Garuda - Garba Rujukan Digital

Pelatihan Pemasaran Digital Pada Sekolah Menengah Kejuruan Negeri 1 Cikarang Danny, Muhtajuddin; Naya, Candra; Mulyana, Iwan; Maringan Hutauruk, Basar
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 2 (2024): Desember 2024
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v2i2.100

The development of digital technology provides great opportunities in the world of marketing, including for Vocational High School (SMK) students who are prepared to enter the world of industry and entrepreneurship. This community service activity aims to improve the understanding and skills of SMKN 1 Cikarang students in digital marketing, so that they are able to utilize technology to market products or services more effectively. The methods used in this activity include training, direct practice, and assistance in creating digital marketing strategies. The materials provided include the use of social media, creative content creation, use of marketplaces, and digital marketing optimization techniques. The results of this activity show an increase in students' understanding and skills in managing digital marketing, which is reflected in their ability to create and implement digital-based marketing strategies. It is hoped that this training can provide long-term benefits for students in facing challenges in the world of work and in developing independent digital-based businesses.

Perancangan Sistem E-Parking Berbasis Arduino dengan Kartu RFID Herdiansyah, Muhammad Ferdi; Danny, Muhtajuddin; Astuti, Retno Fitri
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6467

Security and efficiency of parking management are crucial aspects in the operations of companies and educational institutions. At Pelita Bangsa University, the conventional parking system still uses manual methods and paper-based tickets, which is inefficient and potentially creates security issues. In addition, the lack of integration between the parking system and vehicle identification increases the risk of theft. This research aims to design an RFID-based parking system that can be accessed using a Student Identity Card. The system uses RFID at low frequencies to ensure the security and accuracy of vehicle identification. The results show that the RFID system is able to efficiently replace conventional methods, reduce paper usage, and increase parking access speed. The system is also integrated with the student database, enabling better access control and automatic recording of vehicles. The implementation of the system in Pelita Bangsa University's parking area not only improves security but also user experience, with a faster payment process and structured vehicle data management. Hopefully, this system can be an innovative solution that can be applied in various institutions to face the challenges of parking security and efficiency in the digital age.

Analysis of Batrsiyia Product Sales Prediction Using Linear Regression Method Priana, Firzi Cahya; Danny, Muhtajuddin; Edora, Edora
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5776

The rapidly growing herbal and health industry encourages the need for accurate sales planning to avoid the risk of shortages or excess stock. This research aims to predict sales of Batrsiyia products using the Linear Regression algorithm with RapidMiner tools, through analyzing historical data such as sales time, number of products sold, and unit prices to identify patterns and trends to produce accurate predictions. The results show that the Linear Regression algorithm is able to predict sales with an RMSE value of 96687030.354 +/- 0.000, and a Squared Error of 9348381838748252.000 +/- 25081062946532056.000. This approach helps companies understand sales patterns, predict future trends, and optimize stock and marketing strategies. By utilizing data mining-based prediction methods, companies can make more informed decisions in meeting customer needs, maintaining business stability, and improving operational efficiency.

Membangun Sistem Informasi Administrasi Berbasis Web di RW. 024 Karangsatria, Tambun Utara Bekasi Danny, Muhtajuddin; Muhidin, Asep; Butsianto, Sufajar; Triwibowo, Edi
Lentera Pengabdian Vol. 1 No. 02 (2023): April 2023
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/lp.v1i02.37

Administrative services are very important and become a routine for village government. One of them is in the office of the Rukun Warga 024 Karangsatria Village, North Tambun, Bekasi. The importance of letter administration services in government agencies requires accuracy and service optimization, so that this letter administration service runs optimally and there should be no more errors and mistakes in carrying out this administrative service. With the development of information technology, it gives color to the author to create a web-based administrative information sistem at the Rukun Warga office 024 Karangsatria Village, Tambun Utara, Bekasi. This sistem will make it easy for the public to apply for letters such as a certificate of incapacity and a certificate of domicile, and also provide information.. Keywords: Administration, Web, PHP

