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All Journal Riau Journal of Computer Science Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Computer Networks, Architecture and High Performance Computing Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Mardi Turnip, Mardi
Universitas Prima Indonesia
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media

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Journal : Journal of Computer Networks, Architecture and High Performance Computing

 1 
Implementation of Data Mining Using the K-Nearest Neighbor Method to Determine the feasibility of a lecturer's functional promotion Andreas Theo Pilus Alista Teles Siahaan; Mardi Turnip
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 1 (2022): Article Research Volume 4 Number 1, Januay 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i1.1242

Abstract

As we know now, every lecturer is obliged to determine the use of the momebase for managing the national lecturer identification number, after getting this, the lecturer concerned can already apply for an academic rank. The data that will be processed in this system is where every lecturer has legal rules to propose academic ranks. The data tested are expert assistant lecturers, lectors (L) lectors are divided into 2 lectors 200 (coordinators/III-C) and 300 lectors (administrators (TKT-1/III-D), head lectors are divided into 3 coaches/(IV-A ) kum 400, supervisor of TKT-1/(IVB) kum 550, main coach of junior/(IV-C) 700, professor of intermediary main coach/(IV-D), KEY Advisor/(IV-E). has shown results by displaying data from lecturers who are eligible to apply for a rank using the K-NN method.
Utilization of Artificial Intelligence in Predicting Crime Joan Stacia Carissa; Mardi Turnip
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3208

Abstract

The problem of crime in Indonesia is an urgent issue, with crime rates continuing to increase. High crime rates have serious impacts on societal security, social stability, and economic development. Amidst the complexity of types of crime, motives and methods of handling them, Artificial Intelligence (AI) and Machine Learning (ML) technology has emerged as a promising solution. Through analysis of a literature review with the keywords "AI and crime," this research aims to understand the differences between the use of AI in crime prediction and traditional methods. The literature review method will identify and analyze the latest knowledge regarding the use of AI technology in overcoming crime problems. The use of AI in analyzing crime data, identifying complex patterns, and providing accurate predictions will be emphasized. The research will also explain how AI is able to overcome problems that are difficult to solve with conventional methods. It is hoped that the results of this literature review will provide deeper insight into the potential of AI in reducing crime rates and c reating a safer environment for people in Indonesia.
Utilization of Artificial Intelligence in Predicting Crime Carissa, Joan Stacia; Turnip, Mardi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3208

Abstract

The problem of crime in Indonesia is an urgent issue, with crime rates continuing to increase. High crime rates have serious impacts on societal security, social stability, and economic development. Amidst the complexity of types of crime, motives and methods of handling them, Artificial Intelligence (AI) and Machine Learning (ML) technology has emerged as a promising solution. Through analysis of a literature review with the keywords "AI and crime," this research aims to understand the differences between the use of AI in crime prediction and traditional methods. The literature review method will identify and analyze the latest knowledge regarding the use of AI technology in overcoming crime problems. The use of AI in analyzing crime data, identifying complex patterns, and providing accurate predictions will be emphasized. The research will also explain how AI is able to overcome problems that are difficult to solve with conventional methods. It is hoped that the results of this literature review will provide deeper insight into the potential of AI in reducing crime rates and c reating a safer environment for people in Indonesia.
APPLICATION OF KNN METHOD FOR CLASSIFICATION OF ARRHYTHMIA TYPES BASED ON ECG DATA Manao, Sonatafati; Sitanggang, Delima; Sagala, Albert; Oktarino, Ade; Turnip, Mardi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6010

Abstract

World Health Organization (WHO) data from June 2024 shows that 31% of adults worldwide or 1.8 billion people do not do physical activity. With that, adults are at higher risk of developing cardiovascular disease and causing an economic and social burden on people with heart disease. K-Nearest Neighbor (KNN) is a machine learning method that can be used to classify or predict heart disease conditions. KNN works by finding the closest data point in the training dataset and then using the class labels of those neighbors to classify new data points. In the context of heart disease, this can be used to predict the likelihood of someone having heart disease. Recording the electrical activity of the heart using a 3-led ECG to determine heart health as well as being material for classification. Exploring the use in the diagnosis of heart disease by focusing on screening and classification of heart disease. By utilizing the KNN method, it has the potential to produce a model that can assist in clinical decision making. Improving the prevention of heart disease and accelerating diagnosis through more sophisticated and technology-based analysis of patient health data.
Co-Authors -, aditya perdana -, Evta Indra -, Ruben Abdi Dharma Ade Irma Suryani Aditya Perdana aditya perdana - ADVENT TORAS MARBUN Albert Sagala, Albert Amri , Ahmad Alfauzan Ananda, Debby Andreas Theo Pilus Alista Teles Siahaan Ardila, Niki Arjon Turnip Astri Milleniar Marbun Banjarnahor, Jepri Bolon, Debby Novriyanti Br Tp. Bunawolo, Methina Cahyadi, Andika Carissa, Joan Stacia Chandra, Angelia Ayu Cindy Cynthia Debby Novriyanti Br Tp.Bolon Dedy Ristanto Hulu Delima Sitanggang, Delima Denny Irvan Sinuhaji Ester Ayu S. Marpaung Evta Indra Felix Widarko Hulu, Dedy Ristanto Hulu, Yosefa Intan Susanti Simarmata Joan Stacia Carissa Johan Libby JOICE ANGELINA PURBA JURMIDA PULUNGAN Kelvin M. Arif Almahdi Manao, Sonatafati MARBUN, ADVENT TORAS Marlince N.K Nababan Nababan, Marlince N.K Ndruru, Jonathan Haris P. Oktarino, Ade Owen Owen Panjaitan, Haposan Daniel Patterson, Jennifer Perangin-angin, Despaleri Priambodo, Ganang Reza PULUNGAN, JURMIDA PURBA, JOICE ANGELINA Roshan, Rohit Salmiati Salsabillah Saragi, Yosua Morales Saut Parsaoran Tamba Sigalingging, Josepta Sihaloho, Theresia Delima Simbolon, Naftalia Sinuhaji, Denny Irvan Sitanggang, Wahyu Adventus Andreas Siti Aisyah Sitompul, Daniel Ryan Hamonangan Sitorus, Dedi Setiadi Situmorang, Andreas Situmorang, Fransido Solly Aryza Sonia Novel Lase Sukhbir Singh Sunnia, Cecilia Tarigan, Julio Putra Tarigan, Richard Fernando Timi Tampubolon Venta Br.Tarigan, Emma Wijaya, Benny Wijaya, Kenrick Alvaro William, David Winarti Pasaribu Wong, Yano Sabar M Yenny Yenny Yoga Tri Nugraha