Novriadi Antonius Siagian

Universitas Katolik Santo Thomas

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Co-Authors

Sawaluddin Sawaluddin

University of North Sumatra

Robenhart Tamba

State University of Medan

Ratiwi Harly Dalimunthe

University of North Sumatra

Niskarto Zendrato

University of North Sumatra

Apriwati Barus

Universitas Dian Nusantara

Sutarman Wage

University of North Sumatra

Sardo Pardingotan Sipayung

Universitas Katolik Santo Thomas

Sutarman Sutarman

University of North Sumatra

Zakarias Situmorang

Universitas Katolik Santo Thomas

Fahmi Izhari

Universitas Pembangunan Panca Budi Medan

Cooperative Institutions

University of North Sumatra

Information Technology University

State University of Medan

Universitas Katolik Santo Thomas

Universitas Islam Negeri Sumatera Utara

Financial Management Association International

Universitas Pembangunan Panca Budi Medan

Institut Agama Islam Negeri Bengkulu

State University of Padang

Universitas Bina Darma

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Comparative Analysis of Accuracy in Identifying Types of Glass

InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) (2021)

Novriadi Antonius Siagian

To see whether the proposed research model is able to improve the performance of the classification of the Glass Type Identification data using the K-Nearest Neighbor (K-NN) method then the results will be compared with the C4.5 method and the Naive Bayes method, a performance analysis of the methods will be carried out. The results are based on the results of the Confusion Matrix tabulation (two-class prediction. In this study, only three preprocessing processes were carried out. The first process is handling missing value. The missing value for attributes with numeric values is replaced by the mean (mean) value of the attributes in the same column. Meanwhile, the missing values for attributes with nominal values are replaced by the most likely values for the attributes in the same column. Then the second process is the handling of duplicated data. The data recorded were 214 data, the number of attributes was 9 attributes and the number of classes was 6 classes.The results of this study show that the highest accuracy value is in the C4.5 method with an accuracy of 73.45% with a value of K = 2 and an error rate of 26.55%, while the method with low accuracy is the KNN method. with an accuracy value of 61.95% and an error rate of 38.05%. Naive Bayes has an accuracy of 63.33% and an error rate of 36.67. Therefore C4.5 is more effective than the two methods.

Confusion matrix

Word error rate

Value (mathematics)

Confusion

10.30743/infotekjar.v5i2.3655

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Dataset Weighting Features Using Gain Ratio To Improve Method Accuracy Naïve Bayesian Classification

IOP Conference Series Earth and Environmental Science (2021)

Novriadi Antonius Siagian Sutarman Wage Sawaluddin Sawaluddin

Abstract The Naïve Bayes method is proven to have a high speed when applied to large datasets, but the Naïve Bayes method has weaknesses when selecting attributes because Naïve Bayes is a statistical classification method that is only based on the Bayes theorem so that it can only be used to predict the probability of the class membership of a class independently. Independent without being able to do the selection of attributes that have a high correlation and correlation between one attribute with other attributes so that it can affect the value of accuracy. Naïve Bayesian Weight has been able to provide better accuracy than conventional Naïve Bayesian. Where an increase in the highest accuracy value obtained from the Water Quality dataset is equal to 88.57% in the Weight Naïve Bayesian classification model, while the lowest accuracy value is obtained from the Haberman dataset which is 78.95% in the conventional Naïve Bayesian classification model. The increase in accuracy of the Weight Naïve Bayesian classification model in the Water Quality dataset is 2.9%. While the increase in accuracy value in the Haberman dataset is 1.8%. If done the average accuracy of each dataset using the Weight Naïve Bayesian classification model is 2.35%. Based on the testing that has been done on all test data, it can be said that the Weight Naïve Bayesian classification model can provide better accuracy values than those produced by the conventional Naïve Bayesian classification model.

10.1088/1755-1315/748/1/012034

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Handling Data Imbalance Problem in Hybrid Resampling Approach to Improve Accuracy of K-Nearest Neighbors Algorithm

Instal Jurnal Komputer (2024)

Novriadi Antonius Siagian Sardo Pardingotan Sipayung

Handling the problem of data imbalance is a crucial challenge in the development of classification models, especially in medical data such as stroke detection. This study proposes a hybrid resampling approach of SMOTE (Synthetic Minority Over-sampling Technique) and NearMiss to improve the accuracy of K-Nearest Neighbors (KNN) algorithm on stroke datasets. Our hybrid resampling approach aims to overcome the shortcomings of each resampling technique, with SMOTE generating minority class samples and NearMiss subtracting samples from the majority class. We test this approach on a stroke dataset that has class imbalance. The method was evaluated using K-NN. The experimental results show that the hybrid approach can improve the accuracy of K-NN in predicting the minority class compared to the conventional approach. It shows that adjusting these parameters can significantly affect the performance of the hybrid approach. In this study, providing the highest accuracy in SMOTE with K-1 neighbors resulted in a 100% improvement in accuracy, followed by a 97% improvement with K-2, and a 93% accuracy with K-3. On the other hand, the undersampling approach using NearMiss showed 100% accuracy improvement with K-1, followed by 74% improvement with K-2, and 76% accuracy with K-3. In conclusion, the use of SMOTE proved to be more consistent in improving accuracy with higher K values. In this case, it is important to consider various parameters in choosing the right resampling technique to handle data imbalance.

