Articles
<![CDATA[Pelatihan Pemanfaatan Teknologi Informasi Untuk Meningkatkan Kompetensi Siswa Yayasan Alby Wan Nur ]]>
<![CDATA[Naya, Candra]]>;
<![CDATA[Butsianto, Sufajar]]>;
<![CDATA[Danny, Muhtajuddin]]>;
<![CDATA[Triwibowo, Edi]]>;
<![CDATA[Hasyim, Wachid]]>
<![CDATA[ VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 1 No. 2 (2023): Desember 2023 ]]>
Publisher : <![CDATA[VINICHO MEDIA PUBLISINDO ]]>
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DOI: 10.61946/vidheas.v1i2.53
<![CDATA[Proses pembelajaran yang efektif dan efisien membutuhkan strategi pembelajaran yang tepat. Seorang guru, harus mampu merancang dan melaksanakan pembelajaran yang baik sehingga mampu mencapai tujuan yang ditetapkan. Untuk dapat merancang dan melaksanakan pembelajaran dibutuhkan pemahaman terkait strategi mengajar serta penguasaan terhadap media ajar. Pembelajaran yang efektif terlihat dari bagaimana pembelajaran tersebut dapat menjawab kebutuhan siswa, serta tuntutan kemajuan jaman. Pelatihan pemanfaatan teknologi dalam mengajar menjadi hal yang tepat mengingat pendidikan di Indonesia harus dapat menyesuaikan dengan kemajuan teknologi. Pemanfaatan teknologi dalam mengajar akan mendorong guru untuk menciptakan proses pembelajaran berbasis teknologi. Pelatihan ini dilakukan pada guru di Siswa Yayasan Alby Wan Nur dengan fokus pemanfaatan teknologi dalam pembelajaran yang dilakukan secara daring. Melalui kegiatan pelatihan ini, ada peningkatan kemampuan pada para guru di Siswa Yayasan Alby Wan Nur dalam hal pengelolaan pembelajaran berbasis teknologi di mana kemampuan tersebut berada pada kompetensi pedagogik. Kata Kunci: Teknologi, Kompetensi, Pembelajaran Efektif]]>
<![CDATA[Penggunaan Teknologi Artificial Intelligence Dalam Penulisan Buku ]]>
<![CDATA[Danny, Muhtajuddin]]>;
<![CDATA[Rilvani, Elkin]]>;
<![CDATA[Edora]]>;
<![CDATA[Mulyana, Iwan]]>
<![CDATA[ VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024 ]]>
Publisher : <![CDATA[VINICHO MEDIA PUBLISINDO ]]>
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DOI: 10.61946/vidheas.v2i1.77
<![CDATA[Training on the use of Artificial Intelligence (AI) technology in book writing is a response to the rapid development of information and communication technology which has influenced various aspects of life, including the publishing and writing industry. AI technology has developed rapidly and shown its potential in various fields, including data analysis, natural language processing (NLP), and machine learning (Machine Learning). AI algorithms can generate text, analyze writing style, and provide relevant recommendations for writers. AI can help writers in various stages of the writing process, from brainstorming ideas, creating initial drafts, to editing. Tools like GPT-4 can generate paragraphs or chapters based on specific instructions, which helps speed up the writing process and overcome creative barriers. The use of AI can improve the quality of writing by providing suggestions for editing and improvement. AI can also ensure consistency in writing style and use of terminology, which is especially important in writing technical books or series. In the digital era, speed and quality of content production are key factors in competition. Authors and publishers who are able to utilize AI technology can have a competitive advantage by producing books faster and with better quality.]]>
<![CDATA[Pelatihan Penggunaan Teknologi Dalam Mengelola Dan Mempromosikan Acara ]]>
<![CDATA[Arwan, Asep]]>;
<![CDATA[Muhtajuddin Danny]]>;
<![CDATA[Andriani]]>;
<![CDATA[Amat Damuri]]>
<![CDATA[ VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Vol. 2 No. 1 (2024): Juni 2024 ]]>
Publisher : <![CDATA[VINICHO MEDIA PUBLISINDO ]]>
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DOI: 10.61946/vidheas.v2i1.82
<![CDATA[Dalam era digital yang semakin maju, penggunaan teknologi menjadi krusial dalam berbagai aspek kehidupan, termasuk dalam pengelolaan dan promosi acara. Pelatihan ini bertujuan untuk membekali peserta dengan pengetahuan dan keterampilan praktis dalam memanfaatkan berbagai alat dan platform teknologi guna meningkatkan efisiensi dan efektivitas manajemen acara serta strategi pemasaran yang lebih luas dan terarah. Materi pelatihan mencakup penggunaan perangkat lunak manajemen acara, teknik pemasaran digital, analitik media sosial, serta pemanfaatan teknologi interaktif untuk meningkatkan keterlibatan peserta. Dengan mengikuti pelatihan ini, peserta diharapkan mampu merancang, mengelola, dan mempromosikan acara dengan lebih profesional dan inovatif, sesuai dengan tuntutan zaman. Studi kasus dan praktik langsung akan menjadi bagian integral dari pelatihan untuk memastikan transfer pengetahuan yang aplikatif dan relevan.]]>
