Article Per Year (5 Year)
p-Index From 2021 - 2026
9.065
P-Index
This Author published in this journals
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) Journal of Renewable Energy, Electrical, and Computer Engineering Jurnal Inkofar 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
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

Published : 75 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 75 Documents
Search

 4   5   6   7   8 
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.
Implementasi Extremely Randomized Trees dengan Optimasi Hyperparameter Accelerated Particle Swarm Optimization untuk Klasifikasi Subtipe Anemia Adelia, Adelia; Trimono, Trimono; Idhom, Mohammad
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11295

Abstract

Anemia is a health problem that negatively affects both medical outcomes and social well-being, highlighting the need for accurate early detection. This study applies a machine learning approach to classify anemia subtypes to support clinical intervention and further examination. The Extra Trees method employs a hierarchical decision-tree structure with extreme randomization, making it robust to overfitting and capable of good generalization on small to medium datasets. Accelerated Particle Swarm Optimization (APSO) is utilized as an efficient optimization technique to improve classification performance. The novelty of this study lies in integrating Extra Trees with APSO to optimize anemia subtype classification. The dataset consists of 385 records collected from a regional hospital in East Java, Indonesia, covering four classes: thalassemia, iron deficiency anemia, anemia of chronic disease, and non-anemia. The features include patient initials, gender, age, and hematological parameters (Hb, HCT, RBC, MCV, MCH, MCHC, RDW). The optimized model achieved 85% accuracy, 87% precision, 85% recall, 85% F1-score, 95% specificity, and 94% AUC, outperforming the non-optimized model. These results indicate that the proposed approach is effective for anemia subtype classification.
PENGARUH PERUBAHAN TAHUN TERHADAP PRODUKSI PERTANIAN DI INDONESIA MENGGUNAKAN PENDEKATAN REPEATED MEASURES MANOVA Rizkiyah, Selly; Rizqin, Indira Zein; Putri, Milla Akbarany Baktiar; Nasrudin, Muhammad; Trimono, Trimono
RAGAM: Journal of Statistics & Its Application Vol 4, No 1 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i1.14970

Abstract

This study aims to analyze significant differences in rice paddy production in Indonesia based on year factors using the Repeated Measures MANOVA method. The data used includes harvest areas, productivity, and total rice production from various provinces during the period 2020-2024.  The results showed that there was a significant relationship between the variables tested, so the independence assumption in the MANOVA method was not met. Therefore, Repeated Measures-MANOVA was used as an alternative approach that is more suitable for repeated data. The analysis showed that there were significant differences in rice production by year, with a p-value of <0.05 in all multivariate statistics. The results highlight the importance of efficient crop land management and increased productivity to support the sustainability of the agricultural sector. The Repeated Measures-MANOVA approach proved effective in identifying variations in production based on time factors and can be a relevant analytical tool.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Nova Auliyatul Maulidiyyah; Trimono Trimono; Aviolla Terza Damaliana; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
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 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 Mas&#039;ad Mas&#039;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, 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 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 Widison, Daffin Tanjiro Yuciana Wilandari Zalfa Assyadida, Azizah