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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 Computer Science and Information Technologies 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 International Journal of Economic, Technology and Social Sciences (Injects) Jurnal Pengabdian Barelang Hanif Journal of Information Systems
Al Khowarizmi
Universitas Muhammadiyah Sumatera Utara
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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
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.
Astrocytoma, ependymoma, and oligodendroglioma classification with deep convolutional neural network Romi Fadillah Rahmat; Mhd Faris Pratama; Sarah Purnamawati; Sharfina Faza; Arif Ridho Lubis; Al-Khowarizmi Al-Khowarizmi; Muharman Lubis
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Glioma as one of the most common types of brain tumor in the world has three different classes based on its cell types. They are astrocytoma, ependymoma, oligodendroglioma, each has different characteristics depending on the location and malignance level. Radiological examination by medical personnel is still carried out manually using magnetic resonance imaging (MRI) medical imaging. Brain structure, size, and various forms of tumors increase the level of difficulty in classifying gliomas. It is advisable to apply a method that can conduct gliomas classification through medical images. The proposed methods were proposed for this study using deep convolutional neural network (DCNN) for classification with k-means segmentation and contrast enhancement. The results show the effectiveness of the proposed methods with an accuracy of 95.5%.
Co-Authors Adidtya Perdana Adila Mawaddah Meuraxa Aisar Novita Ajulio Padly Sembiring Al Hamidy Albara Amrullah Andy Satria Asrar Aspia Manurung Aulia Jannah Bela Bela 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 Yoshida Sary Yuyun Yusnida Lase