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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
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MODEL VECTOR AUTOREGRESSIVE (VAR) UNTUK PREDIKSI INDEKS HARGA KONSUMEN, HARGA BERAS, DAN INFLASI KOTA SURABAYA Suprapto, Rheinka Elyana; Trimono, Trimono; Aviolla Terza Damaliana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): 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.v6i1.965

Abstract

The current global economic conditions face increasingly complex challenges, with projections of economic weakness continuing until 2025. Globalization has reduced the role of domestic factors and strengthened the impact of the global economy on the formation of inflation. From a macroeconomic perspective, the level of economic growth is often used as a leading indicator of a country's success, reflecting continuous changes in the economy with the aim of achieving better conditions over a certain period of time. Historically, the inflation rate in Indonesia tends to be higher compared to other developing countries. Data shows that during the 2010–2020 period, Indonesia's quarterly inflation was consistently higher than other developing countries. This study uses time series data analysis with a multivariate approach that includes three main variables: inflation, rice prices, and the consumer price index (CPI). The method used is Vector Autoregressive (VAR), which is an analysis technique for data with more than one related variable. The results of the analysis show that the VAR method produces a Mean Absolute Percentage Error (MAPE) value of 32.73% for inflation, 6.24% for CPI, and 5.78% for rice prices. These findings indicate that the VAR model has varying levels of accuracy for each variable, with more accurate predictions for CPI and rice prices compared to inflation.
ANALISIS KEMISKINAN DI INDONESIA MENGGUNAKAN LOCAL INDICATOR OF SPATIAL ASSOCIATION DAN SPATIAL ERROR MODEL Khairani, Putri Rahmatun; Kurniawati, Yenni; Amalita, Nonong; Mukhti, Tessy Octavia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): 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.v6i1.966

Abstract

Poverty in Indonesia remains a significant socio-economic challenge with notable regional disparities. The eastern provinces, particularly Papua, Maluku, and East Nusa Tenggara, experience persistently high poverty rates, suggesting a strong spatial influence. This study examines the spatial distribution of poverty using the Local Indicators of Spatial Association and the Spatial Error Model with 2024 data from the Indonesian Central Statistics Agency (BPS) for 38 provinces. The analysis employs a K-Nearest Neighbors weighting matrix (k = 10) for spatial dependencies. The LISA results identify High-High poverty clusters in Papua, Maluku, and East Nusa Tenggara. In contrast, Low-Low clusters are concentrated in Java and Bali, indicating a strong spatial pattern (Moran’s I = 0.4448). SEM findings reveal that the Gini index (β = 29.97) and population density (β = 0.016) significantly influence poverty, whereas inflation and total population do not. The model explains 76.1% of poverty variance (R² = 0.760966), highlighting its superiority over traditional regression models. These findings underscore the need for spatially adaptive policies to address poverty effectively. Policymakers should prioritize equitable economic development, regional investment, and infrastructure improvements, particularly in high-poverty clusters. Integrating spatial econometric models with KNN provides deeper insights into interregional disparities, supporting more precise and inclusive development strategies
ALGORITMA PRIM YANG DIMODIFIKASI PADA MASALAH POHON PEMBANGUN MINIMUM FUZZY Puteri, Ilma; Syafwan, Mahdhivan; Nazra, Admi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): 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.v6i1.967

Abstract

This study aims to find the Minimum Spanning Tree (MST) on graphs with uncertain edge weights, which are modeled using triangular fuzzy numbers. To compare and sum the edge weights in determining the fuzzy MST, the layered average integration method is used. In graphs with crisp edge weights (real numbers), the MST problem can be solved using Prim's algorithm. This research develops and introduces a fuzzy version of Prim's algorithm to address the fuzzy MST problem on graphs with fuzzy edge weights. Additionally, an application example is provided to demonstrate the performance of the modified Prim's algorithm in determining the fuzzy MST. The results of this study offer an effective approach to handling uncertainty in graph edge weights using fuzzy methods and can be applied to various network problems involving data uncertainty.
STATISTIK PENDIDIKAN SEBAGAI INSTRUMEN EVALUASI DAN PENGAMBILAN KEPUTUSAN DALAM PEMBELAJARAN Lamretta, Nanda Akti; Simbolon, Ici Sry Ulina; Dalimunthe, Riska Romayani; Calista, Venecia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): 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.v6i1.968

Abstract

Educational statistics is an important tool in data-driven decision-making in education. However, many teachers and researchers experience misconceptions related to statistics, which can lead to errors in data collection, analysis and interpretation of results. This study aims to identify common errors that occur in educational statistics, including misconceptions often experienced by teachers, and provide solutions to overcome these problems. Using a literature study approach, we analyzed various sources to provide insights into these errors and how to avoid them. It is hoped that the results of this study can improve the quality of educational research and make a more significant contribution to educational policy development. Misconceptions in education statistics not only impact research results, but can also influence decisions taken by stakeholders in education. Therefore, it is important to recognize and address these errors so that the data analysis conducted can be more valid and reliable. The study also provides recommendations for improving the understanding of statistical techniques used in educational research. With the right steps, it is expected that the quality of educational research can be improved and make a greater contribution to the development of data-based educational policies.

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