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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) JSM (Jurnal SIFO Mikroskil) Bulletin of Electrical Engineering and Informatics CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Juripol (Jurnal Institusi Politeknik Ganesha Medan) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Jurnal Mantik Penusa Jurnal Inotera METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Abdi Ilmu Jurnal Ilmu Teknik dan Komputer Computer Science and Information Technologies Prosiding SeNTIK STI&K
Muhammad Zarlis
Universitas Sumatera Utara
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Vehicle detection system based on shape, color, and time-motion Afritha Amelia; Muhammad Zarlis; Suherman Suherman; Syahril Efendi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1070-1082

Abstract

Vehicle detection application can assist in-vehicle surveillance functions and have implications for various fields. A vehicle can be identified through the license number attached to its license plate, the color and its shape. Vehicle detection can make use of multimedia sensors so that the design and detection performances can be optimal. Sensor performances are influenced by factors such as the number of multimedia sensors, sensor placement, sensor positioning, and schemes in case of system failure. This study makes use of multimedia sensors with cameras equipped by a phase detection auto focus (PDAF) technology which is like a pair of eyes to see an object. This study analyses 134 vehicles with number detection and various colors to see the effect on the detection and recognition processes. The cars were passed through the camera 10 times at a speed of 10-15 km/hour with various camera distances and positions. Various values and depths of the images were generated. The farther the distance the higher the disparity values. For maximum distance of 50 m, disparity is 6.20×106 and image depth is 16.88×109. Vehicle color influences detection with orange has the best accuracy, but the gray has the largest path error value.
Investigating the impact of data scaling on the k-nearest neighbor algorithm Muasir Pagan; Muhammad Zarlis; Ade Candra
Computer Science and Information Technologies Vol 4, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i2.p135-142

Abstract

This study investigates the impact of data scaling techniques on the performance of the k-nearest neighbor (KNN) algorithm using ten different datasets from various domains. Three commonly used data scaling techniques, min-max normalization, Z-score, and decimal scaling, are evaluated based on the KNN algorithm's performance in terms of accuracy, precision, recall, F1-score, runtime, and memory usage. The study aims to provide insights into the applicability and effectiveness of different scaling techniques in different contexts, aid in the design and implementation of machine learning systems, and help identify the strengths and weaknesses of each technique and their suitability for specific types of data. The results show that data scaling significantly affects the performance of the KNN algorithm, and the choice of scaling method can have significant implications for practical applications. Moreover, the performance of the three scaling techniques varies across different datasets, suggesting that the choice of scaling technique should be made based on the specific characteristics of the data. Overall, this study provides a comprehensive analysis of the impact of data scaling techniques on the KNN algorithm's performance and can help practitioners and researchers in the machine learning community make informed decisions when designing and implementing machine learning systems.
A stochastic approach for evaluating production planning efficiency under uncertainty Mochamad Wahyudi; Hengki Tamando Sihotang; Syahril Efendi; Muhammad Zarlis; Herman Mawengkang; Desi Vinsensia
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5542-5549

Abstract

Planning production is an essential component of the decision-making process, which has a direct bearing on the effectiveness of production systems. This study’s objective is to investigate the efficiency performance of decision-making units (DMU) in relation to production planning issues. However, the production system in a manufacturing environment is frequently subject to uncertain situations, such as demand and labor, and this can have an effect not only on production but also on profit. The robust stochastic data envelopment analysis model was proposed in this study with maximizing the number of outputs as the objective function thus means of handling uncertainty in input and output in production planning problems. This model, which is based on stochastic data envelopment analysis and a method of robust optimization, was proposed with the intention of providing an efficient plan of production for each DMU of stage production. The model is applied to small and medium-sized businesses (SMEs), with inputs consisting of the cost of labor, the number of customers, and the quantity of raw materials, and the output consisting of profit and revenue. It has been demonstrated through implementation that the proposed model is both efficient and effective.
Implementasi Algoritma Backtracking Pada Knight’s Tourproblem Akbar Serdano; Muhammad Zarlis; Dedy Hartama
Prosiding Seminar SeNTIK Vol. 3 No. 1 (2019): Prosiding SeNTIK 2019
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Implementasi Algoritma Backtracking Pada Knight’s Tourproblem
Co-Authors Ade Candra, Ade Anindra Ramdhan Nugraha Dadang Priyanto Dedy Hartama Devanta Abraham Tarigan Devi Maiya Sari Nasution Efendi, Syahril Efori Buulolo Erna Budhiarti Nababan Ghazali, Alfin Hardisal, Hardisal Hariyati Lubis, Hariyati Hengki Tamado Sihotang Hengki Tamando Sihotang Hengki Tamando Sihotang Herdianto Herdianto Herimanto Herimanto Herman Mawengkang Husain Husain Husain Husain Julpan Julpan Junaedi, Ifan M Anggi Rivai Nst M Safii Mochamad Wahyudi Muasir Pagan Muhammad Rusdi dan Afritha Amelia - Muhammad Safii Nugroho Syahputra Pahala Sirait Poltak Sihombing Rahmat Widia Sembiring - Rika Permata Sari Siregar Serdano, Akbar Sinuraya, Junus Sri Wahyuni Suci Ramadani Sudarto Suherman Suherman Sulindawaty, Sulindawaty Verdi Yasin Vinsensia, Desi Zakarias Situmorang Zulkifli Nasution