Wulandari, Maya Nurlita (2016) Penggalian Pola Sekuensial Interval Waktu Fuzzy Pada Pergerakan Harga Saham Di Indonesia Menggunakan Algoritma Fp-Growth – Prefixspan. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
1212100030-undergraduate-theses.pdf - Published Version Download (2MB) | Preview |
Preview |
Text
1212100030-paperpdf.pdf - Published Version Download (714kB) | Preview |
Preview |
Text
1212100030-presentationpdf.pdf - Published Version Download (2MB) | Preview |
Abstract
Saham merupakan salah satu instrument investasi yang
populer saat ini karena saham mampu memberikan tingkat
keuntungan yang menarik. Pada kalangan investor pergerakan
harga saham merupakan salah satu topik yang menarik, karena
pergerakan harga saham bersifat dinamis sehingga akan
berpengaruh terhadap nilai investasi mereka. Hal ini
mengakibatkan perlu adanya visualisasi data pada pasar saham
sebagai penentu investasi. Untuk analisis data dan
memvisualisasikannya dapat menggunakan data mining. Data
mining merupakan proses ekstraksi pola yang penting dari data
dalam jumlah besar. Metode data mining yang akan digunakan
adalah metode penggalian pola sekuensial (sequence pattern
mining) menggunakan algoritma FP-Growth–PrefixSpan, selain
itu digunakan juga pendekatan fuzzy untuk mendapatkan interval
waktu yang bervariasi pada data yang dianalisis sehingga pola
yang dihasilkan berupa pola sekuensial interval waktu fuzzy.
Hasil analisis menunjukkan bahwa terdapat pengaruh antara nilai
minimum support terhadap banyaknya hasil pencarian pola
sekuensial interval waktu fuzzy. Hasil dari penggalian pola
sekuensial interval waktu fuzzy pada aktivitas pergerakan harga
saham ini selanjutnya bisa dijadikan bahan pertimbangan dalam
pengambilan keputusan investasi.
===========================================================================
Stock is one of the popular investment instrument at this
time because it was able to provide the level of benefits. Among
the investors, stock price movement is one of the interesting
topics, because the stock price movement is dynamic so it will
have an effect on the value of their investment. This resulted in
the need for data visualization on the stock market as a
determinant of investment. For data analysis and visualization
can use data mining. Data mining is the process of extracting the
important patterns of large amounts of data. Data mining
methods that will be used is a method of extracting patterns of
sequential (sequence pattern mining) using FP-Growth algorithm
– PrefixSpan, besides fuzzy approach also used to obtain the time
interval that varies on the data to be analyzed so that the
resulting pattern of sequential pattern time interval fuzzy. The
results of the analysis show that there are influences between the
minimum support value against the large number of sequential
pattern search results of fuzzy time intervals. The results of the
excavations of sequential pattern time interval fuzzy on this stock
price movement activity next can be used as consideration in
making investment decisions.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSMa 518.1 Wul p |
| Uncontrolled Keywords: | Saham, Data Mining, Sequence Pattern, Interval Waktu Fuzzy. |
| Subjects: | T Technology > T Technology (General) > T57.6 Operations research--Mathematics. Goal programming |
| Divisions: | Faculty of Mathematics, Computation, and Data Science > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | EKO BUDI RAHARJO |
| Date Deposited: | 04 Oct 2019 03:18 |
| Last Modified: | 04 Oct 2019 03:18 |
| URI: | http://repository.its.ac.id/id/eprint/70987 |
Actions (login required)
 |
View Item |