Pelatihan E-Commerce untuk Pemula pada Sekolah Menengah Kejuruan Negeri 1 Cikarang Danny, Muhtajuddin; Muhidin, Asep; Mulyana, Iwan; Hutauruk, Basar Maringan
VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 3 No. 1 (2025): Juni 2025
Publisher : VINICHO MEDIA PUBLISINDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61946/vidheas.v3i1.114

This community service activity aims to provide basic training on using e-commerce platforms to students at State Vocational High School 1 Cikarang. This training is motivated by the need for digital literacy and entrepreneurship in the digital economy era, particularly for vocational high school students who are geared towards becoming work-ready workers or young entrepreneurs. The activity was implemented face-to-face through material delivery, live demonstrations on the use of e-commerce platforms such as Tokopedia and Shopee, as well as account creation and product marketing simulations. The results of this activity demonstrated an increased understanding of the students' basic e-commerce concepts, how to create an online store, and effective digital marketing strategies. This activity also stimulated students' interest in trying online entrepreneurship. It is hoped that this training will provide students with the initial foundation for utilizing digital technology for productive economic activities in the future.

Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning Danny, Muhtajuddin; Fatchan, Muhamad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1259

The Informatics Engineering Undergraduate Program, Faculty of Engineering, Pelita Bangsa University, implements Outcome Based Education (OBE) by emphasizing the achievement of student Learning Outcomes (LO) as an indicator of the quality of learning in higher education. LO achievement measurement has been mostly done manually through academic assessments, so it is less than optimal in predicting student performance comprehensively. This study aims to build a prediction model for student Learning Outcomes achievement using machine learning algorithms. Research data were obtained from academic results, attendance, lecture activities, and student skill indicators. The prediction model was developed by comparing the Support Vector Machine (SVM), Random Forest, Decision Tree, and Artificial Neural Network (ANN) algorithms, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm provided the best performance with more stable accuracy compared to other algorithms. Furthermore, the distribution of Program Learning Outcomes (PLO) in the curriculum shows: PLO 1 (57 courses), PLO 2 (10 courses), PLO 3 (3 courses), PLO 4 (27 courses), PLO 5 (8 courses), PLO 6 (20 courses), PLO 7 (33 courses), PLO 8 (10 courses), PLO 9 (54 courses), and PLO 10 (57 courses). Based on student scores in 57 courses, the distribution of assessment categories is as follows: Very Good 38.1%, Good 46.3%, Fair 8.4%, and Fail 7.2%. Thus, the PLO achievement of the Informatics Engineering Undergraduate Study Program reached 84.4% in the Good and Very Good categories. This finding provides a significant contribution to efforts to monitor and plan strategies for improving the quality of OBE-based learning adaptively and data-driven.

Model Prediksi Ketercapaian Learning Outcome Based Education Mahasiswa di Program Studi Teknik Informatika Menggunakan Algoritma Machine Learning Danny, Muhtajuddin; Fatchan, Muhamad
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 3 (September 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i3.1259

The Informatics Engineering Undergraduate Program, Faculty of Engineering, Pelita Bangsa University, implements Outcome Based Education (OBE) by emphasizing the achievement of student Learning Outcomes (LO) as an indicator of the quality of learning in higher education. LO achievement measurement has been mostly done manually through academic assessments, so it is less than optimal in predicting student performance comprehensively. This study aims to build a prediction model for student Learning Outcomes achievement using machine learning algorithms. Research data were obtained from academic results, attendance, lecture activities, and student skill indicators. The prediction model was developed by comparing the Support Vector Machine (SVM), Random Forest, Decision Tree, and Artificial Neural Network (ANN) algorithms, with performance evaluation using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest algorithm provided the best performance with more stable accuracy compared to other algorithms. Furthermore, the distribution of Program Learning Outcomes (PLO) in the curriculum shows: PLO 1 (57 courses), PLO 2 (10 courses), PLO 3 (3 courses), PLO 4 (27 courses), PLO 5 (8 courses), PLO 6 (20 courses), PLO 7 (33 courses), PLO 8 (10 courses), PLO 9 (54 courses), and PLO 10 (57 courses). Based on student scores in 57 courses, the distribution of assessment categories is as follows: Very Good 38.1%, Good 46.3%, Fair 8.4%, and Fail 7.2%. Thus, the PLO achievement of the Informatics Engineering Undergraduate Study Program reached 84.4% in the Good and Very Good categories. This finding provides a significant contribution to efforts to monitor and plan strategies for improving the quality of OBE-based learning adaptively and data-driven.

