Judul: Penentuan Metode Pendeteksian Pencilan Terbaik: Penerapan Pada Data Kualitas Udara Indonesia
Abstrak: Outliers detection in univariate data aims to see the characteristics of a single variable before entering the next analysis. There are many methods for detecting outliers, but only a few methods consider the data distribution. This research determines the best outlier detection method on univariate data with a skewed distribution. Outliers detection methods used in this research are Tukey's boxplot, adjusted boxplot, sequential fences, and adjusted sequential fences. It identifies areas of concern due to poor air quality during the Implementation of Micro-Community Activity Restrictions. The data used in this research is air quality index data for each province in Indonesia on June 08, 2021. The adjusted boxplot method performs best based on the number of outliers detected, error rate, accuracy, precision, specificity, sensitivity, and robustness. Adjusted boxplot and adjusted sequential fences can detect tails that contain outliers accurately because the skewness coefficient makes them more robust. Meanwhile, Tukey's boxplot and sequential fences are poor methods since they couldn’t detect true outliers and outliers tail. Based on the results, adjusted boxplot is the best method. Then, areas that need attention due to poor air quality include South Sumatera, South Sulawesi, West Java, Riau, North Sumatera, Jambi, Jakarta, and East Java. Keywords: adjusted boxplot, adjusted sequential fences, outlier, sequential fences, Tukey's boxplot., Pendeteksian pencilan pada data univariat bertujuan untuk melihat karakteristik peubah tunggal sebelum memasuki analisis selanjutnya. Ada banyak metode untuk mendeteksi pencilan, tetapi dalam pembentukannya hanya beberapa metode yang mempertimbangkan sebaran data. Penelitian ini menentukan metode pendeteksian pencilan terbaik pada data univariat dengan sebaran menjulur. Metode pendeteksian pencilan yang digunakan adalah Tukey’s boxplot, adjusted boxplot, sequential fences, dan adjusted sequential fences. Selain itu, dilakukan pengidentifikasian wilayah yang menjadi perhatian karena memiliki kualitas udara buruk selama penerapan Pembatasan Kegiatan Masyarakat Mikro. Data yang digunakan dalam penelitian ini adalah data indeks kualitas udara setiap provinsi di Indonesia pada tanggal 08 Juni 2021. Metode adjusted boxplot memiliki kinerja terbaik berdasarkan jumlah pencilan yang berhasil terdeteksi, tingkat kesalahan, akurasi, presisi, spesifisitas, sensitivitas, dan resistensi terhadap pencilan. Adjusted boxplot dan adjusted sequential fences dapat mendeteksi sisi ekor data yang mengandung pencilan secara akurat karena memiliki koefisien kemenjuluran yang membuatnya lebih kuat. Sementara itu, Tukey’s boxplot dan sequential fences merupakan metode yang buruk karena tidak berhasil mendeteksi pencilan dan sisi ekor yang mengandung pencilan. Berdasarkan hasil tersebut, adjusted boxplot merupakan metode terbaik. Wilayah yang perlu menjadi perhatian akibat kualitas udara buruk antara lain Sumatera Selatan, Sulawesi Selatan, Jawa Barat, Riau, Sumatera Utara, Jambi, Jakarta, dan Jawa Timur. Kata kunci: adjusted boxplot, adjusted sequential fences, pencilan, sequential fences, Tukey's boxplot
Keyword: adjusted boxplot, adjusted sequential fences, outlier, sequential fences, Tukey's boxplot

Judul: Analysis of Air Quality in Indonesia Using Tensor Decompositon-Based Clustering
Abstrak: Indonesia faces severe challenges in addressing high levels of air pollution. To anticipate and address the effect of air pollution on human health and the environment, all parties need concerted efforts. Grouping regions based on similarities in air quality will help the government tailor policies more effectively. This research aims to conduct a cluster-based analysis of air quality in terms of not only its pollutant compound but also its spatial- and temporal context using tensor decomposition. The cluster elements were then analyzed with similar occurrence patterns to obtain potential factors. Air quality data containing time, region, and air quality parameters are processed to construct a tensor. Air quality parameters being addressed are carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM10 and PM2.5). The data was retrieved from 31 representative monitoring stations across provinces in Indonesia from October 2023 to December 2023. Tensor decomposition with three latent features in each dimension was used for k-medoid clustering using the silhouette and elbow evaluation. Tucker decomposition is used for the general method in tensor decomposition because of its ability to control the decomposition rank. This means regulating the number of latent features or components to be extracted during the decomposition process. The analysis resulted optimal cluster numbers for region, time, and pollutant dimensions. It suggested that four clusters were identified based on region dimension. Meanwhile, in terms of temporal patterns, three clusters resulted. Furthermore, three clusters were identified based on pollutant types. The most concerning region cluster, including Palembang and Pontianak, have PM10 and PM2.5 concentrations above the safe level.
Keyword: air quality, carbon emission, clustering, spatial, temporal

Judul: Hydrothermal Synthesis of Biphasic Calcium Posphate
Abstrak: Biphasio calsium phosphate (BCP), which was a mixture of hydroxypatite (HA) and tricalcium phosphate (TCP), was synthesized by hydrothermal method followed by sintering process. Hydroxyapatite was produced by using natural calcium source CaO from hen's eggshell and (NH4)2HPO4 as phosphate souce, other hydroxyapatite sample was produce by using synthetic calcium source Ca(OH) and H3PO4 as phosphate source.
Keyword: