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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Bulletin of Electrical Engineering and Informatics Jurnal Infinity Journal of Telematics and Informatics SAMUDERA Scientific Journal of Informatics CESS (Journal of Computer Engineering, System and Science) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi Informasi dan Komunikasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Jurnal Inotera MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISTech (Journal of Islamic Science and Technology) Building of Informatics, Technology and Science Jurnal Mantik MES: Journal of Mathematics Education and Science Jurnal Varian International Journal of Advances in Data and Information Systems Computer Science and Information Technologies Randwick International of Social Science Journal Journal of Research in Mathematics Trends and Technology Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Journal for Lesson and Learning Studies International Journal of Humanities Education and Social Sciences Jurnal MathEducation Nusantara International Journal of Community Service Implementation Jurnal Infinity
Herman Mawengkang
Faculty Of Matthematics And Natural Science Universitas Sumatera Utara, Medan, 20155, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa Oktaviana Bangun; Herman Mawengkang; Syahril Efendi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4572

Abstract

The accumulation of student databases can occur if students are unable to complete their studies, namely graduating at a predetermined time. Data mining techniques are often used to process student data so that they can produce predictions of student graduation in order to graduate at a predetermined time. One of the data mining techniques that is often used is the Support Vector Machine (SVM) algorithm. This study aims to analyze the performance of the SVM algorithm to produce a predictive model of student graduation in order to graduate at a predetermined time in the Public Health Study Program, Faculty of Public Health, Deli Husada Health Institute. The method used in this study is a linear SVM algorithm starting from data retrieval by selecting the attributes that will be used for the next stage, data processing consists of cleaning data whose contents do not exist and data transformation which is the determination of the category of each data, modeling is done with the SVM algorithm. from training data and testing and evaluation data to validate and measure the accuracy of the model. The test results with the amount of training data as much as 70% and testing data as much as 30% shows that the linear SVM algorithm provides an accuracy value of 90%
Improved Benders decomposition approach to complete robust optimization in box-interval Hendra Cipta; Saib Suwilo; Sutarman Sutarman; Herman Mawengkang
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4394

Abstract

Robust optimization is based on the assumption that uncertain data has a convex set as well as a finite set termed uncertainty. The discussion starts with determining the robust counterpart, which is accomplished by assuming the indeterminate data set is in the form of boxes, intervals, box-intervals, ellipses, or polyhedra. In this study, the robust counterpart is characterized by a box-interval uncertainty set. Robust counterpart formulation is also associated with master and subproblems. Robust Benders decomposition is applied to address problems with convex goals and quasiconvex constraints in robust optimization. For all data parameters, this method is used to determine the best resilient solution in the feasible region. A manual example of this problem's calculation is provided, and the process is continued using production and operations management–quantitative methods (POM-QM) software.
Model Dynamic Facility Location in Post-Disaster Areas in Uncertainty lili Tanti; Syahril Efendi; Maya Silvi Lydia; Herman Mawengkang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2095

Abstract

Indonesia has many disaster-prone areas, natural disasters that occur in Indonesia in 2021 are 5,402 disasters. For disaster management in post-disaster areas, logistical planning is needed in the distribution of logistical assistance, it is estimated that the logistics costs of disaster assistance reach approximately 80% of the total costs in disaster management so that logistical assistance is an expensive activity of disaster relief. However, so far the process of distributing logistical assistance to disaster posts has not been evenly distributed. One of the causes of the unequal distribution is the inappropriate selection of distribution post locations. The facility location model is dynamic and has the objective function of minimizing the distance between emergency posts and refugee posts in terms of distribution of disaster relief goods in one cluster group. For grouping unsupervised learning data using a machine learning clustering algorithm, k-means. Model validation has been carried out using max run and max optimization 1000 times with results reaching 90%. This proves that the emergency facility location model can be used to determine the location of the emergency center, where the determination of the location of the emergency center has the closest distance to the request point/post shelter for disaster victims
Predictive Analysis of Spatial Data and Time Series to Predict Earthquake Magnitudes by Using Data Mining Approach Ignazio Ahmad Pasadana; Herman Mawengkang; Syahril Efendi
Randwick International of Social Science Journal Vol. 4 No. 1 (2023): RISS Journal, January
Publisher : RIRAI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47175/rissj.v4i1.607

Abstract

The suggested methodology presented in this work uses data mining to identify seismic zones and time series to forecast earthquake magnitudes. Utilize historical earthquake data gathered from the United States Geological Survey (USGS) and obtained by utilizing hierarchical and fartherst first clustering to predict seismic activity. Latitude and longitude cluster data were used to create a prediction model to forecast the size of upcoming earthquakes in the Nanggroe Aceh Darussalam region and its nearby areas.
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.
Analisis Kesalahan Siswa Dalam Menyelesaikan Soal Matematika Pada Materi Trigonometri Berdasarkan Analisis Newman Dedi Siswo; Firmansyah Firmansyah; Herman Mawengkang
Jurnal MathEducation Nusantara Vol 6, No 2 (2023): July 2023
Publisher : Universitas Muslim Nusantara Al Washliyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32696/jmn.v6i2.308

