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: Perbandingan Model SARIMA dan Intervensi dalam Peramalan Jumlah Penumpang Domestik di Bandara Internasional Soekarno-Hatta
Abstrak: Pandemi Covid-19 membawa dampak yang cukup besar pada sektor transportasi udara. Bandara Internasional Soekarno-Hatta (Soetta) mengalami penurunan jumlah penumpang akibat pandemi Covid-19, meskipun demikian Bandara Soetta tetap beroperasi normal. Peramalan jumlah penumpang perlu dilakukan oleh pihak Bandara untuk menentukan kebijakan yang tepat dan mempersiapkan fasilitas-fasilitas yang disediakan jika terjadi kenaikan jumlah penumpang pada masa pandemi Covid-19. Penelitian ini membandingkan model SARIMA dan intervensi dalam peramalan jumlah penumpang domestik di Bandara Soetta. Hasil penelitian menunjukkan bahwa model SARIMA terbaik yaitu ARIMA(0,1,0)(1,0,0)12 dengan MAPE dan RMSE masing-masing sebesar 55,18% dan 588887,4. Model intervensi terbaik yang diperoleh yaitu ARIMA(0,1,1)(1,0,0)12 b = 0, s = 5, r = 1 dengan MAPE sebesar 35,25% dan RMSE sebesar 238563,4. Model intervensi lebih baik jika dibandingkan dengan model SARIMA dalam melakukan peramalan jumlah penumpang domestik di Bandara Soetta berdasarkan nilai MAPE dan RMSE yang diperoleh., The Covid-19 pandemic has had a significant impact on the air transportation sector. Soekarno-Hatta International Airport (Soetta) experienced a decrease in the number of passengers due to the Covid-19 pandemic, although Soetta Airport continued to operate normally. Forecasting the number of passengers needs to be done by the airport to determine the right policy. Forecasting is also necessary so that the airport can prepare the needed facilities in the event of an increase in passengers during the Covid-19 pandemic. . This study compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The results showed that the best SARIMA model was ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55.18% and 588887.4, respectively. The best intervention model obtained was ARIMA(0,1 ,1) (1,0,0)12 b = 0, s = 5, r = 1 with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values obtained indicate that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport during the Covid-19 pandemic.
Keyword: ARIMA, Covid-19, number of passengers, intervention

Judul: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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