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All Journal CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Infomedia KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Bulletin of Information Technology (BIT) Jurnal Minfo Polgan (JMP) Prosiding Snastikom Jurnal Sistim Informasi dan Teknologi Prosiding Seminar Nasional Teknik UISU (SEMNASTEK) The Journal of Information Technology, Computer Science, and Electrical Engineering
Herdianto Herdianto
Dosen Sistem Komputer Universitas Pembangunan Panca Budi Medan Jl Gatot Subroto KM 4,5, Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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IMPLEMENTASI METODE CNN UNTUK KLASIFIKASI OBJEK Herdianto Herdianto; Darmeli Nasution
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 1 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No1.pp54-60

Abstract

Objects can be interpreted as all inanimate and living things that have various shapes and sizes. For humans to determine the presence of objects, to classify and estimate the distance of objects around them is not difficult. But for a computer to do the work mentioned above with an accuracy level that reaches up to greater than 90% is not easy. Object detection is important in the field of computer vision because it is used to monitor and track objects, while robots that use cameras as sensors are used to avoid obstacles, follow objects, classify and so on. Therefore, the purpose of this study was to determine the level of accuracy of the CNN method in classifying objects. The steps used to complete this research were literature study, collecting digital image data, determining training, and testing data, designing the CNN program, conducting training and testing. From the results of testing the CNN method that has been carried out, it is known that the level of accuracy in classifying objects reaches 98%.
Sistem Monitoring dan Deteksi Dini Pencemaran Udara Berbasis Internet Of Things (IOT) Muhammad Hasanuddin; Herdianto Herdianto
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4034

Abstract

The problem of environmental pollution has been a concern for a long time and continues to be an important issue today. One form of pollution that is very detrimental is air pollution, which causes a decrease in air quality and disrupts its normal function. . The ISPU Scale usually consists of several categories that describe the level of air quality, such as the range 1-50 is said to be "Good", the range 51-100 is said to be "Moderate", the range 101-200 is said to be "Unhealthy", the range 201-300 is said to be "Very Unhealthy", and a range of 301+ is said to be "Dangerous". To monitor the level of air pollution, it is necessary to use a tool that can measure air quality accurately. Indoor pollution, as is often the case in office environments, is one of the most serious threats to the health of modern society. A worker can spend up to eight hours a day in the room. The air we breathe everyday comes from the building's air duct system. However, these duct systems not only circulate air, they can also spread contaminants from one room to another. Therefore, it is important to maintain indoor air quality for the comfort and health of workers, because this has a direct impact on their productivity. Based on this, an idea emerged to develop a prototype detector of carbon monoxide gas that is sensitive to indoor air pollution using a microcontroller and web-based. This research begins with analyzing and designing the system, both in terms of hardware and software. After that, the next step involves coding and programming configuration and setup on the Thingspeak web. The last stage is testing tools and systems to ensure that they operate according to the plan that has been set. This system is designed to be able to detect and monitor the level of air pollution, as well as provide a graphical display on Thingspeak. This tool works by using a microcontroller to read the MQ-135 sensor attached to the Ardino Wemos ESP8266. This sensor is used to measure gas levels in a predetermined room. The tool will connect to the Wi-Fi hotspot on the Android device to provide notifications via Thigspeak based on a graph
Sistem Absensi Karyawan Berbasis Web Menggunakan Metode QR Code pada Kantor Desa Cinta Raja Yumna Tamimah Bilqis; Herdianto Herdianto; Hendry Hendry
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14625

Abstract

Sistem absensi karyawan yang efisien sangat penting dalam meningkatkan kinerja dan akurasi pencatatan kehadiran. Penelitian ini bertujuan untuk mengembangkan sistem absensi karyawan berbasis web menggunakan metode QR Code di Kantor Desa Cinta Raja. Metode Pengumpulan data dilakukan melalui observasi, wawancara, dan studi pustaka. Hasil penelitian menunjukkan bahwa sistem absensi berbasis QR Code lebih efektif dibandingkan dengan metode manual, baik dari segi kecepatan pencatatan maupun akurasi data kehadiran. Perbedaan signifikan terlihat pada efisiensi waktu, di mana sistem berbasis teknologi mampu mengurangi kesalahan pencatatan dan mempercepat proses absensi. Dengan demikian, sistem ini dapat menjadi solusi yang inovatif untuk meningkatkan efisiensi administrasi kehadiran karyawan di lingkungan pemerintahan desa.
Rancang Bangun Aplikasi Data Penjualan Pada Ausy Coffee Berbasis Web Monica R Sihombing; Aminuddin Indra Permana; Herdianto Herdianto
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14744

