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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CESS (Journal of Computer Engineering, System and Science) Jurnal Pilar Nusa Mandiri JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah EXPLORE Jurnal Manajemen Informatika dan Sistem Informasi JISA (Jurnal Informatika dan Sains) INFOKUM Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Publikasi Teknik Informatika (JUPTI) Explore
Salman Salman
Universitas Teknologi Mataram
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images Imran, Bahtiar; Delsi Samsumar, Lalu; Subki, Ahmad; Zaeniah, Zaeniah; Salman, Salman; Rijal Alfian, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1625-1631

Abstract

This research uses the gray level co-occurrence matrix (GLCM) and artificial neural networks to classify COVID-19 images based on chest X-ray images. According to previous studies, there has never been a researcher who has integrated GLCM with artificial neural networks. Epochs 10, 30, 50, 70, 100, and 120 were used in this research. The total number of data points used in this investigation was 600, divided into 300 normal chests and 300 COVID-19 data points. Epoch 10 had 91% accuracy, epoch 30 had 91% accuracy, epoch 50 had 92% accuracy, epoch 70 had 91% accuracy, epoch 100 had 92% accuracy, and epoch 120 had 90% accuracy in categorization. As indicated by the results of the classification tests, combining GLCM and artificial neural networks can produce good results; a combination of these methods can yield a classification for COVID-19.
DESIGNING CLASS SCHEDULE INFORMATION SYSTEM BY USING TABOO-SEARCH METHOD Zaeniah, Zaeniah; Salman, Salman
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1661

Abstract

Drafting of class schedule at the Faculty of Information and Communication Technology, Mataram University of Technology (FTIK UTM) is still done manually. So that, there are some problems such as lecturer teaching schedule at the same time at one time as well as student learning time at the same time at one time and studying more than 3 times a day. Therefore, manual scheduling requires a lot of time and it must be done very carefully. The method used to solve this problem is the Taboo- Search Method which is used to solve the problem of scheduling. The Taboo-Search Method is a method that seeks the best solution from existing solutions by creating a list of solutions or taboo lists, solutions that have been used previously will no longer be displayed for the next problem. The research method used in this research is the method of research and research and development which starts from the preliminary stage to find problems that occur up to the implementation stage so that it is generated an information system of course schedule at the Faculty of Information and Communication Technology, Mataram University of Technology. The purpose of this research is to produce a class schedule information system so that it can help arrange class schedules more quickly and precisely.
MAPPING LOCATIONS AND SHORTEST ROUTE OF TOURISM OBJECTS IN CENTRAL LOMBOK USING GIS-BASED A-STAR ALGORITHM Muslim, Rudi; Hidayatullah, Beni Ari; Imran, Bahtiar; Yani, Ahmad; Salman, Salman
Jurnal Pilar Nusa Mandiri Vol 18 No 2 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i2.3927

Abstract

Central Lombok tourism is a tourism that foreign and domestic tourists often visit. There are many tourist objects offered by the Central Lombok Government, such as waterfall tours, beach tours, traditional village tours, cultural tours, and Pertamina Mandalika International Street Circuit. However, there are many tourist objects, and not all tourists know the location of these tourist objects. Tourists often experience constraints, are the location of tourist objects that is not quite right, it is still difficult to determine the shortest route to the location, and the lack of complete information about existing tourist objects, which can hinder the journey of tourists to the destination location. This study aims to map the location and shortest route of tourism objects in Central Lombok using an Android-based Geographic Information System by applying the A-Star algorithm. The results of this study are to develop an Android-based Geographic Information System or GIS by applying the star algorithm to Central Lombok tourism objects. So that the mapping of the location and information of tourist objects and obtain the search for the shortest route to tourist objects. The A-Star algorithm uses heuristic principles to find the shortest route to a tourism object and is optimal in finding the shortest route to tourism objects
Analisis Manipulasi Splicing pada Citra Digital menggunakan Metode Discrete Cosine Transform (DCT) dan Scale Invariant Feature Transform (SIFT) Efendi, Muhamad Masjun; Salman, Salman
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.53156

Abstract

Pemalsuan dalam citra digital seringkali terjadi di era teknologi saat ini. Bantuan software pengolahan citra memudahkan dan mempercepat proses manipulasi, mendorong orang untuk melakukan perubahan sebelum citra dipublikasikan di internet atau media sosial. Meski kegiatan ini umum dilakukan, seringkali merugikan orang lain dan merupakan bentuk penipuan publik terhadap keaslian citra. Salah satu metode manipulasi yang kerap kali digunakan adalah splicing, splicing adalah menambah objek dalam citra, contohnya meletakkan suatu objek pada citra target yang seolah-olah objek tersebut berada disana. Penelitian ini bertujuan untuk mendeteksi manipulasi jenis splicing dengan menggunakan metode Discrete Cosine Transform (DCT) dan Scale Invariant Feature Transform (SIFT). Metode DCT mentransformasikan blok piksel citra menjadi koefisien, sedangkan SIFT digunakan untuk menemukan frekuensi pada citra grayscale dengan mendeteksi keypoint yang sama. Metode ini mampu mendeteksi objek citra yang dimanipulasi dengan baik dan akurat. Dari hasil pengujian yang dilakukan, nilai akurasi deteksi image splicing pada citra dari internet dan koleksi citra hasil koleksi pribadi mencapai 100%. Harapannya, hasil penelitian ini dapat bermanfaat bagi masyarakat dalam membedakan citra yang asli dengan yang sudah dimanipulasi melalui teknik splicing.
STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD Salman, Salman; Ramdan, Hendri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6157

Abstract

Mataram University of Technology (UTM) still relies on a manual attendance process, such as signing paper-based attendance lists, which are prone to fraud and difficult to manage on a large scale. This study develops a face recognition-based attendance system using Convolutional Neural Network (CNN), which can automatically recognize visual patterns and unique facial features. CNN has advantages in extracting significant facial features, allowing it to recognize faces under various lighting conditions and viewing angles. The dataset used consists of 5,820 facial images from 97 students, with 60 augmented images per student. The results indicate that this system can be implemented in a lecture environment, achieving a validation accuracy of 98.5% at the 150th epoch. However, the model has some limitations, such as a relatively small dataset size and challenges in recognizing faces under extreme lighting conditions or unusual angles, which can affect accuracy in real-world applications. Additionally, although this system has the potential for real-time implementation, further optimization is required to ensure fast and accurate responses on a large scale. To overcome these limitations, future research can explore the use of direct camera input to enhance efficiency and user experience. Furthermore, improving dataset quality by incorporating variations in lighting and image angles, as well as exploring alternative deep learning architectures such as Vision Transformers (ViT) or Swin Transformer, can enhance model performance and generalization. By implementing these improvements, the facial recognition-based attendance system can be more optimal in enhancing accuracy and ease of use in academic environments.
Co-Authors ahmad yani Ahmad Yani Ardiyallah Akbar Efendi, Muhamad Masjun Erfan Wahyudi erniwati, surni Haikal Wahyudi Hidayatullah, Beni Ari Imran, Bahtiar Kirana, Bq. Ajeng Liesna Putri Dende Kirana Lalu Delsi Samsumar, M.Eng. Moh. Subli Muhammad Masjun Efendi Muhammad Rijal Alfian Ramdan, Hendri Rudi Muslim Sriasih, Sriasih Subki, Ahmad Zaeniah Zaeniah Zaeniah Zaenudin Zaenudin