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All Journal Jurnal Ilmiah Teknik (JUIT) International Journal Science and Technology (IJST) Jurnal Teknik dan Science (JTS)
Ivan Maurits
Universitas Gunadarma
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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SISTEM DETEKSI PEMAKAIAN MASKER PADA WAJAH SECARA REAL TIME MENGGUNAKAN FRAMEWORK TENSORFLOW DAN LIBRARY OPENCV Althaf Adhari Rachman; Ivan Maurits
Jurnal Ilmiah Teknik Vol. 2 No. 1 (2023): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v2i1.496

Abstract

At the end of December 2019, there was a mysterious virus that attacked the city of Wuhan in China. Which at the beginning of 2020 the new virus was a new type, namely (SARS-CoV-2) and the disease was called Coronavirus disease 19 (COVID-19). The World Health Organization (WHO) provides instructions to maintain a minimum distance of 1 meter, always wash hands with soap, and always use a mask when leaving the house. The purpose of this writing is to create a program to detect wearing masks on the face, which makes it easier for users to detect who is wearing a mask or not. The method used in this writing uses the SDLC model or System Data Life Cycle by carrying out five stages namely, planning, analysis, design, implementation, and testing. The design uses flowcharts and navigation structures as well as two UML diagrams, namely use case diagrams and activity diagrams. Then for making the program using Python as a programming language, Tensorflow as a framework, Keras and Opencv as libraries, and Visual Studio Code as a text editor. From the results of trials that have been carried out using three different devices and with different specifications, this system can detect mask objects very well and this detection system can also detect more than one person wearing a mask, or not wearing a mask.
APLIKASI MEMO ONLINE (E-MEMO) LABORATORIUM TEKNIK INFORMARTIKA BERBASIS ANDROID Robby Nugraha; Ivan Maurits; M Achsan Isa Al Anshori
Jurnal Ilmiah Teknik Vol. 2 No. 1 (2023): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v2i1.503

Abstract

Practicum is one of the academic activities that must be carried out by students at Gunadarma University. It's not just one department that does practicums, but all majors do practicums. One of the practicums that students must do is the Informatics Engineering Laboratory which is intended for students of the Faculty of Industrial Technology. In practicums in several majors, including practicum in the Informatics Engineering Laboratory, if the practitioner does not take part in the practicum for any reason, except for getting a special case, they must make a memo as the practitioner's identity to take part in the practicum again. To make a memo, the practitioner must come to the Informatics Engineering Laboratory Staff room to make a memo. The practitioner can make memos if the practitioner follows the specified time to make memos. Not only does the memo maker have to comply with the time the memo is written, but taking memos also must comply with the time the memo is taken. It can take quite a long time to get the memo because you have to come on time to the Informatics Engineering Laboratory Staff room and wait to pick up the memo. Therefore, the author took the initiative to create a mobile application called "Online Memo Application (E-Memo) Android Based Informatics Engineering Laboratory". With this application, the practitioner can make memos anywhere and anytime and also the practitioner can come to the Informatics Engineering Laboratory Staff room to validate the memo which is useful for checking the authenticity of the memo. This application is made with the Dart programming language with the Flutter framework. This application is also made using Android Studio and Firebase as a place to accommodate data on practitioners who have made memos. This application runs on Android Version 5.0 with the code name Nougat and above.
HUMAN GENDER DETECTION SYSTEM BASED ON FACIAL IMAGE USING CONVOLUTIONAL NEURAL NETWORK ALGORITHM Abdul Roid; Ivan Maurits
International Journal Science and Technology Vol. 2 No. 1 (2023): March: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v2i1.847

Abstract

The demand for system automation has been continuously increasing with the current technological developments. One of these advancements is in the implementation of face recognition. Camera capabilities have evolved from merely capturing images or videos to being able to process the resulting images. Facial images contain a wealth of information, one of which is the gender information of the individuals. To obtain this information, facial image classification using deep learning is required. In this scientific paper, the author utilizes the Convolutional Neural Network algorithm implemented with the Python programming language and employs TensorFlow as its framework. The research aims to predict human gender based on facial images. The dataset used in this study is obtained from the kaggle.com dataset provider, consisting of 9,600 male facial data and 9,600 female facial data. The data is divided into a training and testing set, with an 80% ratio for training data and a 20% ratio for testing data from the total available data. The model training process is performed for 15 epochs with 768 steps in each epoch. The testing results show that the Convolutional Neural Network method achieves a validation accuracy of approximately 91%. The developed program runs well through a webcam.
PERAN SISTEM INFORMASI AKUNTANSI (SIA) TERHADAP KEUANGAN Wahyu Widjayanti; Windy Dwiparaswati; Ivan Maurits
Jurnal Teknik dan Science Vol. 3 No. 3 (2024): Oktober: Jurnal Teknik dan Science
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jts.v3i3.1913

