Article Per Year (5 Year)
p-Index From 2020 - 2025
0.751
P-Index
This Author published in this journals
All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI STATISTIKA Jurnal Kajian dan Terapan Matematika
Fitri Amanah
Universitas Islam Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Kajian Regularized Generalized Structured Component Analysis untuk Mengatasi Multikolinearitas pada SEM Berbasis Komponen Fitri Amanah; Fitri Rahmawati
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.28069

Abstract

Multicollinearity is one of the issues that may arise in the analysis of Structural Equation Modeling (SEM). An indication of multicollinearity is the high correlation between latent variables and the correlation between indicators forming the latent construct. Multicollinearity causes the interpretation of SEM analysis to be inappropriate. In this study, Regularized generalized structured component analysis (RGSCA) is used as a solution to overcome multicollinearity in component-based SEM. The research aims to apply RGSCA to East Java poverty data, which contains multicollinearity. The first step is analyze data using GSCA, however the weights of  and  indicators are not significant, and the three estimated path coefficients are also not significant at the 95% confidence interval. The high correlation value between the  indicators further indicates the presence of multicollinearity. Futhermore, the data are analyzed using RGSCA with ridge parameters namely   which provides minimum prediction error (CV). The results of the analysis reveal that all estimation of loading factors, weights and path coefficients are significant at 95% confidence intervals. The interpretation of the path coefficient results suggests that education, health, and economy significantly influence poverty, while health and economy also have a significant effect on education, and health additionally exhibits a significant effect on economy. The overall model evaluation results obtained a FIT value of 0.662, indicating that the model can explain about 66.2% of the data variation.
ANALISIS TRACKING ERROR UNTUK MENGUKUR KINERJA PORTOFOLIO MODEL BLACK-LITTERMAN Fitri Amanah; Retno Subekti
Jurnal Kajian dan Terapan Matematika Vol 5, No 5 (2016): Jurnal Matematika
Publisher : Jurnal Kajian dan Terapan Matematika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Salah  satu  factor  utama  dalam  investasi  adalah  risiko.  Risiko  portofolio  yang  relatif  terhadap  benchmarkdisebut  tracking  error,  selanjutnya  disebut  tracking  error  volatility  (TEV).  Tujuan  penelitian  ini  adalah  untuk menjelaskan  aplikasi  TEV  serta  menganalisis  sensitivitas  TEV  terhadap  views  pada  portofolio  Black-Litterman. Tahapan  dalam  perhitungan  TEV  adalah  menentukan  bobot  portofolio  Black-Litterman  dan  bobot  benchmark.Model CAPM merupakan model ekuilibrium diasumsikan sebagai ukuran benchmark. Selisih kedua bobot tersebut disebut  bobot  aktif  .  Selanjutnya  TEV  dinyatakan  dalam  fungsi    sedangkan    adalah  fungsi dalam    yang  merepresentasikan views. Sehingga dengan aturan rantai dapat ditentukan sensitivitas TEV terhadap views. Pembentukan  views  dilakukan  dengan metode  moving average  dari data return 10 hari terakhir.  Diperoleh nilai TEV dari tujuh saham LQ-45 sebesar 0.99% artinya portofolio Black-Litterman lebih berisiko dibandingkan benchmark.  Sensitivitas TEV terhadap  view  pertama    bernilai negatif yaitu  -0.05, artinya dengan mengurangi nilai  maka nilai TEV akan meningkat. Sensitivitas TEV terhadap view kedua   dan view ketiga   bernilai positif yaitu 0.11 dan 0.45, artinya dengan memperbesar nilai   atau  maka nilai TEV akan meningkat.Kata kunci: Black-Litterman, TEV, sensitivitas, benchmark, view
Penerapan K-Means Cluster untuk Pembentukan Portofolio Model Black-Litterman Fitri Amanah; Fauziah Roshafara; Puri Indah Lestari; Salwa Salsabila; Renita Maharani
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.34165