Resampling

10.54209/jurnalinstall.v16i02.207

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Analysis of neural network algorithm in determining high school student department

IOP Conference Series Materials Science and Engineering (2020)

Novriadi Antonius Siagian Muhammad Zarlis Zakarias Situmorang

Abstract In senior high schools, especially in the first class were required to place a department that is in accordance with the value produced. The application predicts student majors based on the value of students using artificial neural network algorithms using rapid miner to be able to produce more precise and faster accuracy results. The results obtained from the analysis carried out obtained an accuracy value of 71.86%.ANN has a network architecture that is a single layer net. Networks that have more than one layer are called multilayer net and competitive layer networks (competitive layer net). The shape of a multilayer net 1 or more has between the input layer and the output layer, which weighs between 2 adjacent layers. ANN architecture using 3 layers is 7 input layers, 6 hidden layers, and 2 output layers. 20 neurons are the number of neuron outputs to artificial neural networks

Net (polyhedron)

Value (mathematics)

10.1088/1757-899x/725/1/012109

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Analisis Algorithms Support Vector Machine dengan Naive Bayes Kernel pada Klasifikasi Data

Prosiding SiManTap : Seminar Nasional Matematika dan Terapan (2019)

Novriadi Antonius Siagian Sutarman Sutarman Sawaluddin Sawaluddin

Peneliti melakukan penelitian terhadap metode support vector machine dan naive bayes pada dataset haberman’s survival dengan membandingkan tingkat kinerja algoritma terhadap kedua algoritma dalam aspek precision, class recall, dan akurasi. Hasil dari pengujian terhadap dataset haberman’s survival dengan menggunakan metode Support vector Machine lebih baik dibandingkan dengan Naive Bayes dalam melakukan klasifikasi data. Dari percobaan kedua algoritma tersebut memiliki selisih 0.37% dalam precision, class recall 3.57% dan 3.22% dalam akurasi. Penentuan data uji juga dapat mempengaruhi hasil pengujian, karena pola data training tersebut akan dijadikan sebagai rule untuk menentukan kelas pada data testing. Sehingga besar atau kecilnya prosentase tingkat precision, recall, dan accuracy dipengaruhi juga oleh penentuan data training.

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Marketing Effect on Andaliman’s Derivative Products for Regional Development Area of Toba Samosir

Novriadi Antonius Siagian Suhardi Suhardi Erlina Erlina Ratiwi Harly Dalimunthe

10.5220/0009901900002480

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Pembobotan Fitur Dataset Menggunakan Gain Ratio Guna Meningkatkan Akurasi Metode Naïve Bayesian Classifier

Novriadi Antonius Siagian

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Implementasi Mandeley Bagi Mahasiswa/I Dalam Penulisan Karya Ilmiah Pada Akademi Pariwisata Nusantara Medan

Jurnal Masyarakat Indonesia (Jumas) (2023)

Novriadi Antonius Siagian Syahril Syahril Apriwati Barus Robenhart Tamba

Mahasiswa tingkat akhir kurang dalam memahami penulisan karya ilmiah yang membuat isi skripsi menjadi tidak sesuai dengan aturan yang berlaku, mengingat bahwa kutipan yang dijadikan referensi. Kurangnya pengetahuan dalam pembuatan tugas akjir, skripsi, tesis, disertasi maka akan berdampak pada hasil karya ilmiah menjadi kurang baik yang diakibatkan kurang nya pemahaman dalam Mencari referensi dan mencitasi kutipan untuk karya ilmiah, dengan adanya bantuan aplikasi/tools Mendeley sangat membantu untuk menambah referensi di karya ilmiah, dengan mengimplementasikan Mendeley karya ilmiah menjadi lebih baik dan penyusunannya menjadi lebih rapi dan terstruktur, pada pemaparan penggunaan aplikasi Mendeley style yang digunakan style IEEE sebab style ini sangat umum digunakan untuk karya ilmiah nasional maupun internasional. Mahasiswa menjadi lebih mudah dalam Menyusun daftar Pustaka, referensi yang ada pada karya ilmiah menjadi lebih terarah atau tepat sasaran yang sesuai dengan topik judul karya ilmiah. Mahasiswa sangat mudah dalam memahami tools Mendeley. Mahasiswa tidak memiliki kendala lagi setelah belajar penggunaan Mendeley.

10.54209/jumas.v2i01.32

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Bigdata Clustering using X-means method with Euclidean Distance

Journal of Physics Conference Series (2020)

Niskarto Zendrato Hanna Willa Dhany Novriadi Antonius Siagian Fahmi Izhari

Abstract Centroid is the central point of data in the grouping process, it is necessary to analyze the centroid in determining the initial value in the initial clustering process. So it is used as a cluster center point in the X-Means algorithm clustering process. Determine cluster center points or centroid, measure the performance of the X-Means algorithm with range cluster parameters by measuring distances between centroid for a fast and efficient way to group unstructured data, and to speed up the model construction process and divide several centroid in half to match the data as a test tool for the analysis of the X-Means method. From testing using the X-Means algorithm with the determination of the number of Centroid clusters carried out by modifying the X-Means method to do some determination of the centroid to get the results of 11 iterations. From the results of these tests produce good cluster members the level of similarity of data with other data and in determining the number of clusters, using the modification of the Euclidean distance method, get better results of the similarity level of each member compared to randomly determining the number of clusters with several iterations.

Centroid

Similarity (geometry)

k-medians clustering

Complete-linkage clustering

Data point

10.1088/1742-6596/1566/1/012103

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Citations (9)