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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Sosiohumaniora Jurnal Ilmu Dasar Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Journal of the Indonesian Mathematical Society Jurnal Matematika & Sains Jurnal Optimasi Sistem Industri SEMIRATA 2015 International Journal of Advances in Intelligent Informatics Indonesian Journal of Science and Technology Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal TAM (Technology Acceptance Model) BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Supply Chain Management Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik Journal of Islamic Monetary Economics and Finance Indonesian Journal of Electrical Engineering and Computer Science JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Prosiding Seminar Nasional Official Statistics Jurnal Matematika Integratif Literatur : Jurnal Bahasa dan Sastra Jurnal TAM (Technology Acceptance Model)
Dedi Rosadi
Department Of Mathematics, Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry 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 Physics Social Sciences Transportation Other

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Integrating IndoBERT and balanced iterative reducing and clustering using hierarchies of BERTopic in Indonesian short text Muhajir, Muhammad; Gunardi, Gunardi; Danardono, Danardono; Rosadi, Dedi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4192-4201

Abstract

Short text topic modeling remains challenging due to data sparsity, limited word co-occurrences, and unstable clustering results, particularly for Indonesian texts. This study proposes an improved BERTopic framework that integrates IndoBERT embeddings, best match 25 (BM25)-based topic representation, and balanced iterative reducing and clustering using hierarchies (BIRCH) clustering to address these issues. IndoBERT generates contextual embeddings adapted to Indonesian linguistic features, and BM25 weighting improves keyword relevance by considering document length and term saturation. BIRCH clustering minimizes outliers by assigning most documents to valid clusters, which enhances data utilization and topic stability. Experiments on Indonesian datasets from X (formerly Twitter), Google Reviews, and YouTube demonstrate that the proposed approach consistently achieves higher topic coherence. The proposed method yields stable topic diversity values between 0.91 and 0.94, maintains embedding density from 0.60 to 0.66, and achieves intra-topic similarity between 0.39 and 0.41 across increasing dataset sizes. The proposed framework successfully reduces outlier proportions to 1-5%, which significantly outperforms standard BERTopic and K-Means. Furthermore, the model maintains stable topic counts as the data volume grows, confirming robustness and scalability for sparse short text modeling. Overall, integrating IndoBERT, BM25, and BIRCH provides a more coherent, stable, and effective solution for Indonesian short text topic modeling.
Model Pengoptimuman Portofolio Mean-Variance dan Perkembangan Praktisnya Ezra Putranda Setiawan; Dedi Rosadi
Jurnal Optimasi Sistem Industri Vol. 18 No. 1 (2019): Published April 2019
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v18.n1.p8-16.2019

Abstract

Many research about portfolio optimization in Indonesia still uses the ‘original’ mean-variance model as proposed by Markowitz more than 60 years ago. This article reviews the development and modification of the Markowitz’s mean-variance model, especially that dealing with real stock-market features, which could help the investor to create their own portfolio. There were several real-stock market features that implemented in the modification of mean-variance portfolios optimization models, such as the minimum transaction lots, the transaction cost, the cardinality constraint, the weight constraint, and the sectoral constraint. To implement these features, several heuristic methods were used to obtain the optimal portfolio weight, such as genetic algorithm, Tabu search, bee colony algorithm, particle swarm algorithm, and simulated annealing. These methods become alternative to the mathematical programming method.
RPLUGIN.ECONOMETRICS: PAKET GRAPHICAL USER INTERFACE OPEN SOURCE UNTUK ANALISIS RUNTUN WAKTU MENGGUNAKAN PERANGKAT LUNAK R Dedi Rosadi; Adi Marhadi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 4, Juli 2009
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (279.502 KB) | DOI: 10.12962/j24068535.v7i4.a85

Abstract

R (R Development Core Team, 2009) is one of the open source software that is popular and has become "lingua franca" or standard language for the purposes of computing the current statistics. In this paper, will be introduced and discussed RcmdrPlugin.Econometrics package (Rosadi, Marhadi and Rahmatullah, 2009), which is a GUI version (Graphical User Interface) of R for the purposes of econometric analysis or time series. RcmdrPlugin.Econometrics package is an additional menu (plug-in) which provided for the R Commander, which is the most popular GUI of R. To illustrate the design philosophy of this package, provided also illustrate the usage of the RcmdrPlugin.Econometrics package for the exponential smoothing.
Analysis of Seismic Data in Sumatra using Robust K-Means Clustering Ulfasari Rafflesia; Dedi Rosadi; Devni Prima Sari; Pepi Novianti
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.523

