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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik International Journal of Advances in Data and Information Systems Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Muhammad Itqan Mazdadi
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Comparison Algorithm for Diabetes Classification with Consideration of Mutual Information and Information Feature Rahmat Ramadhani; Triando Hamonangan Saragih; Muhammad Itqan Mazdadi; Muliadi Muliadi
Jurnal Komputasi Vol. 11 No. 1 (2023)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i1.6649

Abstract

Diabetes is a prevalent disease in humans that is caused by excessive sugar levels in the body. If left untreated, it can lead to severe consequences such as paralysis, decay in certain parts of the body, and even death. Unfortunately, early detection of diabetes is difficult, and many cases go untreated until it is too late. However, the development of technology has opened up new possibilities for early detection and treatment of diabetes. One such approach is classification, a commonly used method in the field of Computer Science. Classification is used in various fields, including health, agriculture, and animal diseases, to draw conclusions based on input data using cause-and-effect relationships. Many different learning concepts and methods can be used in classification, with the Decision Tree concept being one of the most popular examples. This study compares several classification methods, including Decision Tree, Random Forest, AdaBoost, and Stochastic Gradient Boost, with feature selections carried out using MI and IF. The study aims to evaluate the effectiveness of these methods and the influence of feature selection on improving their performance. Based on the results of the study, it can be concluded that feature selection using Mutual Information and Importance Feature can improve the classification accuracy in some methods, particularly in Random Forest, AdaBoost, and Stochastic Gradient Boost. However, the Decision Tree algorithm did not show any improvement in accuracy after feature selection. The best classification accuracy was achieved with the Stochastic Gradient Boost method using the original dataset without feature selection, while the Random Forest method showed the highest accuracy after using all the features. Overall, the results suggest that feature selection can be a useful technique for improving the performance of classification algorithms in diabetes prediction. The study suggests that future research could investigate other classification methods, such as Neural Network or Deep Learning, and use optimization algorithms like Genetic Algorithm or Particle Swarm Optimization to improve feature selection results.
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Hana, Elvina Nur; Faisal, Mohammad Reza; Kartini, Dwi; Mazdadi, Muhammad Itqan; Saputro, Setyo Wahyu; Indriani, Fatma; Satou, Kenji
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes Siti Fathmah; Dwi Kartini; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21528

Abstract

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.
Co-Authors AA Sudharmawan, AA Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Ade Agung Harnawan, Ade Agung Adela Putri Ariyanti Afifa, Ridha Ahdyani, Annisa Salsabila Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Shofi Khairian Ahmad Tajali Aidil Akbar Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Amalia, Raisa Andi - Farmadi Andi Farmadi Andi Farmadi Anna Khumaira Sari Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Antoh, Soterio Ardiansyah Sukma Wijaya Athavale, Vijay Anant Athavale, Vijay Annant budiman, irwan Buih, Putri Helena Junjung Deni Sutaji Dina Arifah Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Fatma Indriani Fitriani, Karlina Elreine Fitrinadi Friska Abadi Haekal, Muhammad Hafizah, Rini Hana, Elvina Nur Helma Herlinda Herteno, Rudi Herteno, Rudy Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman Irwan Budiman M. Apriannur M. Khairul Rezki Mafazy, Muhammad Meftah Muflih Ihza Rifatama Muhamad Fawwaz Akbar Muhamad Ihsanul Qamil Muhammad Adika Riswanda Muhammad Haekal Muhammad Khairin Nahwan Muhammad Mada Muhammad Mirza Hafiz Yudianto Muhammad Mursyidan Amini Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Nabella, Putri Noorhafizi, Muhammad Normaidah, Normaidah Nugraha, Muhammad Amir Nursyifa Azizah P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Putri Nabella Radityo Adi Nugroho Rahmah, Indah Noor Rahmat Hidayat Rahmat Ramadhani Rahmat Ramadhani Rahmawati, Nanda Hesti Rahmawati, Nanda Putri Ramadhani, Muhammad Irfan Ramadhani, Rahmat Ratnapuri, Prima Happy Riadi, Agus Teguh Rifki Izdihar Oktvian Abas Pullah Rifki Rinaldi Rizky, Muhammad Miftahur Rozaq, Hasri Akbar Awal Rozaq, Hasri Awal Akbar Rudy Herteno Saputra, Adryan Maulana Saragih, Triando Hamonangan Satou, Kenji Satrio Yudho Prakoso Setyo Wahyu Saputro Shalehah Siti Fathmah Syahputra, Muhammad Reza Tajali, Ahmad Totok Wianto Wahyu Dwi Styadi Wijaya Kusuma, Arizha Yanche Kurniawan Mangalik YILDIZ, Oktay Yoga Pambudi Yudha Sulistiyo Wibowo Zaini Abdan