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"Indeks Sentimen Konsumen (ISK) banyak digunakan sebagai pengukur keyakinan pembelian di masa mendatang untuk memprediksi perilaku pembelian agregat di masa datang. Studi ini secara empiris membandingkan antara dua model proyeksi : model pengharapan (the expectation model) yang memasukkan ISK sebagai variabel penjelasan dan model tradisional (the traditional model) yang tidak memasukkan ISK. Kedua modal tersebut digunakan untuk mengestimasi permintaan agregat atas mobil baru di Amerika sejak 1976 sampai 1984. Hasil studi mengindikasikan bahwa ISK memiliki hubungan positif dengan penjualan mobil baru. Namun, berdasarkan kemungkinan kriteria dominan (the likelihood dominance criterion), model tradisional lebih baik dibandingkan model pengharapan. Selain itu, kemampuan memprediksi dari model pengharapan sedikit lebih rendah dibandingkan model tradisional.

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The index of consumer sentiment (ICS) has been widely employed as a proxy for future buying confidence to predict future aggregate buying behavior. This study empirically compares two forecast models: the expectation model that includes the ICS as an explanatory variable and the traditional model that does not include the ICS. The models are employed to estimate the aggregate demand for new cars in the U.S. from 1976 to 1984. The results indicate that the ICS has a positive relation with new car sales. On the basis of the likelihood dominance criterion, however, the traditional model is preferred to the expectation model. Furthermore, the forecast ability of the expectation model is slightly inferior to the traditional model."

"Peramalan permintaan bantuan logistik merupakan acuan untuk kegiatan distribusi yang optimal pasca gempa bumi. Peramalan permintaan yang akurat dan efisien dapat mencegah habisnya ketersediaan bantuan logistik, mempercepat waktu distribusi, dan menjamin setiap korban gempa bumi memperoleh bantuan logistik yang dibutuhkan, sehingga dapat mengurangi penderitaan dan menyelematkan hidup mereka. Penelitian ini bertujuan untuk merancang model peramalan permintaan bantuan logistik pasca gempa bumi. Pendekatan Case-based Reasoning (CBR) dengan dukungan internet of things (IoT) digunakan pada penelitian ini. Gempa bumi Lombok utara yang terjadi pada tahun 2018 digunakan sebagai kasus target yang akan diramal permintaan bantuan logistiknya. Hasil peramalan diperoleh berdasarkan kasus gempa bumi yang paling similar dengan kasus target. Similaritas kedua kasus ditentukan berdasarkan enam atribut yaitu: magnitudo, kedalaman gempa bumi, jarak episentrum, jumlah populasi terdampak, durasi tanggap darurat (hari), dan Modified Marcelli Intensity (MMI). Penerapan IoT dapat memberikan nilai atribut secara real time sehingga hasil peramalan diperoleh secara cepat. Hasil peramalan permintaan bantuan logistik menunjukkan MAPE di bawah 20%, sehingga dikategorikan sebagai hasil peramalan yang baik dan akurat.

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The demand forecasting of emergency logistic relief is a premise and basis for optimal emergency distribution after earthquake. Accurate and efficient demand forecast will prevent stock-out, save time, and ensure every victims get the critical supplies to reduce their suffering and save their life. This paper aims to design demand forecasting model of emergency logistic relief after earthquake. Case-based Reasoning (CBR) method supported by Internet of Things (IoT) is applied to develop the model. This paper uses eartquake incident which struck North Lombok regency in 2018 as target case.

The demand forecasting result is obtained based on the historical case that are the most similar to the target case. The similarity is determined by six attributes: earthquake magnitude, depth of hypocenter, epicentrum distance, total affected population, duration of response phase (day), and modified marcelli intensity (MMI). IoT supports the model to acquire real-time attributes value when earthquake occurs so the forecasting result will be obtained quickly. From the target case, the results shows overall forecast error lower than 20% and open the door for conducting emergency logistic relief demand forecast with quantitative and qualitative approach."

""ABSTRAK"Tugas akhir ini bertujuan untuk mencari kriteria seleksi model untuk model linier campuran. Kriteria seleksi model adalah kriteria yang dapat menyeleksi model terbaik dari sehimpunan model kandidat dari suatu data yang sama. Kriteria seleksi model yang dibahas pada tugas akhir ini yaitu kriteria seleksi model yang berdasarkan pada seleksi subset yang bertujuan untuk mendapatkan model kandidat yang paling sesuai untuk memodelkan data. Seleksi subset bekerja berdasarkan nilai discrepancy terkecil. Karena discrepancy tidak dapat dihitung secara langsung, maka ditaksir dengan kriteria seleksi model. Kriteria seleksi model yang digunakan untuk menaksir discrepancy pada tugas akhir ini yaitu Mallow rsquo;s Conceptual Predictive Statistic marginal MCp , dan improved MCp IMCp . Sebagai pembanding dari kedua kriteria seleksi model tersebut akan dibahas juga mengenai Akaike Information Criterion marginal mAIC . Untuk menilai kemampuan ketiga kriteria seleksi model tersebut dalam memilih model, dilakukan simulasi sebanyak 1000 kali. Dua ukuran efek acak yang berbeda dan dua nilai yang berbeda dari korelasi antar pengamatan dari suatu efek acak yang sama diterapkan pada simulasi untuk melihat kondisi kerja optimal dari kriteria seleksi model tersebut. Berdasarkan simulasi yang telah dilakukan diperoleh hasil bahwa kriteria seleksi model MCp, dan IMCp bekerja lebih optimal saat ukuran efek acak kecil dibandingkan saat ukuran efek acak besar. Sementara besarnya korelasi antar pengamatan dalam efek acak yang sama tidak terlalu mempengaruhi kinerja MCp, dan IMCp. mAIC bekerja lebih optimal saat ukuran efek acak kecil dan korelasi antar pengamatan dari efek acak yang sama kecil dibandingkan dengan kondisi yang lain. "

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""ABSTRACT"This final project aims to find the model selection criterion for linear mixed model, that is a criterion that can identify the best model provided a set of candidate models. The criterion discussed in this study is based on a subset selection. The subset selection works by finding the smallest discrepancy value of all candidate models. Since the discrepancy can not be directly calculated, it is estimated by the model selection criterion. The selection criterion that is used in this study is based on Mallow 39 s Conceptual Predictive Statistic Marginal MCp , and Improved MCp IMCp . Akaike Information Criterion marginal mAIC will also be discussed as a comparison to the MCp and IMCp. To assess the performances of the three criteria 1000 simulations were conducted. Two different sizes of random effects and two different values of correlation between observations of a same random effects were design to the simulation. Based on the simulation, MCp, and IMCp performed better for data with small size of random effects compared to that with large random effects. The correlations between observations of the same random effect did not significantly affect MCp 39 s, and IMCp rsquo s performance. mAIC performed better with small size of random effect and small correlations between observations of the same random effects."

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