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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal TIMES CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jambura Journal of Electrical and Electronics Engineering JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Engineering, Science and Information Technology Yayasan Cita Cendikiawan Al Khwarizmi Djtechno: Jurnal Teknologi Informasi JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen INCODING: Journal of Informatics and Computer Science Engineering EXPLORER Prosiding Snastikom Jurnal ABDIMAS Budi Darma Journal of Practical Computer Science (JPCS) Jurnal Informatika Teknologi dan Sains (Jinteks) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Indonesia Sosial Teknologi Jurnal Ilmu Komputer dan Sistem Informasi CompTech : Jurnal Ilmu Komputer dan Teknologi
Rahman, Sayuti
Universitas Medan Area, Medan, Indonesia
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Analisis Perbandingan Akurasi Pre-Trained Convolutional Neural Network Untuk Klasifikasi Kelompok Usia Pengunjung Rumah Sakit Arnes Sembiring; Sayuti Rahman; Dodi Siregar; Muhammad Zen; Suriati Suriati
Journal of Information System Research (JOSH) Vol 4 No 2 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i2.2913

Abstract

Children are not allowed to visit the hospital. Children should not visit the hospital for two reasons, namely the patient's side and the child's side. On the patient's side, patients need peace of mind during treatment and recovery. The noise generated by children makes the atmosphere not conducive and increases the patient's stress level. On the child's side, there are two factors, namely immunity, and trauma. Children have incomplete immunity so they are easily infected by viruses and bacteria. A child's immune disorder will harm the child's development. Apart from viruses and bacteria, in hospitals, there are also patients with major injuries such as those resulting from accidents. Children who see these large wounds can traumatize themselves and interfere with the child's growth and development. The age classification of visitors supports for hospital management to limit visitors based on age. Visitors categorized as children are visitors aged 12 years or younger. The method used for age group classification is the pre-trained CNN, including Alexnet, VGGNet, GoogleNet, ResNet, and AqueezeNet. We conducted a preliminary study using the All-Age-Faces (AAF) dataset as test data that represents the age of hospital visitors. The dataset is divided into two classes, namely children and adults. Based on the SqueezeNet test, it is a better method in terms of training accuracy and validation. Based on the order of accuracy validation, SqueezeNet succeeded in recognizing age groups with an accuracy of 93.09%, VGGNet 92.72%, AlexNet 91.44%, GoogleNet 90.92%, and ResNet 90.62%. This research is expected to contribute to helping control visitors to the hospital.
RANCANG BANGUN MINIATUR SISTEM ALAT PENGUKUR STANDAR KEBISINGAN KNALPOT SEPEDA MOTOR BERBASIS ARDUINO UNO Budi Santoso; Sayuti Rahman; Arnes Sembiring
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 9 No. 1 (2023): Volume 9 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v9i1.1366

Abstract

Sound is one element of the vibration of an object whare the vibration occurs around the air that propagates from all directions whare the results vary form the air pressure that applies to the surface of the eardrum that is heard, Motorcycles are vehicles that are ofthen used as transportation that is used with this transportation to make it easier for users and the sound produced from the exhaust is a noise that is often heard on the highway and there based on these problems, the authors design a miniature system for measuring standard on the entire LM 393 Sound Sensor series to measure the level of noise produced by the exhaust and OLED which serves to display sensor values. The way of application is that the system and gets information on the sound sensor value from a miniature standard exhaust noise gauge, to fine out whether the value generated by the user can see the result on the OLED screen.
Prototype Mesin Penakar Gula Pasir Berbasir Ardiuno Uno Pada UMKM Alfyanang Fattulah; Sayuti Rahman; Imran Lubis
SNASTIKOM Vol. 2 No. 1 (2023): SEMINAR NASIONAL TEKNOLOGI INFORMASI & KOMUNIKASI (SNASTIKOM) 2023
Publisher : Unit Pengelola Jurnal Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.024 KB)

Abstract

Advances in technology today have provided many benefits in various sectors, one of which is in the trade sector. Generally, traders, especially SMEs, still use conventional equipment in their activities. For example, they still use conventional scales to weigh or measure sugar, rice and so on. This activity takes a long time because traders have to reduce or add sugar if the weight is not suitable. This process often makes the weight of sugar imprecise and causes losses for traders. To overcome losses for traders, a tool is needed that can facilitate the activity of weighing the sugar. With today's very rapid technological advances, a tool can be made that can weigh sugar accurately, automatically and can be adjusted to the needs of traders. The tool uses the Arduino Uno microcontroller as the center of the system being built and also as a liaison between the sensors used. The working system is where there is a sugar storage device and automatic sugar weighing, the maximum sugar storage is 5000 grams. On the tool there are several buttons such as the 250 gram, 500 gram kg and 1000 gram buttons. When the button is pressed, the tool will remove the sugar from the storage area and weigh according to the button pressed
KLASIFIKASI JENIS KENDARAAN PADA JALAN RAYA MENGGUNAKAN YOLOV7 Bayu Aditya Pratama; Sayuti Rahman; Arnes Sembiring
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.3493

