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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Journal of Information Systems and Informatics International Journal of Engineering, Science and Information Technology
Riyadi, Tri Agus
Universitas Gunadarma
Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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RANCANG BANGUN PURWARUPA SISTEM PENGUNCI LEMARI DENGAN PENGENALAN SUARA Dzulfikar, Laksamana Akbar; Haryatmi, Emy; Riyadi, Tri Agus
Jurnal Ilmiah Teknologi dan Rekayasa Vol 24, No 3 (2019)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2019.v24i3.2398

Abstract

Sistem keamanan berbasis teknologi modern telah banyak diterapkan di kehidupan sehari-hari salah satunya adalah penerapan sistem keamanan pada kunci pintu lemari. Perintah suara merupakan salah satu media pengoperasian sistem home automation yang banyak diminati. Pada penelitian ini  dibuat sistem pengunci lemari dengan metode pengenalan suara. Sistem keamanan ini menggunakan suara untuk membuka kunci pintu lemari dan menguncinya. Alat ini dirancang dengan menggunakan modul elechouse voice recognition v3, Arduino Uno R3 sebagai mikrokontroler, motor servo 9g dan lampu LED. LED berfungsi sebagai indikator tambahan pada saat penguncian lemari. Perancangan sistem pengunci lemari ini menggunakan pengenalan suara dengan dua kondisi pergerakan motor servo. Hasil penelitian menunjukkan kondisi pertama jika suara pengguna terdeteksi sebagai suara dengan signature “on” maka motor servo akan bergerak membuka kunci lemari dan LED sebagai indikator tambahan akan menyala. Kondisi kedua yaitu jika suara pengguna terdeteksi sebagai suara dengan signature “off” maka motor servo akan bergerak menutup kunci lemari dan LED sebagai indikator tambahan akan mati. Baud rate yang memiliki tingkat keberhasilan paling tinggi adalah 2400 bps.
ANALISIS SISTEM PEMANTAUAN VIDEO MENGGUNAKAN IP CAMERA PADA SUATU UNIT USAHA DI PTN Riyadi, Tri Agus
Jurnal Ilmiah Teknologi dan Rekayasa Vol 22, No 2 (2017)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sistem pemantauan video dengan menggunakan IP camera pada saat ini sudah banyak digunakan untuk keamanan suatu tempat. Parameter yang menentukan kualitas video diantaranya resolusi dan frame rate. Salah satu implementasi IP camera pada suatu unit usaha di PTN untuk memantau keadaan terutama pada saat perkuliahan berlangsung. Tujuan dari penelitian ini adalah merancang dan melakukan ujicoba terhadap rancangan penggunaan IP camera disuatu unit usaha di PTN serta menganalisis hasil video yang dihasilkan dari IP camera. Video hasil pemantauan menggunakan resolusi sebesar 640x480 dan 160x120 dengan frame rate sebesar 5 fps dan 30 fps. Penggunaan resolusi sebesar 640x480 dengan frame rate sebesar 5 fps dan 30 fps menghasilkan kualitas gambar video yang lebih baik dibandingkan dengan penggunaan resolusi sebesar 160x120. Hasil video dengan menggunakan 30 fps dan resolusi sebesar 640x480 menghasilkan kualitas video yang lebih baik dibandingkan dengan video yang menggunakan frame rate sebesar 5 fps. Semua responden yang menilai video menyatakan bahwa kualitas video dengan resolusi 640x480 dan frame rate 30 fps lebih baik dibandingkan dengan video lainnya. Kata Kunci : IP Kamera, Video, Citra, Resolusi, Frame Rate
PURWARUPA ALAT PENDETEKSI BAYI KUNING DAN SUHU TUBUH PADA BAYI BARU LAHIR BERBASIS SENSOR WARNA DAN SENSOR SUHU Bagaskoro Bagaskoro; Emy Haryatmi; Tri Agus Riyadi
Jurnal Ilmiah Informatika Komputer Vol 27, No 3 (2022)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2022.v27i3.7725

Abstract

Jaundice adalah perubahan warna kuning pada kulit atau bagian putih mata yang sering terlihat pada bayi baru lahir. Perubahan warna ini disebabkan oleh zat kuning yang disebut bilirubin. Bayi dengan kadar darah tinggi bilirubin, yang disebut hiperbilirubinemia, berkembang menjadi warna kuning ketika bilirubin terakumulasi di kulit. Jaundice juga membutuhkan perawatan khusus agar bayi tersebut dapat segera ditangani. Alat ini dirancang menggunakan mikrokontroler ESp8266, serta dengan menggunakan Aplikasi Blynk yang berfungsi untuk media interaksi antara user dengan alat itu sendiri. Tujuan dari penelitian ini adalah merancang dan melakukan uji coba terhadap alat pendeteksi warna kuning pada kulit bayi baru lahir dan suhu berbasis mikrokontroler. Metode penelitian yang digunakan adalah melakukan studi literature, disain, pengujian dan analisis alat. Tahap pengujian alat dilakukan terhadap sensor warna yang dapat mendeteksi kadar bilirubin pada kulit bayi serta menggunakan sensor suhu mendeteksi suhu bayi tersebut. Berdasarkan hasil pengujian, sensor suhu tubuh dapat bekerja dengan baik dengan mendeteksi suhu tubuh pada orang dewasa ataupun bayi berada pada kisaran 32-36°C. Pengujian terhadap kulit manusia dan berdasarkan warna referensi menunjukkan bahwa terdapat empat kondisi yaitu normal, ringan, berat (severe) dan critical (kritis).
Classification of Tomato Ripeness Based on Convolutional Neural Network Methods Ayunda, Nur Azizah; Haryatmi, Emy; Riyadi, Tri Agus
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.613

