Article Per Year (5 Year)
p-Index From 2021 - 2026
4.719
P-Index
This Author published in this journals
All Journal Dinamik Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Global Jurnal Teknologi Dan Sistem Informasi Bisnis JISA (Jurnal Informatika dan Sains) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Lentera Pengabdian JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Proceeding International Pelita Bangsa VIDHEAS: Jurnal Nasional Abdimas Multidisiplin SAINTEK Dedikasi : Jurnal Pengabdian Lentera
Zy, Ahmad Turmudi
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry 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 Library & Information Science Social Sciences Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Comparative Analysis of Earthquake Prediction with SVM, Naïve Bayes, and K-Means Models: Comparative Analysis of Earthquake Prediction with SVM, Naïve Bayes, and K-Means Models Muttaqin, Ahmad Fadhiil; Sunge, Aswan Supriyadi; Zy, Ahmad Turmudi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5085

Abstract

Earthquakes are natural disasters with significant impacts on people and the environment, so effective methods for prediction are needed to improve preparedness and risk mitigation. This study analyzes the performance of three algorithms Support Vector Machine (SVM), Naïve Bayes, and K-Means in predicting earthquakes in Indonesia using a dataset containing 4,645 historical data from BMKG processed through preprocessing, data separation, analysis, and performance evaluation with RapidMiner tools. The results show that SVM has the best performance with 99.87% accuracy, 99.83% precision, and 95.61% recall, making it highly relevant for earthquake prediction. Naïve Bayes achieved 90.31% accuracy and 95.08% recall, but the low precision (57.24%) shows the limitations of this model. K-Means successfully clusters earthquakes into two categories: small (3,661 data) and large (55 data) earthquakes, with a Davies-Bouldin Index value of 0.579, reflecting good clustering quality. Based on these results, SVM is recommended as a superior earthquake prediction model, while Naïve Bayes and K-Means are more suitable for additional analysis. This approach confirms the potential of machine learning algorithms in supporting future earthquake risk mitigation.
Optimalisasi Load Balancing Menggunakan Metode NDLC untuk Meningkatkan Kualitas Layanan Jaringan Internet Isro, Aditya Bani; Zy, Ahmad Turmudi; Andika, Sophian
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5484

Abstract

This research focuses on the main problem of internet network instability that is often disconnected and the low quality of network services characterized by slow access speeds, high latency, and fluctuations in network performance at SMK Al-Manar Islamic School. This instability causes disruptions in the teaching and learning process, such as slow access to educational websites, difficulty in accessing online materials, and disruption in the use of internet-based applications. The purpose of this research is to improve the quality of network services through the implementation of load balancing using Mikrotik router. The method used is Network Development Life Cycle (NDLC), which includes the stages of analysis, design, simulation, implementation, monitoring, and management. Data was collected through literature study, field study, and observation using Wireshark to measure QoS parameters such as throughput, packet loss, delay, and jitter before and after the implementation of load balancing. The results showed that the implementation of load balancing successfully increased throughput from 14215.591 kbps to 46460.8675 kbps, reduced delay from 135 ms to 7 ms, and decreased jitter from 135.615 ms to 73.293 ms. The packet loss value remains 0%, both before and after implementation. In conclusion, the implementation of load balancing using Mikrotik router has successfully improved the quality of internet network services at SMK Al-Manar Islamic School, making it faster, more stable and efficient in accordance with TIPHON standards. It is recommended that schools continue to monitor and manage the network regularly to maintain optimal performance.
Implementasi Metode Decision Tree pada Sistem Prediksi Status Kualitas Produk Minuman A Anshor, Abdul Halim; Zy, Ahmad Turmudi
Jurnal Ilmiah Informatika Global Vol. 15 No. 1: April 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i1.3778

