Keputusan Petani dalam Memilih Mengelola Sagu (Metroxylon Sago Rottb) dan Faktor-Faktor yang Mempengaruhi di Kecamatan Malangke Barat, Kabupaten Luwu Utara

The purpose of this study is 1) measuring the level of decision of sago farmers in terms of external factors 2) analyze the relationship between external factors and internal factors of sago farmers. The research data was collected from 60 respondents with a questionnaire filling method and analyzed through descriptive statistical analysis, frequency tabulation and spearman rank correlation. The study was conducted in March to April 2019 in West Malangke District. The results showed that the preference level of managing sago in Malangke Barat District was high with an average value of 30.85. Economic factors obtained from planting sago dominate the reasons for people's interest in managing sago, followed by food needs and ecological factors. Socio-economic characteristics of respondents such as education level, age, number of family dependents, farming experience, sago land area and income, has a non-significant correlation in the level of preference, because the correlation value obtained is low. Keywords : sago; preference level; socio-economic characteristics. Tujuan dari penelitian ini adalah 1) mengukur tingkat keputusan petani sagu ditinjau dari faktor eksternalnya;  2) menganalisis hubungan antara faktor eksternal dan faktor internal petani sagu. Data penelitian dikumpulkan dari 60 responden dengan metode pengisian kuesioner dan dianalisis melalui analisis statistika deskriptif, tabulasi frekuensi dan korelasi spearman rank. Penelitian dilakukan pada bulan Maret sampai April 2019 di Kecamatan Malangke Barat. Hasil penelitian menunjukkan bahwa tingkat preferensi mengelola sagu di Kecamatan Malangke Barat tergolong tinggi dengan nilai rata-rata 30,85. Faktor ekonomi yang didapat dari menanam sagu mendominasi alasan ketertarikan masyarakat mengelola sagu, disusul oleh faktor kebutuhan pangan dan faktor ekologi. Karakteristik sosial ekonomi responden seperti tingkat pendidikan, usia, jumlah tanggungan keluarga, pengalaman berusahatani, luas lahan sagu dan penghasilan, memiliki korelasi yang tidak signifikan dalam tingkatan kesukaan, karena nilai korelasi yang didapatkan rendah

Preventing recession through GDP growth prediction: A classical and machine learning classification approach

Classification methods are a popular method applied in many various fields of science. To represent the effect of predictor factors on categorical response variables, different machine learning classification algorithms are used, namely logistic regression, neural network (NN), random forest, support vector machine (SVM), and bayesian model averaging (BMA). Every classifier has its unique characteristic, performing well in certain datasets but not in others. Hence, it is always a quest to find the best classifier to use for a certain dataset. Economic growth, most commonly using a gross regional domestic product, is experiencing a recession or acceleration, especially before and during the COVID-19 pandemic. This research proposed a comparison of classification methods using regional GDP data for 2019-2020, before and during the COVID-19 pandemic, by predictor variables; percentage of workers, foreign direct investment (PMA), regional revenue (PAD), general allocation fund (DAU), revenue sharing fund (DBH), and the dummy of COVID-19.  The results are that all selected machine learning models can classify the regional GDP growth perfectly for the training data, but, NN model outperforms the other methods with an accuracy of 100% in training and testing data. COVID-19 and the PMA are the most significant variables predicting regional GDP growth for all models. Further research relating to interpretable machine learning, such as feature interaction, global surrogate, and Shapley values, is also necessary to predict regional GDP growth using machine learning methods