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All Journal GRADIEN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Matematika UNAND International Journal of Research in Community Services Jurnal Eurekamatika Aceh International Journal of Science and Technology
Fachri Faisal
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, University Of Bengkulu Jalan Raya Kandang Limun, Bengkulu 38371, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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MAPPING EARTHQUAKE MAGNITUDES IN BENGKULU PROVINCE AND SURROUNDING AREAS USING ROBUST ORDINARY KRIGING Swita, Baki; Astuti, Mulia; Faisal, Fachri; Nuryaman, Aang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1537-1552

Abstract

Bengkulu Province, situated in a subduction zone between the Indo-Australian and Eurasian plates, is highly susceptible to significant seismic activity, including major earthquakes in 2000 and 2007 with magnitudes exceeding 7. This research investigates the geographical distribution of earthquake magnitudes in Bengkulu Province and surrounding areas from 2000 to 2023. Understanding these spatial patterns is crucial for enhancing disaster preparedness and risk mitigation strategies in this high-risk region. Previous studies on earthquake distribution in Indonesia have provided valuable insights but often struggle with outliers and data variability, limiting their accuracy. Conventional Ordinary Kriging methods, though widely used, are sensitive to outliers, leading to potential inaccuracies. This study addresses these limitations by applying a robust Ordinary Kriging approach, which effectively mitigates the influence of outliers, thereby improving prediction reliability. The research utilizes earthquake data, including geographical coordinates and recorded magnitudes. It applies both classical and robust experimental semivariograms (Cressie-Hawkins) to model the spatial structure using theoretical variogram models—spherical, exponential, and Gaussian. The best-fit model is determined based on the lowest root mean square error (RMSE), ensuring accurate representation of spatial patterns. The results demonstrate that robust Ordinary Kriging accurately maps the spatial distribution of earthquake magnitudes, revealing clusters of higher magnitude events in specific regions of Bengkulu Province. These findings identify high-risk areas, providing essential data for disaster mitigation and risk management planning. This study significantly contributes to the field of seismology and geostatistics by enhancing the accuracy of magnitude distribution mapping. The resulting maps support local governments, urban planners, and disaster response organizations in developing more effective mitigation strategies, improving infrastructure resilience, and strengthening early warning systems. Ultimately, this research aims to foster safer, more prepared communities in Bengkulu Province and beyond.
Enhancing Data Visualization Competencies Through Power BI Training Agwil, Winalia; Sunandi, Etis; Rizal, Jose; Faisal, Fachri; Nugroho, Sigit; Syahada, Sri; Hermalia, Hermalia
International Journal of Research in Community Services Vol. 6 No. 2 (2025)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijrcs.v6i2.901

Abstract

Vocational High School (SMK) aims to prepare students with the skills and knowledge required to meet industry demands. Recognizing the importance of data analysis and visualization in the workforce, this community service focuses on enhancing these competencies among SMKN 04 Kota Bengkulu students, particularly those in the Software Engineering program. A community service program was conducted to train students in utilizing Power BI for real-time and interactive data visualization. The training program included preparatory surveys, module development, and practical workshops. Students actively participated, demonstrating a greater interest and understanding of data visualization concepts. Evaluation results showed that 89% of participants found the training beneficial, and 84% mastered Power BI’s visualization techniques. The outcomes highlight the program's effectiveness in equipping students with industry-relevant skills, emphasizing the need for similar initiatives targeting broader student groups. This project bridges the gap between vocational education and the digital economy's demands.
THE UNINFORMATIVE PRIOR OF JEFFREYS’ DISTRIBUTION IN BAYESIAN GEOGRAPHICALLY WEIGHTED REGRESSION Faisal, Fachri; Pramoedyo, Henny; Astutik, Suci; Efendi, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1229-1236

Abstract

When using the Bayesian method for estimating parameters in a geographically weighted regression model, the choice of the prior distribution directly impacts the posterior distribution. The distribution known as the Jeffreys prior is an uninformative type of prior distribution and is invariant to reparameterization. In cases where information about the parameter is not available, the Jeffreys' prior is utilized. The data was fitted with an uninformative Jeffreys' prior distribution, which yielded a posterior distribution that was utilized for estimating parameters. This study aims to derive the prior and marginal posterior distributions of the Jeffreys' and in Bayesian geographically weighted regression (BGWR). The marginal posterior distributions of and can be obtained by integrating the other parameters of a common posterior distribution. Based on the results and discussion, the Jeffreys prior in BGWR with the likelihood function is . On the other hand, the marginal posterior distribution of follows a normal multivariate distribution, that is, , while the marginal posterior distribution of follows an inverse gamma distribution, that is, . As further research, it is necessary to follow up on several limitations of the results of this research, namely numerical simulations and application to a particular case that related to the results of the analytical studies that we have carried out.
Integral Hypergraphs Of The Cartesian Product Of Fano Plane And Latin Squares Of Order 3 Astuti, Mulia; Mayasari, Zulfia Memi; FAISAL, FACHRI; AFANDI, NUR
Jurnal Matematika UNAND Vol. 14 No. 4 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.4.333-340.2025

Abstract

Operasi pada hipergraf adalah suatu cara untuk mengkonstruksi suatu hipergraf dengan struktur yang lebih besar. Salah satu operasi pada hipergraf yang biasa dipelajari adalah operasi kali Kartesius. Suatu hipergraf dikatakan integral jika semua nilai karakteristik dari matriks ketetanggaannya adalah bilangan bulat. Dalam makalah ini, dipelajari dua kelas hipergraf yaitu, bidang Fano dan latin square orde 3. Dapat ditunjukkan bahwa kedua kelas hipergraf tersebut adalah integral. Selanjutnya, ditentukan hipergraf hasil operasi kali Kartesius dari kedua hipergraf tersebut. Dapat dibuktikan bahwa operasi kali Kartesius pada hipergraf mempertahankan sifat keintegralan.
Co-Authors Aang Nuryaman Achmad Efendi Agwil, Winalia Cinta Rizki Oktarina Halauddin Halauddin Henny Pramoedyo Hermalia, Hermalia Jose Rizal Jose Rizal Jose Rizal Mudin Simanihuruk Mulia Astuti Nur Afandi Pepi Novianti Pratiwi, Stevy Cahya Refrizon Refrizon Rini, Dyah Setyo Sigit Nugroho Suci Astutik Suhendra Suhendra Sunandi, Etis Swita, Baki Syahada, Sri Tasyah, Qhiky Lioni Zulfia Memi Mayasari