Abstract

This research aims to develop a classification system capable of identifying types of vehicles on the highway using YOLOv7 (You Only Look Once version 7), a deep learning-based object detection model that can be used for real-time object detection. With the rapid growth of traffic conditions, monitoring and managing traffic become increasingly important to reduce congestion and improve road safety. The research involves collecting image data and labeling the types of vehicles found on the highway. Subsequently, training the YOLOv7 model using the obtained dataset to classify various types of vehicles such as cars, motorcycles, trucks, and buses. The results of this study indicate that YOLOv7 can be efficiently used to classify types of vehicles on the highway with a fairly good level of accuracy, reaching a maximum of 86% for video and 91% for image detection.
Pelatihan Media Website Sebagai Sarana Promosi Produk UMKM Desa Sei Limbat Muhammad Zen; Chairul Rizal; Sayuti Rahman; Risko Liza; Rachmat Aulia
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 02 (2023): EDISI SEPTEMBER 2023
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v5i02.850

Abstract

Pengembangan UMKM Desa merupakan pendorong utama pertumbuhan ekonomi di wilayah pedesaan. UMKM Desa bertujuan untuk menghasilkan produk dengan tingkat daya saing yang tinggi, memastikan produk-produk tersebut telah memenuhi standar kualitas, pengemasan, perizinan, serta sertifikasi yang diperlukan. Salah satu produk unggulan yang telah siap untuk dipasarkan adalah gula aren, yang memiliki beberapa varian produk. Strategi pemasaran saat ini dilakukan secara tradisional, baik melalui penjualan langsung, pameran produk, maupun melalui platform pasar digital.Pengembangan UMKM yang mendapatkan bimbingan dari pemerintah desa memiliki nilai tambah yang signifikan dalam membangun kepercayaan pelanggan terhadap produk. Oleh karena itu, diperlukan solusi untuk mempromosikan produk-produk UMKM sebagai produk yang didukung secara aktif oleh Desa Sei Limbat. Dalam rangka meningkatkan keterampilan masyarakat Desa Sei Limbat, terutama generasi muda, dapat dilakukan pelatihan untuk mengembangkan media website menggunakan Content Management System (CMS) seperti Wordpress yang merupakan salah satu CMS populer saat ini. Tujuan dari pelatihan ini adalah untuk memberikan pengetahuan tambahan kepada masyarakat desa dalam bidang teknologi serta memungkinkan produk-produk yang dihasilkan untuk dipromosikan melalui platform website yang mereka bangun. Hal ini diharapkan dapat meningkatkan visibilitas dan penetrasi pasar bagi UMKM Desa Sei Limbat.
Analisis Existing Convolutional Neural Network Untuk Klasifikasi Usia Pengunjung Rumah Sakit: Studi Kasus Pemantauan Anak dan Dewasa Harahap, Herlina; Rahman, Sayuti; Zen, Muhammad; Suriati, Suriati
Explorer Vol 4 No 1 (2024): January 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i1.881

Abstract

The purpose of this study is to examine the Convolutional Neural Network (CNN) model for classifying the age groups of hospital visits, both children and adults. Hospitals serve as treatment facilities for a variety of ailments caused by viruses, germs, car accidents, and other factors. Children are not permitted to visit the hospital due to hurdles to patient comfort as well as hazards associated with immunity and trauma to children. As a result, a digital strategy is required to monitor the presence of youngsters in the hospital setting. The notion of computer vision and the Convolutional Neural Network (CNN) are employed in this study to attain this goal. The dataset utilized is All-Age-Faces (AAF), which includes photos of human faces ranging in age from 2 to 80 years. To categorize visitors into children or adults, two CNN architectures, ResNet and SqueezeNet, are used with fine-tuning (FT) and full retraining (FR) approaches. The accuracy of FR-ResNet was 97.22%, beating the accuracy of the previous research FT-SqueezeNet, which was 93.09%, better to 4.13%. This study confirmed that the use of CNN, namely the FR-ResNet technique, was effective in accurately categorizing the age of hospital visits. Controlling children's access to hospital areas can help reduce the danger of illness transmission.
IMPLEMENTASI TRANSFER LEARNING UNTUK KLASIFIKASI PENYAKIT PADA DAUN CABAI MENGGUNAKAN CNN Setyadi, Rahmat Arief; Rahman, Sayuti; Manurung, Dionikxon; Hasanah, Mardiatul; Indrawati, Asmah
Djtechno: Jurnal Teknologi Informasi Vol 5, No 2 (2024): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v5i2.4642

