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Bulletin of Informatics and Data Science
Published by Asosiasi Peneliti Data Science Indonesia
ISSN : -     EISSN : 25808389     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Bulletin of Informatics and Data Science journal discusses studies in the fields of Informatics, DSS, AI, and ES, as a forum for expressing research results both conceptually and technically related to Data Science
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 3 Documents
 1 
Search results for , issue "Vol 3, No 2 (2024): November 2024" : 3 Documents clear
Selection of the Best E-Commerce Platform Based on User Ratings using a Combination Entropy and SAW Methods Ulum, Faruk; Wang, Junhai; Setiawansyah, Setiawansyah; Aryanti, Riska
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.92

Abstract

Choosing the right e-commerce platform has a crucial role for consumers and business actors. For consumers, a reliable and user-friendly platform provides a safe, convenient, and efficient shopping experience. Considering various aspects of choosing the right e-commerce platform is a strategic investment that can provide long-term added value for all parties involved in the digital ecosystem. The purpose of this study is to identify and determine the best e-commerce platforms based on user experience and assessment with an objective and structured decision-making approach using a combination of Entropy and SAW methods. The results of the ranking of the best e-commerce platform selection determined through the combination of the Entropy and SAW methods, obtained that Shopee ranked first with the highest preference value of 0.9819, followed by Tokopedia in second place with a value of 0.973. Furthermore, Blibli is in third place with a score of 0.9401, followed by Lazada with a score of 0.9305, and the last is Bukalapak with a score of 0.9021. This research makes a significant contribution to multi-criteria decision-making by applying a combination of Entropy and SAW methods to evaluate and determine the best e-commerce platform based on user assessments. The results of this research can be used as a practical reference as a basis for strategic decision-making in choosing the e-commerce platform that best suits market needs
Hybrid Gradient Boosting and SMOTE-ENN for Toddler Nutritional Status Classification on Imbalanced Data Sinlae, Alfry Aristo Jansen; Erkamim, Moh.; Fitriyadi, Farid; Suhery, Lilik; Destriana, Rachmat
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.93

Abstract

Stunting in toddlers remains a serious global health issue with long-term impacts on children's physical and cognitive development. One of the main challenges in classifying nutritional status is class imbalance, where the number of normal cases significantly exceeds that of minority classes such as stunted and severely stunted. This study aims to develop a hybrid approach by integrating the Gradient Boosting algorithm with the SMOTE-ENN (Synthetic Minority Oversampling Technique–Edited Nearest Neighbors) method to improve classification performance on imbalanced data. The dataset used was obtained from the Kaggle platform, consisting of 121,000 entries with four nutritional status categories. Data preprocessing included label encoding, numerical feature standardization, and stratified data splitting with an 80:20 ratio. The model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The proposed hybrid model successfully increased the recall for the stunted class from 61.80% to 98.41%, and the F1-score from 71.93% to 83.58%. Overall accuracy improved from 92.39% to 93.35%, while the ROC-AUC score increased from 99.08% to 99.63%. These findings demonstrate that integrating Gradient Boosting with SMOTE-ENN is effective in enhancing sensitivity to minority classes and improving overall multi-class classification performance.
Optimizing Autoencoder-Based Feature Selection for Attack Detection in IoT Networks via Machine Learning Approaches Winanto, Eko Arip; Kurniabudi, Kurniabudi; Sharipuddin, Sharipuddin
Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i2.104

Abstract

The Internet of Things (IoT) presents significant security challenges as the number of connected devices continues to grow. One critical approach in developing efficient attack detection systems is the selection of relevant features to reduce model complexity without compromising accuracy. This study evaluates the effectiveness of Autoencoders as a feature reduction method for IoT network intrusion detection systems. Three machine learning algorithms are employed for comparative analysis: K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM). The dataset is evaluated both before and after feature reduction using an Autoencoder, with performance assessed based on accuracy, precision, recall, F1-score, training time, and the number of features. Experimental results demonstrate that the Autoencoder can reduce the number of features by up to 30% without significantly degrading performance. In fact, the NB and SVM models exhibit improvements in both accuracy and training efficiency. The KNN model shows a minimal performance decline, which remains within acceptable limits. Overall, the Autoencoder proves to be an effective method for feature reduction, maintaining or even enhancing detection efficiency and performance. These findings support the use of Autoencoders as an efficient feature selection technique in IoT-based attack detection systems.

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