Abstract

The Accounting Information System (AIS) plays a crucial role in optimizing financial processes across various types of organizations. AIS implementation enables real-time integration of financial data, improves the accuracy of financial reporting, and facilitates better strategic decision-making. This study aims to evaluate the effectiveness of AIS in enhancing operational efficiency and financial reporting accuracy. The study employs a case analysis method on organizations that have implemented AIS. The results indicate that AIS significantly contributes to improving financial management efficiency, accelerating report processing time, and strengthening internal controls. Thus, AIS proves to be an effective tool for transparent and accountable financial management.
Implementation of Laplacian-Based Image Sharpening on X-Ray Images Priyo Sarjono Wibowo; Ivan Maurits
Jurnal Ilmiah Teknik Vol. 5 No. 2 (2026): Mei: Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v5i2.2867

Abstract

X-ray imaging plays an essential role in modern medical diagnostics; however, the resulting images often suffer from low contrast and unclear edge structures. These limitations reduce anatomical visibility and may negatively affect diagnostic accuracy. Therefore, there is a need for a simple, effective, and computationally efficient image enhancement method that can improve X-ray image sharpness while preserving critical diagnostic information. Objective: This study aims to implement and analyze the Laplacian method for enhancing the sharpness of chest X-ray images using digital image processing techniques based on Python and OpenCV. The study also evaluates the effectiveness of the proposed method in improving the visibility of anatomical structures in radiographic images. Methodology: This research adopts a quantitative experimental approach based on computational implementation. The dataset consists of digital X-ray images processed through several stages, including image acquisition, grayscale conversion, Laplacian filtering, image sharpening, and result visualization. The entire implementation is conducted using Python and OpenCV in the Google Colab cloud computing environment. Data analysis is performed using a descriptive-visual approach by comparing original and enhanced images. Findings: The results show that the Laplacian method significantly improves edge visibility in X-ray images. Anatomical structures such as ribs, lung boundaries, and fine edge details become more distinguishable compared to the original images. Local contrast enhancement is also observed, indicating that high-frequency information is effectively amplified. However, a slight increase in noise is detected due to the sensitivity of the Laplacian operator to high-frequency components. Implications: The findings suggest that the Laplacian method can be effectively used as a lightweight preprocessing technique for medical image enhancement, particularly in cloud-based environments such as Google Colab. The method is suitable for educational purposes, research applications, and engineering systems that require low computational cost while maintaining effective image enhancement performance. Originality: The originality of this study lies in the development of a simple, reproducible, and cloud-based implementation framework for Laplacian-based X-ray image enhancement using Python and OpenCV. The main contribution is a lightweight computational approach that balances implementation simplicity with effective image sharpening performance.
Implementation of Machine Learning for Freshwater Fish Detection Ivan Maurits; Priyo Sarjono Wibowo; Mochammad Akbar Marwan
Jurnal Ilmiah Teknik Vol. 5 No. 1 (2026): Januari: Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v5i1.1427

Abstract

Recent advancements in mobile technology and machine learning have enabled the development of practical tools, such as Android applications, to assist in real-time fish species identification, particularly in the context of freshwater fisheries in Indonesia. Objective: This research aims to design and implement an Android application that helps anglers accurately identify and categorize freshwater fish species native to Indonesia. The app integrates machine learning-based image recognition to provide a practical tool for fishing enthusiasts while supporting conservation efforts for Indonesia’s freshwater biodiversity. Methodology: A quantitative approach was employed, focusing on mobile application development using Kotlin for Android. The application uses a TensorFlow Lite-based image recognition model for real-time image processing on mobile devices. Data for the model were gathered from publicly available fish species datasets. The system was tested across multiple Android devices to evaluate compatibility and efficiency. Findings: The application successfully identifies and classifies various freshwater fish species in Indonesia, providing users with accurate species profiles, biological characteristics, and appropriate bait recommendations. The system operates efficiently in real-time on mobile devices without relying on cloud computing, ensuring accessibility in remote areas. Testing results across different Android devices confirm the app's robustness and user-friendly interface. Implications: This research demonstrates the integration of mobile technology and machine learning in fisheries, offering a valuable tool for both recreational and professional anglers. The app promotes awareness of freshwater fish species preservation and supports sustainable fishing practices. Additionally, it can serve educational purposes by enhancing knowledge of local biodiversity and fostering fish conservation efforts. Originality: This research introduces an innovative mobile-based solution to freshwater fish identification. Unlike previous studies, which focused on desktop-based methods, this study offers a practical mobile application that operates efficiently in real-time on-site. The originality lies in combining machine learning and mobile technology to address fish identification challenges while contributing to biodiversity conservation.
Co-Authors Abdul Roid Althaf Adhari Rachman M Achsan Isa Al Anshori M. Achsan Isa Al Anshori Mochammad Akbar Marwan Priyo Sarjono Wibowo Priyo Sarjono Wibowo Robby Nugraha Wahyu Widjayanti Windy Dwiparaswati