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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Technomedia Journal International Journal of Natural Science and Engineering J-SAKTI (Jurnal Sains Komputer dan Informatika) Data Science: Journal of Computing and Applied Informatics Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jurnal Informasi dan Teknologi Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Applied Data Sciences Narra J J-SAKTI (Jurnal Sains Komputer dan Informatika) Action Research Literate (ARL) Proceeding Of International Conference On Education, Society And Humanity
Muhammad Zarlis, Muhammad
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Hybrid Cryptosystem Analysis RSA Algorithm And Triple DES Algorithm Liana, Liana; Zarlis, Muhammad; Tulus, Tulus
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12467

Abstract

Data security is needed in terms of communication. To guarantee data security, a technique is needed to make data and information called Critography. This study aims to analyze the process of Super Encryption in symmetric and asymmetric criterias using the Triple DES Algorithm and the RSA Algorithm. This can improve data security so that data is more confidential. The method used in Triple DES which is also called the symmetric algorithm is the OFB (Output feeback) method, and the RSA Algorithm (Riverst - Shamir-Adleman) which is an asymmetric algorithm using a random number system so that when these two algorithms are combined in the Super Encryption process the more accurate the data security. Super DES Triple Encryption and RSA algorithm analysis shows that the data created by text will be encrypted into ciphertext using both methods and re-described, so that the security of the data is relatively safe. Super Encryption on the two methods Algorithm is done because the level of complexity is difficult to make Cryptanalysts to steal data and the Encryption process becomes slow but data security becomes safer and not easy to attack Cryptanalysts. The problem in this research is how to increase encryption security and speed up the encryption process by combining the RSA and Triple DES methods.
Analysis of The Use of Nguyen Widrow Algorithm in Backpropagation in Kidney Disease Damanik, Romanus; Zarlis, Muhammad; Situmorang, Zakarias
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13608

Abstract

Fast and accurate diagnosis is very important for kidney disease. This research conducts and analyzes by using Nguyen Widrow Algorithm in Back Propagation method in artificial neural network for kidney disease diagnosis with the aim to improve the accuracy in predicting and time efficiency in diagnosing. The Nguyen Widrow algorithm is very capable of accelerating convergence and stabilizing the learning process in artificial neural networks, which is also expected to present a meaningful contribution to the handling of health data. This study uses MATLAB as a platform for algorithm implementation and a dataset of medical records of kidney disease patients collected from a hospital that specializes in treating kidney disease patients. The data pre-processing and artificial neural network modeling stages use the Nguyen Widrow algorithm, while the model training process uses the Back Propagation method. The results showed that the Nguyen Widrow algorithm was able to improve the accuracy of predicting someone suffering from kidney disease compared to using only the Back Propagation method. Analysis of the performance of the model shows a significant improvement in stability and convergence speed during the learning process. This indicates that data processing and medical decision making becomes more efficient. On the other hand, this research also studied the challenges and limitations that will be faced in terms of implementation of the Nguyen Widrow algorithm. Also the sensitivity of the initialization parameters, the need for the quality of the dataset to be used in training the model.This research reveals the ability of the Nguyen Widrow algorithm to improve the performance of artificial neural networks in diagnosing kidney disease. By implementing this algorithm in MATLAB, the results show that the use of the latest data processing technology and analysis tools can provide significant improvements in accuracy and efficiency in the medical field. In addition, this research is expected to provide a new direction in the development of machine learning algorithms for applications in the healthcare field, especially for diagnosing kidney disease. By further utilizing this technology, it contributes significantly to improving the quality of healthcare and treatment outcomes for patients suffering from kidney disease.
Determinants of COVID-19 severity and mortality in children: A retrospective and multicenter cohort study in Medan, Indonesia Airlangga, Eka; Wahyuni, Arlinda S.; Siregar, Jelita; Malisie, Ririe F.; Lubis, Bugis M.; Adisasmito, Wiku B.; Zarlis, Muhammad; Pasaribu, Ayodhia P.
Narra J Vol. 4 No. 2 (2024): August 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i2.865

