Article Per Year (5 Year)
p-Index From 2020 - 2025
8.058
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Jupiter Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA BAREKENG: Jurnal Ilmu Matematika dan Terapan JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Terpadu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) MULTINETICS Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Sains, Aplikasi, Komputasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Journal of Software Engineering, Information and Communication Technology Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Asian Journal of Community Services (AJCS) eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat Journal of Law, Education and Business Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ichwanul Muslim Karo Karo
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 60 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
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.
Sistem Perangkingan Menentukan Fakultas Terbaik Penerapan Zona Integritas Menggunakan Metode SAW Dewi, Sri; Karo, Ichwanul Muslim Karo; Barus, Eviyona Laurenta Br
Jurnal Teknologi Terpadu Vol 11 No 1 (2025): Juli, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i1.1823

Abstract

The Integrity Zone (ZI) is defined as a designation given to government institutions that demonstrate a strong commitment from their leadership and all levels in realising a Corruption-Free Area (WBK) and/or a Clean and Serving Bureaucracy Area (WBBM). This commitment is realised through bureaucratic reform, particularly in encouraging the prevention of corrupt practices and improving the quality of public services. The purpose of this study is to build a system that will be used to see the ranking of the implementation of the faculty Integrity Zone in an effort to support corruption prevention and improve the quality of public services, so that it can assist stakeholders in decision making. Simple Additive Weighting (SAW) was applied in this study involving 7 Faculties at Medan State University, and 8 criteria were used for evaluation. The results of the study are a website-based Integrity Zone ranking system with several features that can be accessed by visitors, namely: Home, Ranking and Login, while the admin can access the Dashboard, Criteria, Alternatives, Simple Additive Weighting Calculation, and Decision Results features. The system was tested with Black Box Testing to see that the menu functions run well.
Co-Authors Abil Mansyur, Abil Adawiah Hasyani, Rabiahtul Ade Amelia, Tasya Adidtya Perdana, Adidtya Aditia Sanjaya Ahyar, Khoirul Ananda Khosuri Angelina Prima Kurniati Anggraini, Nisa Putri Aqila Aqila, Aqila Azizul Azhar Ramli Azizul Azhar Ramli Bachruddin Saleh Luturlean Bakti Dwi Waluyo Darari, Muhammad Badzlan Daulay, Leni Karmila Dedy Kiswanto Dian Septiana Dimas Pebrian Supandi Ester Berliana Ritonga, Yolanda Eviyona Laurenta Br Barus Fadillah, Wahyu Nur Falah, Miftahul Fitri Rahayu Fitria, Nur Anisa Gea, Kurnia Mildawati Ginting, Manan Gunawan, Rizky Habibi, Rizki Haraha, Melyana Hariyanto HARIYANTO HARIYANTO Hariyanto Hariyanto Hariyanto, Hariyanto Hendriyana Hendriyana Heru Nugroho Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Jodi Kusuma Juan Steiven Imanuel Septory Justaman Arifin Karo Karo Karo karo, Justaman Arifin Karo Karo, Justaman Arifin Landong, Ahmad Lorinez S, Yohana Manan Ginting Mardiana Mardiana Maretha Br. Simbolon, Silvana Maulana Malik Fajri Maulidna, Maulidna Melania Justice Panggabean Miftahul Falah Miftahul Falah Mohd Farhan Md Fudzee Mohd Farhan Md Fudzee Molliq Rangkuti, Yulita Mufida, Yasmin Muhammad Yusuf Mutiara Sihaloho, Laura Adelia Nasution, Aurela Khoiri Natasya, Amanda Nelza, Novia Nur Hafni Nurul Ain Farhana Nurul Ikhsan Panggabean, Suvriadi Permata Putri Pasaribu, Yohanna Purba, Desni Paramitha Putri Harliana Putri Maulidina Fadilah Ramadhani, Fanny Ramanti Dharayani Rangkuti, Y. M Reinaldo Kenneth Darmawan Rennyta Yusiana Retno Setyorini Roby Dwi Hartanto Rohmat Saragih Romia Romia Said . Iskandar Salsabila, Aqila Shahreen Kasim Shahreen Kasim Simamora, Elmanani Sisti Nadia Amalia Sri Dewi Sri Dewi Sri Dewi Sri Suryani Supra Yogi Syahrin , Alvin Valentino, Bob Wahyu Nur Fadillah Wardhani Muhamad Warjaya, Angga Wibowo, Adinda Widi Astuti winsyahputra Ritonga Yahya Peranginangin Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yunianto Yunianto Yunianto Yunianto Yunianto Yunianto, Yunianto ZK Abdurahman Baizal