Garuda - Garba Rujukan Digital

MEMBANGUN JIWA PEKA (PRODUKTIF, EDUKATIF, KOOPERATIF, DAN AKSI) PADA KARANG TARUNA DI DESA WANI II KECAMATAN TANANTOVEA KABUPATEN DONGGALA Rizal; Nursalim
Sambulu Gana : Jurnal Pengabdian Masyarakat Vol. 4 No. 3 (2025): September 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/sambulu_gana.v4i3.8528

This community service program aims to develop a sensitive spirit among the younger generation by strengthening the productive, educational, cooperative, and action-oriented aspects of Karang Taruna members in Wani II Village, Tanantovea District, Donggala Regency. The challenges faced include low youth participation in positive activities, underutilization of their potential, and the influence of negative lifestyles that can harm their future. The program's implementation methods included outreach, workshops, skills training, mentoring, and social action activities involving all Karang Taruna members. Results demonstrated increased motivation and social awareness among members, improved organizational management and entrepreneurship skills, and the creation of stronger collaboration among youth. Through this program, Karang Taruna in Wani II Village has transformed into a platform for developing youth character and creativity, while simultaneously serving as a driving force for social development at the village level

UNDERSTANDING MICROCONTROLLERS AND ROBOTICS THROUGH THE APPLICATION OF INQUIRY METHODS IN BASIC ELECTRONICS COURSES Cut Susan Octiva; Maryadi, Maryadi; Dikky Suryadi; Nursalim, Nursalim; T. Irfan Fajri
Jurnal Cakrawala Ilmiah Vol. 3 No. 12: Agustus 2024
Publisher : Bajang Institute

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This analysis aims to explain the understanding of microcontrollers and robotics through the application of inquiry methods in basic electronics courses. The type of research used is classroom research. The sample of this study was 40 private campus students in Medan City who were taken randomly. This quantitative method uses inquiry methods learning model. The results of the study indicate that the application of the inquiry method can make students understand microcontrollers and robotics in basic electronics courses

Scale-Up Strategy of Village-Owned Enterprises in Sigi District: Institutional Transformation Towards Digitalization Umar Umar; Cahyaning Raheni; Nursalim Nursalim; Rizkiani Iskandar; Tovan Tovan; Mutmainah Mutmainah
Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE) Vol 8 No 2 (2025): Sharia Economics
Publisher : Universitas KH. Abdul Chalim Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/iijse.v8i2.6109

This study examines the strategy for improving Village-Owned Enterprises in Sigi Regency. This research aims to develop strategies through institutional transformation towards digitalization. This type of research is descriptive qualitative which aims to explore information by investigating events on the object of research through observation, interviews, and focus group discussions. The results show that out of 153 Village-Owned Enterprises in Sigi Regency, only 17 are productive out of 77 that are still active, so there is a need to increase human resource capacity and develop digital infrastructure through multi-party collaboration. The Asset-Based Community Development (ABCD) approach has an impact on the development of Village-Owned Enterprises.

Deep Learning Based Augmented Reality for 3D Object Recognition Muhamad Ziaul Haq; Nursalim Nursalim
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5431

Augmented Reality (AR) technology is being widely adopted in various fields such as education, entertainment, and creativity. However, there are still some challenges to be overcome in recognizing and rendering three-dimensional (3D) objects accurately and in real-time. We implemented an AR system that utilizes deep learning techniques to recognize 3D objects with improved accuracy levels. Our approach involved training a Convolutional Neural Network (CNN) model using 3D object datasets captured from different viewpoints. The development included designing the network architecture, training the model, evaluating its accuracy, and integrating it into an AR platform based on Unity 3D and Vuforia SDK. The results indicated that the system could achieve recognition of the 3D objects with an average accuracy of 93.7%, precision of 92.4%, and recall of 91.8%, all while keeping response times below 0.8 seconds. Objects with complex geometries like cars and chairs had recognition rates above 94%, while those with similar textures had lower accuracy because of detailed surface complexities. It allows stable interactive visualization of objects in augmented reality even under different lighting conditions and camera angles. Combining deep learning with AR improves the quality of object recognition and provides a more realistic interactive experience. This paper discusses the advances made in AR technology toward better adaptability and efficiency, which can be applied to interactive education, industrial simulation, architecture, and medical fields.

AUTOMATED ESSAY SCORING FOR STUDENT EXAMS USING DEEP NLP MODELS Andi Kaimuddin; Bryant Ritchie Trisnodjojo; Muhamad Ziaul Haq; Nursalim; Riezky Purnama Sari
JTH: Journal of Technology and Health Vol. 4 No. 1 (2026): July: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v4i1.858

