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: Kajian Perbandingan Regresi Conway-Maxwell-Poisson dan Poisson-Tweedie dalam Mengatasi Overdispersi pada Regresi Poisson
Abstrak: Regresi Poisson adalah model yang umum digunakan untuk memodelkan data cacah. Memodelkan data cacah sering terjadi overdispersi sehingga regresi Poisson kurang tepat digunakan karena regresi Poisson mengasumsikan data equidispersi. Overdispersi dapat diatasi menggunakan regresi Conway-Maxwell-Poisson (COM-Poisson) dan Poisson Tweedie (Poisson-Tw). Model terbaik ditentukan berdasarkan RMSE, absolute bias, ragam penduga parameter, AIC, dan BIC terkecil. Penelitian ini menggunakan data simulasi dan data riil. Peubah respon pada data simulasi dibangkitkan menyebar Generalized Poisson dengan berbagai kombinasi parameter ϕ dan n. Kajian data simulasi menunjukkan bahwa regresi COM-Poisson dan Compound Poisson-Tw adalah model alternatif untuk memodelkan data cacah overdispersi, tetapi COM-Poisson lebih baik digunakan untuk tingkatan dispersi yang tinggi. Kajian data riil menunjukkan bahwa regresi COM-Poisson lebih baik digunakan dengan peubah yang berpengaruh terhadap kasus kusta di Jawa Timur tahun 2020 adalah jumlah penduduk miskin (X3) pada taraf nyata 0,05., Poisson regression is a standard method to model count data. Modeling count data frequently causes overdispersion which means that Poisson regression is less precise to model it as Poisson regression has the assumption of equidispersion. Overdispersion can be overcome by using Conway-Maxwell-Poisson (COM-Poisson) and Poisson Tweedie (Poisson-Tw) regression. The best model is determined based on the lowest value of RMSE, absolute bias, variance of parameter estimator, AIC, and BIC. This research uses both simulation and real data. The response variable of simulation data is generated to follow Generalized Poisson distribution with combinations of ϕ and n. The result of simulation study shows that COM-Poisson and Compound Poisson-Tw are the alternatives to model overdispersed count data, but COM-Poisson is better to overcome overdispersion with higher dispersion parameter level. Real data is modeled using COM-Poisson regression to analyze the number of leprosy cases in East Java 2020 and shows that the number of people living in poverty (X3) affects the number of leprosy cases in East Java 2020 at a 0,05 level of significance.
Keyword: COM-Poisson, Compound Poisson-Tw, overdispersion, Poisson

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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