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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Riset Statistika Bandung Conference Series: Statistics Statistika Jurnal Kajian dan Terapan Matematika
Fitri Amanah
Prodi Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Islam Bandung, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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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.
Time Series Clustering and Mean–Variance Portfolio Modeling on IDX Sharia Growth Stocks Fitri Amanah; Fauziah Roshafara; Nafa Nurhanifah; Novianda Dwi Tanti Ramdani
Statistika Vol. 26 No. 1 (2026): 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.v26i1.9723

Abstract

Abstract. A stock portfolio represents an investment strategy aimed at maximizing returns while minimizing risk. Stock data, as a form of time series data, relies on historical information to evaluate performance and identify price movement patterns. Consequently, stock selection is a critical component in asset diversification. The novelty of this study is to apply time series clustering methods to stock data and utilize the resulting clusters to construct a mean-variance portfolio. The data used are the weekly closing prices of 10 Sharia-Growth Index stocks for the period December 2022 to November 2024. Using Dynamic Time Warping (DTW) distance with average linkage, two clusters were identified: cluster 1 (MPMX, TLKM) and cluster 2 (MAPI, HEAL, ISAT, AKRA, BMTR, SIDO, KLBF, PWON), with a Silhouette coefficient of 0.9471 indicating strong clustering performance. To construct the mean-variance portfolio, three stocks with positive expected returns were selected: HEAL, ISAT, and MAPI, which are members of Cluster 2. The optimal weights obtained using Lagrange optimization are HEAL (0.49%), ISAT (99.51%), and MAPI (0%). The Lagrange method allocates 0% to MAPI because its risk level (variance of 0.00322) is the highest among the three candidate stocks, thereby not helping to minimize the overall portfolio risk. Therefore, the optimal portfolio formed by combining HEAL and ISAT would have provided an estimated return of 0.56% and a risk of 4.1%.
Penerapan Support Vector Regression untuk Prediksi Indeks Harga Saham Gabungan (IHSG) di Indonesia Fanny Ahdasabilla; Fitri Amanah
Jurnal Riset Statistika Volume 6, No. 1, Juli 2026, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v6i1.10027

Abstract

Abstract. The Composite Stock Price Index (IHSG) is the main indicator of capital market performance in Indonesia and is influenced by macroeconomic factors such as inflation, interest rates, and exchange rates. The high volatility of the IHSG highlights the need for accurate prediction methods. This study aims to model and predict the IHSG using Support Vector Regression (SVR) with three types of kernels: linear, polynomial, and Radial Basis Function (RBF). The data used consist of monthly observations from January 2016 to September 2025, which underwent outlier detection and treatment, min-max scaling normalization, training and testing data partitioning, and optimal parameter selection using grid search. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R^2). The results indicate that the Radial Basis Function (RBF) kernel with optimal parameters C = 4, ε = 0,1, dan γ = 0,5 provides the best performance compared to the other kernels. The SVR model using the RBF kernel is able to produce accurate predictions and demonstrates good generalization ability on both training and testing data. These findings contribute to the advancement of machine learning-based forecasting methods in financial time series and provide valuable insights for investors and policymakers in making more informed decisions regarding investment strategies and capital market stability. Abstrak. Indeks Harga Saham Gabungan (IHSG) merupakan indikator utama kinerja pasar modal di Indonesia yang dipengaruhi oleh faktor makroekonomi, seperti inflasi, suku bunga, dan nilai tukar. Fluktuasi IHSG yang tinggi mendorong perlunya metode prediksi yang akurat. Penelitian ini bertujuan untuk memodelkan dan memprediksi IHSG menggunakan Support Vector Regression (SVR) dengan tiga jenis kernel, yaitu linear, polinomial, dan Radial Basis Function (RBF). Data yang digunakan adalah data bulanan periode Januari 2016 hingga September 2025, yang melalui tahapan deteksi dan penanganan outlier, normalisasi min-max scaling, pembagian data training dan testing, serta penentuan parameter optimal menggunakan grid search. Kinerja model dievaluasi menggunakan Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan koefisien determinasi (). Hasil penelitian menunjukkan bahwa kernel Radial Basis Function (RBF) dengan parameter optimal C = 4, ε = 0,1, dan γ = 0,5 menghasilkan performa terbaik dibandingkan kernel lainnya. Model SVR dengan menggunakan kernel RBF mampu memberikan hasil prediksi yang akurat serta memiliki kemampuan generalisasi yang baik pada data training dan testing. Temuan ini memberikan kontribusi dalam pengembangan metode prediksi berbasis machine learning pada data keuangan serta dapat menjadi referensi bagi investor dan pembuat kebijakan dalam mengambil keputusan yang lebih tepat terkait investasi dan stabilitas pasar modal.
Peramalan Pendapatan PT Telkom Indonesia (Persero) Tbk Berdasarkan Data Periode 2016-2024 Menggunakan Metode Double Exponential Smoothing-Holt.: Indonesia Jasmine Devanti Bilbina Heriaty; Fitri Amanah
Bandung Conference Series: Statistics 115-122
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.24628

