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All Journal Annual Research Seminar Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Komputer Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Sistem Informasi dan Aplikasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Applied Science, Engineering, Technology, and Education Jurnal Generic KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Ekonomi Jurnal Algoritma ABDIKOM The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Teknik Informatika dan Desain Komunikasi Visual
Dedy Kurniawan
Universitas Sriwijaya
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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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.
Penerapan Random Forest dan XGBoost untuk Analisis Sentimen pada Ulasan Aplikasi M-Pajak Cherliana; M. Rudi Sanjaya; Dwi Rosa Indah; Dedy Kurniawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3370

Abstract

This study aims to evaluate the quality of digital tax services by analyzing the sentiment expressed in user reviews of the M-Pajak app. A dataset of 6,829 reviews was classified into negative, neutral, and positive sentiment, and the study tested the performance of the Random Forest and XGBoost algorithms. Although the test results showed high accuracy rates of 85.21% in the hold-out validation scheme and 90.14% in stratified k-fold cross-validation, an in-depth evaluation using a confusion matrix revealed significant model bias toward the majority class. Key findings indicate that these accuracy figures are misleading because both models completely failed to classify the neutral class, yielding extremely low F1-scores (0.00–0.11). This phenomenon confirms that the primary issue lies not in algorithm selection, but in the extreme data distribution imbalance and the ambiguity of rating-based labeling. The scientific contribution of this research lies in demonstrating that the evaluation of sentiment classification systems must go beyond conventional accuracy metrics. By prioritizing performance stability across each class, the resulting system is expected to provide fairer and more objective evaluation results for public data.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi m.tix – XXI Di Google Play Store Menggunakan Metode Decision Tree Dan Support Vector Machine (SVM) Rahma Ardhia Cahyani; M. Rudi Sanjaya; Dwi Rosa Indah; Dedy Kurniawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3371

Abstract

This study aims to analyze review sentiment and compare the performance of the Decision Tree and Support Vector Machine (SVM) algorithms on a dataset of 14,000 reviews from the m.tix – XXI app, which were classified as 57.4% positive, 33.8% negative, and 8.8% neutral. Through the pre-processing stage, 200 data points were deemed invalid, leaving 13,800 data points suitable for analysis. The dataset was then split into two categories: 80% training data (11,040 reviews) and 20% testing data (2,760 reviews), to support more accurate model performance measurement. The main contribution of this study lies in identifying the advantages of SVM in handling review data, with evaluation results showing that SVM achieved an accuracy of 86%. This is evidenced by the significant superiority of SVM’s F1-score across all categories, particularly for positive sentiment (0.93), negative sentiment (0.80), and neutral sentiment (0.08) compared to the Decision Tree’s accuracy of 83%, with F1-scores of 0.92 for positive sentiment, 0.73 for negative sentiment, and 0.03 for neutral sentiment. This research can be utilized by m.tix-XXI management as a foundation for evaluating and improving the quality of the m.tix-XXI application’s services.
PENERAPAN METODE AHP DAN SMART DALAM PEMILIHAN PROVIDER WIFI DI KOTA PALEMBANG Michael Darwin; M. Rudi Sanjaya; Dedy Kurniawan; Rizka Dhini Kurnia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6910

Abstract

The demand for fast and stable internet services continues to grow in line with the advancement of digital technology in Palembang City. The wide range of Wi-Fi service providers (Internet Service Providers) with varying characteristics makes it difficult for users to objectively determine the best option. This study aims to apply the Analytic Hierarchy Process (AHP) and Simple Multi Attribute Rating Technique (SMART) methods to support decision-making in selecting the best Wi-Fi provider in Palembang City. The AHP method is used to determine the priority weights of each criterion, including price, internet speed, connection stability, and after-sales service. Meanwhile, the SMART method is used to calculate the preference value of each provider alternative based on the obtained weights. The results indicate that internet speed is the most influential criterion in users’ decision-making, followed by stability, price, and after-sales service. Based on the calculation results, Biznet Home ranked highest with a score of 0.966 (on a maximum scale of 1.0) and is therefore recommended as the best Wi-Fi provider in Palembang City. The integration of AHP and SMART methods proves effective in producing systematic, objective, and easily interpretable decision outcomes for users.
ANALISIS SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN BANTUAN SOSIAL MENGGUNAKAN METODE WEIGHTED PRODUCT Lulu Monica Sari; M. Rudi Sanjaya; Dedy Kurniawan; Dwi Rosa Indah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6966

