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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Statistika Jurnal Sains Dasar CAUCHY: Jurnal Matematika Murni dan Aplikasi Kubik Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information Systems Engineering and Business Intelligence BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Honai Math Jurnal Cendekia : Jurnal Pendidikan Matematika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam JURNAL CEMERLANG: Pengabdian pada Masyarakat JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) KUBIK: Jurnal Publikasi Ilmiah Matematika Journal of Science and Science Education Jurnal Pengabdian Papua
Felix Reba
Universitas Cenderawasih, Papua
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Advanced inferential statistics and data mining for chlorophyll distribution clustering Felix Reba; Toha Saifudin; Rimuljo Hendradi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2081-2091

Abstract

This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.
Clustering for Mapping Food Insecurity in the Land of Papua: A Five-Year Multiyear Analysis with Spatial Interpretation (2020-2024) Ishak Semuel Beno; Alvian M Sroyer; Felix Reba; Remuz M. B. Kmurawak; Antonius A. P. Tama
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40366

Abstract

Food insecurity in the Land of Papua remains a critical issue due to extreme geographical conditions, limited infrastructure, and unstable food distribution systems. This study aims to map food vulnerability across 42 districts/cities in Papua using insufficient food consumption data from 2020 to 2024. Clustering was performed using five methods—Single Linkage, Complete Linkage, Ward, K-Means, and Gaussian Mixture Model (GMM)—and evaluated using three validation indices: Silhouette, Davies–Bouldin Index (DBI), and Calinski–Harabasz Index (CHI). To obtain a balanced and comprehensive model selection, a Performance-Based Weighting (PBW) framework was applied. In this framework, the DBI was first transformed to ensure a consistent higher-is-better orientation, and all validation indices were normalized to the [0,1] range prior to computing variance-based weights. This normalization step mitigates potential scale dominance, particularly from the unbounded CHI metric, ensuring proportional contribution from each validation criterion in the aggregated score. Although individual validation indices exhibited varying optimal values of k, the integrated PBW evaluation consistently identifies the two-cluster configuration as the most stable and interpretable overall structure. Specifically, Complete Linkage with k = 2 achieved the highest combined PBW score (0.8658), reflecting strong cluster separation and consistency across validation measures. Spatial interpretation of the resulting clusters reveals that the first cluster predominantly consists of high-risk mountainous districts with persistently elevated levels of food consumption inadequacy, particularly during 2021–2022, while the second cluster represents coastal and urban regions with comparatively lower and improving prevalence in 2023–2024. These findings provide a multiyear clustering perspective with geographic insight into regional disparities in food insecurity across Papua. Overall, this study presents a data-driven and reproducible multiyear clustering framework that integrates multiple validation criteria to enhance robustness in model selection and support evidence-based regional policy formulation.
Clustering and Mixture Distribution Analysis of Average Years of Schooling in Papua (2010–2023) Alvian Sroyer; Henderina Morin; Felix Reba; Jonathan Wororomi; Agustinus Languwuyo
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.32988

Abstract

The purpose of this research is to analyze the distribution of the Human Development Index (HDI) in Papua based on the average years of schooling during the 2010–2023 period using the Gaussian Mixture-based Clustering approach. Data from 28 districts are grouped into five clusters according to their distributional characteristics. Each cluster is modeled using one of the four probability distributions: Inverse Gaussian, Rician, Weibull, or Nakagami. Parameter estimation was performed using the Maximum Likelihood Estimation (MLE) method, and the best distribution for each cluster was selected based on several information criteria (AIC, BIC, AICc, CAIC, and HQC) and validated through Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) tests. The analysis results show that the Inverse Gaussian distribution fits Cluster 1 and Cluster 3, which represent districts with lower HDI schooling patterns. Cluster 2 is best described by the Rician distribution, indicating moderate HDI variability. The Weibull distribution fits Cluster 4, representing areas with moderately improving education. Cluster 5, with the highest and most stable HDI levels, is best modeled using the Nakagami distribution. The resulting mixture model, combining these four distributions, accurately reflects the HDI distribution patterns across Papua. Policy implications from this study include the development of cluster-based educational strategies tailored to regional characteristics to improve educational equity and human development across the province.
PELATIHAN DAN PENERAPAN PENDIDIKAN KARAKTER CERDAS FORMAT KELAOMPOK (PKC-KO) GURU BIMBINGAN DAN KONSELING SMA NEGERI 2 JAYAPURA DALAM RANGKA MEMBENTUK GENERASI BERKARAKTER DAN CERDAS Yansen Alberth Reba; Sally Putri Karisma; Felix Reba; Irmawati Irmawati
JURNAL CEMERLANG: Pengabdian pada Masyarakat Vol 6 No 1 (2023): JURNAL CEMERLANG: Pengabdian Pada Masyarakat
Publisher : LP4MK STKIP PGRI Lubuklinggau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31540/jpm.v6i1.2631