Abstract

Penelitian ini bertujuan untuk mengetahui persentase kesalahan dalam penyelesaian matematika soal-soal trigonometri berdasarkan analisis Newman, (1) soal kesalahan pembacaan, (2) kesalahan pemahaman masalah, (3) kesalahan transformasi, (4) kesalahan keterampilan proses, (5) kesalahan penulisan jawaban akhir dan untuk mengetahui kesalahan apa saja yang dilakukan sebagian besar siswa. Jenis penelitian ini adalah penelitian deskriptif. Teknik pengumpulan data menggunakan tes. Instrumen penelitian ini adalah tes. Teknik analisis data menggunakan Miles dan Huberman. Hasil penelitian menunjukkan bahwa analisis kesalahan penyelesaian masalah matematika trigonometri berdasarkan Newman Analisis pada siswa kelas X MIA SMA Al'Mashum Kisaran Barat adalah error soal membaca sebesar 4%, error soal pemahaman sebesar 54,4%, error transformasi sebesar 63,6%, error keterampilan proses sebesar 64,%, error dalam menulis. jawaban akhir sebesar 80,9% dan kesalahan yang paling banyak dilakukan siswa pada kesalahan penulisan jawaban akhir sebesar 80,9% 
Community Empowerment Training Through Local Potential Sugiyarmasto Sugiyarmasto; Herman Mawengkang
International Journal of Community Service Implementation Vol. 1 No. 1 (2023): IJCSI JUNE 2023
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijcsi.v1i1.142

Abstract

University as an academic institution has a massive potential to built-in entrepreneurs with extensive concept. This have been formed by Setia Budi University with funding the Community Service Programmed which aimed to increase the quality of community in economic, academic and prosperity. Based on that program, the community serviced program about training of making Souvenir will be held in Tambora Tengah , Kelurahan Mojosongo, KecamatanJebres where placed in Setia Budi University area. This district has 21;RW, and 04 RT which has about 30 families each.According to that situation, come up the idea to help each family to be more productive and earning trough training. This training of making souvenir amimed to increase the productivity and passive income to develop and self-supporting the family prosperity.This entrepreneurship in developing souvenir business identic as woman business activity which has time and energy effective and not disturbing their activity and responsibility as a housewife.
Analysis Of The Role Of Blockchain Technology In Recording Motor Vehicle Ownership Data Santoso, Ahmad Imam; Zarlis, Muhammad; Mawengkang, Herman
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10938

Abstract

The importance of the role of motor vehicles in human life is revealed through its transformation from a tertiary need to a secondary one. In Indonesia, the number of motor vehicles reached 136.191 million units in 2020, prompting the government to regulate registration and identification through Presidential Regulation No. 5 of 2015. This research addresses issues related to motor vehicle administration in Indonesia, focusing on transparency, data integrity, and information availability. Inspired by the rapid growth in the number of motor vehicles and the increasing cases of theft, this research aims to design and implement an innovative motor vehicle ownership recording system using blockchain technology. This approach aims to enhance the accessibility of information on the origin and ownership of vehicles or Vehicle Registration Numbers (VRN), which is currently limited in both manual and online forms. The research methodology involves the design and implementation of blockchain technology, tested to verify its effectiveness in achieving transparency and data security goals. The research results indicate that this system can provide more comprehensive and distributed information to the general public, overcoming the limitations of centralized systems. The conclusion of this research affirms that the application of blockchain technology can be an innovative and effective solution to improve motor vehicle administration. The implications of this research include increased efficiency, security, and availability of information in handling vehicle administration, potentially having a positive impact on the general public and related industries
Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index Zarkasyi, Muhammad Imam; Mawengkang, Herman; Sitompul, Opim Salim
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2458

