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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Community Service International Journal of Data Science, Engineering, and Analytics (IJDASEA) Al Khidma: Jurnal Pengabdian Masyarakat Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Inkofar Kreatifitas: Jurnal Ilmiah Pendidikan Islam Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial Baitul Hikmah: Jurnal Ilmiah Keislaman STATISTIKA Kohesi: Jurnal Sains dan Teknologi Information Technology International Journal (ITIJ) Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Trimono, Trimono
Data Science Department, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
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Journal : bit-tech

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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.
Rice Price Forecasting Using an Ensemble GRU–SVR Model with Enhanced Feature Engineering Faizi, Dandi Nur; Trimono, Trimono; Saputra, Wahyu Syaifullah Jauharis
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3532

Abstract

Rice price volatility significantly impacts economic stability and food security in Indonesia, particularly in East Java, where fluctuations in staple food prices affect household purchasing power and inflation management. This study addresses the limitations of existing rice price forecasting models, which often struggle to capture the complex, nonlinear dynamics of agricultural prices influenced by multiple factors such as climate variability and market conditions. Accurate and reliable price forecasting is essential to support effective policy formulation, market intervention, and food price stabilization strategies. This research develops an ensemble forecasting framework integrating Gated Recurrent Unit (GRU) and Support Vector Regression (SVR) with enhanced feature engineering to predict daily medium rice prices using historical price and weather data. The dataset comprises daily observations from 2021 to 2025, including rice prices, average temperature, relative humidity, rainfall, and sunshine duration. In this framework, GRU serves as a temporal feature extractor to learn complex temporal dependencies, while enhanced feature engineering generates complementary statistical features from sliding windows to enrich GRU's output. The combined feature set is provided to an SVR model with a Radial Basis Function kernel for final regression. Experimental results show that the proposed model achieves a high forecasting accuracy with an MAPE of 0.109%, demonstrating stable predictive behavior and making it a valuable tool for monitoring rice prices. The model's effectiveness in capturing temporal dependencies and nonlinear patterns suggests potential applicability beyond East Java, offering broader insights for agricultural price forecasting in other regions.
Implementation of Multiplex Leiden Algorithm for Clustering Ancol Visitors Wardani, Ajeng Puspa; Trimono, Trimono; Nasrudin, Muhammad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3608

Abstract

Ancol is the largest recreational destinations, attracting visitors from diverse backgrounds. However, in 2024 the company experienced an 11.96% decline in visitor numbers. This condition highlights the urgent need for more accurate customer segmentation to support targeted and effective marketing strategies. Accordingly, this study investigates whether a Multiplex Leiden can produce coherent visitor segments, while also examining the relative contribution of each layer to community formation. Unlike prior multilayer segmentation studies, this study leverages the Multiplex Leiden algorithm, which guarantees well-connected communities and has been shown to achieve higher modularity. This is among the first applications of Multiplex Leiden for visitor segmentation, offering improved community coherence and interpretability in a multi-layer behavioral network. To balance network structures and reduce cross-layer density bias, kNN backbone preprocessing was applied before community detection. The results reveal 18 distinct visitor communities with substantial variation in size. Layer-wise quality analysis shows that the socioeconomic status layer contributes the strongest influence on the detected communities, followed by spending behavior and experiential preferences. The clustering quality was evaluated using multiple metrics. An Adjusted Rand Index (ARI) of 0.617 indicates a stable, non-random visitor segmentation, while a positive total quality score of 1.086 reflects strong cross-layer community structure. A mean conductance value of 0.548 suggests moderately well-separated yet realistically overlapping communities. Overall, the findings empirically confirm the effectiveness and interpretability of the Multiplex Leiden algorithm with backbone preprocessing for visitor segmentation in multi-layer networks. Future research may extend this framework by incorporating additional behavioral or temporal data.
Analysis of the LQ45 Stock Portfolio Using Mean–Variance Method and Cornish–Fisher Expansion Putri, Shafira Amanda; Trimono, Trimono; Muhaimin, Amri
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3640

