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All Journal Jurnal Mahasiswa TEUB Sistem : Jurnal Ilmu-Ilmu Teknik SMARTICS Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUKATIF : JURNAL ILMU PENDIDIKAN PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Buletin Ilmiah Sarjana Teknik Elektro Jurnal IKA PGSD : Ikatan Alumni PGSD UNARS Journal of Electrical Engineering and Computer (JEECOM) Akiratech
As’ad Shidqy Aziz
Universitas Wisnuwardhana Malang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : smartics journal

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PENGARUH FITUR DAN ANGLE PADA EKSTRAKSI CIRI GLCM TERHADAP AKURASI UNTUK KLASIFIKASI OBJEK Aziz, As'ad Shidqy; Putra, Firnanda Al Islama Achyunda
SMARTICS Journal Vol 8 No 2 (2022): SMARTICS Journal (Oktober 2022)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v8i2.7627

Abstract

The cars that can move on their own or have the ability to drive without assistance from humans are called autonomous cars. The development of various types of driverless vehicles is currently underway. Where in the future the computer system will replace the role of humans in driving vehicles. However, the problem in autonomous cars that deserves attention is the need for high security. Early warning systems are needed in autonomous car systems to detect objects in front of them. This is necessary to avoid accidents, especially when on the highway. In this study, researchers designed a system for vision-based vehicle detection in detecting cars in front of them. The detection algorithm used has two main components, namely color feature extraction using GLCM values, and 6 parameter testing of GLCM dissimilarity, correlation, homogeneity, contrast, ASM and energy. In this study using the SVM (Support Vector Machine) algorithm for the classification algorithm. Good accuracy results are found in the ASM feature and using an angle of 450, which is 88%.
Akurasi Sensor TCS230 dalam Media Pembelajaran Bahasa Inggris Aziz, As'ad Shidqy; Tri Kristianti
SMARTICS Journal Vol 9 No 1 (2023): SMARTICS Journal (April 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i1.8236

Abstract

English as an international language is expected to be able to make a real contribution to future superior generations who will take part in the international world. One of the right strategies for early childhood education in learning English is to listen and repeat. Thus, English Color Assistant learning media is needed to support early childhood English learning. The main components used to realize the tool include the TCS230 sensor and Arduino Uno. The results obtained from the design and manufacture of the tool prototype include the data generated by the sensor having a high level of precision, which is below 0.5. The accuracy of the tool in color reading is 100% when there is no distance between the tool and the detected media and 90% accuracy when there is a distance between the tool and the detected media.
Tinjauan Perkembangan Kecerdasan Buatan Berbasis Arsitektur Transformer Firmanto, Bayu; As'ad Shidqy Aziz; Jendra Sesoca
SMARTICS Journal Vol 10 No 1 (2024): SMARTICS Journal (April 2024)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v10i1.8351

Abstract

Artificial Intelligence, especially technique utilizing machine learning using transformer architecture has experienced rapid progress. The transformer architecture was first introduced in 2017 and laid the foundation for the development of larger and more accurate models in NLP, some of which use BERT and GPT. This review examines five studies that have made significant contributions to the development of the transformer architecture, including research by Vaswani, Devlin, Brown, and Dai. The results of this study shows that the transformer architecture is capable of improving training efficiency, accuracy, and long-context understanding in various NLP tasks. However, there are still some issues with this technology that need to be addressed further.
Pemanfaatan Metode Multiclass Support Vector Machine dalam Klasifikasi Penyakit Daun Kacang Tanah Fakhrunnia, Brahma Ratih Rahayu; Aziz, As'ad Shidqy; Sesoca, Jendra
SMARTICS Journal Vol 9 No 2 (2023): SMARTICS Journal (Oktober 2023)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v9i2.9077

Abstract

Peanuts are one type of agricultural crop from commodity crops that can provide additional income opportunities for farmers in Indonesia. In addition, the benefits of peanuts are as a source of protein and vegetable fat for human body, so they are also much needed by the food industry. However, in increasing soil productivity there is a decrease in quality and quantity caused by one of the factors, namely plant diseases. Efforts that can be made in maintaining peanut productivity are to prevent early by applying early detection technology. This study presents the application of digital image processing application-based technology using the Multiclass SVM One-Against-One (OAO) strategy to classify the types of leaf disease of peanut plants based on texture feature extraction on the diseased parts of peanut leaves using the Gray Level Co-Occurrence Matrix (GLCM) method. In the classification process using the M-SVM method the OAO strategy will use three kernels, namely polynomial kernel, linear kernel and RBF kernels. Based on the experimental results, the best accuracy is obtained, namely by using GLCM texture feature extraction with a distance of d = 1 and angle 90 degree of and classified using the M-SVM method, the OAO strategy with polynomial kernels provides the highest accuracy results, namely 96.39% for leaf spot class, 92.79% for leaf rust class, 96.39% for eye spot class and 100% for normal class
Co-Authors Abdul Hamid Bachtiar Abualigah, Laith Aditya Chandra Hermawan Amaliah, Fithrotul Irda Arif Budijanto Ayusta Lukita Wardani Azis, Muhammad Nur Abdul B, Nur Vidia Laksmi Bayu Firmanto Devita Anggraini Dian Utami Ikhwaningrum Fakhrunnia, Brahma Ratih Rahayu Jendra Sesoca Kristianti, Tri Mahendra Widyartono Nur Vidia Laksmi B Nurin Fitriana Nurus Sa'adah Oliva, Diego Putra, Firnanda Al Islama Achyunda Rahayu, Brahma Ratih Rahmadian, Reza Rahmatullah, Daeng Sesoca, Jendra Sunyoto Widi Aribowo Wirawan Balol Zainuri, Akhmad Zangana, Hewa Majeed