Abstract

This study investigated indicators of the severity and mortality of COVID-19 in children in Medan, Sumatera Utara Province, Indonesia. The aim of this study was to identify determinants of severity and outcome of children with COVID-19 as the lesson learned from the COVID-19 pandemic, particularly the limited health facilities in Indonesia. This retrospective cohort study was conducted in 2020, 2021, and 2022 at multiple centers. Inpatient and outpatient children confirmed to be SARS-CoV-2 positive were randomly recruited in the selected hospitals. Baseline data (demographic, clinical, laboratory and radiological data) were collected, and outcomes were classified as recovered/deceased (for the inpatient group) or returned to the hospital (for the outpatient group). Severity status was identified based on the Indonesia COVID-19 guidelines. The laboratory data were categorized according to international standards, and data were analyzed using univariate analyzes followed by multivariate logistic regression. A total of 303 inpatient and 114 outpatient children were included in the analysis. Out of the total inpatient cases, 11 patients died with 3.6 mortality rate. Our final multivariate indicated that the presence of shortness of breath (SOB), anemia, and abnormal C-reactive protein (CRP) levels were significantly associated with the severity or the presence of emergency signs, while the presence of SOB and comorbidities were significantly associated with mortality in inpatient children with COVID-19. The presence of fever, cough, SOB, muscle ache and diarrhea were the reasons why the children were returned to the hospital from self-isolation at home among outpatient COVID-19 cases; however, the cough was the only significant factor in the final multivariate mode. This study highlights important determinants of COVID-19 severity and mortality in children, which should be considered during clinical decision-making in low-resource settings of healthcare centers in Indonesia.
Ubiquitous-cloud-inspired deterministic and stochastic service provider models with mixed-integer-programming Sumarlin, Sumarlin; Zarlis, Muhammad; Suherman, Suherman; Efendi, Syahril
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1304-1311

Abstract

The ubiquitous computing system is a paradigm shift from personal computing to physical integration. This study focuses on the deterministic and stochastic service provider model to provide sub-services to computing nodes to minimize rejection values. This deterministic service provider model aims to reduce the cost of sending data from one place to another by considering the processing capacity at each node and the demand for each sub-service. At the same time, stochastic service provider aims to optimize service provision in a stochastic environment where parameters such as demand and capacity may change randomly. The novelties of this research are the deterministic and stochastic service provider models and algorithms with mixed integer programming (MIP). The test results show that the solution found meets all the constraints and the smallest objective function value. Stochastic modeling minimizes denial of service problems during wireless sensor network (WSN) distribution. The model resented is the ability of wireless sensors to establish connections between distributed computing nodes. Stochastic modeling minimizes denial of service problems during WSN distribution.
Smart agriculture model in detecting oil palm plantation diseases using a convolution neural network Gunawan, Gunawan; Zarlis, Muhammad; Sihombing, Poltak; Sutarman, Sutarman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3164-3171

Abstract

Planning models for sustainable crop care in the context of smart agriculture are complex issues as they involve many factors such as productivity, quality, growth sustainability, workforce use, and information technology use. In this study, we will create an optimized model using a convolution neural network (CNN) that can classify and monitor plant diseases. Part of the plant care system is to be aware of plant diseases and to be able to deal with them immediately. This study aims to acquire a new smart farming model for integrated crop care. The results of this research are findings in the form of a CNN model for classifying plant diseases detected from the leaves of the plants studied in oil palm. Testing using Google Colab obtains 100% accuracy and 99% accuracy using a teachable machine. The contributions of this paper create a new model in the field of informatics, especially in the field of intelligent agriculture based on information technology.
A novel approach to optimizing customer profiles in relation to business metrics Elveny, Marischa; Nasution, Mahyuddin K. M.; Zarlis, Muhammad; Efendi, Syahril; Syah, Rahmad B. Y.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp440-450

Abstract

Business is very closely related to customers. Each user owns the data, and the data is used to identify cross-selling opportunities for each customer. For example, the type of product or service purchased, the frequency of purchases, geographic location, and so on. By doing so, you can gain the ability to manage and analyze customer data, allowing you to create new opportunities in industries that were previously difficult to enter. The purpose of optimizing user profiles is to determine minimum or maximum business value and improve efficiency by determining user needs. In this study, multivariate adaptive regression spline (MARS) is a statistical model used to explain the relationship between the response variable and the predictor variable. Robust is used to find variable relationships to make predictions. To improve classification performance, the model is validated using a confusion matrix. The results show an accuracy value of 84.5%, with better time management (period management) reflected in the number of hours spent by merchants as well as discounts during that time period, which has a significant impact on any business. In addition, the distance between customers and merchants is also important, as customers prefer merchants who are closer to them to save time and transportation costs.
PARAMETER ASOSIASI UNTUK MENENTUKAN KORELASI JURUSAN DAN INDEKS PRESTASI KUMULATIF Buaton, Relita; Jollyta, Deny; Mawengkang, Herman; Zarlis, Muhammad; Effendi, Syahril
Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (892.061 KB) | DOI: 10.33480/pilar.v15i1.285

