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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Masyarakat Informatika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Jurnal Ecodemica : Jurnal Ekonomi Manajemen dan Bisnis Jurnal Informatika Jurnal Pilar Nusa Mandiri Forum Ekonomi : Jurnal Ekonomi, Manajemen dan Akuntansi JURNAL MANAJEMEN JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) INTI Nusa Mandiri Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma International Journal of Advanced Science Computing and Engineering Jurnal Pengabdian Ibnu Sina Journal Medical Informatics Technology SmartComp
Dwiza Riana
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Journal Medical Informatics Technology

 1 
Performance Comparison of Three Classification Algorithms for Non-alcoholic Fatty Liver Disease Patients Using Data Mining Tool Octaviantara, Adi; Abbas, Moch Anwar; Azhari, Ahmad; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.2

Abstract

This study aims to carry out a comparative analysis of the three classification algorithms used in research on Nonalcoholic Fatty Liver Disease (NAFLD) Patients. NAFLD is a liver condition associated with the accumulation of fat in the liver in individuals who do not consume excessive alcohol. The algorithms used in the analysis are Decision Tree, Naïve Bayes, and k-Nearest Neighbor (k-NN), with data processing using RapidMiner software. The data used is sourced from Kaggle which comes from the Rochester Epidemiology Project (REP) database with research conducted in Olmsted, Minnesota, United States. The measurement results show that the Decision Tree algorithm has an accuracy of 92.56%, a precision of 93.24%, and a recall of 99.08%. The Naïve Bayes algorithm has an accuracy of 89.93%, a precision of 95.40% and a recall of 93.56%. While the k-Nearest Neighbor algorithm has an accuracy of 91.33%, a precision of 91.94%, and a recall of 99.27%. ROC curve analysis, all algorithms show "Excellent" classification quality. However, only the k-NN algorithm reached 1.0, showing excellent classification results in solving the problem of classifying Nonalcoholic Fatty Liver Disease patients. This study concluded that the k-NN algorithm is a better choice in solving the problem of classifying Non-alcoholic Fatty Liver Disease patients compared to the Decision Tree and Naïve Bayes algorithms. This study provides valuable insights in the development of classification methods for the early diagnosis and management of NAFLD.
Logistic Regression with Hyper Parameter Tuning Optimization for Heart Failure Prediction Herwanto, Teguh; Kodri, Wan Ahmad Gazali; Aziz, Faruq; Hewiz, Alya Shafira; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.3

Abstract

Heart failure is a major public health concern that causes a substantial number of deaths worldwide. Risk factor analysis is required to diagnose and treat patients with heart failure. The logistic regression with hyper parameter tuning optimization is presented in this research, with ejection fraction, high blood pressure, age, and  serum creatinine as relevant risk factors. This study indicates that better data preparation utilizing Deep Learning with hyper parameter adjustment be used to determine the best parameter that has a substantial influence as a risk factor for heart failure. The experiments employed data from the Faisalabad Institute of Cardiology and Allied  Hospital in Faisalabad (Punjab, Pakistan), which included 299 samples. The experimental findings reveal that the proposed approach obtains a recall of 63.16% greater than related works.
Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means Dwi Saputra, Heru; Efendi, Ade Irfan Efendi; Rudini, Edwin; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.21

Abstract

Hepatitis is a serious disease that causes death throughout the world. It is responsible for inflammation in the human liver. If we manage to detect this life-threatening disease early, we can save many lives from it. In this research paper, we predict hepatitis disease using data mining techniques. We have attempted to propose a feasible approach to improve the performance of our prediction models in our research. We address the problem of missing values in the dataset by replacing them with the mean value. Nine algorithms were applied to the hepatitis disease dataset to calculate prediction accuracy. We measure accuracy, precision, recall, ROC and best score, and we compare them with random search hyperparameter tuning. It is hoped that by using them we will find the optimal combination of hyperparameters to improve the performance of machine learning models which helps us compare the performance of classification models.
Cervical Cancer Papsmear Classification through Meta-Learning Technique using Convolution Neural Networks. Mahendra, M; Jumadi, J; Riana, Dwiza
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.23

