This research aims to predict the end of the COVID-19 pandemic in Indonesia based on parametric growth models. The models are chosen by considering their fitness with the data of Taiwan which is believed to have passed over the peak of the pandemic and have gone through all phases in the growth curves. The models are parameterized using the nonlinear least squares method. The deviation and confidence interval of each parameter is estimated using the k-fold cross-validation and the bootstrap techniques. Using the total cases per million population data from March 2 to June 18, 2020, it was found that two growth models fit the data, i.e. logistic and modified Gompertz, where the latter performs better. Using the information about the deviation of each model parameter, a simulation model is developed to predict the time at which the total cases curve starts to flatten, which is an indication of the end of the pandemic. It was found with 95% confidence level that based on the modified Gompertz model the pandemic will end somewhere between March 9 – September 7, 2021 with total cases per million of 206 - 555. Meanwhile, based on the logistic growth model, the end of the pandemic is between August 28 – September 23, 2020 with total cases per million of 180 - 375. This model can be extended by making comparative scenario with Taiwan based on measures that represent the quality of the pandemic mitigation such as test ratio and the intensity of social restriction.

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This paper deals with re-modelling of a fuzzy linear programming (FLP) for an optimal product-mix decision problem and its solution. Database of a chocolate exporting company has been used here to show the practicability of using the proposed model. The proposed model includes a non-linear membership function (MF), a logistic function, which resemblances the realistic behaviour of the solution. A software platform LINGO ® has been utilized to find the optimal solution.

Model klasifikasi berbasis pembelajaran mesin untuk mendeteksi anomali biasanya didasarkan pada data dengan proporsi yang tidak seimbang. Proporsi data anomali biasanya jauh lebih kecil dibandingkan proporsi data non anomali. Ketidakseimbangan data menyebabkan model klasifikasi lebih banyak melakukan pembelajaran dengan data non anomali sehingga model bisa bias. Salah satu metode yang banyak digunakan untuk mengatasi masalah ini adalah oversampling sintetis. Oversampling sintetis umumnya didasarkan pada jarak dan didominasi metode berbasis k-Nearest Neighbor. Secara umum, pola data bisa berdasarkan jarak atau hubungan korelasional. Penelitian ini bertujuan menawarkan metode oversampling sintetis berdasarkan hubungan korelasional dalam bentuk distribusi probabilitas bersama dari data aslinya. Distribusi probabilitas bersama direpresentasikan dengan kopula Gaussian, sedangkan distribusi probabilitas marjinalnya direpresentasikan menggunakan tiga alternatf distribusi, yaitu sistem distribusi Pearson, distribusi empiris, dan sistem distribusi Metalog. Metode ini dibandingkan dengan beberapa metode oversampling lain yang umum digunakan untuk data yang tidak seimbang. Implementasi dilakukan dalam masalah kredit macet nasabah kartu kredit di suatu bank dengan metode klasifikasi k-Nearest Neighbor dengan ukuran kinerja akurasi total dengan metode validasi k-fold cross validation. Didapati bahwa model klasifikasi dengan metode oversampling usulan menggunakan distribusi marjinal Metalog memiliki akurasi total tertinggi.

The Government of Bali is planning to develop a battery tram mass rapid transport in the Kuta area as an effort to resolve severe congestion problem in that area. This research aims to measure consumers’ willingness to pay for the battery tram services in the Kuta corridor. The willingness to pay is measured using a survey based stated preference technique, the Contingent Valuation Method, with the Dichotomous Choice with Follow Up elicitation technique. An electronic survey targeting local residences and tourists that are considered potential users managed to collect 635 data. The survival analysis statistical technique is used to measure the willingness to pay assuming a lognormal distribution. The Survival Package from R software is used to produce the price-response function. The result shows that 50% of local residents are willing to pay up to Rp978/km, while 50% of tourists are willing to pay up to Rp4,402/km for the battery tram services. In general, the price-response curve shows that tourists are willing to pay Rp3,000-Rp5,000/km higher than local residents, most of the respondents that are reluctant to use the battery tram consider the offered prices in the CVM questionnaire too high or tend to avoid using public transport for health reason following the Covid-19 pandemic.