Abstract

One of the problems in higher education is the mistake of prospective students in majors selection. This is caused by not paying attention to the suitability of the major in the original school with the chosen major in higher education so that it impacts not only non optimal processing and learning outcomes, such as the low GPA, but also on social life, such as increasing unemployment. The selection of the right major is very important and to help prospective students in choosing it requires an online system that can be accessed by everyone and select original school majors to see conformity with majors in higher education. This system uses association rules and parameters of support and confidence in data mining. The purpose of this research is to determine the correlation between majors in the original school, majors in higher education and the achievement of the GPA through the use of support and confidence parameters that process the knowledge base in the form of an alumni database on the online system created. Training or testing was conducted on 10,254 data in the database and produced new information and knowledge that between the majors of the original school, the choice of majors in higher education and GPA had a strong correlation with the value of confidence reaching 100%.
Evaluasi Sistem Oracle Fusion Human Capital Management Pada Proses Performance Appraisals di PT. XYZ Nurwita, Siti Rakhmawati; Zarlis, Muhammad
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.7871

Abstract

The implementation of the Enterprise Resource Planning (ERP) system in the human resources scope at PT. XYZ, one of the credit companies in Jakarta, plays a role in managing the performance appraisal process. Oracle Fusion Human Capital Management system with the Talent Management module were used to support the integrated performance appraisal process. In its implementation, there were issues regarding the system's function not aligning with the expected business processes within the system. This study aims to analyze the issues and evaluate the Oracle Fusion HCM system at PT. XYZ, with the goal of providing appropriate recommendations to PT. XYZ to enhance the utilization of the Oracle Fusion HCM system in managing the current performance appraisal process. The research methods used requirement gathering, literature study, and Fit/Gap Analysis (FGA). The research results provided an evaluation and recommendation for the system function misalignment issues faced by PT. XYZ, with 70% of the requirements are Fit and 30% are Partial Fit. The recommended solution is to customize the system's functions to align with company requirements, with a degree of fit of Partial Fit.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT PENGGUNAAN PRODUK ARTIFICIAL INTELLIGENCE PADA VOICE ASSISTANT Tobing, Ricardo Joynest; Zarlis, Muhammad
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 11 No 3 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i3.9185

Abstract

Artificial intelligence technology is currently developing rapidly. This is caused by the need for users to innovate to help users in their daily activities, which of course is supported by the current internet network which is starting to be very adequate in various regions. The research currently being conducted aims to analyze and understand how the user experience is in using artificial intelligence technology, especially in voice assistant products, namely smarthome or smartspeaker. The data collection method used is through a questionnaire. Questionnaires will be distributed to 500 respondents in the DKI Jakarta area, who use voice assistants on smartphones, smarthomes or smartspeakers. The data that has been obtained is then processed using SmartPLS to analyze the validity and hypotheses that have been determined. The research model used is the UTAUT (Unified Theory of Acceptance and Use of Technology) model. The variables in this study are performance expectancy, effort expectancy, social influence, facilitating conditions, towards behavioral intention to use behavior in using voice assistants on smartphones, smarthomes or smartspeakers. This variable will measure the level of user product usage and what are the issues for users when using voice assistant products on smartphones, smarthomes or smartspeakers. The results of the data collected and analyzed in this study will show how acceptance from users of voice assistant products can help user activities, as well as what factors are obstacles for users in using voice assistants.
Pengembangan Sistem Informasi Manajemen untuk Mutu Manufaktur Kosmetik Bersertifikasi Andrian, Kevin; Zarlis, Muhammad
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v5i1.263

