Judul: Pemodelan Jumlah Penumpang Kereta Api di Sumatera Menggunakan First-order dan Higher-order Markov Chain
Abstrak: First-order Markov chain model is a Markov chain model that depends only on the last state, while higher-order Markov chain model is a Markov chain model that relies on past some states. The purposes of this research are to study the first-order and higher-order Markov chain models and their parameter estimation and to apply these models for modeling the number of train passengers in Sumatera. The data used in this research are data of the number of train passengers issued by Statistics Indonesia starting from January 2007 until December 2013. Numerical calculation is performed using the softwares Mathematica 9.0 and Microsoft Excel 2010. For 4-state and 5-state models, we obtain the accuracy of model of 51.81%. Based on these results, we can conclude that first-order and higher-order Markov chain models are not suitable models for the number of train passengers in Sumatra.
Keyword: modeling the number of train passengers, higher-order Markov chain model, first-order Markov chain model

Judul: Penerapan Metode SARIMA Intervensi dalam Peramalan Jumlah Penumpang Kereta Api di Pulau Jawa
Abstrak: Peramalan merupakan metode analisis statistik yang digunakan untuk mengetahui fenomena yang terjadi di masa depan. Salah satu metode peramalan untuk data berpola musiman adalah SARIMA. Pola data deret waktu biasanya mengalami perubahan pada pola rataan yang terjadi di luar dugaan. Peristiwa tersebut dinamakan Intervensi. Pandemi Covid-19 memberikan dampak negatif pada sektor transportasi darat jarak jauh, salah satunya yaitu transportasi kereta api. Jumlah penumpang kereta api setiap tahun cenderung mengalami kenaikan dan membentuk pola musiman, namun adanya pandemi Covid-19 menyebabkan terjadinya penurunan penumpang diluar dugaan. Tujuan penelitian ini adalah menerapkan dan mengevaluasi metode SARIMA Intervensi yang terbentuk serta meramalkan jumlah penumpang kereta api di Pulau Jawa. Data yang digunakan dalam penelitian memiliki periode bulanan. Hasil penelitian menunjukkan bahwa model sebelum Intervensi adalah SARIMA (0,1,1)(0,1,0) dengan nilai BIC 995,1037 sedangkan model Intervensi terbaik adalah SARIMA (0,1,1)(0,1,0) b = 0, s = 3, r = 1 dengan nilai BIC 1685,2870. Bentuk interensi yang digunakan pada data jumlah penumpang kereta api periode April 2013 – Maret 2022 adalah fungsi step. Nilai MAPE yang dihasilkan model sebelum Intervensi dan saat Intervensi masing-masing sebesar 2,0081% dan 33,8308%., Forecasting is a method of statistical analysis used to determine phenomena that occur in the future. One of the forecasting methods for data with a seasonal pattern is the SARIMA. Time series data patterns usually experience changes to the average pattern that occur unexpectedly. This event is called Intervention. The Covid-19 pandemic has had a negative impact on the long-distance land transportation sector, one of which is rail transportation. The number of train passengers every year tends to increase and forms a seasonal pattern, but the Covid-19 pandemic has caused an unexpected decrease in passengers. The purpose of this study is to apply and to evaluate the SARIMA Intervention method that was build, and to predict the number of train passengers on the island of Java. The data used in the study has a monthly period. The results showed that the model before the intervention was SARIMA (0,1,1)(0,1,0) with a BIC value of 995,1037 while the best intervention model was SARIMA (0,1,1)(0,1,0) b = 0, s = 3, r = 1 with a BIC value of 1685,2870. The form of intervention used in data on the number of train passengers for the period April 2013 – March 2022 is the step function. The MAPE values generated by the model before the intervention and during the intervention were 2,0081% and 33,8308%, respectively.
Keyword: BIC, Fungsi Step, MAPE, SARIMA Intervensi

Judul: Isolasi Senyawa Lutein dari Ekstrak Bunga Brokoli sebagai Antioksidan
Abstrak: Antioxidant activity is important to protect our body from free radicals. Green vegetables such as broccoli has high content of lutein which can act as an antioxidant. The objective of this study was to isolate and characterize lutein extract of broccoli flowers, and then further evaluated its antioxidant activity by using α,α-diphenyl-β-picrylhydrazyl (DPPH). The extraction used n-hexane as solvent and KOH/ethanol for saponification process. Extracted lutein was characterized with preparative thin layer chromatography (TLC). Four fractions were obtained from preparative TLC and fraction 3 had the same Rf compared with lutein standard. This fraction was further analyzed with Fourier transform infrared (FTIR) spectrophotometer and had similar functional groups with lutein standard. Based on this result, it can be concluded that broccoli flower extract has lutein compound and potential as an antioxidant with 99.321 μg/mL IC50 value.
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