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All Journal E-Jurnal Matematika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains Matematika dan Statistika Format : Jurnal Imiah Teknik Informatika EIGEN MATHEMATICS JOURNAL International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Informatika Jurnal Eurekamatika UPGRADE: Jurnal Pendidikan Teknologi Informasi Diophantine Journal of Mathematics and Its Applications
Moch Anjas Aprihartha
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Optimization of Classification Algorithms Performance with k-Fold Cross Validation Aprihartha, Moch. Anjas; Idham, Idham
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.212

Abstract

Supervised learning is a predictive method used to make predictions or classifications. Supervised learning algorithms work by building a model using training data that includes both independent and dependent variables. Several methods for building classification include Logistic Regression, Naive Bayes, K-Nearest Neighbor (KNN), decision tree, etc. The lack of capacity of a classification algorithm to generalize certain data can be associated with the problem of overfitting or underfitting. K-fold cross-validation is a method that can help avoid overfitting or underfitting and produce a algorithm with good performance on new data. This study will test the Naive Bayes, K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Logistic Regression methods with k-fold cross-validation on two different datasets. The values of k set for cross-validation are 2, 3, 5, 7, and 10. The analysis results concluded that each classification algorithm performed best at 10-fold cross-validation. In DATA 1, the Naive Bayes algorithm has the highest average accuracy of 0.67 (67%) and the error rate is 0.33 (33%), followed by the CART algorithm, KNN, and finally logistic regression. While DATA 2, the KNN algorithm has the highest average accuracy of 0.66 (66%) and an error rate of 0.34 (34%), followed by the CART algorithm, Naive Bayes, and finally logistic regressionbut can be a reference if you want to predict the growth direction of the accommodation and food service activities sector.
Comparison of Discrete Adaptive Boosting Algorithms for Classification and Regression Tree and Naive Bayes in Pistachio Nut Classification Aprihartha, Moch. Anjas; Azzahro, Salwa Paramita; Azizah, Rahmatul; Andrianza, Muhammad Rafly
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 7 No 1 (2025): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v7i1.396

Abstract

Machine learning is an effective tool for identifying and classifying various conditions, such as predicting shoe sales, classifying raisin types, classifying fruit productivity, and so on. This technique is widely used in various sectors. One example is pistachio sorting. In some places, pistachio sorting is still done traditionally by humans. This is disadvantageous because the costs tend to be high, and the sorting process becomes inconsistent and less effective. The use of machine learning algorithms can be a breakthrough in overcoming this problem. Naive Bayes and Classification and Regression Tree (CART) are machine learning algorithms commonly used in the classification process. To improve classification accuracy, these two basic models are integrated with the Discrete Adaptive Boosting (Discrete AdaBoost) algorithm. This study aims to assess the effectiveness of machine learning algorithms in identifying the characteristics of pistachios. Algorithm testing was carried out using the k-fold cross-validation technique. The estimated average performance results of all classification models do not show significant differences. The Discrete AdaBoost CART model has the best accuracy, specificity, and f1-score, at 86.49%, 85.78%, and 88.32%, respectively. Therefore, the Discrete AdaBoost CART model is a suitable model for classifying pistachio types. This shows that ensemble approaches such as Discrete AdaBoost CART can make a significant contribution to improving the performance of classification systems, especially in the context of data with many relevant features. This study was limited to identifying binary classes of pistachios. In further research, it is recommended to explore machine learning algorithms for multiclass of pistachio nuts.
Perbandingan Algoritma Real Adaptive Boosting pada Regresi Logistik, CART, dan Naive Bayes dalam Klasifikasi Biji Labu Aprihartha, Moch Anjas; Fallo, Sefri Imanuel; Rasikhun, Hady
Jurnal Sains Matematika dan Statistika Vol 11, No 2 (2025): JSMS Juli 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i2.36859

