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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Computer Science and Information Technologies Indonesian Journal of Data and Science Innovation in Research of Informatics (INNOVATICS) Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Mandiri Open Community Service Journal Bulletin of Social Informatics Theory and Application NERO (Networking Engineering Research Operation) International Journal of Artificial Intelligence in Medical Issues
Huzain Azis
Unviersitas Muslim Indonesia
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : NERO (Networking Engineering Research Operation)

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ANALISIS KINERJA ALGORITMA PEMBELAJARAN MESIN ENSEMBEL PADA DATASET MULTI KELAS CITRA JAFFE Azis, Huzain; Alisma, Alisma; Purnawansyah, Purnawansyah; Nirmala, Nirmala
NERO (Networking Engineering Research Operation) Vol 9, No 2 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27872

Abstract

This research aims to develop a facial expression recognition system based on the JAFFE dataset which includes seven classes of emotional expressions, namely happy, sad, angry, afraid, disgusted and neutral expressions. The first step taken is canny segmentation on each dataset to maintain essential information on each face. Next, extraction was carried out using the hu moments method to gain an in-depth understanding of the important characteristics of facial expressions. The next process involves ensemble voting using five classification methods, namely Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Process Classifier (GPC), and Decision Tree. The results of these five methods are then ensembel using voting techniques, and the final results are evaluated using performance metrics such as accuracy, precision, recall, and F-1 score. Evaluation is carried out by comparing the final results with the original data from the JAFFE dataset, by measuring accuracy , precision, recall, and F1 Score value to evaluate system performance. The results of this research show that the ensemble voting approach using a combination of classification methods is able to significantly improve facial expression recognition capabilities. The resulting accuracy, precision, recall, and F1 Score values provide a comprehensive picture of system performance.  This research contributes to the development of facial emotion recognition technology and can be applied in various contexts. Includes human-computer interaction as well as applications in the fields of artificial intelligence.Keywords: Performance Analysis, Ensemble, Jaffe Image, Classification, Multiclass
PREDIKSI ANEMIA DARI PIXEL GAMBAR DAN LEVEL HEMOGLOBIN MENGGUNAKAN RANDOM FOREST CLASSIFIER Azis, Huzain; Rismayanti, Nurul
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.27916

Abstract

Anaemia is a widespread blood disorder characterized by a deficiency of red blood cells or hemoglobin, which can lead to severe health complications if not diagnosed and treated promptly. This research aims to develop a machine learning model to predict anaemia based on hemoglobin levels and image pixel distributions, leveraging a dataset from Kaggle. The dataset includes features such as percentages of red, green, and blue pixels in images and hemoglobin levels. We applied a Random Forest Classifier, a robust machine learning algorithm, and evaluated its performance using 5-fold cross-validation. The data pre-processing involved removing irrelevant columns, encoding categorical variables, and scaling numerical features. The model achieved a mean accuracy of 97.05%, precision of 97.02%, recall of 97.05%, and F1-score of 96.88%, indicating its high reliability in predicting anaemia. Visualizations such as Correlation Heatmaps, 3D PCA, Parallel Coordinates Plots, 3D t-SNE, and Violin Plots were used to understand feature relationships and distributions. These results underscore the potential of machine learning in providing a non-invasive, cost-effective diagnostic tool for anaemia, especially in resource-limited settings. Future research should address dataset imbalance and potential biases, explore additional features, and test other machine learning models to further enhance the predictive accuracy. This study contributes to the field of medical diagnostics by demonstrating the efficacy of integrating hemoglobin levels and image data for anaemia prediction, paving the way for improved early detection and treatment strategies.Keywords: Anaemia, Hemoglobin, Machine Learning, Random Forest.
Co-Authors Abdul RAchman Manga Admojo, Fadhila Tangguh Alfa Faridh Suni Alfiyyah, Nurul Alisma, Alisma Alwi Alwi Anas, Risnayanti Andi Tenri Sumpala, Andi Tenri As'ad, Ihwana Baharuddin, Mus Mulyadi Bustam, Faida Daeng Dolly Indra Erick Irawadi Alwi Erma Susanti Fadhila Tangguh Admojo Faradibah, Amaliah Farniawati Fattah Farniwati Fattah Fattah , Farniwati Fattah, Farniawati Fattah, Farniwati Felix Andika Dwiyanto Fika Hastarita Rachman, Fika Hastarita Firdaus Solihin Harlinda Lahuddin Hasanuddin, Tasrif Haviluddin Haviluddin Herdianti Darwis Herdianti Darwis Herman Herman Ika Oktavia Suzanti Inggrianti Pratiwi Putri jabir, sitti rahmah Kamil, Fajrul Ihsan Lokapitasari Belluano, Poetri Lestari M. Imam Maulana Manga, Abdul Rachman Muh Nurtanzis Sutoyo Muhammad Syahrizal Darwis Muis, Ismunandar Mus Mulyadi Baharuddin Nirmala Nirmala, Nirmala Nirwana Nirwana Nur bahri, Nur Bahri Nurul A'ayunnisa Nurul Fitri H, Sarah FIla Pradinata, Awal Purnawansayah Purnawansayah Purnawansyah Purnawansyah Puspitasari, Yustisia Putri Utami, Aulia Putri, Aulia Maharani Putri, Inggrianti Pratiwi Rachman Manga, Abdul Ramadan, Syahril Ramdan Satra Rasman Rasman Rismayanti, Nurul Rochman, Eka Mala Sari Sainlia, Ahmad Fauzan Salim, Yulita Saly, Intan Novita Selvi Marcellia Shidiq Arif Siwiantoko Syafie, Lukman Tasrif Hasanuddin Tenripada, Andi Ulfa Widyawati, Dewi Yulita Salim