Abstract

This study uses convolutional neural networks (CNNs) and meta-learning techniques to create an accurate and efficient model for classifying the risk factors of cervical cancer. The dataset includes four types of cervical lesions, and the main objective is to categorize these lesions as either benign or malignant. This classification is essential for early and succesfull treatment of cervical cancer. The challenge arises from the complexity and variations in the images, resulting in the inability of conventional machine learning and deep learning approaches to provide correct classifications. Meta ensemble learning approaches are employed to improve the model's classification accuracy. The dataset of cervical cancer risk factors is preprocessed before being used to train and evaluate numerous CNNs utilizing pre-trained models and various architectures. Subsequently, a meta-learning is employed to optimize the learning process, and used to aggregate the outputs of the multiple CNNs. Moreover, the assessment findings show the model achieves high accuracy and effectiveness. Finally, the suggested model's accuracy score will be contrasted against the current cutting-edge methods used by other existing systems.
Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method Hadianti, Sri; Aziz, Faruq; Nur Sulistyowati, Daning; Riana, Dwiza; Saputra, Ridwan; Kurniawantoro
Journal Medical Informatics Technology Volume 2 No. 2, June 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i2.37

Abstract

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.
Analyzing User Experience and User Satisfaction: Evaluating User Acceptance of the Halo Hermina App Edi Sabara; Mahendra; Riana, Dwiza
Journal Medical Informatics Technology Volume 2 No. 3, September 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i3.45

Abstract

This research investigates the factors influencing user acceptance of the Halo Hermina mobile health application through an analysis of user experience and satisfaction. The study utilized a survey method to gather feedback from Halo Hermina users, assessing the questionnaire's validity and reliability. The results indicate strong validity across most items, with correlation values between 0.779 and 0.828 for performance expectancy and over 0.77 for effort expectancy. The reliability analysis shows high internal consistency, with Cronbach's Alpha values exceeding 0.976. User satisfaction scored the highest mean (4.027), indicating a consistent high level of satisfaction among users. The correlation analysis reveals significant relationships between performance expectancy, effort expectancy, facilitating condition, and behavioral intention, with the strongest correlation found between performance expectancy and effort expectancy (0.8796). Overall, the study emphasizes the crucial role of enhancing user experience and satisfaction to boost the adoption of mobile health applications like Halo Hermina, providing valuable insights for developers and stakeholders to enhance application features and service quality to meet user expectations effectively.
Co-Authors Abbas, Moch Anwar Ahmad Azhari Anton Anton Arif Budiarto Astuti, Rachmawati Darma Aziz, Faruq Choerunisya, Hana Dinar Ajeng Kristiyanti, Dinar Ajeng Dwi Saputra, Heru Dwinanda, Brama Rizky Setia Edi Sabara Efendi, Ade Irfan Efendi eka rahmawati Ernawati, Muji Evi Sopandi, Evi Ferda Ernawan Gata, Windu Hakim, Valianda Farradillah Hasanah, Riyan Latifahul Herwanto, Teguh Hewiz , Alya Shafira Hewiz, Alya Shafira Ilhami, Azriel Noer Jamal Maulana Hudin Jamil, Muh. JARWADI, JARWADI Jumadi, J Kahfi, Ahmad Hafidzul Kodri, Wan Ahmad Gazali Kurniawantoro Laela Kurniawati, Laela Luh Putu Ratna Sundari Mahendra Mahendra, M Marasabessy, Fahmi B. Muhamad Hasan Muhammad Qomaruddin Na'am, Jufriadif Nur Sulistyowati, Daning Nurhasanah, Fitri Yani Octaviantara, Adi Pardede, Hilman F Pardede, Hilman Ferdinandus Rahman, Irfan Fadholur Ramanudin, Eko Rosdiana, Tjaturningsih Rudini, Edwin Rumintarsih, Ade Sahidillah Nurdin Saputra, Ridwan Saputra, Surya Fajar Sri Hadianti Sri Hardianti Sulistyowati, Daning Nur Susilawati, Desi Susilawati Thira, Indra Jiwana Tohir, Herdian Waeisul Bismi Wati, Elga Watmah, Sri Widagdo, Galih Widanto, Frantina Andri Widianto, Tetra Wildan Wiguna WITRIANA ENDAH PANGESTI Zevana, Alda