Abstract

Labu merupakan spesies tanaman yang bernilai ekonomis dan medis. Hampir setiap bagian dari labu dapat dikonsumsi terutama pada bijinya. Minyak dari biji labu dapat juga digunakan sebagai saus untuk salad, produk kosmetik, sabun dan lilin. Keterampilan dalam mengklasifikasikan biji labu dengan tepat sangat dibutuhkan diberbagai sektor, seperti pertanian dan industri pangan. Dibutuhkan teknologi pengembangan yang dapat mengidentifikasi dan mensortir biji labu dengan mudah dan cepat. Beberapa algoritma yang umum dapat digunakan untuk mengidentifikasi jenis biji labu seperti algoritma regresi logistik (RL), Classification and Regression Tree (CART), dan Naive Bayes (NB). Penelitian ini bertujuan mengeksplorasi model RL, CART, dan NB pada dua jenis varietas biji labu, yaitu Ürgüp Sivrisi dan Çerçevelik berdasarkan karakteristik fisiknya. Selain itu, digunakan pendekatan Real Adaptive Boosting (RAB) untuk meningkatkan kinerja model dasar. Teknik ini bekerja dengan kemampuan menggabungkan beberapa model homogen secara berulang untuk menghasilkan model yang kuat. Hasil uji kinerja model klasifikasi diperhitungkan melalui metrik evaluasi. Model RAB-RL memiliki performa tertinggi pada akurasi, presisi, dan f1-score sehingga menjadikan model terbaik dalam mengklasifikasikan jenis biji labu dibandingkan model-model lainnya. Dalam model dasar, model RL memiliki performa terbaik dibawah model RAB-RL
Klasifikasi Produktivitas Buah Nanas Menggunakan Algoritma Classification and Regression Tree (CART) Aprihartha, Moch. Anjas; Putrawan, Zulhandi; Zulhan , Dicky; Ahardika Nurfaizal, Fatma
Diophantine Journal of Mathematics and Its Applications Vol. 3 No. 1 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v3i1.34193

Abstract

Indonesia is one of the countries that has a variety of fruits cultivated. One of them is the pineapple fruit. Various pineapple-based products such as pineapple juice, canned foods, pineapple jam, etc. The high demand for pineapples presents an opportunity for companies to increase pineapple product processing. The increase in pineapple productivity is influenced by several factors, one of which is the extent of land and the type of pineapple produced. To improve pineapple productivity, it can be done by classifying the types of pineapples based on productive and non-productive categories. The purpose of this classification is to enable farmers or plantation managers to allocate resources more efficiently by providing more intensive care for productive category pineapples. The classification method that can be used to classify productive and non-productive pineapples is the Classification and Regression Tree (CART) algorithm. The CART method is a method that produces decision tree models that are used to solve classification and regression problems. This research uses the CART method to classify pineapple productivity. The research results obtained accuracies, sensitivities, specificities, and precisions of 97.06%; 92.31%; 100%; 100% respectively. Meanwhile, the AUC obtained is 0.962 which indicates that the model is very good at predicting pineapple productivity correctly.
Co-Authors Achmad Wahid Kurniawan Ahardika Nurfaizal, Fatma Akrom, Muhamad Alam, Taufik Nur Andrianza, Muhammad Rafly Ayu Pertiwi Azizah, Rahmatul Azzahro, Salwa Paramita Budi, Setyo Dicky Zulhan Fatma Ahardika Nurfaizal Fitri Astutik Hady Rasikhun Harun Al Azies Herowati, Wise Hidayat, Novianto Nur Husniyadi, M. Idham Idham Ignatius Moses Setiadi, De Rosal Jus Prasetya JUS PRASETYA Muhammad Naufal, Muhammad Muljono Muljono Nani Sulistianingsih Noor Ageng Setiyanto, Noor Ageng Nurfaizal, Fatma Ahardika Prabowo, Wahyu Aji Eko Prasetya, Jus Putrawan, Zulhandi Safitri, Aprilyani Nur Sefri Imanuel Fallo Sudibyo, Usman Supriadi Rustad T. Sutojo Trisnapradika, Gustina Alfa Zulhan , Dicky Zulhan, Dicky Zulhandi Putrawan