Abstract

Social assistance is one of the government’s efforts to reduce social inequality and achieve social justice for all Indonesian citizens. However, limited budget allocations necessitate the development of an objective selection system to determine eligible recipients. This study aims to apply the Weighted Product (WP) method in analyzing a Decision Support System for determining social assistance recipients in Tanjung Raja Village. Five criteria were used in this study, namely income amount, number of dependents, housing condition, employment status, and asset ownership. The data were obtained through questionnaires that were validated using the Content Validity Index (CVI), yielding a CVI value of 1, which indicates full expert agreement regarding the suitability of the criteria. The results show that the income criterion (C1) has the highest weight of 0.2461, followed by the number of dependents (C2) at 0.1936, employment status (C4) at 0.1907, housing condition (C3) at 0.1897, and asset ownership (C5) with the lowest weight of 0.1797. In the ranking results, alternative A5 obtained the highest vector V value of 0.2076, followed by A3 (0.2061), A4 (0.2024), A1 (0.1931), and A2 (0.1905), indicating that candidate A5 is the most eligible to receive social assistance. The strong validity of the criteria (CVI = 1) and the measurable ranking results demonstrate that the application of the Weighted Product method effectively supports an objective, fast, and accurate decision-making process.
Sentiment and Topic Analysis of Digital Community Application Gamer Reviews using SVM-LDA and CRISP-DM Muhammad Mayda Ary Pratama; Dedy Kurniawan; Ahmad Rifai; Ken Ditha Tania
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5746

Abstract

Impatient behavior among gamers is often reflected in sharp and emotionally charged digital reviews, particularly in the use of community applications such as Discord. This study explores expressions of impatience through sentiment and topic analysis. By adopting the CRISP-DM framework, a total of 10,000 Indonesian-language reviews collected from the Google Play Store were analyzed. The analytical process begins with sentiment labeling using IndoBERT, followed by polarity classification using the Support Vector Machine (SVM) algorithm, and topic exploration through the Latent Dirichlet Allocation (LDA) method. The results indicate that 57.4% of the reviews express positive sentiment, primarily related to voice communication quality and community interaction features. In contrast, 42.6% of the negative reviews commonly convey frustration regarding login issues and verification processes. The SVM model optimized using Bayesian Optimization achieved an accuracy of 90.46%. This study highlights that Discord serves not only as a communication platform but also as a reflection of users’ high expectations for system speed and stability. The main contribution of this research lies in the integration of SVM–LDA methods within the CRISP-DM framework to better understand the digital behavior of Indonesian gamers. The practical implications of these findings provide strategic insights for developers to improve authentication reliability and community features in alignment with user characteristics.
Co-Authors A. Salwa Aurelya Putri Ahmad Rifai Ahmad Rifai Ahmad Rifai Ali Ibrahim Alif, Rahmat Allsela Meiriza Allsela Meiriza, Allsela Amalsyah, Muhammad Rizky Anik Rahmawati, Anik Annisa Khoiriah Annisa Khoiriah Apriansyah Putra Apriansyah Putra Arba'i, Sultan Ari Wedhasmara Ariansyah Saputra Ariansyah Saputra Ariansyah Saputra Athiyyah Nuha Rotifa Bayu Wijaya Putra Billan, Angel Caroline Cherliana Dwi Rosa Indah Endang Lestari Ruskan Endang Lestari Ruskan Enjelina Tampubolon Fathoni - Fathoni Fathoni Fatimah Azzahrah Fidela Tertia Alfino Fika Febrika Hanifah, Izzati Millah Hikmah, Lailatul Huda Ubaya Huda Ubaya Huda Ubaya Jaidan Jauhari Karisa Anjani Fakhri Kemahyanto Exaudi Ken Ditha Tania Ken Ditha Tania Lulu Monica Sari M Rudi Sanjaya M Rudi Sanjaya Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya Sanjaya Michael Darwin Muhammad Mayda Ary Pratama Muhammad Syarief Albani Nadia Anggraini Nilda Syah, Naziah Oktadini, Nabila Rizky Pacu Putra Perdani, Tharisa Antya Purwita Sari Purwita Sari Purwita Sari, Purwita Puti Chalisa Wardhana Putri Eka Sevtiyuni Putri Eka Sevtiyuni Putri, Naila Raihana Rahma Ardhia Cahyani Rahmad Fadli Isnanto Rahmat Alif Akbari Rahmat Fadli Isnanto Rahmat Fadli Isnanto Risyahputri, Aliyananda Rizka Dhini Kurnia Rossi Passarella Rudi Sanjaya Rudi Sanjaya Sanjaya, M Rudi Sevtiyuni, Putri Eka Sultan Arba'i Sutarno Sutarno Sutarno Sutarno Syamsuryadi Syamsuryadi Yadi Utama