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All Journal Electronic Journal of Graph Theory and Applications (EJGTA) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pusat Inovasi Masyarakat Milang Journal of Mathematics and Its Applications MILANG Journal of Mathematics and Its Applications Indonesian Actuarial Journal
Fendy Septyanto
IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics Physics Social Sciences

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PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR SINGAPURA, BAHT, DAN PESO MENGGUNAKAN METODE GSTAR Budiarti, Retno; Rahmawati, D. S.; Septyanto, Fendy; Purnaba, I Gusti Putu
MILANG Journal of Mathematics and Its Applications Vol. 20 No. 1 (2024): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.20.1.1-13

Abstract

The Generalized Space-Time Autoregressive (GSTAR) model is an extension of the Space-Time Autoregressive (STAR) model. The difference between the two models lies in the parameter assumptions. In the STAR model, the parameters are assumed to be independent of location, so this model is only suitable for data with homogeneous locations. Meanwhile in the GSTAR model, the parameters are assumed to change for each different location. This research aims to develop the best model for forecasting the Rupiah exchange rate against the Singapore Dollar, Thai Baht, and Philippine Peso. The appropriate model used for the Rupiah exchange rate data is the GSTAR(51)I(1) model. The weights used in this study are uniform location weights and inverse distance. The modeling results show that the best model is the model with inverse distance weighting, which has an MSE value of 371.8907 with MAPE values for each of the Rupiah exchange rate data against the Singapore Dollar, Thai Baht, and Philippine Peso of 0.3154214%, 0.8369436%, and 0.6237245%, respectively.
PEWARNAAN SIMPUL UNTUK SELEKSI ASET PADA PEMBENTUKAN PORTOFOLIO INVESTASI SAHAM Prastiwi, Diah; Septyanto, Fendy
MILANG Journal of Mathematics and Its Applications Vol. 20 No. 2 (2024): MILANG Journal of Mathematics and Its Applications
Publisher : School of Data Science, Mathematics and Informatics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.20.2.111-121

Abstract

Dalam membentuk portofolio investasi biasanya terdapat tahap seleksi dan alokasi/pembobotan. Seleksi aset kerap dilakukan dengan melihat kinerja dari aset tersebut. Dalam penelitian ini, seleksi aset dilakukan dengan menggunakan konsep-konsep teori graf. Konsep dasar yang digunakan adalah independent set dan clique. Dengan independent set, dipilih sekumpulan aset yang berkorelasi rendah sehingga risikonya juga rendah. Dengan clique, ditemukan sekumpulan aset yang berkorelasi tinggi sehingga dapat dipilih salah satunya sebagai representasi. Lebih lanjut, digunakan pewarnaan simpul pada graf korelasi untuk menghasilkan partisi aset-aset menjadi beberapa independent set, yang masing-masing dapat dibentuk menjadi portofolio terdiversifikasi. Dengan menerapkan pewarnaan simpul pada graf antikorelasi, aset-aset dipartisi menjadi beberapa clique, yang masing-masing dapat dipilih satu aset untuk mereplikasi pasar. Dipilih pembobotan sederhana yaitu equal weight. Sebagai studi kasus, diambil saham-saham LQ45 tahun 2023. Dihasilkan sebuah portofolio yang terdiversifikasi dengan baik, serta sebuah portofolio yang mampu mereplikasi pasar dengan efisien. Keduanya memiliki return lebih tinggi dari return bebas risiko sehingga layak diinvestasikan.
Modelling Dependencies of Stock Indices During Covid-19 Pandemic by Extreme-Value Copula Budiarti, Retno; Intansari, Kumala; Purnaba, I Gusti Putu; Septyanto, Fendy
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.15109

Abstract

Quantifying dependence among variables is the core of all modelling efforts in financial models. In the recent years, copula was introduced to model the dependence structure among financial assets return, and its application developed fast. A large number of studies on copula have been performed, but the study of multivariate extremes related with copulas was quite behind in comparison with the research on copulas. The COVID-19 pandemic is an extreme event that has caused the collapse of various economic activities which resulted in the decline of stock prices. The modelling of extreme events is therefore important to mitigate huge financial losses. Extreme-value copula can be suitable to quantify dependencies among assets under an extreme event. In this paper, we study the modelling of extreme value dependence using extreme value copulas on finance data. This model was applied in the portfolio of the IDX Composite Index (IHSG), Straits Times Index (STI) and Kuala Lumpur Stock Exchange (KLSE). Each individual asset return is modelled by the ARMA-GARCH and the joint distribution is modelled using extreme value copulas. This empirical study showed that Gumbel copula is the most appropriate extreme value copulas for the three indices. The results of this study are expected to be used as a basis for investors in the formation of a portfolio consisting of 2 financial assets and a portfolio consisting of 3 financial assets. 
Precision-Oriented Churn Prediction with a Fine-Tuned Meta-Learner Stack Model and SHAP: A Case Study on IBM Telco Ghaza Antani, Tajmahal; Hakim, Adhan Haidar; Nurrizky, Rayna; Annelia Einstania Vyorra, Venny; Septyanto, Fendy
Indonesian Actuarial Journal Vol. 1 No. 2 (2025): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol01no2pp137-151

Abstract

Customer churn prediction is essential in the telecommunications industry, where maintaining existing customers is significantly more cost-effective than acquiring new ones. This study introduces a precision-oriented stacked ensemble model to predict churn using the IBM Telco Customer Churn dataset. Emphasis is placed on maximizing precision to reduce false positives, thereby minimizing unnecessary and costly intervention efforts. The proposed architecture employs LightGBM, CatBoost, and Logistic Regression as base learners, with a fine-tuned ElasticNet serving as the meta-learner. Evaluation results show that the stacking model achieves strong overall performance, attaining an AUC of 0.917 and the highest precision among all models tested. To ensure interpretability, SHapley Additive exPlanations (SHAP) are applied to identify key drivers of churn such as number of referrals, contract type, monthly charges, and tenure. These findings demonstrate that a precision-focused approach can balance business efficiency and predictive power, offering a robust framework for proactive and cost-sensitive churn management.
Identifying Weakly Correlated Dominating Stocks using Maximum Independent Set Gimanjar, Yeremia Bertolla; Prastiwi, Diah; Septyanto, Fendy
Indonesian Actuarial Journal Vol. 1 No. 2 (2025): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol01no2pp128-136

Abstract

In this paper we consider the problem of efficiently finding a (small) set of stocks taken from an index that can replicate the index performance. Furthermore, we add the requirement that the set’s returns have weak correlation with each other. Such a selection of stocks may be useful for investors who want to simplify their analysis of the stock index, trying to capture market movement with reduced risk. To solve this problem, we use maximum independent set, a concept from graph theory. As a case study we consider IDX80 in the year 2024.
Co-Authors Adawiyah, Syifa Adilla, Indrya Annelia Einstania Vyorra, Venny Berlian Setiawaty D. S. Rahmawati Fadhla, Syahira Ghaza Antani, Tajmahal Gimanjar, Yeremia Bertolla Hakim, Adhan Haidar I Gusti Putu Purnaba Indrya Adilla Intansari, Kumala Khasanah, Jumrotul Kiki A. Sugeng Nisa, Afifa Ahmi Nurdin, Jasmine Mutiara Indira Nurrizky, Rayna Prastiwi, Diah Rahmawati, D. S. Ramaditya, Daiva Aqilyanda Retno Budiarti Ruhiyat Ruhiyat Ruhiyat Sarifah, Sirfi Tozzar, Aidil Amin Windiani Erliana Windiani Erliana Windiani Erliana Yuda Ardiansyah Yuda Ardiansyah