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Purpose – The purpose of this study was to invent morphological North Aceh durian data as germplasm information.

Methodology – The research was conducted at Langkahan and Sawang, North Aceh Region, from March to August 2014. The material used was the durian plant that should be 20 years and preferred by the local community. Exploration as the first step of experiment was done by purposive sampling. Identification was done on the source of durian germplasm. The source of durian germplasm as the experimental object was observed for its growth and morphology. Data analysis for morphological characteristics was done by using NTSYSpc (Numerical Taxonomy and Multivariate Analysis) NTSYSpc versi 2.02.

Originality – The results showed that there were 25 accessions superior durian in Langkahan and 26 accessions superior durian in Sawang. They had different characters in the vegetative parts of the plant. The durian coefficient value of similarity in Langkahan ranged from 0.33 to 0.94, while in Sawang, it ranged from 0.24 to 0.86. The diversity of the morphological character in superior durian of Langkahan and Sawang was seen from the qualitative character (surface and color of bark, crown shape, top surface color of leaves, and leaf shape) and quantitative character (plant height, stem diameter, crown diameter, length, width, and leaf area).

The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly.

In this paper, the authors proposed a big data management and analytic approach for disability datasets.

By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach.

The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.