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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING The IJICS (International Journal of Informatics and Computer Science) Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Ihsan: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences International Journal Of Science, Technology & Management (IJSTM) Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Prosiding Snastikom sudo Jurnal Teknik Informatika Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Blend Sains Jurnal Teknik Wahana Jurnal Pengabdian kepada Masyarakat International Journal of Economic, Technology and Social Sciences (Injects) Jurnal Pengabdian Barelang Hanif Journal of Information Systems
Al Khowarizmi
Universitas Muhammadiyah Sumatera Utara
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation Other

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MODEL CLASSIFICATION OF NOMINAL VALUE AND THE ORIGINAL OF IDR MONEY BY APPLYING EVOLUTIONARY NEURAL NETWORK Al-Khowarizmi, Al-Khowarizmi
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.274 KB) | DOI: 10.31289/jite.v3i2.3284

Abstract

Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better
THE E-BUSINESS COMMUNITY MODEL IS USED TO IMPROVE COMMUNICATION BETWEEN BUSINESSES BY UTILIZING UNION PRINCIPLES Fauzi, Fauzi; Al-Khowarizmi, Al-Khowarizmi; Muhathir, Muhathir
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (362.166 KB) | DOI: 10.31289/jite.v3i2.3260

Abstract

Business is an interpersonal and organizational activity that involves the process of selling, purchasing both goods and services with the aim of making a profit. But to get a large profit, it takes many partners who have a high desire to move forward. Information technology provides services for business people so that media information is available as a sign of obstacles. In addition it is necessary to do modeling where the process of communication between businesses running on information technology has a different profit from the business being run. Thus the union has the principle of kinship and has the principle of profitability divided by the amount of contribution given so that the creation of a model in electronic business (e-business) in the hope of having a family principle that is able to provide special profits for businesses other than the profits that run on certain businesses.
Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices Albara; Al-Khowarizmi; Riyan Pradesyah
International Journal of Science, Technology & Management Vol. 2 No. 6 (2021): November 2021
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v2i6.403

Abstract

Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.
Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor Al Khowarizmi; Rahmad Syah; Mahyuddin K. M. Nasution; Marischa Elveny
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2696-2703

Abstract

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.
Classification of skin cancer images by applying simple evolving connectionist system Al-Khowarizmi Al-Khowarizmi; Suherman Suherman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp421-429

Abstract

Simple evolving connectionist system (SECoS) is one of data mining classification techniques that recognizing data based on the tested and the training data binding. Data recognition is achieved by aligning testing data to trained data pattern. SECoS uses a feedforward neural network but its hidden layer evolves so that each input layer does not perform epoch. SECoS distance has been modified with the normalized Euclidean distance formula to reduce error in training. This paper recognizes skin cancer by classifying benign malignant skin moles images using SECoS based on parameter combinations. The skin cancer classification has learning rate 1 of 0.3, learning rate 2 of 0.3, sensitivity threshold of 0.5, error threshold of 0.1 and MAPE is 0.5184845 with developing hidden node of 23. Skin cancer recognition by applying modified SECoS algorithm is proven more acceptable. Compared to other methods, SECoS is more robust to error variations.
Automatic face recording system based on quick response code using multicam Julham Julham; Muharman Lubis; Arif Ridho Lubis; Al-Khowarizmi Al-Khowarizmi; Idham Kamil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp327-335

Abstract

This research mainly talks about the use of quick response (QR) code reader in automating of recording the users' face. The applied QR code reader system is a dynamic type, which can be modified as required, such as adding a database, functioning to store or retrieve information in the QR code image. Since the QR code image is randomly based on its information, a QR code generator is required to display the image and store the information. While the face recorder uses a dataset available in the OpenCV library. Thus, only the registered QR code image can be used to record the user's face. To be able to work, the QR code reader should be 10 to 55 cm from the QR code image.
Information technology based smart farming model development in agriculture land Al-Khowarizmi Al-Khowarizmi; Arif Ridho Lubis; Muharman Lubis; Romi Fadillah Rahmat
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp564-571

