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All Journal bit-Tech Jurnal Informasi dan Teknologi Jurnal PETISI (Pendidikan Teknologi Informasi)
Kartika Maulida Hindrayani
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Education

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ARIMA-TGARCH Model for Return Prediction and Risk Estimation with VaR Imanta Ginting; Trimono Trimono; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3090

Abstract

Investment activity in the Indonesian capital market has experienced significant growth, driven by increasing public awareness and accessibility to financial instruments. Stocks remain the most favored investment tool due to their potential for high returns, though they come with higher risks. Accurate modeling of return dynamics and risk estimation is thus crucial for informed investment decisions. This study analyzes the return and volatility of PT Telekomunikasi Indonesia Tbk (TLKM) stock using a hybrid time series approach that combines the Autoregressive Integrated Moving Average (ARIMA) model and the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) model. The analysis uses daily closing price data from 2020 to 2024, with 1,210 observations. The best-fitting model, ARIMA(2,0,2)–TGARCH(1,1), resulted in low Root Mean Squared Error (RMSE) values of 0.0188 for both training and testing datasets, indicating strong prediction accuracy. Forecasting over a five-day horizon revealed fluctuating returns and a decreasing trend in volatility, from 0.0230 to 0.0198. Additionally, the study utilized the Value at Risk (VaR) method to estimate potential losses under normal market conditions. At a 95% confidence level, the predicted daily loss for a capital investment of IDR 50,000,000 ranged between IDR 1,633,108 and IDR 1,859,355. The combination of ARIMA and TGARCH, integrated with VaR, provides a comprehensive framework for capturing both linear return trends and asymmetric volatility, offering investors a robust quantitative tool for managing risks and optimizing strategies.
Heckman Probit Two-Step Regression Approach for Analyzing Open Unemployment Factors in West Java Province Holly Patrycia; Dwi Arman Prasetya; Kartika Maulida Hindrayani
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3096

Abstract

Open unemployment remains a major socio-economic challenge in Indonesia, with West Java recording the highest national rate in August 2024 at 6.75%. This study investigates the determinants of open unemployment using the Heckman Probit Two-Step model, an approach rarely applied in Indonesian labor market research. Unlike conventional regression methods, this model corrects for sample selection bias by simultaneously estimating labor force participation and unemployment status. Data are drawn from the 2024 Survei Angkatan Kerja Nasional (SAKERNAS) conducted by Badan Pusat Statistik (BPS), covering working-age individuals in West Java Province. The first stage models labor force entry, while the second stage incorporates the Inverse Mills Ratio (IMR) to adjust for selection effects. Results show that the IMR coefficient (–0.3100, p = 0.0412) is statistically significant, confirming the necessity of the two-step correction. The explanatory power of the model is substantial, with Pseudo-R² values of 0.385 for labor force participation and 0.381 for open unemployment. Marginal effects indicate that being married reduces unemployment probability by 5.50%, each additional year of age decreases it by 2.79%, whereas a longer job search increases it by 3.35%. Training experience lowers unemployment risk, while disabilities and larger household size increase vulnerability. Methodologically, the study demonstrates the advantages of Heckprobit in producing unbiased estimates compared to descriptive or conventional probit approaches previously used in Indonesia. Nonetheless, the cross-sectional design and focus on a single province limit generalizability. Findings provide valuable evidence for policymakers to design targeted, inclusive employment strategies aligned with regional development goals
Interpretive Structural Modeling-Based Decision Support System for Marine Tourism Strategy Kartini; Kartika Maulida Hindrayani; Endang Tri Wahyurini; Aang Kisnu Darmawan; Hilya Zada Mardhatilla Al Haadiy; Maudi Adella; Rizky Fatkhur Rohman
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.649

Abstract

Marine tourism in Madura has great potential for economic growth, but its unsustainable management threatens the ecosystem and community welfare. A development strategy is needed that balances economic, social, and environmental aspects. The main challenge is the complexity of sustainable marine tourism development, where various factors are interrelated and require a holistic approach. Previous studies have identified factors that influence marine tourism, but have been lacking in integrating them into a comprehensive decision-making framework. This study aims to fill this gap by developing a Decision Support System (DSS) to help stakeholders formulate sustainable marine tourism development strategies. The main objective of this study is to develop a DSS based on Interpretive Structural Modeling (ISM) to map the relationships between key variables and provide strategy recommendations. The ISM approach is used to identify, analyze, and interpret the relationships between key variables. Data were collected through expert interviews, surveys, and literature studies. The study produced a hierarchical model that describes the influence and relationships between variables, as well as a DSS that is able to provide development strategy recommendations based on priorities and objectives. This study contributes to providing a structured and evidence-based decision-making tool for sustainable marine tourism development in Madura. The originality of this study lies in the integration of ISM into DSS for sustainable marine tourism, offering a new perspective in strategic decision-making.
Pengujian Fungsional Website Crusher Report Berbasis Machine Learning Menggunakan Metode Robustness Testing Chelsea Ayu Adhigiadany; Kartika Maulida Hindrayani; Dwi Arman Prasetya
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.2014

Abstract

Website dan Machine Learning menjadi kebutuhan penting perusahaan dalam rangka meningkatkan efektivitas kinerja. Salah satu implementasi integrasi website dengan Machine Learning adalah website Crusher Report milik PT XYZ. Website yang dirancang dengan memanfaatkan LARS, PostgreSQL, dan Flask ini sudah diuji secara ketangkasan model dalam memprediksi. Penelitian ini bertujuan untuk menguji keandalan website Crusher Report sebagai user interface milik PT XYZ menggunakan pendekatan Black Box Testing dengan metode Robustness Testing. Skenario pengujian yang digunakan yaitu dengan memberikan input diluar ketentuan website. Hasil pengujian menunjukkan bahwa website mampu menangani seluruh input tidak valid dengan baik melalui notifikasi kesalahan dan pengaturan nilai input otomatis, menghasilkan tingkat keberhasilan pengujian sebesar 100%. Temuan ini menunjukkan bahwa website Crusher Report efektif dalam mendeteksi dan mengelola kesalahan input, serta layak digunakan sebagai platform pendukung operasional crusher PT XYZ.
Co-Authors Aang Kisnu Darmawan Chelsea Ayu Adhigiadany Dwi Arman Prasetya Endang Tri Wahyurini Hilya Zada Mardhatilla Al Haadiy Holly Patrycia Imanta Ginting Kartini Maudi Adella Rizky Fatkhur Rohman Trimono, Trimono