<![CDATA[Desain Media Pembejaran Berbasis Digital Pada TK Islam Pelita Insan Perum. Bumi Citra Lestari ]]>
<![CDATA[Wiyanto, Wiyanto]]>;
<![CDATA[Nugroho, Agung]]>;
<![CDATA[Suwarno, Agus]]>;
<![CDATA[Danny, Muhtajuddin]]>
<![CDATA[ Jurnal Pelita Pengabdian Vol. 2 No. 1 (2024): Januari ]]>
Publisher : <![CDATA[DPPM Universitas Pelita Bangsa ]]>
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DOI: 10.37366/jpp.v2i1.3966
<![CDATA[Tridharma Perguruan Tinggi merupakan kewajiban bagi Dosen yaitu untuk memenuhi bisang pendidikan, penelitian, dan pengabdian. Dalam rangka pemenuhan kewajiban tersebut, berbagai macam bentuk pengabdian terhadap sesama hendaknya dapat dilakukan oleh Dosen dan dapat melibatkan mahasiswa Universitas Pelita Bangsa khususnya pada Prodi Teknik Informatika dan Prodi Teknik Industri. Program pengabdian yang dilakukan ini adalah melakukan pembuatan desain Media Pembelajaran Berbasis Digital pada TK Islam Pelita Insan yang beralamat di Perumahan Bumi Citra Lestari Jl. Cempaka XI Blok C66 No. 19, Ds. Waluya, Kec. Cikarang Utara, Kab. Bekasi, yang merupakan wadah pembekalan Dosen dan juga sebagai pembinaan mahasiswa untuk menyalurkan minat dan bakatnya dalam mengamalkan profesionalisme disiplin ilmu ke tengah masyarakat. Manfaat lain dari desain Media Pembelajaran Berbasis Digital adalah meningkatkan pemahaman bagaimana cara menyajikan pembejaran yang interaktif dan menyenangkan untuk Siswa/I TK Islam Pelita Insan. Media pembelajaran digital ini dibuat dengan Microsoft Power Point dan disimpan dalam format *.ppsx. Pengabdian ini diselenggarakan dengan menggunakan metode mendesain Media Pembelajaran Berbasis Digital yang interaktif dan menyenangkan serta mudah dipahami oleh Siswa/I TK Islam Pelita Insan]]>
<![CDATA[Digitalisasi Kurikulum Dinniyah TK Islam Pelita Insan Perum Bumi Citra Lestari Kabupaten Bekasi ]]>
<![CDATA[Wiyanto, Wiyanto]]>;
<![CDATA[Nugroho, Agung]]>;
<![CDATA[Suwarno, Agus]]>;
<![CDATA[Danny, Muhtajuddin]]>
<![CDATA[ Dedikasi: Jurnal Pengabdian Lentera Vol. 1 No. 08 (2024): September 2024 ]]>
Publisher : <![CDATA[Lentera Ilmu Nusantara ]]>
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DOI: 10.59422/djpl.v1i08.511
<![CDATA[Dalam rangka pemenuhan kewajiban Tridharma Perguruan Tinggi yang merupakan kewajiban bagi Dosen yaitu untuk memenuhi bidang pendidikan, penelitian, dan pengabdian, dengan berbagai macam bentuk pengabdian terhadap masyarakat maka dapat dilakukan oleh Dosen dan dapat melibatkan mahasiswa. Dalam hal ini Universitas Pelita Bangsa khususnya pada Prodi Teknik Informatika dan Prodi Teknik Industri melakukan program pengabdian yang berkelanjutan dari semester Ganjil 2023-2024 lalu. Kelanjutan dari program pengabdian pada periode ini yaitu mengembangkan digitalisasi kurikulum dinniyah yang digunakan TK Islam Pelita Insan agar mudah diterapkan dan interaktif pada proses pembelajaran Dinniyah. Hal ini merupakan wadah pembekalan Dosen dan juga sebagai pembinaan mahasiswa untuk menyalurkan minat dan bakatnya dalam mengamalkan profesionalisme disiplin ilmu ke tengah masyarakat. Manfaat lain dari digitalisasi kurikulum dinniyah adalah meningkatkan pemahaman bagaimana cara menyajikan pembejaran yang interaktif dan menyenangkan untuk Siswa/I TK Islam Pelita Insan. Pengabdian ini diselenggarakan dengan menggunakan metode mendesain Media Pembelajaran Berbasis Digital yang interaktif dan menyenangkan serta mudah dipahami oleh Siswa/I TK Islam Pelita Insan.]]>
<![CDATA[Application of the K-Nearest Neighbor Machine Learning Algorithm to Preduct Sales of Best-Selling Products ]]>
<![CDATA[Danny, Muhtajuddin]]>;
<![CDATA[Muhidin, Asep]]>;
<![CDATA[Jamal, Akhiratul]]>
<![CDATA[ Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024 ]]>
Publisher : <![CDATA[Yayasan Cita Cendekiawan Al Khwarizmi ]]>
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DOI: 10.47709/brilliance.v4i1.4063
<![CDATA[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.]]>
<![CDATA[Sentiment Analysis on Social Media X (Twitter) Against ChatGBT Using the K-Nearest Neighbors Algorithm ]]>
<![CDATA[Arwan Sulaeman, Asep]]>;
<![CDATA[Danny, Muhtajuddin]]>;
<![CDATA[Butsianto, Sufajar]]>;
<![CDATA[Pratama, Suria]]>
<![CDATA[ Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024 ]]>
Publisher : <![CDATA[Yayasan Cita Cendekiawan Al Khwarizmi ]]>
