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All Journal Desimal: Jurnal Matematika Telcomatics MDP Student Conference
Fajri Farid
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Mathematics Social Sciences

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Product Quality Classification Based on Machine Learning in the Quality Control System of the Laser Metal Deposition Process Elok Fiola; Rahma Neliyana; Try Yani Rizki Nur Rohmah; M. Syamsuddin Wisnubroto; Fajri Farid
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11432

Abstract

Industry 4.0 revolutionizes modern manufacturing by enabling the active integration of smart sensors and machine learning to optimize product quality control systems. This research focuses on classifying product quality in the Laser Metal Deposition (LMD) process by applying three machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). The dataset consists of four numerical sensor variables, including Optical Sensor, Laser Power, Pressure, and Temperature, with Defect Label as the binary target variable. The Synthetic Minority Oversampling Technique (SMOTE) is used to balance the class distribution. Correlation analysis reveals weak linear relationships among all variables, suggesting the presence of complex non-linear interactions. The Random Forest model produces the best performance with accuracy of 0.88, recall of 0.79, and AUC of 0.80, outperforming Decision Tree and SVM. These findings indicate that ensemble-based methods effectively capture complex patterns within sensor data and offer reliable predictions for quality control in metal manufacturing industries, particularly within Laser Metal Deposition processes.
Application of the DAG-SVM for multi-class mobile phone price classification Sihombing, Natanael Oktavianus Partahan; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529450

Abstract

This study investigated the application of multiclass Support Vector Machine (SVM) strategies for smartphone price range classification using the Mobile Price Classification dataset (N = 2,000). The aim was to assess whether the Directed Acyclic Graph SVM (DAG-SVM) could provide improvements in predictive performance or computational efficiency compared with the conventional One-vs-One (OvO) and One-vs-Rest (OvR) approaches. The dataset’s twenty features were standardized using Z-score normalization and split into training and testing sets with an 80:20 ratio. All models were implemented using a linear kernel and evaluated based on accuracy, macro-precision, macro-recall, macro-F1, and execution time. The results showed that both OvO and DAG-SVM achieved the highest performance, with an accuracy and macro-F1 score of 96.25%, while OvR performed substantially lower. Despite the theoretical efficiency of DAG-SVM, its Python-based sequential elimination process led to slower prediction time than OvO. This study contributed empirical evidence that execution time can diverge from theoretical expectations in practical implementations and demonstrated the importance of computational efficiency analysis when comparing multiclass SVM architectures for mobile price classification.
Klasifikasi Multikelas Varietas Kacang Kering Menggunakan Metode Hybrid SVM Berbasis DAG Dinda Nababan; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14094

Abstract

This study analyzes the performance of three conventional SVM strategies, namely One-vs-One (OvO), One-vs-Rest (OvR), and Directed Acyclic Graph OvO (DAG-OvO), compared with the hybrid approach Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) in the context of multiclass classification using the Dry Bean Dataset. All models are evaluated based on accuracy and macro metrics to measure the consistency of predictions between classes. The results show that both conventional and hybrid methods achieve the same high level of accuracy, namely 0.92, with Precision, Recall, and F1-score Macro values ​​that were also identical between approaches. The main difference between the approaches lies in computational efficiency. OvO and DAG-OvO show the fastest training time, while DAG-RvR is the most efficient method in the inference stage. These findings confirm that the hybrid DAG-RvR structure can accelerate the prediction process without compromising accuracy, making it worthy of consideration for applications that require fast inference.
Klasifikasi Varietas Beras Menggunakan Hybrid SVM Berbasis DAG–OVO dan RVR Marleta Cornelia Leander; Christyan Tamaro Nadeak; Linda Rassiyanti; Fajri Farid
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.14108

Abstract

This research proposes a hybrid Support Vector Machine (SVM) strategy for multiclass rice variety classification by combining Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) with K-Means clustering. Five rice varieties were analyzed using 16 morphological and texture features extracted from the Rice Image Dataset. Three conventional SVM methods—One-vs-One (OvO), One-vs-Rest (OvR), and DAG-OvO—were evaluated as baselines. Two hybrid schemes were then developed: DAG-RvR K-Means–OvO and DAG-RvR K-Means–K-Means. Experimental results show that all methods achieve high accuracy of approximately 99%, indicating strong feature separability among rice varieties. However, the proposed DAG-RvR K-Means–OvO provides the most efficient performance, achieving the fastest training time while maintaining competitive testing speed and the highest accuracy of 0.99040. The findings demonstrate that integrating K-Means–based class partitioning with pairwise SVM classification improves computational efficiency without reducing predictive performance, making the hybrid approach suitable for fast and accurate multiclass classification tasks.
Co-Authors Christyan Tamaro Nadeak Dinda Nababan Elok Fiola Linda Rassiyanti M. Syamsuddin Wisnubroto Marleta Cornelia Leander Rahma Neliyana Sihombing, Natanael Oktavianus Partahan Try Yani Rizki Nur Rohmah