Abstract

Indonesia is located within the Pacific Ring of Fire and frequently experiences significant seismic activities, rendering the region susceptible to hazards. Specifically, Sumatra is an island in the western part of the country, near the Eurasian and Indo-Australian tectonic plates. Over the past five years, an observable uptick in seismic events has been recorded in Sumatra. This research aimed to cluster the Sumatra region’s seismic data using the k-means algorithm and its extensions, including trimmed and robust sparse k-means, to determine the characteristics and patterns of seismic events. The k-means clustering algorithm operates effectively on many data but needs to work better in the presence of outliers. Meanwhile, the data identification reports the presence of outliers in the seismic data. The clustering analysis identified two main clusters, supported by multivariate and spatial outlier detection during preprocessing. The first cluster, encompassing 62% of seismic events, is located offshore near the Mentawai seismic gap, characterized by shallow depths (33–41 km) and magnitudes of 4.5–5.0 Ms. The second cluster, representing 28% of events, includes both mainland and offshore regions, associated with the Sumatran Fault system and slab deformation zones, at moderate depths (54–154 km) with magnitudes of 4.3–4.4 Ms. Rare deep-focus events exceeding depths of 214 km were identified as outliers. Evaluation using Silhouette, Davies-Bouldin, and Dunn indices determined that k=2 was the optimal number of clusters. This study contributes by integrating robust clustering methods to handle outliers, enhancing the reliability of seismic data analysis. This study demonstrates the value of applying trimmed and robust sparse k-means algorithms to improve clustering performance in regions with complex tectonic activity.
The PELT-ensemble NARX Hybrid Method for Short-Term Forecasting Time Series Data with Change Points and Its Application Hermansah Hermansah; Dedi Rosadi; Faihatuz Zuhairoh; Shelton Peiris; Pepi Novianti; Widyastuti Andriyani; Muhammad Irsyam
Journal of Applied Data Sciences Vol 7, No 4: December 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i4.1085

Abstract

This study proposes a new approach using the PELT-ensemble NARX hybrid method for short-term forecasting of time series data containing change points (CPs). The Pruned Exact Linear Time (PELT) method was employed to detect CPs and to determine cut-off points for the observation data, while the ensemble Nonlinear Autoregressive Neural Network with External Inputs (NARX) was used for forecasting. The method was applied to health data, specifically confirmed Covid-19 cases in the provinces of Java and Bali, Indonesia, from March 2020 to October 2021. Several Covid-19-related variables such as the number of recovered cases and deaths, and the weather variables, i.e., the maximum temperature, the average temperature, and humidity, are used as external inputs for the NARX model. The results indicate that the PELT method effectively identified the first day in the last segmentation for each province, and subsequent forecasting using the ensemble NARX method achieved high accuracy. Forecasting performance was evaluated using the Mean Absolute Percentage Error (MAPE) and several other metrics, showing that the PELT-ensemble NARX hybrid method significantly outperformed the single NARX approach and other forecasting methods, i.e. Exponential Smoothing, Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) model. These findings highlight the effectiveness of integrating change point detection with ensemble neural network modelling to substantially improve short-term forecasting accuracy for time series subject to structural change.
Co-Authors Abda Abda Abdul Hoyyi Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Adhitya Ronnie Effendie, Adhitya Ronnie Adi Marhadi Andriyani, Widyastuti Asrirawan Asuri, Afriza Baehaqi Budi Nurani Ruchjana Danardono Danardono Danardono Danardono Dara Puspita Anggraeni Deasy Arisanty Dede Maryana Devni Prima Sari Di Asih I Maruddani Edi Faizal Epha Diana Supandi Ezra Putranda Setiawan Ezra Putranda Setiawan Faihatuz Zuhairoh Fajri Farid Firmansyah, Hilmi Gubu, La Gumgum Darmawan Hendra Perdana Hermansah Hermansah Hermansah Hermansah Hermansah hermansah hermansah Hermansah Hermansah, Hermansah Herni Utami Herni Utami Iman Sanjaya Indradewa, Rhian Iskandar, Edi Izza Dinikal Arsy Jana, Padrul Khabib Mustofa La Gubu Malim Muhammad Media Rosha Muhammad Irsyam Muhammad Muhajir, Muhammad Pepi Novianti Resa Septiani Pontoh Retno Subekti Rizal, Ahmad Ashril Ruchjana, Budi N Rupilu, Rina Apriany Helen Wite Setialaksana, Wirawan - Shelton Peiris Sifriyani, Sifriyani Sri Redjeki Ulfasari Rafflesia Ulfasari Rafflesia