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All Journal TEKNIK INFORMATIKA Telematika : Jurnal Informatika dan Teknologi Informasi InComTech: Jurnal Telekomunikasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Technology and Computer Engineering IJID (International Journal on Informatics for Development) IJISTECH (International Journal Of Information System & Technology) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Berdaya Mandiri IJISTECH JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Parta: Jurnal Pengabdian Kepada Masyarakat Applied Science and Technology Research Journal
Eko Dwi Nugroho
Institut Teknologi Sumatera
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Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naive Bayes at Institut Teknologi Sumatera Algifari, Muhammad Habib; Ashari, Ilham Firman; Nugroho, Eko Dwi; Afriansyah, Aidil; Vebriyanto, Mario
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6761

Abstract

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.
Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans Nugroho, Eko Dwi; Verdiana, Miranti; Algifari, Muhammad Habib; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Rizkita, Alya Khairunnisa; Winarta, Richard Arya; Gunawan, David
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8886

Abstract

Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
Online Integrated Development Environment (IDE) in Supporting Computer Programming Learning Process during COVID-19 Pandemic: A Comparative Analysis Kusumaningtyas, Kartikadyota; Nugroho, Eko Dwi; Priadana, Adri
IJID (International Journal on Informatics for Development) Vol. 9 No. 2 (2020): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09202

Abstract

COVID-19 has spread to various countries and affected many sectors, including education. New challenges arise in universities with study programs related to computer programming, which require a lot of practice. Difficulties encountered when students should setting up the environment needed to carry out programming practices. Furthermore, they should install a text editor called Integrated Development Environment (IDE) to support it. There is various online IDE that supports computer programming. However, students must have an internet connection to use it. After all, many students cannot afford to buy internet quotas to access online learning material during the COVID-19 pandemic. According to these problems, this study compares several online IDEs based on internet data usage and the necessary supporting libraries' availability. In this study, we only compared eleven online IDEs that support the Python programming language, free to access, and do not require logging in. Based on the comparative analysis, three online IDEs have most libraries supported. They are REPL.IT, CODECHEF, and IDEONE. Based on internet data usage, REPL.IT is an online IDE that requires the least transferred data. Moreover, this online IDE also has a user-friendly interface to place the left and right sides' code and output positions. It prevents the user from scrolling to see the results of the code that has been executed. The absence of advertisements also makes this online IDE a more focused appearance. Therefore, REPL.IT is highly recommended for users who have a limited internet quota, primarily to support the learning phase of computer programming during the COVID-19 pandemic.
The Evaluation of LSB Steganography on Image File Using 3DES and MD5 Key Ashari, Ilham Firman; Nugroho, Eko Dwi; Andrianto, Dodi Devrian; Yusuf, M. Asyroful Nur Maulana; Alkarkhi, Makruf
JITCE (Journal of Information Technology and Computer Engineering) Vol. 8 No. 1 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.1.8-18.2024

Abstract

Information security is paramount for individuals, companies, and governments. Threats to data confidentiality are increasingly complex, demanding strong protection. Therefore, cryptography and steganography play pivotal roles. This study proposes the utilization of LSB (Least Significant Bit) steganography on image files employing the 3DES (Triple Data Encryption Standard) algorithm. The aim is to facilitate secure transmission and reception of data, including confidential messages, within digital information media. The research methodology involves implementing 3DES + LSB using Image Citra and innovating 3DES + MD5 Hash in .txt files. The results and discussions described include, among others, Pseudocode, Cryptographic Testing, and Steganography Testing. Based on the results of program analysis and testing, it can be concluded that the more messages that are inserted in the image, the more pixel differences there are in the stego image. The more colors in the image to which the message will be inserted, the more pixel differences in the stego image will be. The images that stego objects can present are only images with .png and .jpeg extensions. Testing from the fidelity aspect, the average PSNR obtained is 66,365, meaning that the stego image quality is very good. Testing from the recovery aspect, from 4 tested stego images, showed that messages can be extracted again. Testing of the robustness spec using two attack techniques, namely rotation, and robustness, shows that the message cannot be extracted from the image. Testers of the computation time, from testing 1-1000 characters, show the average time required for computation is about 0.798 seconds.
COMPARATIVE OF LSTM AND GRU FOR TRAFFIC PREDICTION AT ADIPURA INTERSECTION, BANDAR LAMPUNG Ilham Firman Ashari; Verlina Agustine; Aidil Afriansyah; Nela Agustin Kurnianingsih; Andre Febrianto; Eko Dwi Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6569

Abstract

The Tugu Adipura intersection in Bandar Lampung is a vital traffic hub connecting four major roads. Rapid population growth and increasing vehicle numbers challenge traffic flow and urban quality of life. Despite its importance, there is limited research using predictive models to analyze traffic patterns at complex intersections in mid-sized Indonesian cities. This study addresses that gap by examining traffic growth on four connected roads using deep learning models. Traffic data were collected hourly from June 1, 2021, to July 31, 2023. A comparative analysis of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models was conducted, with SGD and Adam as optimizers. Results show the GRU model with Adam achieved the lowest RMSE (0.23) on road section 1, indicating its superior ability to model short-term fluctuations and non-linear growth in traffic volume. The study offers practical implications for traffic management by highlighting GRU’s capacity to capture seasonal trends and rapid growth, supporting proactive infrastructure planning and congestion mitigation strategies. These findings demonstrate the value of data-driven approaches in enhancing transportation systems in growing urban areas.
Evaluation of the Implementation of Augmented Reality on Vegetable Objects Using Marker-Based Tracking and SUS Adrian Putradinata, Gusti Made; Ashari, Ilham Firman; Nugroho, Eko Dwi
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 2 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i2.24770

