Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Jurnal Simetris Scientific Journal of Informatics
Soeleman, M. Arief
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
CLUSTERING TRAFO DISTRIBUSI MENGGUNAKAN ALGORITMA SELF-ORGANIZING MAP Khotimah, Tutik; Syukur, Abdul; Soeleman, M. Arief
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 8, No 1 (2017): JURNAL SIMETRIS VOLUME 8 NO 1 TAHUN 2017
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (219.586 KB) | DOI: 10.24176/simet.v8i1.808

Abstract

Salah satu cara untuk mengetahui beban sebuah trafo distribusi PLN masih memenuhi batas normal atau overload adalah dengan melakukan pengukuran beban trafo tersebut. Pada PLN Area Pelayanan Jaringan Kudus, pengukuran beban dilakukan baik pada siang hari mau pun pada malam hari. Hasil pengukuran tersebut memiliki kemungkinan berbeda. Hal ini disebabkan pada siang hari penggunaan beban cenderung kecil, sedangkan pada malam hari pemakaian beban lebih besar. Hal ini menyebabkan sulitnya menentukan beban trafo tersebut masih normal atau overload. Untuk memetakan beban trafo distribusi secara cepat dan akurat, diperlukan teknik data mining yaitu clustering. Penelitian ini dilakukan dengan menerapkan algoritma Self Organizing Map (SOM). Dengan SOM dihasilkan nilai akurasi sebesar 93% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 84% terhadap hasil pengukuran beban trafo distribusi pada malam hari. Sedangkan error yang dihasilkan dari pemetaan dengan SOM sebesar 7% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 16% terhadap hasil pengukuran beban trafo distribusi pada malam hari.
Hybrid Top-K Feature Selection to Improve High-Dimensional Data Classification Using Naïve Bayes Algorithm Wibowo, Riska; Soeleman, M. Arief; Affandy, Affandy
Scientific Journal of Informatics Vol 10, No 2 (2023): May 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i2.42818

Abstract

Abstract. Purpose: The naive bayes algorithm is one of the most popular machine learning algorithms, because it is simple, has high computational efficiency and has good accuracy. The naive bayes method assumes each attribute contributes to determining the classification result that may exist between attributes, this can interfere with the classification performance of naive bayes. The naïve bayes algorithm is sensitive to many features so this can reduce the performance of naïve bayes. Efforts to improve the performance of the naïve bayes algorithm by using a hybrid top-k feature selection method that aims to handle high-dimensional data using the naïve bayes algorithm so as to produce better accuracy.Methods: This research proposes a hybrid top-k feature selection method with stages 1. Prepare the dataset, 2. Replace the missing value with the average value of each attribute, 3. Calculate the weight of the attribute value using the weight information gain method, 4. Select attributes using the top-k feature selection method, 5. Backward Elimination with the naïve bayes algorithm, 6. Datasets that have been selected new attributes, then validated using 10 fold-cross validation where the data is divided into training data and testing data, 7. Calculate the accuracy value based on the confusion matrix table.Result: Based on the experimental results of performance and performance comparison of several methods that have been presented (Naïve Bayes, deep feature weighting naïve bayes, top-k feature selection, and hybrid top-k feature selection). The experimental results in this study show that from 5 datasets from UCI Repository that have been tested, the accuracy value of the hybrid top-k feature selection method increases from the previous method. From the accuracy comparison results that the proposed hybrid top-k feature selection method is ranked the first best method.Novelty: Thus it can be concluded that the Hybrid top-k feature selection method can be used to handle dimensional data in the Naïve Bayes algorithm. 
Co-Authors Abdul Syukur Affandy Affandy Tutik Khotimah Wibowo, Riska