Application of the K-Nearest Neighbor Machine Learning Algorithm to Preduct Sales of Best-Selling Products Danny, Muhtajuddin; Muhidin, Asep; Jamal, Akhiratul
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4063

The development of increasingly intense competition in the business world, accompanied by advances in information technology, has brought retail companies into a situation of tighter and more open competition. PT LG Innotek Indonesia is the only company that produces tuners in Indonesia. Looking at consumer demand, PT LG Innotek must improve product quality, and add products that consumers like and frequently purchase. For this reason, PT LG Innotek Indonesia needs an analysis that can help the company identify products that tend to sell well. This analysis can be carried out through the application of machine learning algorithms, especially the K-Nearest Neighbor method. The aim of this research is to find out how the KNN algorithm performs in predicting products that are selling well and not selling well at PT LG Innotek Indonesia. Based on the analysis results, prediction results were obtained with an accuracy level of 94.74% and an error rate of 5.26%. With this high level of accuracy and low error rate, it can be concluded that the K-Nearest Neighbor method is effectively used to predict sales of PT LG Innotek Indonesia's best-selling products.

Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm Arwan Sulaeman, Asep; Danny, Muhtajuddin; Butsianto, Sufajar; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4105

This research aims to analyze the public's response to ChatGPT through data obtained from Twitter. Apart from that, it is also to understand whether people's responses tend to be positive or negative towards ChatGPT, as well as to test the performance of the K-Nearest Neighbors (KNN) method in classifying sentiment patterns in tweet data. The sentiment analysis method is carried out by dividing public responses into positive and negative categories. Next, the performance of the K-Nearest Neighbors (KNN) method was tested with varying k values ??to classify sentiment patterns in tweet data. This testing includes dataset division, vectorization of text data using TF-IDF, initialization and training of the KNN model, and evaluation of model performance using metrics such as precision, recall, and f1-score. The results of sentiment analysis show that the majority of people's responses to ChatGPT are positive (74.3%), while 25.7% of responses are negative. Performance testing of the KNN model shows that the highest accuracy of 88% is achieved when the k value is 5. Evaluation of model performance also shows satisfactory levels of precision, recall and f1-score. Based on the research results, it was concluded that sentiment analysis and classification using KNN were effective in understanding people's responses to ChatGPT

Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner Surojudin, Nurhadi; Ermanto, Ermanto; Danny, Muhtajuddin; Pratama, Suria
Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i1.4136

Until now there is no treatment that can specifically treat heart failure problems. Heart failure treatment only functions to control symptoms, improve quality of life so that patients can carry out normal activities, and reduce the risk of complications due to heart failure such as heart rhythm disturbances, kidney and lung function disorders, stroke, and sudden death. Heart failure is a condition when the heart pump weakens so that it is unable to circulate sufficient blood throughout the body. This condition is also called congestive heart failure. Until now there is no treatment that can specifically treat heart failure problems. This research is a descriptive study which aims to describe the condition of heart failure. By using classification techniques in data mining on data from patients suffering from heart failure using the Naive Bayes algorithm. By using the Rapid Miner tool, data processing is based on the dataset, using classification techniques and data mining stages to classify data on patients suffering from heart failure. By using the Rapid Miner tool, the data processing that will be used as a data collection in this research is collected into 90% training data and 10% testing data. The research results showed an accuracy rate of 80.00%, precision of 66.67% and recall of 100.00%. Based on the research that has been conducted, it is concluded that classification techniques using the Naive Bayes algorithm can be used to determine the potential for life and death in heart failure sufferers.

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