Abstract

Public interest in stock market investment in Indonesia has increased alongside growing awareness of financial planning and portfolio management. The LQ45 Index, consisting of stocks with high liquidity, large market capitalization, and strong fundamentals, is widely used as a benchmark for portfolio analysis. However, many portfolio studies still rely on conventional Value at Risk (VaR), which assumes normally distributed returns and may underestimate extreme losses, making it less effective in capturing tail risk. This study addresses this research gap by integrating Mean–Variance Optimization (MVO) with the Cornish–Fisher VaR approach, which incorporates skewness and kurtosis to accommodate non-normal return distributions. Daily adjusted closing price data of LQ45 stocks from January to December 2025 were obtained from Yahoo Finance, and logarithmic returns were calculated. Based on the highest Sharpe Ratios, BRPT, EXCL, and ANTM were selected as portfolio constituents. Correlation analysis shows low dependency among the selected stocks, supporting diversification, while normality tests confirm deviations from normality, justifying the use of Cornish–Fisher VaR. The optimal portfolio allocates 10.6% to BRPT, 65.5% to EXCL, and 23.9% to ANTM, producing an expected return of 65.7%, portfolio risk of 26.2%, and a Sharpe Ratio of 2.5, indicating strong risk-adjusted performance. Cornish–Fisher VaR estimates potential losses of 2.23%, 3.09%, and 5.30% at the 90%, 95%, and 99% confidence levels. These results demonstrate that combining MVO and Cornish–Fisher VaR offers a more robust framework for portfolio optimization in the Indonesian stock market.
Stacked LSTM Integrated with Big Data Pipelines for Automated Food Beverage Stock Price Prediction Asfiani, Ilil Musyarof; Prasetya, Dwi Arman; Trimono, Trimono
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3687

Abstract

Stock price volatility in the Food and Beverage (F&B) sector presents persistent challenges for investors and decision-makers, particularly in emerging markets. This study proposes an automated stock price prediction framework whose primary contribution lies in the system-level integration of a Stacked Long Short-Term Memory (LSTM) model with a scalable big data orchestration pipeline, rather than in introducing a new forecasting algorithm alone. The system targets three Indonesian F&B companies PT Indofood CBP Sukses Makmur Tbk, PT Mayora Indah Tbk, and PT Garudafood Putra Putri Jaya Tbk using historical daily stock price data. The dataset spans multiple years of trading records retrieved from the Yahoo Finance API, and predictions are generated for a seven-day forecasting horizon. Methodologically, the approach combines a multi-layer LSTM architecture with Apache Spark for distributed data preprocessing, Apache Airflow for automated workflow orchestration, and PostgreSQL for structured data storage. This integration enables scheduled data ingestion, reproducible model training, and continuous forecasting within an end-to-end analytics pipeline. Model performance is evaluated using error-based metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), and is benchmarked against a conventional single-layer LSTM without pipeline orchestration. Empirical results show that the proposed pipeline-based Stacked LSTM achieves lower prediction error, with MAPE values ranging between approximately 1.1% and 2.2% across the evaluated stocks, indicating improved stability and accuracy. Overall, the findings demonstrate enhanced forecasting reliability and deployment readiness through automated pipelines.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Ade Irma Agustian Adelia Adelia, Adelia Adiwidyatma, Afdhal Reshanda Afidria, Zulfa Febi Aliya Dasa Pramesthi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Asfiani, Ilil Musyarof Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Faiz, Mochammad Abudrrochman Faizi, Dandi Nur Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto Icha Rohmatul Jannah idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Imanta Ginting Imelda Widya Ningrum Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Kartika Maulida Hindrayani Kartika Maulida Hindrayani Kartini Kartini Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Marwani, Arrum Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Ningrum, Imelda Widya Ningtiyas, Rona Wulan Nova Auliyatul Maulidiyyah Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putra, Andrawana Putri, Irma Amanda Putri, Milla Akbarany Baktiar Putri, Nabila Rizky Amalia Putri, Nevia Desinta Putri, Shafira Amanda Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Ratna Yulistiani Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Riyantoko, Prismahardi Aji Rizkiyah, Selly Rizqin, Indira Zein Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Saputra, Wahyu Syaifullah Jauharis Sekar Arum Melati Sihananto, Andreas Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Syukri Syukri Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wardah Ariij Adibah Wardah, Salsabila Wardani, Ajeng Puspa Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widayawati, Eny Widduro, Bagus Widison, Daffin Tanjiro Yuciana Wilandari yuliza, eva Zalfa Assyadida, Azizah