Abstract

Abstract. Revenue is one of the primary indicators of a company’s financial performance and serves as a basis for strategic business planning. Therefore, an accurate forecasting method is essential to support decision-making. This study aims to forecast the revenue of PT Telkom Indonesia (Persero) Tbk using the Double Exponential Smoothing-Holt method based on quarterly revenue data from 2016 to 2024 obtained from the company’s financial statements. The smoothing parameters for level and trend were determined through a trial and error approach by selecting the combination that produced the lowest Mean Absolute Percentage Error (MAPE). The results showed that the optimal parameters were and , yielding a MAPE value of 2,84%, indicating excellent forecasting accuracy. The forecast for the next four periods showed a stable upward trend in revenue, increasing from IDR 38.989 trillion in period 37 to IDR 39.736 trillion in period 40. These findings indicate that the Double Exponential Smoothing-Holt method is effective for forecasting the revenue of PT Telkom Indonesia (Persero) Tbk and can support financial planning and strategic decision-making. Abstrak. Pendapatan merupakan salah satu indikator utama yang mencerminkan kinerja keuangan perusahaan dan menjadi dasar dalam penyusunan strategi bisnis. Oleh karena itu, diperlukan metode peramalan yang akurat untuk mendukung pengambilan keputusan. Penelitian ini bertujuan meramalkan pendapatan PT Telkom Indonesia (Persero) Tbk menggunakan metode Double Exponential Smoothing-Holt berdasarkan data pendapatan kuartalan periode 2016-2024 yang diperoleh dari laporan keuangan perusahaan. Parameter pemulusan level dan tren ditentukan melalui pendekatan trial and error dengan memilih kombinasi yang menghasilkan nilai Mean Absolute Percentage Error (MAPE) terkecil. Hasil penelitian menunjukkan bahwa parameter optimum diperoleh pada dan dengan nilai MAPE sebesar 2,84%, yang termasuk dalam kategori sangat baik. Hasil peramalan empat periode ke depan menunjukkan tren peningkatan pendapatan yang stabil, yaitu dari Rp38.989 triliun pada periode ke-37 menjadi Rp39.736 triliun pada periode ke-40. Dengan demikian, metode Double Exponential Smoothing-Holt efektif digunakan untuk meramalkan pendapatan PT Telkom Indonesia (Persero) Tbk serta dapat mendukung proses perencanaan keuangan dan pengambilan keputusan perusahaan.
Analisis Korelasi Valid Day, Duration, dan Diamond Dalam Live Streaming TikTok Nadya Ayu Aprilia; Fitri Amanah
Bandung Conference Series: Statistics 189-196
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.25072

