Judul: Perbandingan Model-model Regresi dengan Peubah Respon Cacah yang Memiliki Permasalahan Overdispersi
Abstrak: Regresi Poisson adalah pendekatan yang umum digunakan untuk menganalisis data cacah. Namun, dalam penerapannya, asumsi equidispersi pada regresi Poisson sering kali tidak terpenuhi. Kondisi overdispersi sering ditemukan dalam pemodelan regresi Poisson. Beberapa metode dapat digunakan untuk mengatasi hal tersebut, diantaranya dengan menggunakan model alternatif seperti Conway-Maxwell-Poisson (COM-Poisson), Poisson Tweedie (Poisson-Tw), dan regresi binomial negatif. Penelitian ini menggunakan data jumlah kasus kekerasan pada perempuan di setiap kabupaten/kota di Jawa Barat pada tahun 2022. Pemilihan model terbaik dipilih berdasarkan nilai Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), dan Bayesian Information Criterion (BIC) terendah. Hasil penelitian menunjukkan bahwa ketiga model alternatif memiliki performa yang cukup baik dalam mengatasi overdispersi karena memiliki nilai RMSE, AIC, dan BIC yang tidak jauh berbeda. Namun, model Poisson-Tweedie dipilih sebagai model terbaik karena memiliki nilai AIC dan BIC terkecil (AIC = 276,471 dan BIC = 284,246) dibandingkan model lainnya. Berdasarkan pemodelan, diketahui bahwa terdapat empat peubah yang dapat memengaruhi jumlah kekerasan pada perempuan di kabupaten/kota di Jawa Barat. Tingkat pengangguran terbuka (??1) dan jumlah penduduk yang memiliki akta cerai (??9) berpengaruh positif sedangkan pendapatan per kapita perempuan (??2) dan indeks kedalaman kemiskinan (??5) memiliki pengaruh negatif terhadap jumlah kekerasan pada perempuan di setiap kabupaten/kota di Jawa Barat., Poisson regression is a commonly used approach to analyze count data. However, in its application, the equidispersion assumption in Poisson regression is often not met. Overdispersion conditions are often found in Poisson regression modeling. Several methods can be used to overcome this, including using alternative regression models such as Conway-Maxwell-Poisson (COM-Poisson), Poisson Tweedie (Poisson-Tw), and negative binomial regression. This research uses data on the number of cases of violence against women in each district/city in West Java in 2022. The best model was selected based on the lowest Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) values. The research results show that the three alternative models have quite good performance in overcoming overdispersion because they have RMSE, AIC, and BIC values that are not much different. However, the Poisson-Tweedie model was chosen as the best model because it has the smallest AIC and BIC values (AIC = 276.471 and BIC = 284.246) compared to the other models. Based on modeling, it is known that there are four variables that can influence the amount of violence against women in each districts/cities in West Java. The open unemployment rate (??1) and the number of people who have a divorce certificate (??9) have a positive influence, while women's per capita income (??2) and the depth of poverty index (??5) have a negative influence on the amount of violence against women in each district/city in West Java.
Keyword: COM-Poisson, negative binomial, overdispersion, Poisson-Tw, violence against woman

Judul: Comparison Between Poisson, Quasi-Poisson, and Binomial Negative Regression in Analyzing Under-Five Children Malnutrition Cases in East Java
Abstrak: Model regresi paling sederhana dalam memodelkan data dengan peubah respon berupa data cacahan adalah regresi Poisson. Masalah yang sering dijumpai saat menggunakan regresi Poisson adalah masalah overdispersi. Alternatif pemodelan lain yang dapat digunakan pada data dengan kondisi overdispersi adalah regresi quasi-Poisson dan binomial negatif. Pada penelitian ini, akan ditentukan model regresi terbaik yang sesuai dalam memodelkan jumlah kasus gizi buruk balita di Jawa Timur yang merupakan data cacahan dengan kondisi overdispersi. Data diperoleh dari Buku Profil Kesehatan Jawa Timur tahun 2018. Perbandingan model antara regresi Poisson, quasi-Poisson, dan binomial negatif dilakukan dengan melihat plot perbandingan hasil prediksi, plot estimasi rataan dan ragam, serta plot perbandingan pemberian bobot pada algoritma IWLS. Hasil perbandingan menunjukkan bahwa regresi quasi-Poisson merupakan model yang lebih sesuai dalam memodelkan jumlah kasus gizi buruk balita di Jawa Timur. Hasil pengujian pada taraf nyata 10% membuktikan bahwa persentase balita yang menerima ASI eksklusif, persentase balita yang menerima pelayanan kesehatan minimal 8 kali, dan persentase penduduk dengan akses terhadap sanitasi layak merupakan faktor-faktor yang berpengaruh nyata dan signifikan terhadap jumlah kasus gizi buruk balita di Jawa Timur. Penggerombolan dilakukan pada 37 kabupaten/kota di Jawa Timur berdasarkan ketiga faktor tersebut. Kabupaten/kota digerombolkan menjadi tiga zona dengan karakteristiknya masing-masing., The most straightforward counted data regression is Poisson regression. The problem often discovered in Poisson regression is overdispersion. Some alternatives regression that can be used in an overdispersed counted data are quasi-Poisson and binomial negative regression. This study will identify the most appropriate and suitable regression in modelling the number of under-five children malnutrition cases in East Java as an overdispersed counted data. The data was obtained from 2018th East Java Health Profile Book. Comparison between Poisson, quasi-Poisson, and negative binomial regression will be made based on a prediction plot, a mean-variance plot, and a comparison plot of observation weight in IWLS algorithm. The comparison shows that quasi-Poisson regression is more suitable for modelling the number of under-five children malnutrition cases in East Java. Hypothesis testing result in 10% significance level shows that the percentage of under-five children who receive exclusive breastfeeding, the percentage of under-five children who receive health services at least 8 times, and the percentage of population with proper sanitation access are factors that significantly affect the number of under-five children malnutrition cases in East Java. Based on the three significant factors, 37 regions in East Java later clustered into three clusters with their characteristics.
Keyword: binomial negative regression, overdispersion, Poisson regression, quasi-Poisson regression, under-five children malnutrition

Judul: Sintesis Flavonol melalui 2’-Hidroksikalkon
Abstrak: Flavonol is a class of flavonoids commonly found in fruits and vegetables. Flavonol is very useful, especially in food sector and health. In this study, flavonol unsubstituted was successfully synthesized from an intermediate material 2’-hydroxychalcone. The 2’-hydroxychalcone was synthesized by Claisen-Schmidt condensation method between o-hydroxyacetophenone and benzaldehyde using 4 different catalysts, namely KOH 60%, solid pellet of KOH-grinding technique, BF3-Et2O, and NaH in DMF. The KOH 60% was the best catalyst, giving the highest yield (57%). Afterward, the flavonol was synthesized in 1-stage oxidation cyclization of o-hydroxyacetophenone using Algar-Flynn-Oyamada (AFO) reaction, giving 76%. The flavonol total yield from 2’-hydroxychalcone was 43%. All synthesized products were characterized by their melting points and their spectra. The success of the synthesis flavonol opens the way to synthesis of useful flavonol derivatives such as quercetin and kaempferol
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