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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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Journal : International Journal of Electrical and Computer Engineering

 1 
Analysis of earthquake hazards prediction with multivariate adaptive regression splines Dadang Priyanto; Muhammad Zarlis; Herman Mawengkang; Syahril Efendi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2885-2893

Abstract

Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu.
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.
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