Abstract

The quality of a beverage product is one of the important items that beverage product entrepreneurs must pay attention to. Good quality beverage products will have an impact on consumers' health. UMKM Buah Sabar is one of the MSMEs located in Bekasi district which produces beverage products A. In the distribution of these beverage products, MSME workers in the delivery section have conditions where the product is out of stock or left over. The reseller must be able to understand whether the status of the remaining product is still of good quality or has been damaged. This is very important to pay attention to because the cooling conditions of each reseller have varying degrees of cold, sometimes also influenced by blackouts and unstable electricity voltage. This condition can cause the quality of product A to decrease. The large number of resellers and products sent will make it difficult for MSME workers to detect the quality of beverage product A. To overcome this problem the researchers found a solution that requires a machine learning method to predict the quality status of product A. In this research, the researchers used the decision tree method to predict the quality status of the product Drink A. The data used are 500 samples of drink product A in the production period from November 2023 to February 2024. The parameters used include temperature, color, taste, aroma, and quality status class of drink product A. The results of this research will show the presentation The accuracy value for the quality of product A is 99.59%, this shows that the decision tree algorithm has very good performance in the process of classifying the quality of beverage product A.
DECISION SUPPORT SYSTEM FOR DETERMINING DEPARTMENT USING THE PROFILE MATCHING INTERPOLATION METHOD AT WIKRAMA VOCATIONAL SCHOOL, BOGOR Pranoto, Gatot Tri; Nugroho, Agung; Zy, Ahmad Turmudi
JISA(Jurnal Informatika dan Sains) Vol 6, No 1 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i1.1625

Abstract

Wikrama Vocational High School is one of the schools that routinely carries out the determination of majors every year. The majors process at Wikrama is carried out in the tenth grade by the Guidance and Counseling Teacher (BK Teacher) and the Head of Expertise Competence (Kakomli). BK and Kakomli teachers have difficulty determining the results of majors when there are more interest in one major than other majors, there is a mismatch of majors results because they are not in accordance with the existing majors in the chosen field of expertise and the process of majors is not accurate and fast. This is because it has not used an objective mechanism for determining majors, there is no weighting process, and there is no information system available. Therefore, it is necessary to develop a decision support system (DSS) to assist the process of determining majors using Profile Matching and Interpolation methods. The Profile Matching method is used for appraising decisions, while the Interpolation method is used for the weighting process. The criteria used in each field of expertise are Informatics Engineering with 11 criteria and Computers, Business Management with 8 criteria, and Tourism with 7 criteria. Based on the results of testing and validation that have been carried out by experts, it has an accuracy value of 93%. The accuracy value indicates that the system can provide recommendations for determining the right major. In addition, the interpolation weighting method is proven to increase the accuracy value compared to the ordinal weighting value in Profile Matching. The results of this study are in the form of a decision support system that helps in determining majors objectively, quickly and accurately.
Co-Authors Abdul Azis Abdul Ghofir Abdul Halim Anshor Adrianna Syariefur Rakhmat Afriantoro, Irfan Ainur Rofieq Alfian Ma’arif Ananda, Angga Thifal Anaoval, Abdul Aziz Andika, Sophian Anwar, M Syaibani Arwan, Asep Astuti, Asri Liya Aswan Supriyadi Sunge Ayassy, Bayu Maulana Djoko Nugroho Edora, Edora Effendi, M Makmun Effendi, Muhammad Makmun Fadlurrohman, Muhammad Shiddiq Fahamsyah, Mohammad Hatta Gatot Tri Pranoto Gunawan, G Handono, Bagus Tri Hartono, Rudi Hidayatullah, Cahyo Ikhsan Romli Isarianto, Isarianto Isro, Aditya Bani Jeckson Sidabutar Khotimah, Nur Maharani, Siti Mutia Maheswari, Diyan Arum Muhamad Ekhsan Muhammad, Najamuddin Dwi Miharja Muttaqin, Ahmad Fadhiil Nadya Safitri, Nadya Novitasari, Aas Noviyanti, Rosa Nugraha, Dimas Reza Nurmaulida*, Irma Pamungkas, Bagas Perdana, Anggi Permatasari, Antini Pranoto, Kirana Astari PUTRI, TIARA Rahma, Syifa Aurellia Ramadhan, Yogi Reza Rozikin, Zaenur s, suherman S.Djawas, Fathia Wardah Saiful Muktiali Sanudin Sanudin Sari, Nita Winda Sasongko, Ananto Tri Sen , Tjong Wan Shintania, Mira Simanjuntak, Antonius Simarangkir, Manase Sahat H Sinaga, Frans Putra Siregar, Amril Mutoi Sunge, Aswan S Sunge, Aswan S. Sunita Dasman Surojudin , Nurhadi Surojudin, Nurhadi Suryadi, Dikky Wahyu Caesarendra Wangsadanureja, Miftah Wijaya, Kurnia Difa Yoga Pratama Zahri, M. Hannata