Investment is the wealth of one or more assets in the hope of future benefits. Things to consider in investing are profit and risk. So investors need to diversify their investments, which means investors need to form a portfolio through the selection of several assets so that risk can be minimized without reducing expected profits. The COVID-19 pandemic period had a big impact on the economy, especially for investors in making optimal portfolio formation. This study aims to determine the optimal portfolio formation during the COVID-19 pandemic using the Single Index Model. In this study a Single Index Model was be studied systematically and then translated into a programming. The data used are data of consistent shares included in the Jakarta Islamic Index (JII) shares over the past two years. Furthermore, these stocks are chosen which have an average return that is higher than the profits obtained if investors save their money in the bank. The results showed six JII companies included in the candidate for optimal portfolio formation. After the analysis, two shares were produced, namely BRPT with a proportion of 63.8043% and EXCL 36.1957%. The proportion is expected to provide a profit of 1.57% per week and a risk of 6.06% per week. With the proportions obtained, an investment simulation was then carried out during the COVID-19 pandemic. The results of the simulation obtained a gain of 0.0771504% every week. These results are below the risk-free return of assets (SBIS) during the COVID-19-19 pandemic with an average profit of 0.087445% per week. It was concluded that optimal portfolio formation with the Single Index Model did not provide optimal benefits during the COVID-19 pandemic.
Penelitian tentang perangkingan dokumen pada temu kembali informasi saat ini mudah ditemukan, hal ini terkait perkembangan keilmuan dibidang penggalian informasi yang bergerak sangat cepat. Namun, Walaupun sudah penelitian yang menggunakan Bahasa Arab sebagai objek masih terbatas. Karena keterbatasan penggunaan dokumen Bahasa Arab untuk penelitian bidang penggalian informasi maka penulis mencoba melakukan pendekatan sederhana, yaitu dengan mengimplementasikan metode klasifikasi naïve bayes dan k-Nearest Neighbor (k-NN). Tujuan dari penelitian ini adalah untuk mengetahui apakah metode klasifikasi terutama naïve bayes dan k-NN dapat digunakan untuk melakukan perangkingan, dan juga membandingkan akurasi dari kedua metode tersebut. Berdasarkan penelitian yang dilakukan, didapatkan hasil bahwa perangkingan dengan metode klasifikasi dapat dilakukan dengan tingkat akurasi metode Naïve Bayes lebih baik dibandingkan dengan metode k-NN dengan rata-rata nilai F1 Measure mencapai 72%, rata-rata nilai precision mencapai 75%, dan rata-rata nilai recall mencapai 80%. Sedangkan hasil dari metode k-NN diperoleh rata-rata nilai F1 Measure mencapai 70%, rata-rata nilai precision mencapai 76%, dan rata-rata nilai recall mencapai 79%. Namun penelitian ini masih kurang dari segi teknik yang dilakukan, yaitu dengan menghilangkan proses stemming. Sehngga penulis memberikan saran untuk penelitian selanjutnya supaya bisa dilakukan proses stemming dan menggunakan metode perangkingan yang lebih baru.
Controversies about veil and hijab are often occur in society. Especially in today’s digital era, public opinion expressed through social media can greatly influence the others opinions, regardless of whether it is positive or negative. Therefore, this research was aiming to conduct an approach through analysis sentiment of public opinion about the veil and hijab to know how much accurate the sentiment analysis predict the positive, negative, or other sentiments with using Twitter data as the research object. The algorithm used in this study is Support Vector Machine (SVM) because of its fairly good classification model though it trained using small set of data. The SVM on this research was combined with Radial Base Function (RBF) kernel because of its numerical difficulties that are fewer than linear and polynomial kernel and also because this research doesn’t have a large feature. The amount of data used is 3556 tweets data. Tweets data, which is numbered 1056, is classified manually for the learning process. The remaining 2500 data will be classified automatically with the classifier model that has been created. A total of 1056 tweets data that have been classified manually is separated into training and testing data with a ratio of 8: 2. The result of the sentiment analysis process using Support Vector Machine algorithm RBF kernel with C=1 and γ=1 has an accuracy score of 73.6% with precision to negative opinions are 62%, positive opinions are 83%, neutral opinions reach 53% and irrelevant opinions that talk about hijab and veil reach 98%. It shows that sentiment analysis can be used for predicting the negative, positive or other sentiments of a sentence based on a certain topic, in this case veil and hijab.
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