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All Journal Al-Jabar : Jurnal Pendidikan Matematika MANAJERIAL Desimal: Jurnal Matematika BERDAYA: Jurnal Pendidikan dan Pengabdian Kepada Masyarakat International Journal of Hydrological and Environmental for Sustainability MDP Student Conference International Journal of Applied Mathematics, Sciences, and Technology for National Defense Jurnal Pengabdian Masyarakat Terapan
Farid, Fajri
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences

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Klasifikasi Multikelas Support Vector Machine dengan Hibrida Directed Acyclic Graph One Vs One dan Rest Vs Rest pada Klasifikasi Tingkat Obesitas Naufal, Daffa Ahmad; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
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.v5i2.14097

Abstract

This research is focused on analyzing how well different multiclass Support Vector Machine (SVM) classification methods can predict obesity levels. It also presents a new hybrid Directed Acyclic Graph Rest-vs-Rest (DAG-RvR) method as a better option. The study utilizes a dataset called the Obesity Risk Prediction Cleaned, which has information on seven different obesity categories. The methods being assessed include One-vs-One (OvO), One-vs-Rest (OvR), DAG-One-vs-One (DAG-OvO), and the new DAG-RvR method. For fine-tuning the parameters, GridSearchCV and the RBF kernel were used. The findings reveal that DAG-RvR achieves an accuracy of 0.91, which is similar to OvO and DAG-OvO, but it trains much quicker, taking just 0.3422 seconds. Even though its precision, recall, and F1-score are a bit lower than the pairwise methods, DAG-RvR still maintains reliable multiclass performance. In summary, this method strikes a good balance between achieving high accuracy and being efficient in computations.
Klasifikasi Varietas Beras Menggunakan Hybrid SVM Berbasis DAG–OVO dan RVR Leander, Marleta Cornelia; Nadeak, Christyan Tamaro; Rassiyanti, Linda; Farid, Fajri
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 Ade Lailani Al Husein, Fulkan Kafilah Amir Machmud, Amir Andriana Putra, Randa Christyan Tamaro Nadeak Dyah Astuti, Rohmi Fauzi Dzaki Arif, Sofyan Hilal Kurniawan, Danang Irsalinda, Nursyiva Lailani, Ade Leander, Marleta Cornelia Lestari, Yusni Puspha Linda Rassiyanti M. Yogi Riyantama Isjoni Muhamad Syazali Nababan, Dinda Naufal, Daffa Ahmad Nurjanah, Fitri Pangesti, Aliffanur Budi Pitri, Rizka Putri, Lutfia Aisyah Rahmi Ramadhani Rassiyanti, Linda Rian Kurnia, Rian Ridwanulloh, M. Ubaidillah Rizqi Prastowo Rohmi Dyah Astuti Ronal, Ronal Saidat, Suhaila Satria, Eggi Setiawan, Dewi Indra Siahaan, Arielva Simon Siatan, Mairizal Salehudin Suciati, Indah Sya’diyah, Khalimatus Vico Luthfi Ipmawan Vidia, Vidia Wisnubroto, M. Syamsuddin Yuliana Yuliana Yustisia Sari, Rizki