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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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PENGGEROMBOLAN KECAMATAN DI PROVINSI JAWA BARAT BERDASARKAN AKSES PENDIDIKAN MENENGAH ATAS (SMA-SEDERAJAT) DENGAN K-PROTOTYPES Sofia Octaviana; Ahmad Syauqi; Anwar Fitrianto; Erfiani Erfiani; Alfa Nugraha Pradana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): 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.v5i1.478

Abstract

Education is an important element for the Indonesian nation and must be felt by all citizens. The availability of educational facilities is important for the realization of overall educational equality for the Indonesian people. The aim of this research is to group sub-districts in West Java Province according to their level of access to Senior High School (SHS-equivalent). The data included in this study comprises both numerical and categorical variables, which were obtained from the 2021 Village Potential Data Collection (PODES) conducted by the Central Statistics Agency. A cluster analysis method that can be used to group objects based on numerical and categorical data is K-Prototypes. The results of the grouping divide the data into 2 groups, where the first group has the characteristics of an urban subdistrict, the topographic area is plain, access to the nearest high school is very easy, and has an average number of high school and equivalent schools of 22 schools per subdistrict, and has an average distance to the nearest high school of 1,86 km. Meanwhile, the second group has the characteristics of subdistricts with rural areas, topography in the form of slopes, easy access to the nearest high school, and has an average number of high schools of 7 per subdistrict, and the average distance to the nearest high school is 4,06 km. The second group is sub-districts that need to be given special attention because they have relatively fewer high schools and the distance to the nearest high school is further
UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: ISOLATION FOREST ALGORITHM APPLICATION TO INDONESIAN EARTHQUAKE DATA Gregorius Airlangga
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.v4i3.479

Abstract

Indonesia's position on the seismically active Pacific "Ring of Fire" necessitates advanced methods for earthquake detection and preparedness. This paper introduces an application of the Isolation Forest algorithm—an unsupervised machine learning technique—for detecting seismic anomalies in Indonesia's complex geotectonic landscape. Unlike traditional methods that rely on predefined thresholds or patterns, the Isolation Forest algorithm isolates anomalies based on their rarity and distinctness without the need for labeled data. We applied this algorithm to a comprehensive dataset from the Indonesian Meteorology, Climatology, and Geophysical (BMKG) Agency, featuring a decade's worth of seismic events, to identify outliers that may signify potential seismic hazards. Our findings reveal that the Isolation Forest algorithm effectively identifies seismic anomalies, with 874 out of tens of thousands of events flagged as statistically significant outliers. A comparative analysis with traditional seismic anomaly detection models highlights the robustness of the Isolation Forest algorithm in handling the high-dimensional and noisy nature of seismic data, emphasizing its superiority in detecting subtle anomalies
APPLICATION OF THE WEIGHT PRODUCT METHOD FOR PRIORITIZING CONSTRUCTION PROJECTS Denny Jean Cross Sihombing
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.v4i3.483

Abstract

As a dynamic and complex sector, the construction industry is often faced with challenges in managing projects and determining their priorities. In the context of intense business competition and changes in the external environment, this research highlights the importance of developing efficient methods for prioritizing construction projects. The focus is on applying the Weight Product (WP) method, a decision-making framework, to score projects based on cost, time, quality, and risk criteria. The method integrates data collection, analysis, modeling, and ranking. The results identify projects that align with the company's priority objectives, while the systematic steps can provide practical guidance for construction companies in optimizing resource allocation. This research also has the potential to contribute to the literature and practice of the construction industry, as well as be the basis for developing more advanced applications and decision-making methods and priority project selection applications
ANALISIS KELAYAKAN EKONOMI TAMBANG BENTONIT MENGGUNAKAN METODE DISCOUNTED CASH FLOW Wahyuni Wahyuni; Diana Purwandari; Tati Febrianti Syantika Rini
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.v4i3.484

Abstract

This research was conducted to analyze and evaluate the feasibility of the bentonite mining business carried out by CV. Bentonit Ariyanto. The calculations in this research use Discounted Cash Flow (DCF). Discounted Cash Flow (DCF) is a cash flow calculation method that calculates the time value of money. Money invested in the present will have a different value in the future. From the calculation results using the Discounted Cash Flow method CV. BENTONIT ARIYANTO has a Net Present Value (NPV) of 2,864,612,232 > 0, Payback Period (PBP) 6 years 9 months < Age of Mine, Internal Rate of Return (IRR) 17.48% > MARR, Profitability Index (PI) 1, 95 > 1 and Sensitivity Analysis has a value when the selling price falls 5% and costs remain the same, the NPV value is 1,741,946,278, IRR 15.97%, PBP 9 years 1 month, PI 1.58. When the selling price falls 15% and costs remain the same, the NPV value is -503,385,631, IRR 10.78%, PBP 10 years 5 months, PI 0.83. When the selling price increases by 12.5% ​​from the current selling price and costs remain the same, the NPV value is 5,671,277,118, IRR 19.9%, PBP 4 years 3 months, and PI 2.89
IMPLEMENTASI GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION PADA LAJU PERTUMBUHAN PENDUDUK DI BOJONEGORO Nur Mahmudah; Nuraini Khoiriyah
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.v4i3.485

