Claim Missing Document
Check
Articles

Found 25 Documents
Search

Utilization of Artificial Intelligence in Making Learning Materials Easier and More Innovative at SMA and SMK Syakhyakirti Palembang: Pemanfaatan Artificial Intelligence Dalam Membuat Bahan Ajar Yang Lebih Mudah dan Inovatif di SMA dan SMK Syakhyakirti Palembang Purwita Sari; Sutarno, Sutarno; Rossi Passarella; Rahmat Fadli Isnanto; Dedy Kurniawan; Huda Ubaya; Ahmad Rifai; Kemahyanto Exaudi
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 2 (2025): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v9i2.26455

Abstract

The use of Artificial Intelligence (AI) in developing teaching materials at SMA and SMK Syakhyakirti Palembang provides convenience and introduces new innovations in the field of education. AI is a technology with tremendous potential to improve the quality of education by offering interactive, engaging, and relevant teaching materials tailored to students' needs. In this study, data was collected through interviews with teachers, observations of the learning process, and analysis of teaching materials created with the assistance of AI technology. The results of the study show that AI can enhance efficiency in the creation of teaching materials, provide a broader range of content, and enable customization of materials based on students' abilities and interests. Additionally, AI supports teachers in monitoring students' learning progress through more accurate and structured data analysis. By implementing this technology, the learning process at SMA and SMK Syakhyakirti Palembang has become more effective, modern, and innovative. This study also provides important recommendations, including training for teachers in the use of AI, the development of supportive technological infrastructure, and strategies for integrating AI into teaching practices. These efforts are expected to be adopted by other educational institutions to foster sustainable improvements in the quality of education.
Sistem Informasi Geografis Pemetaan Menara Telekomunikasi dengan Pengecekan Jarak Menggunakan Metode Haversine Arba'i, Sultan; Wedhasmara, Ari; Fathoni, Fathoni; Putra, Apriansyah; Kurniawan, Dedy
Jurnal Teknik Informatika dan Sistem Informasi Vol 9 No 3 (2023): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v9i3.6219

Abstract

DISKOMINFO OKU Regency provides recommendations for the establishment of telecommunication towers. Then do the planning and arrangement of telecommunication towers. However, this is still done manually based on map visualization in printed books, so when changes occur, you have to reprint the book, and you have to re-plan with Microsoft Excel, which makes the process more difficult and time consuming. In addition, there is no digital tower information service causing a lack of information for tower providers, so that the construction of towers has not been properly organized. So the solution to this problem is to build a system that can manage data, can map telecommunication towers and can plan the need for shared towers for the next 5 years by taking into account population, cellular service users, traffic requirements, number of BTS and tower coverage radius, and can provides information on checking coordinates by applying the haversine method which is useful so that the construction of new towers can be properly arranged in the system, where the system is made based on a website with a design using the waterfall method. The results of this study are an application that can assist the process of planning and arrangement of towers, both visual maps, tower requirements, checking coordinates and so on, which is done computerized.
Rebranding Soklat Brew Batang dan Aplikasinya pada Media Promosi Kurniawan, Dedy; Rahmawati, Anik; Nilda Syah, Naziah
Jurnal Teknik Informatika dan Desain Komunikasi Visual Vol 4 No 2 (2025): Jurnal Teknik Informatika dan Desain Komunikasi Visual
Publisher : Fakultas Komputer Dan Desain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51792/jtd.v4i2.04

Abstract

Soklat Brew is a chocolate beverage business formerly known as Chocolate Mas Mboy Semarang. The renaming represents an adaptive strategy in response to the evolving trends of contemporary beverage markets and aims to establish a more modern brand image. However, this name transformation has not yet been supported by a strong visual identity and consistent promotional strategies. This study aims to redesign the visual identity of Soklat Brew Batang using the Robin Landa method, which consists of the stages of orientation, analysis, concept development, design, and implementation. The outcome of this design process includes a new logo, tagline, and a comprehensive visual identity system applied across various promotional media, both digital and print such as packaging, merchandise, social media, and booth displays. All visual elements are compiled into a Graphic Standard Manual to ensure consistent brand application. Through this approach, Soklat Brew is expected to enhance its market competitiveness and broaden its market reach.
Deteksi Komentar dan Analisis Sentimen Promosi Judi Online pada Youtube Menggunakan IndoBERT dan XGBoost Putri, Naila Raihana; Kurniawan, Dedy; Tania, Ken Ditha
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8421

Abstract

YouTube, as a highly interactive platform, has become a medium for online gambling promotions, raising legal issues under the Electronic Information and Transactions (ITE) Law and social risks, particularly for adolescents. This study aims to analyse public responses to gambling-related comments and to develop an automatic detection system using Natural Language Processing (NLP). The research follows the Knowledge Discovery in Databases (KDD) stages, including web scraping, preprocessing, text transformation, model training, and evaluation. Sentiment analysis was performed on 999 comments labelled positive, negative, and neutral. Detection of promotional content was tested using IndoBERT and TF-IDF-based XGBoost, with 587 training samples and 885 external testing samples at an 80:20 ratio. The results show that the majority of comments (52.65%) are positive with a fairly high average confidence score (0.914), indicating public support for the eradication of online gambling. Meanwhile, negative comments (24.72%) with a confidence score of 0.888 generally contained criticism of the rampant practice of gambling promotion or YouTube's weak moderation system. For automatic detection, IndoBERT achieved superior performance with 0.94 accuracy and F1-score and only 10 misclassifications, significantly outperforming XGBoost, which reached 0.73 accuracy with 47 errors. This study highlights the effectiveness of transformer-based models in detecting gambling promotions while also indicating strong public support for eradication efforts. These findings provide an empirical foundation for advancing research on adaptive automated moderation systems capable of identifying concealed patterns of illicit content in digital platforms, particularly in the detection of online gambling promotional comments within the YouTube ecosystem.
Comparison of XGBoost and LSTM in Knowledge Discovery for GrokAI Mobile Application Sentiment Analysis Risyahputri, Aliyananda; Kurniawan, Dedy; Tania, Ken Ditha
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8651

Abstract

Generative AI has provided real benefits in key sectors of the public sector. However, the rapid expansion of AI assistant services also raises concerns about whether newly released products can consistently meet user expectations, especially as negative experiences are increasingly expressed through public reviews. Its positive impacts encourage competitive rivalry among AI assistant product developers, including xAI, which also participates by formulating the Grok AI application. As a relatively new product with over 50 million downloads, GrokAI needs to perform an evaluation to maintain its competitiveness. This condition leads to the research goal of analyzing user sentiment toward GrokAI application through reviews on Google Play Store and comparing the performance of Machine Learning and Deep Learning classification models within the framework of Knowledge Discovery in Databases (KDD). This study uses 11,108 review data classified using the VADER Lexicon method, resulting in 7,633 positive reviews and 3,475 negative reviews. The data is then tested on XGBoost (Extreme Gradient Boosting) and LSTM (Long-Short Term Memory) models. The results show that the XGBoost model performs slightly better with an accuracy of 87.22%, compared to LSTM, which reaches 86.58%. However, both models exhibit significant performance disparities in classifying negative classes due to the extreme difference in data quantity. The knowledge discovery process reveals that the majority of positive sentiment appreciates the free access and general functions of the application. Meanwhile, negative sentiment focuses on complaints related to response time, output quality, and specific features such as image and voice. The main recommendation is to maintain the advantage of free access also improve features and processing logic to sustain loyalty and service quality. Future research is suggested to test models with more balanced data and optimize dataset cleaning to improve accuracy in minority classes.