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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA CommIT (Communication & Information Technology) Al-Jabar : Jurnal Pendidikan Matematika Union: Jurnal Ilmiah Pendidikan Matematika Jurnal Matematika: MANTIK Wacana Akademika : Majalah Ilmiah Kependidikan Numeracy : Jurnal Ilmiah Pendidikan Matematika IndoMath: Indonesia Mathematics Education BAREKENG: Jurnal Ilmu Matematika dan Terapan Teorema: Teori dan Riset Matematika JURNAL VOKASI TEKNOLOGI INDUSTRI (JVTI) Pedagogik : Jurnal Pendidikan Guru Sekolah Dasar Recent in Engineering Science and Technology Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Applied Mathematics and Statistics
Samsul Arifin
Binus University
Agriculture, Biological Sciences & Forestry Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Journal : Indonesian Journal of Applied Mathematics and Statistics

 1 
Trends, Contributions and Prospects: Bibliometric Analysis of ANOVA Research in 2022-2023 Sutrisno, Utis; Wulandari, Yulia; Usep; Arifin, Samsul; Roni; Manurung, Monica Mayeni; Faisal, Muhamad
Indonesian Journal of Applied Mathematics and Statistics Vol. 1 No. 1 (2024): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v1i1.7

Abstract

This study aims to analyze the development and contribution of research on the topic of ANOVA (Analysis of Variance) using the bibliometric analysis method. ANOVA is a statistical method used to compare the means of three or more groups. Through bibliometric analysis, we explore articles published in journals related to ANOVA within a certain time span, namely 2022-2023. The method of bibliometric analysis involves collecting bibliographic data from relevant sources and analyzing characteristics such as frequency of publication, notable authors, and most frequently cited journals. This study uses a bibliometric analysis method that retrieves 1,911 metadata from Scopus. The results of the bibliometric analysis revealed an increase in the number of publications about ANOVA during the time span studied, namely 2022-2023. These findings indicate that ANOVA remains a relevant and interesting topic for researchers in various disciplines. In addition, there is a wide variety of research topics related to ANOVA, including the use of ANOVA in various contexts, such as laboratory experiments, clinical research, and analysis of survey data. By analyzing the contribution of research on the topic of ANOVA, this study provides valuable insights for us. Moreover, the researcher also discussed prospects for future research on this topic, including the development of new analytical methods and the wider application of ANOVA in various scientific and practical contexts.
Web Application for IHSG Prediction Using Machine Learning Algorithms Wijaya, Andryan Kalmer; Lucky, Henry; Arifin, Samsul
Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 1 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v2i1.21

Abstract

This study investigates the effectiveness of the Long Short-Term Memory (LSTM) method in predicting the stock price of the Composite Stock Price Index (CSPI). LSTM, a variant of Recurrent Neural Networks, is designed to overcome challenges such as the vanishing gradient problem and long-term dependencies in time-series data. Given the dynamic and volatile nature of financial markets, accurate stock price prediction is crucial for investors and analysts. The data set used in this study consists of daily CSPI prices from January 2000 to December 2023, which serve as both training and testing data for model development. The LSTM model is trained to forecast the next day’s stock price, and its performance is compared with traditional statistical models, particularly the Autoregressive Integrated Moving Average (ARIMA) model and linear regression. Performance evaluation is based on the Mean Absolute Percentage Error (MAPE), a widely used metric for assessing predictive accuracy. The results indicate that while the ARIMA model achieves a lower MAPE of 0.7%, demonstrating slightly superior accuracy, the LSTM model also performs well, with a MAPE of approximately 1%. These findings suggest that while statistical models like ARIMA remain highly effective for stock price forecasting, deep learning approaches such as LSTM still offer promising predictive capabilities, especially when handling large and complex datasets. The ability of LSTM to capture non-linear patterns and temporal dependencies makes it a viable alternative for financial forecasting, potentially benefiting traders and market analysts seeking data-driven decision-making tools.
Clustering Analysis: A Note on Methodologies and Trends Raditha, Alya Maura; Arifin, Samsul
Indonesian Journal of Applied Mathematics and Statistics Vol. 2 No. 2 (2025): Indonesian Journal of Applied Mathematics and Statistics (IdJAMS)
Publisher : Lembaga Penelitian dan Pengembangan Matematika dan Statistika Terapan Indonesia, PT Anugrah Teknologi Kecerdasan Buatan PT Anugrah Teknologi Kecerdasan Buatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71385/idjams.v2i2.23

Abstract

This study conducts a bibliometric analysis of clustering techniques in scientific research using VOSviewer and Gen-AI-based Consensus.app. The dataset was collected from Scopus and the Web of Science using predefined queries to filter articles published in 2024 and 2025. VOSviewer was utilized to visualize co-authorship networks, keyword co-occurrence, citation relationships, bibliographic coupling, and co-citation patterns, revealing key research clusters and influential studies. Additionally, Consensus.app was employed to generate AI-driven insights, summarizing key themes and emerging trends in clustering methodologies. The results indicate that clustering research is highly collaborative, with strong institutional networks and interdisciplinary applications. Machine learning, data mining, and network analysis emerge as dominant themes, with key publications shaping methodological advancements. The co-citation network highlights foundational studies that have influenced the field. By combining traditional bibliometric techniques and AI-based analysis, this study offers a comprehensive perspective on clustering research, identifying knowledge gaps and potential future directions. These findings provide valuable insights for researchers seeking to explore emerging topics, collaborate effectively, and contribute to the development of clustering methodologies. However, this study is limited to publications indexed in Scopus and Web of Science within the years 2024–2025, which may not fully capture longer-term developments. Future research could expand the scope to other databases and timeframes for a broader perspective.
Co-Authors Abdillah, Abdul Azis Abdul Azis Abdillah Adi Adi Agus Sukandi Alfian Alfian Arif Rahman Hakim Arumella Surgandini Assagaf, Idrus Bobbi Rahman Dewi Yanti Liliana Dwi Anto, Septian Dwi Wijonarko Edistria, Ega Effendi, Melody Elita Putri, Rizky Ga, Jonri Lomi I Gusti Agung Anom Yudistira Indra Bayu Muktyas Indra Bayu Muktyas Jimy, Valensius Jonathan, Stanley Jufra, Arlita Aristianingsih Jufra, Jufra Klara Iswara Sukmawati Lucky, Henry Manurung , Monica Mayeni Manurung, Mika Meitriana Manurung, Monica Mayeni Muhamad Faisal, Muhamad Muhammad Amien Ibrahim Muhammad Faisal Muktyas, Indra Bayu Muzakki, Zahran Nerru Pranuta Murnaka, Nerru Pranuta Nugraha, Edwin Setiawan Pimpi, La Pratama, Muhammad Nabil Priambodo, Oktanindita Puguh Wahyu Prasetyo, Puguh Raditha, Alya Maura Ramadhani, Sausan Repka, Joe Rezakusuma, Muhammad Rinda Nariswari Rinda Nariswari RONI Rully Charitas Indra Prahmana Rusli P. D. Kolnel Safitri, Raudhatun Seleki, Jacob Stevy Setyawan, Fariz Siti A. M. Karubaba Suharno Suharno Sulistiawati Sulistiawati Sulistiawati Sulistiawati Susyanto, Nanang Sutrisno, Utis Tri Handayani Usep Widyakusuma, Nita Wijaya, Andryan Kalmer Wijayanti, Dian Eka Winarto Selamet, Muhammad Windria, Hening Wiwik Wiyanti, Wiwik Yanuar Yanuar Yulia Wulandari, Yulia Zenklinov, Amanatullah Pandu