Judul: Customer Segmentation of Gas Transportation PT Pertamina Gas Using The K-Prototypes Algorithm
Abstrak: Segmentasi pelanggan merupakan implementasi analisis statistika berupa analisis gerombol dalam dunia bisnis. PT Pertamina Gas (Pertagas), sebagai perusahaan gas yang melayani pelanggan bertipe business-to-business (B2B), diharapkan dapat menginisiasi segmentasi pelanggan produk transportasi gas untuk mengelompokkan pelanggan berdasarkan kemiripannya agar perusahaan dapat mengenal lebih baik kebutuhan pelanggan. Segmentasi dilakukan berdasarkan hasil penggerombolan data rekapitulasi realisasi volume penggunaan gas 2021 yang mencakup 3 peubah kategorik dan 4 peubah numerik. Algoritma K-prototypes digunakan dalam membuat segmentasi pelanggan karena terbukti dapat menyelesaikan kasus penggerombolan data campuran dibandingkan dengan K-means yang hanya dapat menangani kasus data numerik. Hasil penggerombolan menunjukkan k = 4 sebagai banyak gerombol optimal dengan nilai rasio keragaman terendah, yaitu Ri = 0,4810. Secara umum, gerombol-1 dan gerombol-4 digolongkan sebagai gerombol non-outlier dan gerombol lainnya sebagai outlier. Gerombol-1 berisikan pelanggan yang sudah lama berdiri dengan pembelian standar yang konsisten. Pelanggan yang konsisten juga digerombolkan dalam gerombol-4 dengan pembelian yang lebih rendah dan umur lebih muda dibandingkan gerombol-1. Sebaliknya, terdapat satu pelanggan dalam gerombol-2 yang sangat tinggi pembelian dan fluktuasinya tetapi serupa dengan gerombol-4 dalam aspek umur perusahaan. Lalu, pelanggan dengan karakteristik lebih rendah dari gerombol-2 namun lebih unggul dari gerombol-1 digolongkan dalam gerombol-3 dengan proporsi karakteristik peubah kategorik yang paling seimbang., Customer segmentation is an implementation of statistical analysis as in cluster analysis in the business field. PT Pertamina Gas (Pertagas), a gas company targeted for business-to-business (B2B) customers, is in urge to initiate customer segmentation on gas transportation to divide customers based on similarity. Hence, the company would better know the customer’s needs. Segmentation was based on clustering on the realization of gas usage volume in 2021 which included 3 categorical and 4 numerical variables. K-prototypes algorithm was used for clustering since it's proven to solve mixed-type data clustering better than K-means which was limited for numerical-type data. The result concluded that k = 4 was the optimal number of clusters based on the lowest variance ratio, Ri = 0,4810. In general, cluster-1 and cluster-4 were classified as non-outlier clusters and the others as outlier. Cluster-1 gathered aged customers with persistent and standard purchases. Persistent customers were as well allocated to cluster-4 with younger and lower purchases than cluster-1. Contrary, cluster-2 contained one high value with high fluctuation customer, although its company age was similar to cluster-4. Lastly, customers with lower characteristics than cluster-2 but superior to cluster-1 were classified in cluster-3 with the most even categorical variables characteristics proportion.
Keyword: B2B, cluster analysis, mixed-type data, variance ratio

Judul: Segmentasi pelanggan ritel perusahaan alat berat berdasarkan catatan transaksi dengan pembobotan komponen utama
Abstrak: Customer segmentation is allocation of customers into segments according to characteristics similarity to optimize the implementation of customer relationship management. The characteristics of retail customer could be identified based on their transaction record. Principal component analysis is used to summarize the characteristics of PT United Tractors Tbk. spare part retail customer from transaction record in 2012. Variables in the transaction analysis and segmentation are transaction frequency ( ), count of transaction months ( ), count of product ( ), count of product type ( ), and total transaction amount ( ). The weight of those variables are determined by principal component analysis. The weight of each variable obtained is 0.41 for , 0.78 for , 0.61 for , 0.28 for , dan 0.72 for . Transaction score of each customer is calculated by combining the standardized transaction variables. Ideal segmetation is conducted by k-means algorithm. The ideal number of segment is 4. The mean of transaction score for segmen 1 to 4 are 9.09, 3.34, -1.39, and 0.27. Customers are devided into 4 segments by percentage 2.79%, 12.09%, 54.88%, and 30.23% for segment 1 to 4.
Keyword: Bogor Agricultural University, retail customer segmentation., principal component analysis, customer relationship management, cluster analysis

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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