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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik JURNAL BISNIS STRATEGI Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) ANDHARUPA Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Analisis Bisnis Ekonomi EBSJ JURIKOM (Jurnal Riset Komputer) KOMPUTIKA - Jurnal Sistem Komputer EDUMATIC: Jurnal Pendidikan Informatika Abdimasku : Jurnal Pengabdian Masyarakat Indo-MathEdu Intellectuals Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Rekayasa elektrika Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Analisis Bisnis Ekonomi
Muljono Muljono
Department of Pediatrics, Faculty of Medicine, Universitas Indonesia/ Cipto Mangunkusumo Hospital
Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Bagging Nearest Neighbor and its Enhancement for Machinery Predictive Maintenance Arisani, Muhammad Irfan; Muljono, Muljono
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8158

Abstract

K-nearest Neighbor is a simple algorithm in Machine learning for such a prediction classification task which plays in valuable aspects of understanding big data. However, this algorithm sometimes does a lacking job of classification tasks for many different dataset characteristics. Therefore, this study will adopt enhancement methods to create a better performance of the nearest-neighbor model. Thus, this study focused on nearest neighbor enhancement to do a binary classification task from the extremely unbalanced dataset of a machine failure problem. Firstly, this study will create new features from the machinery dataset through the feature engineering processes and transform the chosen numerical features with standardization steps as the proper scaling. Then, the modified under-sampling method will be given which will reduce the amount of the majority class to 4.75 times that of the minority class. Next is the applied grid-search tuning which will find the right parameter combinations for the nearest-neighbor model being applied. Furthermore, the previous pre-processing steps will be combined with an additional bagging method. Finally, the resulting bagged KNN will present a 0.971 rate of accuracy, 0.555 rate of precision, 0.781 rate of recall, 0.649 rate of f1-score, 0.95 auc of ROC curve, and 0.702 auc of precision-recall curve.
Application of MobileNetV2 and SVM Combination for Enhanced Accuracy in Pneumonia Classification Meindiawan, Eka Putra Agus; Muljono, Muljono
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8426

Abstract

Pneumonia is a very common respiratory infection in low- and middle-income countries and is still a leading cause of death, especially among children under five years old. Modern technologies, such as machine learning, offer significant potential in improving the automatic detection of pneumonia through chest X-ray (CXR) image analysis. This study aims to develop a more accurate pneumonia diagnosis system by evaluating various feature extraction methods. CXR datasets of pneumonia patients were resized to 180x180 pixels and balanced using the SMOTE-Tomek technique. Three main approaches were investigated: direct classification using Support Vector Machine (SVM) on the SMOTE-Tomek balanced dataset, feature extraction using Sobel edge detection followed by SVM classification, and feature extraction using MobileNet-V2 followed by SVM classification. The results showed that Scheme 1 achieved 97% accuracy, Scheme 2 decreased to 95%, and Scheme 3 achieved the highest accuracy at 98%. The lower accuracy in Scheme 2 is due to the limitations of Sobel edge detection, which reduces the key features in the CXR image. On the other hand, the improvement in Scheme 3 is due to the effective feature extraction capability of MobileNet-V2. In conclusion, the choice of feature extraction method plays an important role in determining the accuracy of an automated diagnostic system. This study builds on existing research and is expected to make a significant contribution to the development of more accurate and efficient automated diagnostic systems, which can ultimately help reduce pneumonia-related mortality.
Co-Authors Abu Salam Achmad Wahid Kurniawan Ahmad Fanani Alya Salsabila, Anggita Arisani, Muhammad Irfan Astuti, Rahma Hayuning Bayu Aryanto Budi Harjo Budi, Setyo Catur Supriyanto Edi Noersasongko Fandi Yulian Pamuji Fitri Yakub Hanny Haryanto Hartono, Faezal Harun Al Azies Hendarwanto Hendarwanto Herdiman T. Pohan Heribertus Himawan Herowati, Wise Ignatius Moses Setiadi, De Rosal Ika Novita Dewi Junta Zeniarja Jutono Gondohanindijo, Jutono Karmel L. Tambunan Kartini Kartini Kurniawan, Defri Kusumo, Willyanto Kartiko Mahazam Afrad Maulana, Muhammad Naufal Meindiawan, Eka Putra Agus Misdram, Muhammad Moch Anjas Aprihartha Muhammad Naufal, Muhammad Muliadi, Nuraziz nikus, Domi Nugroho, Bagus Nur Rokhman Paramita, Cinantya Prasetyo Prasetyo Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto R. H.H. Nelwan Rachmad Abdullah Raden Arief Nugroho Sari Ayu Wulandari Setyaningrum, Rani Setyaningrum, Rani Silalahi, Rosalia Natal Sudibyo, Usman Sukmono, Indriyo K. Supriadi Rustad Susanto, Al Birr Karim Sutriawan Sutriawan T. Sutojo Taufiq M. Waly Trisnapradika, Gustina Alfa Utomo, Danang Wahyu Valentina Widya Suryaningtyas, Valentina Widya Wuryantoro, Tri Yuspiani, Yuspiani Zahrah, Febrina Nabila