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan penyakit pada daun cabai merah menggunakan metode Convolutional Neural Network (CNN) dengan pendekatan transfer learning, khususnya arsitektur ResNet101. Indonesia, sebagai negara agraris, memiliki banyak petani yang mengandalkan tanaman cabai merah sebagai salah satu komoditas utama. Namun, penyakit daun cabai sering kali menurunkan kualitas dan kuantitas hasil panen. Dalam upaya meningkatkan deteksi dini penyakit, penelitian ini memanfaatkan teknologi deep learning untuk menganalisis citra daun cabai. Dataset yang digunakan terdiri dari citra penyakit daun cabai merah yang telah diaugmentasi, dengan total 2128 gambar yang dibagi menjadi data training sebanyak 1702 citra dan data validasi sebanyak 426 citra. Penelitian ini membandingkan kinerja berbagai arsitektur CNN, termasuk AlexNet, GoogleNet, VGGNet16, dan ResNet50, serta lapisan-lapisan pada arsitektur ResNet. Hasil penelitian menunjukkan bahwa augmentasi dataset meningkatkan akurasi validasi dari 89.72% menjadi 97.18%. ResNet101 mencapai akurasi validasi tertinggi sebesar 98.12%, menunjukkan efektivitas transfer learning dalam tugas klasifikasi penyakit daun cabai. Penelitian ini menunjukkan bahwa penggunaan metode CNN dengan transfer learning, khususnya arsitektur ResNet101, sangat efektif untuk mendeteksi dan mengklasifikasikan penyakit pada daun cabai merah. Peningkatan kinerja model melalui augmentasi dataset dan pemilihan arsitektur yang tepat dapat membantu meningkatkan kualitas dan kuantitas hasil panen, serta mendukung pertanian cerdas di Indonesia
Rancang Bangun Miniatur Sistem Alat Pengukur Standar Kebisingan Knalpot Sepeda Motor Berbasis Arduino Uno Budi Santoso; Sayuti Rahman; Arnes Sembiring
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 2 No. 1 (2023): Januari 2023
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v2i1.40

Abstract

Sound is one of the vibrations of an object where the vibration occurs around the air that propagates from all directions where the results vary and the air pressure that applies to the surface of the eardrum that hear. Motorcycles are vehicles that are often heard on the road and there are some motorcycles that have some non-standard exhausts used on these motorcycles. Based on this problem, the author designed a miniature motorcycle exhaust noise measuring device system. Using this design uses Arduino Uno as the center of control rangkian LM393 sound sensor to measure the noise level of the sound produced by the exhaust and OLED displaying the sensor value. For the application of this tool, simply close the sensor to the motorcycle exhaust that will be tested, then the sound value produced by the motorcycle will be displayed on the miniature OLED of the exhaust noise standard measuring device, to determine whether the value produced by the user can see the result on the OLED screen.
Penerapan Smart Farming Sebagai Upaya Modernisasi Pertanian Cabai Rahman, Sayuti; Indrawati, Asmah; Sembiring, Arnes; Hartono, Hartono; Zuhanda, Muhammad Khahfi; Ongko, Erianto
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 02 (2024): EDISI SEPTEMBER 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i02.1050

