Article Per Year (5 Year)
p-Index From 2021 - 2026
1.244
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian UntukMu NegeRI Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Journal of Software Engineering and Information System (SEIS)
Bayu Anugerah Putra
Unknown Affiliation
Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Software Engineering and Information System (SEIS)

 1 
OPTIMISASI ALGORITMA K-MEANS DENGAN METODE REDUKSI DIMENSI UNTUK PENGELOMPOKAN BIG DATA DALAM ARSITEKTUR CLOUD COMPUTING Putra, Bayu Anugerah; Mukhtar, Harun; Br Bangun, Elsi Titasari; Gusnanda, Alris; Maisyarah, Adila; Kurniawan, Muhammad Irgi; Pradipa, Raditya; Ali, Zurrahman Muhammad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.7616

Abstract

In the era of big data, data clustering becomes a major challenge due to the complexity and huge volume of data. The K-means algorithm is one of the clustering techniques that is often used due to its simplicity. However, K-means faces difficulties in handling high-dimensional and large-volume data. This study proposes an optimization of the K-means algorithm using the Principal Component Analysis (PCA) dimensionality reduction method to improve the efficiency and accuracy of big data clustering in cloud computing architecture. The KDD Cup 1999 dataset is used to test this method. The dataset undergoes pre-processing and dimensionality reduction using PCA, then K-means clustering is applied. The clustering results are evaluated using the Silhouette Score and Davies-Bouldin Index. The implementation is carried out in the Google Colab environment to utilize cloud computing resources. The results show that dimensionality reduction using PCA significantly reduces computational complexity and improves clustering quality. This method is effective in clustering big data, making it an efficient solution for data clustering in cloud computing architecture.
KLASIFIKASI MAKANAN BERDASARKAN NILAI GIZI MENGGUNAKAN ALGORITMA RANDOM FOREST DAN TEKNIK SMOTE Br Bangun, Elsi Titasari; Bayu Anugerah Putra; Aryanto
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9725

Abstract

Classifying food based on nutritional content is essential for developing personalized dietary recommendation systems and promoting healthier eating habits. This study aims to construct a food classification model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance in the dataset. The dataset includes various nutritional attributes such as calories, protein, fat, carbohydrates, fiber, sugar, sodium, and cholesterol, along with additional information such as food category and meal time. After preprocessing, the data were split into training and testing sets, with SMOTE applied to the training data to improve class representation. The model was trained using Random Forest and evaluated using accuracy, precision, recall, and F1-score. The results show that the model achieved an accuracy of 83.35% and an average F1-score above 0.80, with the best performance observed in majority classes. The confusion matrix analysis indicates that most predictions were accurate, although misclassifications occurred among classes with overlapping nutritional values. Protein, calories, and carbohydrates were identified as the most influential features in the classification process. These results show that combining Random Forest and SMOTE works well for creating food classification systems using nutritional data and could be useful in apps for diet recommendations and managing nutrition.
Co-Authors ., Farid Hasfindra Addarisalam, Alif Ali, Zurrahman Muhammad Alris Gusnanda Amran, Hasanatul Fu'adah Andi Sunyoto Anggraini, Chelina Aryanto Baidarus Br Bangun, Elsi Titasari Dian Utami Durades, M.Azri Fakhira Frisya Ramadhani Fatchiyah Maharani, Masti Fitri Handayani Fitri Handayani Gusnanda, Alris Hanum Salsabila Haris, Aidil Harun Mukhtar Jihan Aulia Khusyaini, Ilham Kurniawan, Muhammad Irgi Lorenza, Dina Lutfi Mz, Al Agib Maisyarah, Adila Mardiya, Ainul Muchtadi, Bill Fikra NUR FADILAH Nurwijayanti Pradipa, Raditya Prasasti, Aditia Rahmad Firdaus Ramanda, Yuki Rayhan, Aqeel Rico Apriandika Rizki, Rafi Hamdan Septiana Srinandini Shafwan, Affif Dzaky Soni Sunanto Sunanto Syahrul R, Syahrul Taufiq, Reny Medikawati