Abstract

In K-Means Clustering, the number of attributes of a data can affect the number of iterations generated in the data grouping process. One of the solutions to overcome these problems is by using a reduction technique on the dimensions of the dataset. In this study, the authors apply the Gini Index to perform attribute reduction on the data set to reduce attributes that have no effect on the dataset before clustering with K-Means Clustering. The dataset used to be tested as a testing instrument in this research is Absenteeism at work obtained from the UCI Machine Learning Repository, with 20 attributes, 740 data records and 4 attribute classes. The results of the tests in this research indicate that the number of iterations obtained from the comparison of tests using the K-Means in a Conversional (Without Attribute Reduction) is obtained by the number of 9 iterations, while the K-Means with attribute reduction with the Gini Index obtains the number of iterations totaling 6 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering in a Conversional (Without Attribute Reduction) is 1391.613, while in K-Means Clustering with attribute reduction with a Gini Index, it is 440.912. From the results of the proposed method, it is able to reduce the percentage of errors and minimize the number of iterations in K-Means Clustering by reducing the dimensions of the dataset using the Gini Index
Perfomance analysis of Naive Bayes method with data weighting Afdhaluzzikri, Afdhaluzzikri; Mawengkang, Herman; Sitompul, Opim Salim
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11516

Abstract

Classification using naive bayes algorithm for air quality dataset has an accuracy rate of 39.97%. This result is considered not good and by using all existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 61.76%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using naive bayes algorithm for air quality dataset. While the Water Quality dataset has an accuracy rate of 93.18%. These results are considered good and by using all the existing data attributes. By doing pre-processing, namely feature selection using the gain ratio algorithm, the accuracy of the Naive Bayes algorithm increases to 95.73%. This proves that the gain ratio algorithm can improve the performance of the naive bayes algorithm for air quality dataset classification. Classification using Naive Bayes algorithm for Water Quality dataset. Based on the tests that have been carried out on all data, it can be seen that the Weight nave Bayes classification model can provide better accuracy values ​​because there is a change in the weighting of the attribute values ​​in the dataset used. The value of the weighted Gain ratio is used to calculate the probability in Nave Bayes, which is a parameter to see the relationship between each attribute in the data, and is used as the basis for the weighting of each attribute of the dataset. The higher the Gain ratio of an attribute, the greater the relationship to the data class. So that the accuracy value increases than the accuracy value generated by the Naïve Bayes classification model. The increase in accuracy in the Naïve Bayes classification model is due to the number of weights from the attribute selection in the Gain ratio.
Co-Authors , Rahmad Sembiring Abi Rafdi Afdhaluzzikri, Afdhaluzzikri Afnaria, Afnaria Ahmad Zaki Mubarak, Ahmad Zaki Al Khowarizmi Anggi Anatasia Kinanti Anugreni, Fera Arjon Turnip Asrianda Asrianda Azmi, Zulfian - Badawi, Afif Buaton, Relita Budhiarti, Erna Christefa, Dea Christian Sinaga, Christian Dadang Priyanto Dedi Siswo Defri Muhammad Chan Deny Jollyta Efendi, Syahril Elly Rosmaini Ermawati Ermawati Erna B N Erna Budhiarti Nababan Fatma Sari Hutagalung Firmansyah Firmansyah Firmansyah Firmansyah Fitrie, Rosa Hadistio, Ryan Rinaldi Handayani, Sri Hartama, Dedy Hasugian , Paska Marto Hengki Tamando Sihotang Hengki Tamando Sihotang Heni Pujiastuti Herawati, Elvina Heri Gustami Husain Husain Husain Husain Ignazio Ahmad Pasadana Iin Parlina Indah Purnama Sari Juanda Hakim Lubis K. M. Nasution , Mahyuddin Lestari, Valencya lili Tanti Lismardiana Lismardiana Lusi Herlina Siagian M Safii M Zarlis Mahyuddin K. M Nasution Mardiningsih Mardiningsih, Mardiningsih Marpongahtun Marwan Ramli Maya Silvi Lydia Mochamad Wahyudi Muhammad Arif Satria Nasution Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis, Muhammad Muliawan Firdaus Napitupulu, Fajrul Malik Aminullah Nuraini Nuraini Oktaviana Bangun Opim Salim Sitompul Ovirianti, Nurul Huda Pasaribu, Suhendri Poltak Sihombing Prandana, Randy Pujiastuti, Lise Putri, Mimmy Sari Syah Rahman, Silvi Anggraini Resti, Lady Ichwana Roma Rezeki Ryan Rinaldi Hadistio Saib Suwilo Saib Suwilo Santoso, Ahmad Imam Sarif, Muhammad Irfan Sawaluddin Nasution Sawaluddin Sawaluddin, Sawaluddin Sihotang, Hengki Tamando Sugiyarmasto Sugiyarmasto Sutarman Sutarman Sutarman Sutarman Sutarman Syahmrani, Aghni Syahputra, Muhammad Romi Syahril Effendi Tanjung, Ilyas Tulus Tulus Tulus Tulus Vinsensia, Desi Weber, Gerhard Wilhelm Wiryanto Wiryanto Wisnu Irsandi Pratama Zakarias Situmorang Zarkasyi, Muhammad Imam Zarlis, M Zarlis, M Zoelkarnain Rinanda Tembusai Zulfian Azmi