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.

Online media news portals have the advantage of speed in conveying information on any events that occur in society. One way to know what a story is about is from the title. The headline is a headline that introduces the reader's knowledge about the news content to be described. From these headlines, you can search for the main topics or trends that are being discussed. It takes a fast and efficient method to find out what topics are trending in the news. One method that can be used to overcome this problem is topic modeling. Topic modeling is necessary to help users quickly understand recent issues. One of the algorithms in topic modeling is Latent Dirichlet Allocation (LDA). The stages of this research began with data collection, preprocessing, forming n-grams, dictionary representation, weighting, validating the topic model, forming the topic model, and the results of topic modeling. The results of modeling LDA topics in news headlines taken from www.detik.com for 8 months (March-October 2020) during the COVID-19 pandemic showed that the best number of topics produced each month were 3 topics dominated by news topics about corona cases, positive corona, positive COVID, COVID-19 with an accuracy of 0.824 (82.4%). The resulting precision and recall values indicate that the two values are identical, so this is ideal for an information retrieval system.

No abstract

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.