose of this study is to analyse the effect of human resources, infrastructure, and government policies on the global AI index. The method used to determine the relationship between human resources, infrastructure, and government policies with the global AI index is the multiple linear regression method. From the results of data processing, a linear regression  = - 7,54675 + 0,65972  + 0,25096  + 0,07672 . Based on this model, the influence of human resources, infrastructure, and government policies has a significant positive effect on the Global AI Index. The coefficient of determination of the model is 0.8833, in other words, human resources (), infrastructure (), and government policy () are able to explain the value of the global AI index (Y) by 88.33% and the remaining 11.67% is explained by other variables
ANALISIS DAN SIMULASI MODEL SUSCEPTIBLE INFECTIVE TREATMENT RECOVERY PADA PENYEBARAN PENYAKIT MALARIA DI KOTA MEDAN Eka Yusnita; Machrani Adi Putri Siregar
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.407

Abstract

Malaria is an infectious disease caused by plasmodium, which is a single-celled living creature belonging to the group of protozoan parasites, malaria is transmitted through the bite of a female Anopheles mosquito containing plasmodium in it. Plasmodium carried through mosquito bites will live and reproduce in human red blood cells. This disease attacks all age groups, both men and women. People who are exposed to malaria will have symptoms of fever, chills, sweats, headaches, nausea or vomiting. Patients who show clinical symptoms must undergo laboratory tests to confirm their positive malaria status. The purpose of this study was to determine the model of the spread of Malaria and to analyze the stability of the model of Malaria. This study uses the SITR mathematical model which involves 4 population compartments, namely Susceptible (S), Invective (I), Treatment (T), Recovery (R). The data used is secondary data obtained from the Medan City Health Office through the Medan City Health Office. From this study, the SITR mathematical model was obtained, two equilibrium points, namely the point of disease and endemic from the SITR model with a basic Reproductive value of = 0, 2693449588 or  < 1. The greater the basic reproduction number, the more likely it is that malaria will spread, but the more the smaller the basic reproduction number, the smaller the chance for malaria to spread and will gradually disappear
PENERAPAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES UNTUK ANALISIS FAKTOR YANG MEMPENGARUHI KELAYAKAN NASABAH YANG MENGAJUKAN PEMBIAYAAN Alif Yuanita Kartini; Devy Wulandari
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i3.410

Abstract

Not all customers who apply for financing will be accepted by the bank. This is to avoid risks that often occur in the financing process, namely bad financing. One way to avoid this risk is to find out the factors that affect the eligibility of customers who apply for financing using the MARS method. This research was conducted at BSI Bojonegoro branch office using data on customers who applied for financing from January to March 2023, namely 75 customers. The response variables used are binary with categories of customers who do not get financing and customers who get financing. While the predictor variables used are BI checking (X1), job background (X2), type of financing (X3), number of dependents (X4), working period (X5), income (X6), plafond (X7), margin (X8) and DSR (X9). Based on the analysis, it was found that the factors had a significant influence on the eligibility of customers applying for financing were DSR which contributed 100%, income 48%, employment background 45%, margin 42%, plafond 26% and BI checking 17%. Furthermore, the MARS model obtained is used to classify eligible and unfit customers with an accuracy rate of 92%. From this research, it is expected to minimize customers who are stuck in making payments and minimize financing risks at BSI Bojonegoro branch office
PENERAPAN SISTEM PENDUKUNG KEPUTUSAN (SPK) DENGAN MENGGUNAKAN METODE FUZZY AHP (ANALYTICAL HIERARCY PROCESS) SEBAGAI PENENTUAN PENERIMA BEASISWA PIP Ahmad Al A Dhomul Aflahin; M. Ivan Ariful Fathoni; Festian Cindarbumi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i3.411

Abstract

Determining PIP scholarship recipients is a matter of multi-criteria decision making. For this problem, a decision support system (SPK) was built to determine PIP scholarship recipients using criteria including: family economic condition, average family income, and number of parental dependents. The method used in this problem is the Fuzzy AHP (Analytical Hierarchy Process) method. Based on the results of testing the decision support system using the Fuzzy AHP method, this research succeeded in determining students who were entitled to receive the PIP scholarship, where from all alternative PIP scholarship applicants, the final clusterization value was obtained as 25% of the alternatives were entitled to receive it and 75% of the alternatives were not entitled to receive it. Apart from that, this decision support system is dynamic in nature, where the system can handle changes or additions to criteria

Page 25 of 56 | Total Record : 554

 23   24   25   26   27 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2025): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 4 No. 3 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 4 No. 1 (2023): Jurnal Lebesgue Vol. 4 No. 1 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 3 No. 3 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 3 No. 2 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 3 No. 1 (2022): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 2 No. 3 (2021): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 2 No. 2 (2021): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 2 No. 1 (2021): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 1 No. 3 (2020): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 1 No. 2 (2020): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik Vol. 1 No. 1 (2020): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik More Issue