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk 1) agar guru bimbingan dan konseling mampu melaksanakan PKC-KO ini kepada siswa, 2) agar generasi penerus bangsa yakni siswa memiliki karakter cerdas sesuai norma pancasila dan norma yang berlaku di masyarakat, 3) agar siswa mampu melaksanakan, menghayati, mengamalkan dan menaati norma pancasila dan norma yang berlaku di masyarakat. Metode yang digunakan dalam kegiatan pegabdian kepada masayarakat ini adalah metode pelatihan dan penerapan. Pelatihan dilaksanakan dengan menggunakan buku saku PKC-KO sebagai pegangan dan panduan saat pelaksanaan kegiatan. Pelatihan dan penerapan ini dilaksanakan da dipandu oleh para dosen dan mahasiswa dari program studi bimbingan dan konseling, jurusan ilmu pendidikan fakultas keguruan dan ilmu pendidikan, universitas cenderawasih. Hasilnya diantaranya 1) luaran bagi peserta yang dicapai yaitu peningkatan pemahaman dan ketrampilan pelaksanaan layanan dalam bimbingan dan konseling khususnya dengan menggunakan layanan PKC-KO, 2) bagi tim pengabdi, luaran yang dicapai adalah draf modul yang dapat digunakan kedepan untuk pelatihan dan penerapan PKC-KO guru bimbingan dan konseling, dan 3) Publikasi pada media online, yaitu website Jurnal Cemerlang: Pengabdian Pada Masyarakat yang diterbitkan oleh Lembaga Penelitian, Pengembangan, Pengabdian pada Masyarakat dan Kerjasama (LP4MK) STKIP PGRI Lubuklinggau.
PARAMETER ESTIMATION AND ANALYSIS OF AVERAGE YEARS OF SCHOOLING IN MERAUKE DISTRICT WITH BIRNBAUM-SAUNDERS DISTRIBUTION APPROACH Agustinus Langowuyo; Sara Yokhu; Felix Reba
KUBIK Vol 10 No 1 (2025): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v10i1.42992

Abstract

Average years of schooling is an important indicator in assessing the success of education development in a region. This study aims to analyze data on average years of schooling in Merauke Regency, Papua Province, using the Birnbaum-Saunders (BS) Distribution approach. This distribution was chosen because of its ability to model data that has asymmetric characteristics and low variability. The parameters resulting from the analysis include a scale parameter (β) of 8.35, which reflects the average years of schooling of the population, and a shape parameter (α) of 0.0545, which indicates the low degree of dispersion of the data around the mean. The results of the analysis show that the average length of schooling in Kabupaten Merauke is at the junior high school (SMP) level, with a homogeneous data distribution. This homogeneity reflects good equity in access to education, but also indicates the potential for stagnation at certain levels of education. The Birnbaum-Saunders distribution proved to be effective in modeling education data in this region, providing a more accurate picture than traditional approaches. This research makes an important contribution in understanding the distribution pattern of average years of schooling in Merauke district. The results can be used as a basis for designing more targeted policies in improving the quality and access to education, especially at the senior secondary level. In addition, this approach can serve as a reference for analyzing education in other regions with similar geographical and socio-economic challenges
Co-Authors Agustinus Langowuyo Agustinus Langowuyo Agustinus Langowuyo Agustinus Languwuyo Ahmad Sofyan Alvian M Sroyer Antonius A. P. Tama Asmuruf, Frans Habel Saud Handiko Handiko Henderina Morin Henderina Morin, Henderina Irmawati Irmawati Ishak Semuel Beno Jonathan k. Wororomi Jonner Nainggolan Kalvin Paiki Korinus Rejauw Langowuyo, Agustinus Mandowen, Samuel Aleksander Mikhael Udam Popi Ida Laila Ayer Remuz MB Kmurawak Rimuljo Hendradi Sally Putri Karisma Sara Yokhu Sara Yokhu Tiku Tandiangnga Toha Saifudin Wambrauw, Oscar O. O. Yansen Alberth Reba Yokhu, Sara Yosefina Sarena