Abstract

Population Growth Rate is the rate at which influencing factors increase and decrease population size. The development of large populations in regional governments causes uncontrolled population growth rates. The population growth rate in Bojonegoro Regency from 2019 to 2020 experienced a significant increase of 0.96%. The increase in population has an impact on the emergence of various problems in the economic and social fields. This problem requires effective and comprehensive spatial modeling, namely Geographically Weighted Logistic Regression (GWLR) with Fixed Gaussian and Adaptive Gaussian weighting. GWLR modeling aims to determine the implementation of knowledge and insight into the factors that influence the rate of population growth in each area of ​​Bojonegoro District. based on the results of GWLR modeling with the Akaike Index Criteria (AIC) on the Fixed Gaussian kernel function of 33.91. This value identifies that the population growth rate modeling in each sub-district has different values. This difference can be seen from 6 sub-districts which are significantly influenced by the number of births and 5 sub-districts are significantly influenced by the number of couples of childbearing age who participate in family planning. The results of modeling predictions (GWLR) in Bojonegoro Regency show that 11 sub-districts have low growth rate categories while 17 sub-districts have high population growth rate values
DESAIN PEMBELAJARAN SISTEM PERSAMAAN LINEAR DUA VARIABEL MELALUI CONTEXTUAL TEACHING AND LEARNING Muhammad Iqbal; Sukirwan Sukirwan
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.v4i3.486

Abstract

This research aims to comprehend how the context of buying and selling activities and a hypothetical learning trajectory (HLT) can aid in learning two-variables linear equation system through contextual teaching and learning (CTL) in class 8.1 and in class 8.3 at Madrasah Sanawiah al-Mubarok. The research method used is design research. The essential characteristic of design research is the cycle. The research subjects for the first cycle (preliminary teaching) included 28 students from class 8.1. In this cycle the first HLT is being tested to observe students as they progress through the learning trajectory. The revised HLT will be implemented in the second cycle (teaching experiment). The research subjects for the second cycle included 33 students from class 8.3. The obtained data are analyzed qualitatively. The results of the retrospective analysis indicate that the use of buying and selling activity contexts can facilitate students’ understanding of how the two-variables linear equation system is applied to solve related problems. According to the retrospective analysis results, 1) using context of buying and selling activities can aid students in comprehending how to apply the two-variables linear equation system to solve related problems; 2) the HLT in learning the two-variables linear equation system through CTL includes learning objectives, activities that occur during learning, and student answer hypotheses. The learning trajectory used includes making meaningful connections; engaging in significant activities; achieving high standards; self-regulated learning; nurturing and fostering student personalities; critical and creative thinking; collaboration; authentic assessment
ANALISIS PERBANDINGAN METODE SIMPLE ADDITTIVE WEIGHTING (SAW) DAN WEIGHT PRODUCT (WP) DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN HANDPHONE Annisa Shafira; Irvana Arofah; Besse Arnawisuda Ningsi
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.v4i3.487

Abstract

Penggunaan handphone dalam dunia pendidikan sangat berpengaruh terutama dalam mencari informasi, sehingga para siswa selalu melibatkan handphone dalam proses belajar. Setiap merek handphone mempunyai keunggulannya masing-masing yang membuat calon pembeli bingung untuk menentukan pilihan yang sesuai dengan kriteria dan kebutuhan. Penelitian ini bertujuan untuk mengetahui perbandingan metode Simple Additive Weighting (SAW) dan Weighting Product (WP) dalam sistem pendukung keputusan sesuai dengan kriteria dan kebutuhan. Populasi target dalam penelitian ini adalah keseluruhan siswa/siswi SMA Negeri 11 Tangerang Selatan Tahun Ajaran 2022/2023. Kemudian dipilih kelas 10 sebagai populasi terjangkau sebanyak 396 orang, dari populasi terjangkau diambil sampel sebanyak 80 responden. Konsep dasar Simple Additive Weighting (SAW) adalah menentukan rating kecocokan, membuat matriks keputusan, menormalisasikan matriks, hasil akhirnya diperoleh proses penjumlahan dari perkalian matriks dengan vektor bobot, sehingga di dapatkan urutan rangking dari setiap alternatif. Sedangkan konsep dasar Weighting Product (WP) adalah perbaikan bobot, menentukan nilai vektor S, menentukan nilai vektor V, membandingkan nilai akhir dari vektor V, sehingga di dapatkan nilai urutan rangking dari setiap alternatif. Hasil analisis dengan menggunakan metode Simple Additive Weighting (SAW) dan Weighting Product (WP) dengan hasil kriteria harga, memori internal, RAM, Kamera dan kapasitas baterai alternatif handphone Infinix Note 12 VIP menjadi alternatif terbaik disusul dengan Vivo T1 5G
UNSUPERVISED MACHINE LEARNING FOR SEISMIC ANOMALY DETECTION: LOCAL OUTLIER FACTOR ALGORITHM TO INDONESIAN EARTHQUAKE DATA Gregorius Airlangga
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.v4i3.488