The increasing use of essay-based examinations in higher education has created significant challenges in maintaining efficient, objective, and consistent assessment processes. Manual essay grading is time-consuming and susceptible to subjective judgment, particularly when evaluating large numbers of student responses. Therefore, this study aims to develop and evaluate an Automated Essay Scoring (AES) system based on Bidirectional Encoder Representations from Transformers (BERT) to improve the accuracy and consistency of student essay assessment. This research employed an experimental quantitative approach using 2,500 student essay responses, of which 2,340 valid responses were retained after preprocessing and data cleaning. The dataset was divided into training, validation, and testing subsets using a 70:15:15 ratio. The proposed model was fine-tuned using the AdamW optimizer with a learning rate of 2 × 10⁻⁵, a batch size of 16, and 8 training epochs. Model performance was evaluated using Quadratic Weighted Kappa (QWK), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results demonstrate that the proposed BERT model achieved a QWK score of 0.872, indicating strong agreement with human evaluators, while obtaining an MAE of 0.418 and an RMSE of 0.593, reflecting relatively low prediction errors. Comparative evaluation also showed that the proposed BERT model outperformed conventional baseline approaches in automated essay scoring, confirming the effectiveness of contextual language representations for understanding semantic information in student essays. These findings indicate that the proposed framework provides a reliable and efficient solution for automated essay assessment, offering practical benefits for improving scoring consistency, reducing lecturers' workload, and supporting the implementation of intelligent assessment systems in higher education.

Impact of Using Big Data Analisys in Increasing Personalization of Learning Rahmawati Rahmawati; Nursalim Nursalim; Agry Alfiah; Andi Hasyim; Aldi Bastiatul Fawait
Journal of Computer Science Advancements Vol. 2 No. 2 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i2.906

In today’s digital era, big data analytics has become a very relevant topic to improve learning personalisation as it can collect and analyse very large and complex data. Big data analytics can lead to a more efficient learning system by collecting and analysing huge and complex data. In education, big data analytics can be used to understand students’ learning behaviour, their needs and preferences, so that learning and learning outcomes can be improved. This research is conducted with the aim of using big data analytics to improve learning personalisation. It also aims to find out the challenges of using big data analytics to improve learning personalisation. The method used in this research is quantitative method. This method is a way of collecting numerical data that can be tested. Data is collected through the distribution of questionnaires addressed to students. Furthermore, the data that has been collected from the distribution of the questionnaire, will be accessible in Excel format which can then be processed with SPSS. From the research results, it can be seen that the big data analysis has shown that the use of more detailed and accurate data can help teachers find students’ special needs and improve learning effectiveness. As a result, teachers can create learning strategies that are better suited to students’ needs and improve their learning outcomes. From this study, we can conclude that the use of big data analytics in improving personalisation allows teachers to understand better the individual needs and preferences of students, so that more suitable learning plans can be developed and student engagement can be improved.

Deep Learning Based Augmented Reality for 3D Object Recognition Muhamad Ziaul Haq; Nursalim Nursalim
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5431

Augmented Reality (AR) technology is being widely adopted in various fields such as education, entertainment, and creativity. However, there are still some challenges to be overcome in recognizing and rendering three-dimensional (3D) objects accurately and in real-time. We implemented an AR system that utilizes deep learning techniques to recognize 3D objects with improved accuracy levels. Our approach involved training a Convolutional Neural Network (CNN) model using 3D object datasets captured from different viewpoints. The development included designing the network architecture, training the model, evaluating its accuracy, and integrating it into an AR platform based on Unity 3D and Vuforia SDK. The results indicated that the system could achieve recognition of the 3D objects with an average accuracy of 93.7%, precision of 92.4%, and recall of 91.8%, all while keeping response times below 0.8 seconds. Objects with complex geometries like cars and chairs had recognition rates above 94%, while those with similar textures had lower accuracy because of detailed surface complexities. It allows stable interactive visualization of objects in augmented reality even under different lighting conditions and camera angles. Combining deep learning with AR improves the quality of object recognition and provides a more realistic interactive experience. This paper discusses the advances made in AR technology toward better adaptability and efficiency, which can be applied to interactive education, industrial simulation, architecture, and medical fields.

User Satisfaction Classification of Tiktok Shop Skincare Products Using C4.5 and Random Forest for Recommendation Strategy Nursalim Nursalim; Muhamad Ziaul Haq; A. Nurul Hidayat; Budi Mulyono
Sharia Economic and Management Business Journal (SEMBJ) Vol. 7 No. 2 (2026): Sharia Economic and Management Business
Publisher : Yayasan Darussalam Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62159/sembj.v7i2.2234

Background: TikTok Shop has become an important social commerce platform for skincare purchases; however, product recommendations are not always perceived as relevant by users. A data-driven satisfaction classification model is therefore needed to support more targeted recommendation strategies. Method: This study used a quantitative approach involving 150 TikTok Shop users who had purchased skincare products. Data were collected through an online questionnaire containing 14 Likert-scale items and three recommendation-preference items. Instrument quality was evaluated using corrected item-total correlation and Cronbach Alpha. The C4.5 decision tree and Random Forest models were evaluated using stratified 10-fold cross-validation. Results: All 14 items were valid, with item-total correlations ranging from 0.619 to 0.881, and the overall Cronbach Alpha was 0.969. The satisfaction classes were balanced, consisting of 75 satisfied and 75 unsatisfied respondents. Information gain analysis identified product delivery as the most influential attribute, with a gain value of 0.4551. C4.5 achieved 85.33% accuracy, while Random Forest achieved 83.33% accuracy. Conclusion: C4.5 provided competitive performance and stronger interpretability than Random Forest for this dataset. The resulting classification rules can be used to prioritize delivery reliability, application usability, and product quality in skincare recommendation strategies.

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