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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) CAUCHY: Jurnal Matematika Murni dan Aplikasi JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Inferensi Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Xplore: Journal of Statistics Statistika Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Teknobuga : Jurnal Teknologi Busana dan Boga
Gerry Alfa Dito
Study Program On Statistics And Data Science, IPB University, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences Other

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Exploring a Large Language Model on the ChatGPT Platform for Indonesian Text Preprocessing Tasks Cici Suhaeni; Sabrina Adnin Kamila; Fani Fahira; Muhammad Yusran; Gerry Alfa Dito
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p100-116

Abstract

Preprocessing is a crucial step in Natural Language Processing, especially for informal languages like Indonesian, which contain complex morphology, slang, abbreviations, and non-standard expressions. Traditional rule-based tools such as regex, IndoNLP, and Sastrawi are commonly used but often fall short in handling noisy, user-generated text. This study explores the capability of Large Language Model, particularly ChatGPT-o3, in performing Indonesian text preprocessing tasks, namely text cleaning, normalization, stopword removal, and stemming/lemmatization, and compares it to conventional rule-based approaches. Using two types of datasets, consisting of a small example dataset of five manually constructed sentences and a real-world dataset of 100 tweets about the Indonesian “Makan Bergizi Gratis” program, both preprocessing methods were applied and evaluated. Results show that ChatGPT-o3 performs equally well in text cleaning and significantly better in normalization. However, rule-based methods like IndoNLP and Sastrawi still outperform ChatGPT-o3 in stopword removal and stemming. These findings indicate that while ChatGPT-o3 demonstrates strong contextual understanding and linguistic flexibility, they may underperform in rigid, token-based operations without fine-tuning. This study provides initial insights into using Large Language Models as an alternative preprocessing engine for Indonesian text and highlights the need for hybrid approaches or improved prompt design in future applications.
Stochastic Residual Selection in Simulated Annealing for Clusterwise Panel Optimization Luh Putu Widya Adnyani; Bagus Sartono; Asep Saefuddin; I Made Sumertajaya; Gerry Alfa Dito
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7608

Abstract

Modeling heterogeneity in panel data requires solving a complex combinatorial partition problem under structural constraints. Although clusterwise regression captures latent group structures with distinct parameters, determining the optimal partition remains computationally challenging due to the vast solution space and susceptibility to local minima. This study proposes a modified simulated annealing (SA) algorithm incorporating a Stochastic Residual Selection (SRS) mechanism, in which candidate units are selected from a high-residual subset rather than deterministically relocating only the unit with the largest residual. The stochastic candidate-size parameter was evaluated using m=1 and m=5, where m=1 represents deterministic selection of the largest residual unit, while m=5 randomly selects one unit from the five largest-residual units for clusterreassignment. The stochastic perturbation enhances global exploration and improves convergence stability in non-convex optimization landscape. Simulation experiments involving 200 individuals observed over three time periods demonstrate that the proposed SRS-SA outperforms standard SA, achieving an Adjusted Rand Index of approximately 0.95 at 1,000 iterations while producing lower Mean Absolute Bias and Mean Squared Error. An empirical application to improved sanitation data across districts and municipalities in Java, Indonesia, further confirms its effectiveness in identifying latent structural heterogeneity. These findings highlight the robustness and computational efficiency gained through stochastic diversification in metaheuristic optimization for constrained clusterwise panel modeling.
A Modified Simulated Annealing Algorithm for Clusterwise Linear Regression Model Estimation Luh Putu Widya Adnyani; Bagus Sartono; Asep Saefuddin; I Made Sumertajaya; Gerry Alfa Dito; Krittiya Duangmanee
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/Jmathcos12907

Abstract

Heterogeneous data require modeling methods that capture structural differences across subpopulations rather than imposing a single global relationship. Clusterwise linear regression (CLR) is able to provide this flexibility despite the high computational cost. Although effective, classical simulated annealing (SA) algorithms is sensitive to initial parameters and prone to premature convergence. This study proposes an improved SA-based approach by incorporating a simple stochastic modification. Rather than consistently relocating the observation with the largest residual, the algorithm randomly selects an observation from the top-(m) largest residuals at each iteration.. This mechanism allows the algorithm to explore more possible solutions while still moving toward better results. The proposed method was evaluated through simulation studies and an application to district-level poverty data in Indonesia. The results show that the modified algorithm generally provides better clustering accuracy and smaller parameter bias than the standard simulated annealing algorithm in clusterwise regression models. The real-data application also identified nine clusters with different socio-economic characteristics, showing the importance of using analytical approaches and policies that account for regional diversity.
Classification of Educational Vulnerability Status Using Boosting Methods on Imbalanced Socioeconomic Data Andi Illa Erviani Nensi; Meavi Cintani; Mahda Al Maida; A. Qeis Tenridapi; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.39670

Abstract

Extreme class imbalance in educational data causes classification models to favor the majority class and fail to identify vulnerable students who are not attending school, despite this group requiring the greatest intervention. This study aims to develop a classification model for identifying educational vulnerability status among school-age children. The positive class corresponds to out-of-school children (minority class), while the negative class represents children who are still attending school. The dataset exhibits an extreme imbalance ratio of approximately 1:48, making conventional classification approaches ineffective in detecting minority-class observations.To address this challenge, four imbalance-aware boosting methods, namely AdaBoost-M2, SMOTEBoost, RBBoost, and RUSBoost, were compared. Model selection was conducted using stratified five-fold cross-validation on the training set, followed by final evaluation on an independent test set. Model performance was assessed using sensitivity, specificity, balanced accuracy, and Area Under the Curve (AUC), which are more appropriate than overall accuracy for highly imbalanced data. The results show that SMOTEBoost L50 with a threshold of 0.50 achieved the highest Balanced Accuracy (0.7474), Sensitivity (0.8824), and AUC (0.7745). However, its low minority-class precision (0.0452) and F1-score (0.0860) indicate a substantial false-positive trade-off. These findings demonstrate that integrating minority-class balancing strategies with boosting mechanisms can substantially improve the detection of vulnerable students in highly imbalanced socioeconomic data. Feature importance analysis revealed that education level or class was the most dominant predictor, followed by household size and frequency of internet use. Overall, this study provides empirical evidence regarding the effectiveness of imbalance-aware boosting methods for educational vulnerability detection and highlights their potential application in supporting more targeted educational interventions.
Co-Authors A. Qeis Tenridapi Anang Kurnia Andi Illa Erviani Nensi ASEP SAEFUDDIN Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Carlya Agmis Aimandiga Cici Suhaeni Cici Suhaeni Dela Gustiara Desy Endriani Dewi Fortuna Fani Fahira Fitri Dewi Shyntia Hazelita Dwi Rahmasari I Gusti Ngurah Sentana Putra I Made Sumertajaya I Made Sumertajaya Itasia Dina Sulvianti Krittiya Duangmanee Kusman Sadik Luh Putu Widya Adnyani Luh Putu Widya Adnyani Mahda Al Maida Mardatunnisa Isnaini Mauizatun Hasanah Meavi Cintani Mega Maulina Muh. Sunan Muhammad Syafiq Muhammad Yusran Nabila Syukri Nimas Ayu Hapsari Rahardiantoro, Septian Rizqi Annafi Muhadi Sabrina Adnin Kamila Shalshabilla Shafa Siti Aisyah Siti Nur Azizah Syella Zignora Limba Unique Desyrre A. Resiloy Wawan Saputra Windi Pangesti Yani Prihantini Hiola Yenni Angraini Yusma Yanti Zulhijrah