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All Journal JOIN (Jurnal Online Informatika) Scientific Journal of Informatics
Much Aziz Muslim
Department of Computer Science, Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Optimizing Stacking Ensemble Models for Customer Churn Prediction in the Telecommunications Industry Rofik Rofik; Jumanto Unjung; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1783

Abstract

One of the biggest challenges in the telecommunications industry is predicting churn, which is the condition when a customer unsubscribes and switches to another service provider. In an era of competitive market conditions, retaining customers is much more efficient than acquiring new customers. Conventional prediction models are often unable to capture the complexity of customer behavior patterns, resulting in a lower accuracy than optimal. This study aims to optimize customer churn prediction performance by developing a stacking ensemble model that combines several classification algorithms to improve model performance. Fourteen algorithms were tested, and the six algorithms with the best accuracy were selected as base learners, while Logistic Regression was selected as the meta-learner. The stacking model testing was carried out sequentially through a combination of 6 algorithms with the same meta-learner algorithm. Testing was also carried out with and without using the SMOTE data balancing method to evaluate the effect of data balancing on the prediction results. The results of this study show that the combination of the Adaboost, Ridge Classifier, and Logistic Regression algorithms can produce the highest accuracy of 82.97%, which exceeds the prediction performance of a single algorithm. This research contributes to demonstrating an effective stacking ensemble configuration for predicting customer churn in the telecommunications industry and emphasizes that the selection of the right algorithm combination has a greater impact on model performance than the number of algorithms used.
Sign Language Detection System Using YOLOv5 Algorithm to Promote Communication Equality People with Disabilities Maylinna Rahayu Ningsih; Yopi Julia Nurriski; Fathimah Az Zahra Sanjani; M. Faris Al Hakim; Jumanto Unjung; Much Aziz Muslim
Scientific Journal of Informatics Vol. 11 No. 2: Regular Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.6007

Abstract

Purpose: Communication is an important asset in human interaction, but not everyone has equal access to this key asset. Some of us have limitations such as hearing or speech impairments, which require a different communicative approach, namely sign language. These limitations often present accessibility gaps in various sectors, including education and employment, in line with Sustainable Development Goals (SDGs) numbers 4, 8, and 10. This research responds to these challenges by proposing a BISINDO sign language detection system using YOLOv5-NAS-S. The research aims to develop a sign language detection model that is accurate and fast, meets the communicative needs of people with disabilities, and supports the SDGs in reducing the accessibility gap. Methods: The research adopted a transfer learning approach with YOLOv5-NAS-S using BISINDO sign language data against a background of data diversity. Data pre-processing involved Super-Gradients and Roboflow augmentation, while model training was conducted with the Trainer of SuperGradients. Result: The results show that the model achieves a mAP of 97,2% and Recall of 99.6% which indicates a solid ability in separating sign language image classes. This model not only identifies sign language classes but can also predict complex conditions consistently. Novelty: The YOLOv5-NAS-S algorithm shows significant advantages compared to previous studies. The success of this performance is expected to make a positive contribution to efforts to create a more inclusive society, in accordance with the Sustainable Development Goals (SDGs). Further development related to predictive and real-time integration, as well as investigation of possible practical applications in various industries, are some suggestions for further research.
Co-Authors Dwika Ananda Agustina Pertiwi Fathimah Az Zahra Sanjani Jumanto Unjung M. Faris Al Hakim Maylinna Rahayu Ningsih Rofik Rofik Yopi Julia Nurriski