Abstract

Cabai merupakan salah satu komoditas hortikultura yang memiliki nilai ekonomi tinggi, namun produktivitasnya sering terganggu oleh berbagai penyakit daun yang disebabkan oleh hama, seperti bercak daun, layu fusarium, embun tepung, dan virus kuning. Penyakit-penyakit ini tidak hanya memengaruhi kualitas hasil panen, tetapi juga menyebabkan kerugian ekonomi yang signifikan bagi petani. Untuk mengatasi permasalahan ini, dilakukan pengabdian kepada masyarakat dengan mengimplementasikan teknologi Convolutional Neural Network (CNN) untuk klasifikasi penyakit daun cabai secara cepat dan akurat. Metode yang digunakan melibatkan observasi lapangan untuk mengidentifikasi permasalahan yang dihadapi petani di Desa Lubuk Cuik, Batu Bara, Sumatera Utara. Data berupa gambar daun cabai yang terinfeksi dikumpulkan dan digunakan untuk melatih model CNN. Model yang dikembangkan, efficientChiliNet, mampu mengklasifikasikan penyakit daun cabai dengan akurasi pelatihan 99,8% dan akurasi validasi 96,5%. Aplikasi berbasis web dan desktop kemudian dibuat untuk mempermudah petani dalam mendiagnosis penyakit daun cabai secara mandiri. Aplikasi ini juga disosialisasikan kepada petani melalui pelatihan untuk memastikan implementasi teknologi yang optimal. Hasil pengabdian ini menunjukkan bahwa teknologi berbasis CNN mampu memberikan solusi efektif dalam mengidentifikasi penyakit daun cabai dan membantu petani meningkatkan produktivitas pertanian. Rekomendasi selanjutnya adalah pengembangan fitur tambahan dalam aplikasi untuk memberikan panduan penanganan hama dan integrasi teknologi Internet of Things (IoT) untuk pemantauan lingkungan secara real-time. Dengan pendekatan ini, diharapkan terciptanya modernisasi pertanian berbasis smart farming yang berkelanjutan.
ConciseCarNet: convolutional neural network for parking space classification Ramli, Marwan; Rahman, Sayuti; Bayu Syah, Rahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4158-4168

Abstract

The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.
Co-Authors Abdul Malik Adam Adinda Titania Aditya Pratama, Bayu Ady Pratama, Ramadhan Alfyanang Fattulah Andi Marwan Elhanafi Ari Usman Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arwadi Sinuraya Asih, Munjiat Setiani Asmah Indrawati Bayu Aditya Pratama Bayu Syah, Rahmad Beby Suryani Beby Suryani Fithri Budi Santoso Budi Santoso Chairul Rizal Chairul Rizal Chiuloto, Kalvin Dadan Ramdan Daffa, Daffa Zain Shahriza Deseari Baeha Desi Yanti Dodi Siregar Dodi Siregar Emil Fitranshah Aliff S Erianto Ongko Eswanto, Eswanto Fera Damayanti Finta Aramita Fiqi Arfian Habib Satria Hafifah, Febri Haida Dafitri Haida Dafitri Haida Dafitri, Haida Halawa, Agung Y S Harahap, Herlina Hartono Hartono Hartono Hartono Hartono Hartono Hasibuan, Ade Zulkarnain Hasibuan, Muhammad Ridwan Hasibuan, Nasaruddin Nur Herdianto Herdianto, Herdianto Herlina Andriani Simamora Hutajulu, Olnes Yosefa Ilham Faisal Ilham Faisal Irfandi Irfandi, Irfandi Irwan Irwan Isnaini Khahfi Zuhanda, Muhammad Kharunnisa Kharunnisa Lili Suryati Liza, Risko Lubis, Husni lubis, ihsan M F Verri Anggriawan Manurung, Dionikxon Mardiatul Hasanah Marischa Elveny, Marischa Martini, Dewi Marwan Ramli Marwan Ramli Muchzakhir Bustari Mufida Khairani Mufida Khairani Muhammad Khahfi Zuhanda Muhammad Rizky Irwansyah Muhammad Zen Muhammad Zen, Muhammad Munadi Munadi Muzdalifah Ulfayani Pratama, Bayu Aditya Putra, Andre Kurnia Rachmat Aulia Rachmat Aulia, Rachmat Rafiqi Rahmad B.Y Syah Rahmad Syah, Rahmad Retna Astuti Kuswardani Riki Agusetiawan Risko Liza Ritonga, Iqbal Giffari Robby Darwis Rudi Salman Sembiring, Arnes Setyadi, Rahmat Arief Siregar, Rosyidah Siti Sundari Sri Eka Riyani Harahap Sultan Shidqi Sumi Khairani Suriati Suriati Suriati Suriati Suriati, Suriati Suswati suswati suswati Tanjung, Rino Nurcahyo Fauzi Tanjung, Shabila Shaharani Taufik Siregar Tengku Mhd Diansyah Tengku Mohd Diansyah, Tengku Mohd Ulfa Sahira Winanda, Icha Windy Sri Wahyuni Wiraswan Duha Yasir, Amru Yessi Fitri Annisah Lubis Yuni Syahputri Zealtiel, Billiam Zuhanda, M. Khahfi