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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL INSTEK (Informatika Sains dan Teknologi) ABDIMAS SILIWANGI IKRA-ITH ABDIMAS JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Teknologi Terpadu Building of Informatics, Technology and Science J-PIPS (Jurnal Pendidikan Ilmu Pengetahuan Sosial) Community Empowerment Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Devotion: Journal of Research and Community Service Jurnal Abdimas Kartika Wijayakusuma Journal of Software Engineering, Information and Communication Technology Jurnal PkM MIFTEK J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Tekmulogi: Jurnal Pengabdian Masyarakat Enrichment: Journal of Multidisciplinary Research and Development Fastabiq: Jurnal Studi Islam Engagement: Jurnal Pengabdian Kepada Masyarakat International Journal of Ethnoscience and Technology in Education
Hendriyana Hendriyana
Universitas Pendidikan Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknologi Terpadu

 1 
Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score Ichwanul Muslim Karo Karo; Hendriyana Hendriyana
Jurnal Teknologi Terpadu Vol. 8 No. 2 (2022): December, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.564

Abstract

Diabetes is a deadly and chronic disease. It characterized by an increase in blood sugar. Many complications occur if diabetes does not treat and identified. The common identification process by visits to diagnostic centers and consulting physician. It makes bored patients. Machine learning approach can solve the problem of diabetic identification. However, the unbalanced range of diabetes variable values ​​affects the quality of machine learning results. This study predicts the likelihood of diabetes in diabetic patients from 768 Indian women, using three machine learning classification algorithms and Z-Score normalization method. The machine learning algorithms used are Decision Tree, Support Vector Machine (SVM) and Naive Bayes. Experiments were run on the Pima Indians Diabetes Database (PIDD). Dataset retrieved from the UCI Machine Learning Repository. The performance of the three algorithms was evaluated using accuracy, precision, F1, and recall based on confusion matrix. SVM algorithm is an algorithm that has the highest performance that both algorithm the Naive Bayes and Decision Tre algorithms, the accuracy and F1 is 80.73% and 76%. The Z-Score method has positively contribution to increasing the accuracy of the classification model. Furthermore, this study also managed to get a higher accuracy than previous studies.
Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah Hendriyana Hendriyana; Ichwanul Muslim Karo Karo; Sri Dewi
Jurnal Teknologi Terpadu Vol. 8 No. 2 (2022): December, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.581

Abstract

Blood supplies and stocks are urgently needed. Regular donations from healthy volunteers are the only way to keep up with the blood supply. This research aims to develop and evaluate a machine-learning algorithm to predict whether a volunteer will donate or not. The machine learning algorithms are Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). This study also applies the process of normalizing data with a Z-score to standardize the dataset scale. The dataset is sourced from the Hsin-Chu City Blood Transfusion Service, Taiwan, and stored in the UCI repository. The evaluation methods are accuracy, precision, recall, and F-1 score. The research results with the Naïve Bayes algorithm were 89.90%, Logistic Regression 82.59%, and SVM 94.79%. The normalization process using the Z-Score method contributes positively to improving the performance of the classification model. Based on this performance, it provides predictive results for volunteers who will return to donate blood to offer blood reserves to those in need.
Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score Karo Karo, Ichwanul Muslim; Hendriyana, Hendriyana
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.564

Abstract

Diabetes is a deadly and chronic disease. It characterized by an increase in blood sugar. Many complications occur if diabetes does not treat and identified. The common identification process by visits to diagnostic centers and consulting physician. It makes bored patients. Machine learning approach can solve the problem of diabetic identification. However, the unbalanced range of diabetes variable values ​​affects the quality of machine learning results. This study predicts the likelihood of diabetes in diabetic patients from 768 Indian women, using three machine learning classification algorithms and Z-Score normalization method. The machine learning algorithms used are Decision Tree, Support Vector Machine (SVM) and Naive Bayes. Experiments were run on the Pima Indians Diabetes Database (PIDD). Dataset retrieved from the UCI Machine Learning Repository. The performance of the three algorithms was evaluated using accuracy, precision, F1, and recall based on confusion matrix. SVM algorithm is an algorithm that has the highest performance that both algorithm the Naive Bayes and Decision Tre algorithms, the accuracy and F1 is 80.73% and 76%. The Z-Score method has positively contribution to increasing the accuracy of the classification model. Furthermore, this study also managed to get a higher accuracy than previous studies.
Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah Hendriyana, Hendriyana; Karo Karo, Ichwanul Muslim; Dewi, Sri
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.581

Abstract

Blood supplies and stocks are urgently needed. Regular donations from healthy volunteers are the only way to keep up with the blood supply. This research aims to develop and evaluate a machine-learning algorithm to predict whether a volunteer will donate or not. The machine learning algorithms are Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). This study also applies the process of normalizing data with a Z-score to standardize the dataset scale. The dataset is sourced from the Hsin-Chu City Blood Transfusion Service, Taiwan, and stored in the UCI repository. The evaluation methods are accuracy, precision, recall, and F-1 score. The research results with the Naïve Bayes algorithm were 89.90%, Logistic Regression 82.59%, and SVM 94.79%. The normalization process using the Z-Score method contributes positively to improving the performance of the classification model. Based on this performance, it provides predictive results for volunteers who will return to donate blood to offer blood reserves to those in need.
Co-Authors Acep Supriadi Adela, Yoga Agus Juhana Ahdini, Allifia Aisyah, Risa Aziz, Rafi Nurkholiq Barrios, Rubylin G Basri, M. Hasan Basri, Nurul Ain Nabillah Binti Bauzir, Arij Fathir Bisma, Hartanto Deden Pradeka Destia, Triyanda Putra Dian Anggraini Diana Putri Dita Dwi Andriyani Enanoria, Cristine Joy Fadilah, Luthfi Aqshol Fajri, Bayu Ramadhani Farisi, Farhan Siddiq Al Fauzia, Novia Alifah Febis, Delsa Tria Fitriyani , Anisa Furi Furnamasari, Yayang Gunawan, Sendi Febrian Harlini, Meti Ichwanul Muslim Karo Karo Ilyasa, Salya INDIRA SYAWANODYA intan permata sari Kalimat, Arul Budi Kurnia, Nova Laksono, Alfan Pujo Lulu Nurdini, Lulu M. Ridwan Sutisna Maulana, Ilham Ahmad Mochamad Iqbal Ardimansyah Mochmad Iqbal Ardimansyah Mochmad Iqbal Ardimansyah MUH. HUSEN ARIFIN Muhammad Dzikri Danuarteu Muhammad, Raditya Muntiani, Via Nadia Tiara Antik Sari Nadia Tiara Antik Sari Nadila, Ai Salsa Nisrina, Hanaan Nugraha, Mochammad Suva Nuur Wachid Abdul Majid Ovie Ayunda Rahmaputri Isnawan Padini, Dewi Sri Purkon, Haikal Muhammad Putra, Muhammad Taufik Dwi Raditya Muhamad Raditya Muhamad Raditya Muhammad Resnawati, Panni Retnowati, Yulia Robertus Robet Rusliwa Somantri, Gumilar Sarah Agustin Septiana, Asyifa Imanda Setiawan, Muhamad Yogi Shiddiqi, Qifni Yasa' Ash Sirait, Tongam Siti Nuraisah, Siti Sri Dewi Sri Dewi Syifaul Fuada Tampara, Josua Uyun, Syifa Qurrotul WULANDARI, WIDYA Yazid Hilman Maulana Zhaza Nabila Zahra