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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer Networks, Architecture and High Performance Computing
Rahman, Ben
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Diagnosa Penyakit Tulang Belakang Menggunakan Metode Forward Chaining dan Certainty Factor Jayanti, Riza Dwi; Rahman, Ben; Fitri, Iskandar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3497

Abstract

In the field of medicine, especially orthopedics, there are several types of spinal diseases including scoliosis, lordosis, kyphosis, and spondylosis. Therefore, an expert system is needed to diagnose spinal diseases. Based on this problem, we performed an exponential comparison of the two hybrid methods. With the final value of the combined forward chaining & naive bayes method of 8.89, and 9.40 the highest final value of the combined forward chaining method and certainty factor. So that in this research, forward chaining and certainty factor methods are used which are designed based on a website, using Sublime Text 3 programming tools and PHP and MySQL as databases. From the results of application testing and manual calculations with 30 sample data, it was concluded that 7 users or about 23% entered the level of confidence in the possibility of developing spinal disease and 23 users or 77% of the total testing stated at the level of confidence in the probability of developing spinal disease with a value of the highest confidence of 88.5% in spondylosis disease.
Analisa Penerapan Metode MABAC dengan Pembobotan Entropy dalam Penilaian Kinerja Dosen di Era Society 5.0 Ahyuna, Ahyuna; Rahman, Ben; Nugroho, Fifto; Nirawana, I Wayan Sugianta; Karim, Abdul
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3511

Abstract

In the era of society 5.0, it is very influential on the education sector which is experiencing increasingly sophisticated technological changes, so that lecturers are expected to be able to combine learning with technology so that students' insight into technology is growing. However, in the case of a performance appraisal process, several problems often occur because the large number of lecturers will affect the time of the performance appraisal process. In assessing the performance of lecturers in the era of society 5.0, there are several criteria including Dynamic, Innovative, Number of Scientific Publishes, Discipline and Digital Skills. Therefore, the author applies a MABAC and ENTROPY method in order to find accurate and logical results. So with that, the author adopted a method and produced the highest ranking, namely on behalf of Dito Putro Utomo with a total value of 0.39925
Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3 Saputra, Adi Dwifana; Hindarto, Djarot; Rahman, Ben; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12218

Abstract

Tomato diseases vary greatly, one of which is tomato leaf disease. Some variants of leaf diseases include late blight, septoria leaf, yellow leaf curl virus, bacteria, mosaic virus, leaf fungus, two-spotted spider mite, and powdery mildew. By knowing the disease on tomato leaves, you can find medicine for the disease. So that it can increase the production of tomatoes with good quality and a lot of quantity. The problem that often occurs is that farmers cannot determine the disease in plants, they try to find suitable herbal medicines for their plants. After being given the drug, many plants actually died due to the pesticides given to the tomato plants. This is detrimental to tomato farmers. This problem is caused by incorrect disease detection. Therefore, this study aims to solve the problem of disease detection in tomato plants, in a more specific case, namely tomato leaves. Detection in this study uses a deep learning algorithm that uses a Convolutional Neural Network, specifically GoogleNet and EfficientNetB3. The dataset used comes from kaggle and google image. Both data sets have been pre-processed to match the data set class. Image preprocessing is performed to produce appropriate image datasets and improve performance accuracy. The dataset is trained to get the model. The training using GoogleNet resulted in an accuracy of 98.10%, loss of 0.0602 and using EfficientNetB3 resulted in an accuracy of 99.94%, loss: 0.1966.
Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects Suherman, Endang; Rahman, Ben; Hindarto, Djarot; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12378

Abstract

Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that is well-known for its effectiveness in image recognition tasks, while DETR utilizes Transformers to study object representations directly from images. We tested our system performance on the COCO dataset and demonstrated that ResNet-50 + DETR achieves a better level of accuracy than DETR models that do not use ResNet-50. In addition, we also show that ResNet-50 + DETR can detect objects more quickly than similar traditional CNN models. The results of our research show that the use of ResNet-50 in the DETR system can improve object detection performance in images by about 90%. We also show that using ResNet-50 in DETR systems can improve object detection speed, which is a huge advantage in real-time applications. We hope that the results of this research can contribute to the development of object detection technology in images in the world of computer vision.
Drowsy Detection in the Eye Area using the Convolutional Neural Network Wedha, Alessandro Benito Putra Bayu; Rahman, Ben; Hindarto, Djarot; Wedha, Bayu Yasa
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12386