Abstract

Smart farming in various worlds is not just about applying technology in terms of storing data on agricultural land. However, having a concept of measurable data based on available computational techniques trained and then generating knowledge. As an application, the agri drone sprayer can be used for the process of applying pesticides and liquid fertilizers on each side. In addition, drone surveillance is also useful in implementing smart farming such as mapping land so that farmers will know the condition of their agricultural land. However, the soil and weather sensor will also help the farmers to monitor the farmland as well. Devices with sensors can only obtain data in the form of air and soil humidity, temperature, soil pH, water content and forecasting the harvest period. So that the smart farming model can help farmers to get recommendations, in preventing the predicted damage to their land and crops. However, according to its geographical location, the application of smart farming can be a smart solution to agricultural problems in Indonesia and make the future of Indonesian Agriculture a technology-based smart agriculture.
Optimization of distance formula in K-Nearest Neighbor method Arif Ridho Lubis; Muharman Lubis; Al- Khowarizmi
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.331 KB) | DOI: 10.11591/eei.v9i1.1464

Abstract

K-Nearest Neighbor (KNN) is a method applied in classifying objects based on learning data that is closest to the object based on comparison between previous and current data. In the learning process, KNN calculates the distance of the nearest neighbor by applying the euclidean distance formula, while in other methods, optimization has been done on the distance formula by comparing it with the other similar in order to get optimal results. This study will discuss the calculation of the euclidean distance formula in KNN compared with the normalized euclidean distance, manhattan and normalized manhattan to achieve optimization results or optimal value in finding the distance of the nearest neighbor.
Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network Al-Khowarizmi Al-Khowarizmi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i2.3284

Abstract

Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better
Classification of the IDR-USD Exchange Rate with Multilayer Perceptron Based on Detection Rate Al-Khowarizmi Al-Khowarizmi; Fanny Ramadhani; Fatma Sari Hutagalung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5252

Abstract

Artificial neural network (ANN) is a set of units in processing a model based on the habits of human neural networks. ANN has one of its duties, namely classification with the concept of supervised learning. ANN also has various methods in performing its duties such as Multilayer perceptron (MLP). Where MLP is one of the ANN methods that can classify based on data as conceptualized in data mining. Very useful classifications and trends in the field of research because of the review of data that will generate knowledge. Nominal Exchange Rate is one of the datasets tested in this study. The exchange rate of the Indonesian Rupiah (IDR) against the United States Dollar (USD) is very necessary both in terms of stock movements and other businesses. So that, it is necessary to use classification to predict future exchange rates. In this study, the MLP method was carried out by obtaining a validation test using MAPE based on the detection rate of sebesar 0.500079879%.
Co-Authors Adidtya Perdana Adila Mawaddah Meuraxa Ajulio Padly Sembiring Al Hamidy Albara Amrullah Asrar Aspia Manurung Aulia Jannah Budi Kurniawan Hutasuhut Dicky Apdilah Edy Rahman Syahputra Fanny Ramadhani Fatma Sari Hutagalung Fauzan Nur Ahmadi FAUZI . Fauzi Fauzi Firahmi Rizky Gabriel Ardi Hutagalung Habibi Ramdani Safitri Halim Maulana Has Diana Hasanuddin Hasanuddin Hasdiana Heru Mardiansyah Idham Kamil Ilham Ramadhan Nasution Indah Purnama Sari Indah Purnama Sari Irvan, Irvan Ismail Hanif Batubara Julham Julham Lubis, Arif Ridho M. Iqbal Tanjung Mahyuddin K. M. Nasution, Mahyuddin K. M. Mandra Saragih Marah Doly Nasution Marischa Elveny Mhd Faris Pratama Mhd. Basri Mhd. Basri Muhammad Luthfi Hamzah Muharman Lubis Muhathir Muhathir Muhathir, Muhathir Mulkan Azhari Mulkan Azhari Naibaho, Frainskoy Rio Niken Aprilina Nurhalimah Nadeak Oris Krianto Sulaiman Pipit Putri Hariani Pipit Putri Hariani MD Pradesyah, Riyan Prayudani, Santi Rahmad Syah Rahmat Mushlihuddin Romi Fadillah Rahmat Sarah Purnamawati Sharfina Faza Suherman Suherman Syah, Rahmad Syahril Efendi Vicky Rolanda Yuyun Yusnida Lase