Abstract

Perkembangan teknologi informasi telah mendorong berbagai sektor usaha, termasuk usaha Food and Beverage (F&B), untuk mengadopsi sistem informasi berbasis komputer guna meningkatkan efisiensi dan akurasi dalam proses operasional. Penelitian ini bertujuan untuk merancang dan membangun aplikasi pendataan penjualan berbasis web pada Ausy Coffee, sebuah usaha F&B yang belum memiliki sistem pencatatan penjualan yang terkomputerisasi. Metodologi pengembangan sistem menggunakan pendekatan rekayasa perangkat lunak dengan bahasa pemrograman web dan database MySQL. Hasil dari pengujian aplikasi menunjukkan bahwa sistem yang dibangun mampu mencatat penjualan secara akurat, menampilkan laporan keuangan bulanan, serta memudahkan pengambilan keputusan dalam pengelolaan stok dan evaluasi keuntungan. Sistem ini diharapkan dapat menjadi solusi efektif bagi usaha serupa dalam mengelola data penjualannya.
IMPLEMENTATION OF YOLOv4 TO DETERMINE THE QUALITY OF TOMATOES herdianto herdianto
Journal of Information Technology, computer science and Electrical Engineering Vol. 2 No. 3 (2025): October 2025 - January 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v2i3.228

Abstract

Tomatoes are plants whose fruits can be used to flavor dishes, fried rice, and can also be made into tomato juice. Due to their great benefits, the demand for tomatoes is increasing, even to the point of being exported overseas. For this reason, many farmers in Indonesia cultivate them. However, in order to be exported, the quality of the tomatoes to be shipped must meet the established standard, which is grade 1 quality. Currently, determining the quality of tomatoes is done manually by humans as quality control, which requires a long time, high operational costs, and is prone to errors. Therefore, this study will attempt to use YOLOv4 to identify tomato quality in three levels, namely good, fair, and poor. YOLOv4 will first be trained with 80% of the dataset totaling 300, and 20% will be used for testing. The test results show that YOLOv4 can correctly distinguish tomatoes with good, mid, and bad quality with an accuracy rate of to 66.2% and a detection time of less than 1 second for each object.
Implementasi Metode Yolo pada Deteksi Objek Manusia Herdianto Herdianto; Hafni Hafni; Darmeli Nasution; Syahrul Ramadhan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp234-240

Abstract

Until now, the problem of theft of motorbikes and livestock in North Sumatra is still quite high.  Locations for motorbike theft can occur in many places such as schools, homes, parking lots, offices and so on, while for livestock it can occur on pastures and in pens during the day or night and the perpetrators are men. To make this theft a success, various modes are used in varying human positions, from sitting, squatting to standing. To help overcome this, several object detection methods have been developed such as Background Subtraction, Template Matching, Histogram Oriented Gradient (HOG), Deformable Part-based Model (DPM) and Viola Jones (VJ).   Of the many methods that have been used, there are still shortcomings, namely in terms of time, accuracy and various human positions.  For this reason, research was carried out with the aim of improving the time and level of accuracy in detecting human objects using the YOLO method. The research stages carried out in this research include literature study, collecting data, determining training and test data, creating programs, training, and testing. From the trials carried out, it is known that YOLO can detect humans in various positions with a mAP value of 0.99 and an average detection time of 810.01 ms.
Co-Authors Adisastra Pengalaman Tarigan Amin, Muhammad Aminuddin Indra Permana Ari Pratama Aulia Manurung Ayu Nuriana Sebayang Chairul Rizal Darmeli Nasution Dewi Mahrani Rangkuty Fery Anugrah Hafni Hafni Hendry Hendry Hidayati Purnama Lubis Mita Mutiara Zora Monica R Sihombing Muhammad Hasanuddin Munadi Munadi Mursyidah . Rusli Rusli Silvia Anggraini Syahrul Ramadhan Ucmariance Yumna Tamimah Bilqis