Abstract

Indonesia's location on the "Ring of Fire" poses a high risk for seismic events. Addressing this, our study applied the Local Outlier Factor (LOF) algorithm for advanced seismic anomaly detection, crucial for geotectonic upheaval prediction. The LOF, adept at unsupervised learning in label-scarce datasets, analyzed data from the Indonesian Meteorology, Climatology, and Geophysical Agency, validated for integrity. Our approach, considering local density deviations, offered a refined alternative to conventional threshold-based detection, accommodating seismic data's intrinsic variability. The LOF algorithm successfully pinpointed anomalies, revealing unique seismic events unconstrained by geography or time. A comparative analysis underscored the LOF's superiority in recognizing local deviations and handling disparate data densities. These findings highlight the LOF's utility in strengthening seismic risk mitigation and anticipatory measures. The diverse anomalies identified, varying in magnitude and depth, reflect Indonesia's complex seismic interplay. To conclude, the LOF proves potent for anomaly detection, potentially elevating public safety and disaster preparedness. Future research will compare the LOF with other unsupervised methods, seeking to deepen seismic risk comprehension
ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR SEISMIC ANOMALY DETECTION IN INDONESIA: UNVEILING PATTERNS IN THE PACIFIC RING OF FIRE Gregorius Airlangga
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): 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.v5i1.489

Abstract

This study presents an integrated analysis of machine learning algorithms for the detection of seismic anomalies in Indonesia, a region within the volatile Pacific Ring of Fire. Employing Local Outlier Factor, Isolation Forest, and Elliptic Envelope algorithms, we processed a comprehensive dataset of seismic events characterized by latitude, longitude, depth, and magnitude. Our methodology involved standardizing these features and aggregating model predictions to establish a consensus mechanism for outlier detection. The results indicated that the vast majority of seismic events are consistent with the expected geological patterns, with a negligible percentage exhibiting anomalous behavior across the models. Through statistical analysis and visual mapping, we discerned that while anomalies are varied, they may correlate with specific seismic event features such as higher magnitudes or unique geographic locations. The consensus approach revealed a high-confidence subset of outliers, offering a focused direction for further seismological scrutiny. The study's implications extend to enhancing seismic risk assessment and early warning systems, providing a methodological framework for identifying seismic events that deviate from normative patterns. By outlining a scalable approach for anomaly detection, this research contributes to the predictive analytics tools available for disaster risk management and emergency preparedness, aiming to mitigate the impact of seismic hazards in seismically active regions
ADVANCED MACHINE LEARNING TECHNIQUES FOR SEISMIC ANOMALY DETECTION IN INDONESIA: A COMPARATIVE STUDY OF LOF, ISOLATION FOREST, AND ONE-CLASS SVM Gregorius Airlangga
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): 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.v5i1.490

Abstract

This study presents a comprehensive comparison of three machine learning algorithms for anomaly detection within seismic data, focusing on the unique geographical and geological context of Indonesia, a region prone to frequent seismic events. Local Outlier Factor (LOF), Isolation Forest, and One-Class SVM were assessed using a meticulously curated dataset from the Indonesian Meteorology, Climatology, and Geophysical Agency, standardized to ensure consistent feature scale. Our analysis encompassed both statistical metrics and visualizations to evaluate the performance of each algorithm. The One-Class SVM emerged as the most effective method, achieving the highest silhouette score, indicative of its superior cluster formation and clear distinction between inliers and outliers. The Isolation Forest also demonstrated strong performance with a favorable silhouette score and Davies-Bouldin index, suggesting effective anomaly isolation capabilities. In contrast, the LOF algorithm showed less precision, as indicated by lower silhouette scores and a higher Davies-Bouldin index, suggesting potential challenges in distinguishing between normal and anomalous seismic patterns. Statistical validation using the Kruskal-Wallis H-test confirmed significant differences in the anomaly score distributions of the three algorithms, with a p-value of 0.0. Visualizations through PCA and t-SNE reinforced the quantitative findings, displaying a clear demarcation of anomalies by the One-Class SVM and Isolation Forest, unlike the LOF.The findings underscore the importance of selecting appropriate anomaly detection methods for seismic data analysis, highlighting the robustness of One-Class SVM and Isolation Forest for such applications. The implications of this research are profound for seismic risk management, providing insights that enhance the accuracy and reliability of earthquake prediction systems, which is vital for regions with high seismic activity such as Indonesia.

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