Abstract

Sorting system for tomato is one of the important things to deploy to achieve better quality of tomato. Nowadays, many sorting system is done manually and this could spend a lot of time and become inefficient. One method can be implemented in the sorting system by using Convolutional Neural Network (CNN) method to classify the ripeness of tomatoes. The objective of this research is to classify the ripeness of tomatoes based on the color of tomatoes. There are three categories of color level such as green for raw tomato, turning for half-ripe tomato and red for ripe tomato. Research methodology of this research is data collection, data pre-processing and image maintenance, CNN model, and training data. The image used in this research are 1148 images. These images were taken manually using smartphone camera in outdoor environment. These images were used to build CNN model. The results of this research show that by testing 10 images of tomatoes achieved raw tomatoes close to 90%, ripe tomatoes close to 90% and half-ripe tomatoes close to 80%. Based on the results, CNN can be used as a good alternative in image classification tasks.
Herbal Plant Leaves Classification Using Convolutional Neural Network Models: A Literature Review Fauzi, Alfharizky; Haryatmi, Emy; Riyadi, Tri Agus; Murniyati, Murniyati
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.723

Abstract

Plants are essential to human beings because plants are considered most as foods. Plants can be used for food ingredients, medical purposes, and industrial applications. People inspect plants using traditional methods, such as using the naked eye, which can be time-consuming and expensive. Therefore, the effectiveness and high quality of automated crop identification classification systems are needed for adequate crop protection. This study aims to identify and classify nine plant species using different datasets, focusing on transfer learning from models trained on plant leaf datasets. Most research has shown that increasing the dataset size would significantly improve classification accuracy. The accuracy of the first test using the modified N1 classification model was 99.45%. In the second experiment, the accuracy of the N2 model was 99.65%. The accuracy of the N3 model, despite being slightly less accurate than AlexNet, was 99.55%, and it performed better, while the accuracy of AlexNet was 99.73%. Compared to the AlexNet model, the proposed model performed better and required less training time. The N1 model reduced the training time by 34.58%, the N2 model by 18.25%, and the N3 model by 20.23%. The N1 and N3 resulted in the same size, namely 14.8MB, and the compactness was 92.67%. The size of the N2 model was 29.7MB, and the compactness was 85.29% compactness. The proposed models provide more accuracy and efficiency in classifying plant leaves and can be used as a standalone mobile application that benefits farmers.
Implementation of Intrusion Detection System Using Snort and Log Visualization Using ELK Stack Robbani, Fatih Dien; Haryatmi, Emy; Riyadi, Tri Agus; Supono, Riza Adrianti; Bima Kurniawan, Ary; Rosdiana, Rosdiana
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.901

Abstract

Cyber threats like malware, ransomware, and DDoS attacks demand fast and integrated detection systems. Traditional network monitoring tools often struggle to identify complex real-time attack patterns. This study evaluates the integration of Snort, an Intrusion Detection System (IDS), with the ELK Stack (Elasticsearch, Logstash, Kibana) to detect and visualize cyberattacks effectively. The system was tested against three attack scenarios: a Windows ping flood, port scanning using Zenmap, and SSH brute force attacks via Nmap Scripting Engine (NSE). Wireshark was employed as a supporting tool to monitor raw network traffic. The results indicate that Snort detected all simulated attacks in real time, and the ELK Stack efficiently processed and visualized the alert data. However, limitations in Kibana's dashboard refresh rate slightly hindered real-time monitoring capabilities. Overall, the integration of Snort and the ELK Stack proves effective for network threat detection and analysis, with room for future improvements in visualization performance and automated response mechanisms.
Pengaruh File Apk Terhadap Keamanan Sistem Operasi Android Berdasarkan Analisis Statik dan Dinamik Riyadi, Tri Agus
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 2 (2022): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i2.4677

Abstract

Many devices using Android such as handphone, tablet and others. Android makes the daily works become so easy to activate other devices using IoT technology. To use those facilitate, Android user should install a file such as an apk file. The objective of this research is to examine the impact of an apk file in Android using static and dynamic analysis. First, static analysis was done using Qark and the results are three recommendation such as vulnerable, warning and information. Second, dynamic analysis was done by giving the permission to the apk file when it was installed in Android. The impact of this condition was anyone could access almost all the resources in the Android such as SMS when connected to internet.
Co-Authors Ayunda, Nur Azizah Bagaskoro Bagaskoro Bima Kurniawan, Ary Dzulfikar, Laksamana Akbar Emy Haryatmi Fauzi, Alfharizky Murniyati, Murniyati Riza Adrianti Supono, Riza Adrianti Robbani, Fatih Dien Rosdiana Rosdiana