Abstract

A portfolio in finance is a collection of investment assets that aims to reduce risk by spreading investment across various assets. In building a portfolio, cluster analysis is used to select assets. K-Means cluster is often used because it is considered efficient for handling large data. In addition, the Black-Litterman Model is used because it can combine investor knowledge into asset allocation efficiently, so that the portfolio becomes more diverse, stable and adaptive to economic conditions, and reflects the investment manager's views. The research results show that k-means cluster analysis can be applied in forming the Black-Litterman model portfolio. Two clusters were obtained, namely cluster I consisting of ADRO, AKRA, BRMS, MIKA, TLKM, UNVR shares, and cluster II consisting of INDF, INKP, SMGR, UNTR. The two clusters were then formed into portfolios I and II. The calculation of expected return and portfolio risk shows that portfolio II produces profits (expected return portfolio) that are greater than portfolio I, namely 0.04445 or IDR 4.445.344,00, and the risk level of portfolio II is also smaller than portfolio I, namely 0.02104 or IDR 2.104.400,00
ANALYSIS OF MULTILEVEL STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS METHOD Amanah, Fitri; Abdurakhman, Abdurakhman
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.81-92

Abstract

Generalized Structured Component Analysis (GSCA) is a component-based SEM. One of the developments of GSCA is the GSCA method for multilevel data known as multilevel GSCA. Multilevel data is data that has a nested, grouped, or nested structure. This study aims to apply multilevel GSCA to the data on factors that affect poverty. The data used is on Indonesia's health, education and poverty in 2023.. The result is that all indicators are significant to the latent variables. The structural model shows that the quality of health has a negative and significant effect on poverty, education has a negative and significant effect on poverty, and the quality of health has a positive and significant effect on education. The results of between group show that health quality has a positive and significant effect on education in all regions, health quality has a negative and significant effect on poverty in Bali & Nusa Tenggara, Sulawesi, as well as Maluku and Papua, education has a negative and significant effect on poverty in Sumatra, Java, and Maluku & Papua. The overall goodness of fit value (FIT) is 0.622, meaning the model can explain 62.2% of data variation.
Studi Komparasi Regresi Logistik Biner dan K-Nearest Neighbor Pada Kasus Prediksi Curah Hujan Rahmawati, Fitri; Amanah, Fitri; Fallo, Sefri Imanuel
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.2739

Abstract

ABSTRAK Perubahan iklim yang sedang terjadi di berbagai belahan dunia sebagai akibat dari pemanasan global telah menyebabkan ketidakpastian cuaca. Salah satu perubahan yang dirasakan adalah intensitas curah hujan. Hal ini mengakibatkan prediksi akan curah hujan menjadi penting untuk dilakukan. Ada beberapa teknik analisis data yang digunakan untuk prediksi curah hujan, diantaranya klasifikasi. Pada penelitian ini, dengan menggunakan variabel temperatur, kelembapan, lamanya penyinaran, dan kecepatan angin, akan dilakukan prediksi terhadap klasifikasi curah hujan di Kota Bogor. Model yang digunakan adalah Regresi Logistik Biner dan K-Nearest Neighbor. K yang digunakan pada model K-Nearest Neighbor yaitu 1 hingga 18. Untuk membandingkan kedua model, dibentuk confusion matrix yang selanjutnya digunakan untuk menghitung akurasi model. Akurasi model Regresi Logistik Biner sebesar 92,746%, adapun akurasi model K-Nearest Neighbor adalah sebesar 94,81865%. Dengan demikian, pada penelitian ini model K-Nearest Neighbor lebih baik dibandingkan model Regresi Logistik Biner. ABSTRACT Climate change due to global warming occurring in all parts of the world makes the weather unpredictable. One of the changes felt is the intensity of rainfall. This makes it important to predict rainfall. There are several data analysis techniques used to predict rainfall, including classification. In this research, using the variables temperature, humidity, duration of sunlight, and wind speed, predictions will be made on the classification of rainfall in the city of Bogor. The models used are Binary Logistic Regression and K-Nearest Neighbor. The K used in the K-Nearest Neighbor model is 1 to 18. To compare the two models, a confusion matrix is formed and then used to calculate the model accuracy. The accuracy of the Binary Logistic Regression model is 92.746%, while the accuracy of the K-Nearest Neighbor model is 94,81865%. Thus, in this research the K-Nearest Neighbor model is better than the Binary Logistic Regression model.
Co-Authors Abdurakhman Abdurakhman Fauziah Roshafara Fitri Rahmawati Fitri Rahmawati Puri Indah Lestari Renita Maharani Retno Subekti Salwa Salsabila Sefri Imanuel Fallo