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DOI: 10.47709/brilliance.v4i1.4105
<![CDATA[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]]>
<![CDATA[Implementation of the Naive Bayes Algorithm for Death Due to Heart Failure Using Rapid Miner ]]>
<![CDATA[Surojudin, Nurhadi]]>;
<![CDATA[Ermanto, Ermanto]]>;
<![CDATA[Danny, Muhtajuddin]]>;
<![CDATA[Pratama, Suria]]>
<![CDATA[ Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024 ]]>
Publisher : <![CDATA[Yayasan Cita Cendekiawan Al Khwarizmi ]]>
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DOI: 10.47709/brilliance.v4i1.4136
<![CDATA[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.]]>
<![CDATA[Recruitment Classification of Security Unit PT. Satria Kencana Abadi Using Naïve Bayes Method ]]>
<![CDATA[Rilvani, Elkin]]>;
<![CDATA[Surojudin, Nurhadi]]>;
<![CDATA[Danny, Muhtajuddin]]>;
<![CDATA[Yoga Pratama, Evan]]>
<![CDATA[ Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024 ]]>
Publisher : <![CDATA[Yayasan Cita Cendekiawan Al Khwarizmi ]]>
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DOI: 10.47709/brilliance.v4i1.4138
<![CDATA[To get human resources according to company standards, the problem faced in the company is the difficulty of the selection process with a short time and the complexity of the decision making process resulting in subjective decision making. The purpose of this research is to assist the assessment process in making decisions for determining the selection of security units (SATPAM) to be more targeted so that it can help the company. In this study the data used were 697 data with 558 training data and 139 testing data. This test data was carried out using the Naïve Bayes algorithm method to classify so that it can determine accurate and efficient decision making, using Rapidminer tools which have 82 accuracy, 01%, 81.61% Precision, and 88.75% recall. This shows that the Naïve Bayes algorithm method has a good performance in determining decision making during the selection of security forces (SATPAM) at PT. Satria Kencana Abadi.]]>
<![CDATA[The Sentiment Analysis of Bekasi Floods Using SVM and Naive Bayes with Advanced Feature Selection ]]>
<![CDATA[Amali, Amali]]>;
<![CDATA[Maulana, Donny]]>;
<![CDATA[Widodo, Edy]]>;
<![CDATA[Firmansyah, Andri]]>;
<![CDATA[Danny, Muhtajuddin]]>
<![CDATA[ Brilliance: Research of Artificial Intelligence Vol. 4 No. 1 (2024): Brilliance: Research of Artificial Intelligence, Article Research May 2024 ]]>
Publisher : <![CDATA[Yayasan Cita Cendekiawan Al Khwarizmi ]]>
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DOI: 10.47709/brilliance.v4i1.4268
<![CDATA[Flood management in Bekasi City poses significant challenges, necessitating strategies grounded in an understanding of community sentiment. This study aims to develop and optimize sentiment analysis of social media data related to flooding using Support Vector Machine (SVM) and advanced feature selection techniques. The primary goal is to enhance the accuracy of classifying public sentiment toward flood management efforts in Bekasi City. Data is collected from various social media platforms, preprocessed, and analyzed using SVM with feature selection techniques like Information Gain and Analysis of Variance (ANOVA). (Thoriq et al., 2023) Our findings indicate that using SVM with advanced feature selection significantly improves sentiment classification accuracy compared to standard methods. These results offer insights into public perceptions, helping policymakers improve management strategies and communication for flood events. This method assists in understanding community responses and pinpointing critical areas needing attention. Moreover, this study contributes to disaster management in urban flood-prone areas by presenting a methodological approach applicable to other disaster contexts. Integrating social media sentiment analysis with advanced machine learning techniques offers a robust framework for real-time public sentiment assessment, enhancing disaster response strategies. Furthermore, these techniques help create a more resilient urban environment by improving the efficiency and effectiveness of flood management practices. This comprehensive tool is essential for better preparedness, response, and recovery from flood events, ultimately enhancing community resilience and safety in Bekasi City. This research is part of machine learning in disaster management and a valuable asset for city planners and disaster professionals around the world.]]>