Abstract

Abstract. TikTok is a social networking application used as an entertainment platform that offers various features, one of which is live streaming, where creators can interact directly with viewers in real time. This study aims to analyze the relationship among the variables valid day, duration, and diamond in TikTok live streaming. Valid day refers to the number of verified days within a month during which a creator conducts live streaming. Duration refers to the total amount of time accumulated over one month while a creator performs live streaming. Diamond refers to the accumulation of virtual gifts received by creators during live streaming. The data used in this study are secondary data which were obtained from one of the agency's worksheets under PT Media Generasi Group for June 2025. The method employed in this study is the Spearman Rank correlation, a nonparametric statistical method used to determine the direction and strength of the relationship between variables when the data are not normally distributed. The results indicate significant relationships between valid day and diamond, duration and diamond, as well as valid day and duration. Therefore, it can be concluded that these three variables are positively correlated and serve as supporting factors in increasing the number of diamonds earned by creators during live streaming. Abstrak. TikTok merupakan aplikasi jejaring sosial yang digunakan sebagai media hiburan yang memiliki beberapa fitur salah satunya live streaming, dimana kreator dapat berinteraksi secara langsung dengan penonton dalam waktu nyata. Penelitian ini bertujuan untuk menganalisis hubungan antara variabel valid day, duration, dan diamond dalam live streaming TikTok. Valid day merupakan jumlah hari yang tervalidasi selama satu bulan apabila kreator melakukan live streaming. Duration merupakan akumulasi waktu yang tercapai selama satu bulan ketika kreator melakukan live streaming. Diamond merupakan akumulasi dari hadiah virtual yang didapat oleh kreator selama melakukan live streaming. Data yang digunakan merupakan data sekunder yang berasal dari salah satu lembar kerja Agensi dalam PT. XYZ pada bulan Juni tahun 2025. Metode yang digunakan pada penelitian ini yaitu Rank Spearman, yang merupakan metode statistik nonparameterik untuk mengetahui arah dan kekuatan hubungan antar variabel ketika data tidak berdistribusi normal. Hasil analisis menunjukkan bahwa terdapat hubungan yang signifikan antara variabel valid day dengan diamond, duration dengan diamond, dan valid day dengan duration, sehingga dapat disimpulkan bahwa antar ketiga variabel memiliki hubungan yang positif yang menjadi faktor pendukung dalam perolehan diamond bagi kreator dalam melakukan live streaming.
Analisis Value at Risk Dengan Metode Historical, Variance-Covariance, dan Monte Carlo pada Saham Sektor Konsumsi Primer Nur Sugih Aji; Fitri Amanah
Bandung Conference Series: Statistics 217-224
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.25338

Abstract

Penelitian ini bertujuan mengestimasi kerugian maksimum pada tiga saham defensif sektor konsumsi primer, yaitu PT Indofood Sukses Makmur Tbk (INDF), PT Indofood CBP Sukses Makmur Tbk (ICBP), dan PT Mayora Indah Tbk (MYOR) untuk memitigasi risiko investasi. Data yang digunakan adalah sekunder berupa harga penutupan mingguan periode 3 Januari 2025 hingga 10 April 2026. Pengukuran risiko dihitung menggunakan metode Value at Risk (VaR) dengan pendekatan Historical, Variance-Covariance, dan Monte Carlo. Spesifikasi risiko ditetapkan pada tingkat kepercayaan 95% dengan asumsi penempatan modal awal Rp10.000.000, di mana metode Monte Carlo dibangkitkan melalui 10.000 iterasi. Uji Kolmogorov-Smirnov diaplikasikan untuk memvalidasi kelayakan distribusi normal dari data return. Hasil penelitian menunjukkan variasi estimasi kerugian maksimum dari ketiga metode yang digunakan. Pada metode Historical, nilai VaR pada saham INDF sebesar -5,79% (Rp579.193), ICBP sebesar -5,78% (Rp578.218) dan MYOR sebesar -7,42% (Rp742.092). Pada metode Variance-Covariance, nilai VaR pada saham INDF sebesar -5,29% (Rp529.397), ICBP sebesar -5,68% (Rp568.235) dan MYOR sebesar -7,89% (Rp788.577). Sementara itu, estimasi melalui metode Monte Carlo nilai VaR pada saham INDF sebesar -5,25% (Rp524.740), ICBP sebesar -6,10% (Rp609.990), dan MYOR sebesar -8,12% (Rp812.188). Estimasi VaR komparatif ini dapat menjadi landasan bagi investor dalam membatasi toleransi kerugian secara terukur.
Co-Authors Abdurakhman Abdurakhman Fanny Ahdasabilla Fauziah Roshafara Fitri Rahmawati Jasmine Devanti Bilbina Heriaty Muhamad Arifianto Darmawan Nadya Ayu Aprilia Nafa Nurhanifah Novianda Dwi Tanti Ramdani Nur Sugih Aji Puri Indah Lestari Renita Maharani Retno Subekti Salma Nera Hanifah Salwa Salsabila Sefri Imanuel Fallo