Abstract

Pertumbuhan pasar industri kosmetik yang terus meningkat secara konsisten mendorong produsen untuk terus meningkatkan kualitas sistem usahanya sesuai standar GMP (Good Manufacturing Practice) untuk menghasilkan lebih banyak produk. Inticosmetic, produsen kosmetik (sejak 1979), telah merancang dan menggunakan QMS (sistem manajemen mutu) yang disebut INCOL (Inticosmetic Online Leverage) sejak 2018. Bahkan setelah bertahun-tahun digunakan, perangkat lunak tersebut menyebabkan kegagalan produksi karena ketidakcocokan bahan baku. stok data. Pencatatan manual masih digunakan oleh beberapa manajemen. Penelitian ini dilakukan untuk meningkatkan kualitas sistem informasi dengan menggunakan pendekatan ISO/IEC 25010 Quality in Use Framework. Wawancara dengan manajemen puncak, pengujian kotak hitam, dan survei karyawan perusahaan dilakukan untuk mengumpulkan data. Temuannya adalah bahwa manajemen puncak terlalu membatasi dan mengontrol proses bisnis, yang mengakibatkan lambatnya proses bisnis, pemulihan kesalahan input, dan tumpukan tindakan yang tertunda. Desain input otomatis menggunakan IoT perlu dikembangkan untuk mendelegasikan lebih banyak pekerjaan dari manajemen tingkat bawah ke operator. Perangkat lunak perlu menyesuaikan kembali skema validasi halaman untuk keefektifan karena jumlah data yang berkembang pesat. Dokumen output software telah memenuhi aspek GMP namun masih membutuhkan pengembangan manajemen aset.
Co-Authors , Rahmad Sembiring Achmad Noerkhaerin Putra Adisasmito, Wiku Bakti Bawono Ady Putra, Wahyu Aidil Halim Lubis AIRLANGGA, EKA Aminuyati Andrian, Kevin Ayodhia P. Pasaribu, Ayodhia P. Benfano Soewito Buaton, Relita Budhiarti Nababan, Erna Bugis M. Lubis, Bugis M. Christefa, Dea Cut Ita Erliana Dahlan Abdullah Defi Irwansyah Deny Jollyta Dewi, Rafiqa Efendi, Syahril Efendi, Syahril Eka Irawan Elviwani, Elviwani Erlina Erlina Erma Julita, Erma Erna Budhiarti Nababan Erna Budiarti Ghazali, Alfin Ginting, Emnita Boru Gunawan Gunawan Hadistio, Ryan Rinaldi Haq, Fesa Asy Syifa Nurul Harahap, Eka Purnama Hartama, Dedy Hasibuan, Nisma Novita Herman Mawengkang Hidayati, Indri Husna, Lina Naelal Indra Gunawan Lewis, Andreas Liana Liana Lidya Rosnita Mahyuddin K. M Nasution Malisie, Ririe F. Marischa Elveny, Marischa Marpaung, Tulus Joseph Herianto Mesran, Mesran Miralda, Viya Mohammad Andri Budiman Muhammad Reza Aulia Muliati, Vika Febri Nasution, Zulaini Masruro Novi Dian Nathasia Nurhayati Siregar, Nurhayati Nurwita, Siti Rakhmawati Ovirianti, Nurul Huda Pasaribu, Roni Fredy Halomoan Poltak Sihombing Prayoga, Nanda Dimas Pulungan, Annisa Fadhillah purba, lia cintia Purba, Roimal Hafizi Purnomo Sidi Priambodo Rahmad, Sofyan Rahman Aulia, Rahman Rahman, Abdu Riansyah, Muhammad Romanus Damanik Saib Suwilo Saifullah Saifullah Santoso, Ahmad Imam Sawaluddin Sembiring, Rahmat Widia Siregar, Jelita Siti Sarah Harahap Sri Melvani Hardi Suherman, Suherman Sukiman, T. Sukma Achriadi Sumarno . Sutarman Sutarman Suyanto Suyanto Syah, Rahmad B. Y. Syahputra, Muhammad Romi Syahril Effendi Syauqi, Muhammad Irfan Tanjung, Yulia Windi Tobing, Ricardo Joynest Tulus Tulus Tulus Ucuk Darusalam Wahyuni, Arlinda S. Wardhani, Widiastuti Kusumo Zakaria Zakaria Zakarias Situmorang Zulham Zulkarnain Lubis