Abstract

With the rapid development of technology in the field of information media, which initially received information from mass media, schools, electronic media and the development of Augmented Reality technology that can be applied to the field of information. Augmented Reality is the incorporation of two-dimensional virtual objects into a real environment. AR has many benefits in the field of entertainment and others. The purpose of this study is to implement information media in the form of augmented reality with a Marker-based tracking approach on leaf vegetable objects using markers in the form of paper and can display information in virtual objects, namely the content and benefits, processing of vegetables that appear as objects, producing markers that can be detected with several tests of distance, light, and marker resistance. At a distance of 10 cm – 50 cm the marker can be detected using both smartphones (Vivo and Samsung), but at a distance of 100 cm samsung smartphones are no longer able to detect due to camera differences. Marker light testing can be detected at 5 – 200 Lux light but in >1 lux marker cannot be detected and displays objects. The usability test uses the System Usability Scale (SUS) method which results in a final score of 71,583 which means that the system that has been developed is already feasible to give to users.
Digitalisasi Informasi Sebagai Penunjang Efektivitas Pelayanan Administrasi Koperasi Argo Mulyo Lestari Untoro, Meida Cahyo; Kurniawansyah, Apri; Perdana, Agung Mahadi Putra; Praseptiawan, Mugi; Nugroho, Eko Dwi; Afriansyah, Aidil; Yulita, Winda; Verdiana, Miranti
Parta: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/parta.v4i2.4588

Abstract

Koperasi memiliki peran penting dalam perekonomian Indonesia. Argo Mulyo Lestari, salah satu koperasi yang mengelola dan menyediakan bibit pohon dan buah-buahan serta melakukan pendistribusian keseluruh wilayah Indonesia. Hasil observasi dengan cara wawancara mendapatkan data tentang proses bisnis yang dilakukan koperasi masih tergolong kuno, dengan cara mencatat pada buku, menyimpan pada excel. Proses bisnis yang tidak diimbangi dengan Teknologi informasi dan komunikasi mengakibatka, terjadi duplikasi data dan akses terbatas bagi seluruh anggota koperasi. Tim pengusul membuat usulan untuk menyelesaikan permasalahan dengan cara Teknologi Tepat Guna Digitalisasi Administrasi Koperasi Argo Mulyo Lestari. Tujuan dari digitalisasi, mempermudah, meningkatkan, dan keterbukaan data dalam melaksanakan proses bisnis. Digitalisasi mencangkup proses bisnis administrasi umum, simpan pinjam, keuangan dan pelaporan keuntungan serta kerugian. Teknologi tepat guna akan dievaluasi dengan menggunakan usability test. Hasil dari pengambdian, koperasi Argo Mulyo Lestari sudah menerapkan digitalisasi teknologi yang transparan, dan bertanggung jawab. Digitalisasi administrasi merupakan langkah yang tepat dalam menghadapi perkembangan teknologi informasi yang semakin canggih.
Analysis Comparison of Depression Levels Based on Gender and Academic Factors of Students Verdiana, Miranti; Nugroho, Eko Dwi; Anggraini, Leslie; Bagaskara, Radhinka; Yulita, Winda; Afriansyah, Aidil; Algifari, Muhammad Habib
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.7975

Abstract

This study aims to analyze the level of depression among university students by examining gender and several academic indicators. The dataset includes responses from 27,901 students across various regions, with variables covering age, gender, academic pressure, study satisfaction, work/study hours, CGPA, and depression status. The analytical methods applied in this study include the chi-square test to evaluate the association between gender and depression status, point-biserial correlation to examine the relationship between numeric variables and depression, and logistic regression to develop a prediction model. The chi-square test results revealed no significant relationship between gender and depression (p = 0.774), indicating that depression affects both genders. In contrast, academic pressure exhibited the strongest correlation with depression status (r = 0.47), followed by work/study hours (r = 0.209) and study satisfaction (r = -0.168). The Logistic Regression model constructed using the four most relevant variables demonstrated satisfactory performance, achieving 75.5% accuracy and 82.1% recall in identifying students experiencing depression. These findings highlight the critical role of academic-related factors—particularly academic pressure—in influencing students' mental health. Therefore, targeted academic support strategies are essential to mitigate depression risks in higher education environments.
Co-Authors Adri Priadana Adrian Putradinata, Gusti Made Afriansyah, Aidil Algifari, Muhammad Habib Alkarkhi, Makruf Amelia Oktavia Amirul Iqbal Andre Febrianto Andrianto, Dodi Devrian Anggraini, Leslie Bagaskara, Radhinka David Gunawan, David Firmansyah, Hafiz Budi Idza Ramaulkim Ilham Firman Ashari Kurniawansyah, Apri Kusumaningtyas, Kartikadyota Miranti Verdiana Muhammad Nadhif Athalla Muhammad Reza Kahar Aziz Nashrullah, Muhammad Nela Agustin Kurnianingsih Perdana, Agung Mahadi Putra Praseptiawan, Mugi Prasetyawan, Purwono Radhinka Bagaskara Rizkita, Alya Khairunnisa Untoro, Meida Cahyo URI ARTA RAMADHANI Vebriyanto, Mario Verdiana, Miranti Verlina Agustine Winarta, Richard Arya Winda Yulita Yulita, Winda Yusuf, M. Asyroful Nur Maulana