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Penentuan Jurusan Siswa SMA Berdasarkan Tes Minat Bakat Menggunakan Metode Single, Complete dan Average Linkage Sroyer, Alvian M; Saud, Habel; Reba, Felix
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 2 (2022): Agustus 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i2.2022.72-80

Menurut data yang dirilis oleh United Nations Children's Fund (Unicef) tentang situasi pendidikan di Papua, dimana 30% siswa di Provinsi Papua secara umum tidak menyelesaikan Pendidikan SD dan SMP. Di wilayah pedalaman Papua, kira-kira 50% peserta didik jenjang SD dan 73% SMP lebih memilih putus sekolah. Jika fenomena ini dibiarkan berlanjut, maka para siswa akan kesulitan dalam menentukan jurusan yang sesuai dengan kemampuan mereka baik di jenjang SMA, maupun jika para siswa ingin melanjutkan ke perguruan tinggi. Beberapa tahun terakhirÂ ini, beberapa SMA di kota Jayapura sudah menggunakan hasil Intelligenz Struktur Test (IST) dalam menentukan jurusan siswa. Tim tes psikologi yang sering melakukan tes ini adalah para dosen dari Program Studi Bimbingan dan Konseling (BK) UNCEN. Namun tim Prodi BK juga terkadang mengalami kendala, jika siswa yang mengikuti tes dalam jumlah yang banyak. Akibatnya, waktu yang diperlukan untuk menyelesaikan satu subtes IST relatif lama. Terkadang terjadi error yang tak terduga dalam menentukan hasil tes menggunakan norma. Sehingga penelitian ini, akan digunakan tiga metode cluster yaitu : Single Linkage, Complete Linkage dan Average Linkage dalam menentukan jurusan siswa SMA. Penggunaan tiga metode ini tujuannya adalah bagaimana mengetahui metode yang paling sesuai dalam menentukan jurusan siswa SMA. Hasil dendogram memperlihatkan bahwa, jumlah jurusan yang dapat dibentuk adalah dua jurusan. Selanjutnya hasil analisis menunjukan bahwa, dari 305 siswa kelas X yang mengikuti tes minat dan bakat, terdapat hanya 7 siswa yang memenuhi syarat untuk masuk pada jurusan IPA dan 298 siswa memenuhi syarat untuk masuk pada jurusan IPS. Harapannya metode terbaik dapat menjadi rujukan sebagai bahan validasi. Selain itu, tim tes psikologi Prodi BK UNCEN dapat menjangkau seluruh SMA dan Sederajat di Provinsi Papua untuk melakukan tes psikologi dalam penentuan jurusan siswa.

PARAMETER ESTIMATION AND ANALYSIS OF AVERAGE YEARS OF SCHOOLING IN MERAUKE DISTRICT WITH BIRNBAUM-SAUNDERS DISTRIBUTION APPROACH Langowuyo, Agustinus; Yokhu, Sara; Reba, Felix
KUBIK Vol 10 No 1 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

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

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 (Î²) of 8.35, which reflects the average years of schooling of the population, and a shape parameter (Î±) 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

Clustering and Mixture Modeling of Schooling Expectancy Trends in Papua Province: A Spatial Analysis Using the Mapping Toolbox Wororomi, Jonathan; Reba, Felix; Asmuruf, Frans
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.459-472

Background: Persistent educational inequality in Papua Province, particularly in remote highland districts, is driven by limited infrastructure and accessibility. Although Schooling Expectancy (Harapan Lama Sekolah, HLS) is widely recognized as a forward-looking educational metric, existing studies rarely incorporate probabilistic modeling with spatial analysis to examine regional disparities. ObjectiveThis study aimed to identify spatial and statistical patterns of schooling expectancy across 29 districts in Papua from 2010 to 2023 by combining probabilistic clustering with spatial visualization methods. Methods: The analysis applied Gaussian Mixture Model (GMM) clustering, which was validated using the Silhouette Index and Davies–Bouldin Index (DBI), to group districts based on HLS trends. Fourteen candidate probability distributions were evaluated using Kolmogorov–Smirnov and Anderson–Darling tests. In addition, five model selection criteria (AIC, BIC, AICc, CAIC, HQC) were applied to refine the fit. Cluster-wise mixture model was constructed, and spatial interpretation was improved through MATLAB’s Mapping Toolbox as well as wind rose diagrams. Results: During the process of the analysis, four statistically distinct clusters were identified. Cluster 3 (coastal districts) showed the highest and most stable HLS (12.1–14.0 years), while Cluster 4 (remote highlands) signified the lowest (2.4–5.6 years) with high dispersion. Right-skewed distributions (e.g., Weibull, Gamma) modeled high-performing districts, and heavy-tailed, left-skewed ones (e.g., Stable, Inverse Gaussian) modeled marginalized regions. Spatial visualization confirmed a clear coastal–highland divide in educational attainment. Conclusion: The proposed incorporation of probabilistic modeling and spatial clustering offered a robust analytical tool for capturing intra-regional educational disparities. This framework provided empirical evidence to support geographically differentiated policy interventions in Papua and could be adapted to similar underserved regions in future studies. Keywords: Schooling Expectancy, Gaussian Mixture Model, Probabilistic Modeling, Silhouette Index, Davies–Bouldin Index, Spatial Clustering, Education Inequality, Papua Province.

PARAMETER ESTIMATION AND ANALYSIS OF AVERAGE YEARS OF SCHOOLING IN MERAUKE DISTRICT WITH BIRNBAUM-SAUNDERS DISTRIBUTION APPROACH Langowuyo, Agustinus; Yokhu, Sara; Reba, Felix
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

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 (Î²) of 8.35, which reflects the average years of schooling of the population, and a shape parameter (Î±) 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

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

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, Universitas Islam Negeri Maulana Malik Ibrahim Malang

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

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.

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