Abstract

Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.
Proposed Enterprise Architecture on System Fleet Management: PT. Integrasia Utama Wedha, Alessandro Benito Putra Bayu; Rahman, Ben; Hindarto, Djarot; Wedha, Bayu Yasa
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12387

Abstract

An information technology consulting firm that specializes in Global Positioning Systems provides fleet management services for many of its clients. The systems currently used by companies require more advanced modernization to ensure optimal service delivery. To overcome this challenge, a proposed enterprise architecture on system fleet management is presented in this paper. The proposed enterprise architecture is a comprehensive solution that includes the necessary hardware, software and operational processes to improve fleet management services. The proposed architecture is based on the Enterprise Architecture, which enables the integration of various systems and applications used by companies. The proposed architecture includes modules for vehicle tracking, fuel management, maintenance scheduling and driver performance monitoring. These modules work together to provide real-time data on fleet operations, enabling companies to make informed decisions regarding their fleet management services. The proposed architecture also incorporates an easy-to-use interface that simplifies the fleet management process and enhances customer satisfaction. The proposed system is scalable and easily adaptable to meet service requirements across multiple customers. In conclusion, the proposed enterprise architecture for system fleet management provides a comprehensive solution to the current challenges faced by companies as a corporate fleet service provider. The proposed architecture will improve service, reduce costs, and increase customer satisfaction.
Diagnostic on Car Internal Combustion Engine through Noise Sjah, William Surya; Rahman, Ben; Hindarto, Djarot; Wedha, Alessandro Benito Putra Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12392

Abstract

Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.
Detects Damage Car Body using YOLO Deep Learning Algorithm Gustian, Yonathan Wijaya; Rahman, Ben; Hindarto, Djarot; Wedha, Alessandro Benito Putra Bayu
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12394

Abstract

This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.
Voice Command-Based IoT on Smart Home Using NodeMCU ESP8266 Microcontroller Shakaramiro, Muhammad Ariel; Gunaryati, Aris; Rahman, Ben
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 1 (2024): March 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i1.8287

Abstract

This research focuses on developing a Smart Home prototype that integrates the Internet of Things (IoT) and voice command using the NodeMCU ESP8266 microcontroller. This system allows users to control household devices such as lights, gates, and fans with voice commands through voice-enabled devices. The prototype utilizes NodeMCU ESP8266 to connect the devices to a WiFi network. The developed voice recognition system can accurately identify voice commands and send instructions to NodeMCU ESP8266 to control the corresponding devices. The test results demonstrate the prototype's efficiency in automating household devices through voice commands. Consequently, users can enhance comfort and energy efficiency within their homes. This research opens opportunities for the development of smarter and user-friendly Smart Home systems in the future. Response testing of the Blynk application showed a 100% success rate, with an average response time of less than ten seconds. WiFi network testing was carried out with the IP Address 192.168.101.137, resulting in good functional performance even in the presence of physical obstacles, and the device can operate well up to a distance of 22 meters.
Enhancing IT Employee Placement Using SMARTER with Centroid Rank Order Weighting for Candidate Suitability Rahman, Ben; Adinda, Saskia; Handayani, Adelia Putri
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 1 (2024): March 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i1.9165

Abstract

In an era characterized by constant evolution in digital technology, the significance of Information Technology (IT) within organizations is of utmost importance. Efficient recruitment processes and appropriate placement of IT personnel are crucial for a company's success. This research aims to develop a candidate assessment system using the SMARTER (Smart Simple Multi-Attribute Rating Technique Exploiting Rank) approach combined with the Rank Order Centroid weighting method to assist HR departments in selecting suitable IT candidates. Addressing the challenges faced by HR directors lacking IT expertise, this study offers an innovative solution to enhance alignment between candidates and IT position requirements. Through the analysis of nine specific criteria, including education, work experience, and English proficiency, the system is designed to provide more accurate candidate placement recommendations. The findings of this research demonstrate significant potential in improving IT recruitment processes and contributing significantly to the literature.
Co-Authors Abdul Karim Adinda, Saskia Ahyuna Ahyuna, Ahyuna Aris Gunaryati, Aris Budiman, Tegar Fauziah Fauziah Fifto Nugroho Gustian, Yonathan Wijaya Handayani, Adelia Putri Hindarto, Djarot I Wayan Sugianta Nirawana Iskandar Fitri, Iskandar Jayanti, Riza Dwi Muhammad Syaifudin Putra, Abdul Rahman Wijaya Santoso, Handri Saputra, Adi Dwifana Septi Andryana Shakaramiro, Muhammad Ariel Sjah, William Surya Subagja , Mifta Suherman, Endang Teddy Mantoro Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa