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All Journal TEKNIK INFORMATIKA Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Pemikiran dan Pengembangan Sekolah Dasar (JP2SD) Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains JOIV : International Journal on Informatics Visualization IT JOURNAL RESEARCH AND DEVELOPMENT Journal of Teaching and Learning in Elementary Education Dinamisia: Jurnal Pengabdian Kepada Masyarakat Journal of Psychology and Instructions Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Instructional Development Journal Journal of Applied Engineering and Technological Science (JAETS) International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Aksara: Jurnal Ilmu Pendidikan Nonformal Unri Conference Series: Community Engagement Jurnal Manajemen Informatika Jayakarta Journal for Lesson and Learning Studies Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Teknik Informatika Journal of Applied Business and Technology Jurnal Elementaria Edukasia
Dewi Nasien
Instititut Bisnis Dan Teknologi Pelita Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences Other

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Community Service in Winda Songket Riau: Implementation of Sustainopreneurship and Women's Empowerment: Pengabdian Kepada Masyarakat di Winda Songket Riau: Penerapan Sustainopreneurship dan Pemberdayaan Perempuan Nasien, Dewi; Adiya, M. Hasmil; Siddik, M.; Suroyo, Suroyo; Mukhsin, Mukhsin
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 5 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i5.23057

Abstract

Community Service Activity (PkM) at Winda Songket, Riau, focuses on efforts to preserve the cultural heritage of songket weaving as part of Riau Malay culture, as well as improving the welfare of women through the application of a sustainopreneurship model. This program integrates ecological, economic, and social concepts to increase added value in the production of traditional Riau songket cloth. In addition, this program also emphasizes the importance of women's roles in maintaining and developing the songket business, thereby improving their economic welfare. This service involves socialization, training, and mentoring for songket craftsmen, hoping to encouragethem to adopt innovations in sustainable business models
COSINE SIMILARITY FOR ESSAY ANSWER DETECTION Setiawan, Laurensius Rendi; Nasien, Dewi
Journal of Applied Business and Technology Vol. 1 No. 1 (2020): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.31 KB) | DOI: 10.35145/jabt.v1i1.21

Abstract

Saint Mary Senior High School is the one of the famous private schools in Pekanbaru. examination in Saint Mary Senior High School has used a smartphone for multiple choice questions, while in essay questions the pen-and-paper is still used (manual). The number of examination scripts received from students is a problem for a teacher to see the similarity of students' answers with the answer key. At present, there is no system that can assist in checking answers. This study began with the selection stage, which is done by the interview process and filling out the questionnaire. Then proceed with an analysis of this case is used to look for possible development needs of the system. Then the programming language used is PHP and uses MySql database. Web-Based Essay Answer Detection Application Using Cosine Similarity Method in Saint Mary Senior High School. With the application of the essay answer detection application in Saint Mary Senior High School, it is hoped that it will be easier for the teacher to conduct the exam. Besides, it is expected that the application of a computerized system and the use of a database can accelerate the processing of student ranking grades effectively and efficiently.
Troubleshooting Generator Sets using Expert System Nopendri, Nopendri; Nasien, Dewi
Journal of Applied Business and Technology Vol. 1 No. 2 (2020): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1388.387 KB) | DOI: 10.35145/jabt.v1i2.36

Abstract

PT. Zaman Teknindo (PT. ZT) is a company engaged in Mechanical and Engineering field which is registered as one PT. Telkomsel vendors. The problems that occur at PT. ZT, if the power outage and generator set (generator) does not start automatically. The corrective team on duty at that time will go to the field and find a solution to the problem. With a lack of knowledge from the corrective team, they need help from the mechanical team. The mechanical team is an external team of PT. ZT. To bring a mechanical team requires an enormous cost and a relatively long time needed to get to the location. Based on the problem above, this study proposes a forward chaining expert system that is by depth-first search using the certainty factor method. To prove whether a fact is certain or not, it must be in the metric form in generator troubleshooting. The research methodology used the Software Development Life Cycle (SDLC) starting from problem identification, analysis, design, coding, testing and maintenance. This system is web-based, so users can easily access and choose symptoms of the damage. With this system makes it easy for PT. ZT especially the corrective team in the field can easily find out the damage symptoms without having to meet with experts directly.
Increasing Trust in AI with Explainable Artificial Intelligence (XAI): A Literature Review Dewi Nasien; M. Hasmil Adiya; Devi Willeam Anggara; Zirawani Baharum; Azliza Yacob; Ummi Sri Rahmadhani
Journal of Applied Business and Technology Vol. 5 No. 3 (2024): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v5i3.193

Abstract

Artificial Intelligence (AI) is one of the most versatile technologies ever to exist so far. Its application spans as wide as the mind can imagine: science, art, medicine, business, law, education, and more. Although very advanced, AI lacks one key aspect that makes its contribution to specific fields often limited, which is transparency. As it grows in complexity, the programming of AI is becoming too complex to comprehend, thus making its process a “black box” in which humans cannot trace how the result came about. This lack of transparency makes AI not auditable, unaccountable, and untrustworthy. With the development of XAI, AI can now play a more significant role in regulated and complex domains. For example, XAI improves risk assessment in?finance by making credit evaluation transparent. An essential application of?XAI is in medicine, where more clarity of decision-making increases reliability and accountability in diagnosis tools. Explainable Artificial Intelligence?(XAI) bridges this gap. It is?an approach that makes the process of AI algorithms comprehensible for people. Explainable Artificial Intelligence (XAI) is the bridge that closes this gap. It is a method that unveils the process behind AI algorithms comprehensibly to humans. This allows institutions to be more responsible in developing AI and for stakeholders to put more trust in AI. Owing to the development of XAI, the technology can now further its contributions in legally regulated and deeply profound fields.
Optimization of Body Mass Index Classification Using Machine Learning Approach for Early Detection of Obesity Risk Dewi Nasien; Steven Owen; Fenly Fenly; Johanes Johanes; Frendly Lombu; Leo Leo; Zirawani Baharum
Journal of Applied Business and Technology Vol. 6 No. 3 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i3.201

Abstract

This study aims to optimize the classification of obesity risk at an early stage using Principal Component Analysis (PCA), which is an important technique in machine learning. PCA is used to reduce the dimensionality of data, maintain important information without losing data, and has the advantage of reducing complexity which usually increases the risk of overfitting. The obesity dataset will be classified using algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Gradient Boosting Linear, and XGBoost. Specifically, each algorithm is chosen because of its respective advantages: KNN for nonlinear data, SVM for high-dimensional data, and Random Forest and XGBoost for complex data patterns. Evaluation is carried out using metrics such as accuracy, precision, recall, and F1-score to assess the performance of the algorithm. The results show that the Random Forest and XGBoost algorithms provide the best performance in terms of accuracy, especially when all dataset features are used without PCA reduction. This study is expected to be a consideration in determining the best algorithm for obesity classification, supporting early detection, and facilitating decision making in health analysis.
Automated Waste Classification Using YOLOv11 A Deep Learning Approach for Sustainable Recycling Dewi Nasien; M. Hasmil Adiya; Mochammad Farkhan; Ummi Sri Rahmadhani; Azurah A. Samah
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.205

Abstract

The rapid increase in waste generation due to urbanization and population growth has necessitated more efficient waste management solutions. Traditional waste sorting methods rely on manual labor, which is time-consuming, error-prone, and inefficient at large scales. This paper proposes an automated waste classification system using YOLOv11, the latest iteration of the YOLO family, which is known for its high speed and accuracy in object detection. By leveraging a custom dataset containing 10,464 labeled waste images from various categories—such as biodegradable, plastic, metal, paper, and glass—this study trains and evaluates a deep learning model capable of real-time waste identification and categorization. Experimental results demonstrate that YOLOv11 achieves high detection accuracy, with an overall classification accuracy of 94% and a mean average precision (mAP) exceeding previous methods. The model effectively differentiates between various waste types, though some misclassifications occur, particularly between visually similar materials like transparent plastic and glass. Performance metrics, including precision and recall, indicate the robustness of the proposed system in real-world applications. This research highlights the potential of YOLOv11 for integration into smart waste management systems, such as automated sorting machines and AI-powered recycling bins, to enhance efficiency and reduce environmental impact. Future work will focus on optimizing model performance by incorporating additional training data, applying advanced image augmentation techniques, and exploring hybrid approaches such as texture analysis and spectral imaging to improve classification accuracy. The implementation of this technology is expected to streamline waste recycling processes, minimize contamination in recyclable materials, and contribute to sustainable waste management practices.
Pemberdayaan Guru SD Kabupaten Bengkalis melalui Integrasi Kegiatan Coding Berbasis STEM untuk Meningkatkan Kemampuan Computational Thinking Rifqa Gusmida Syahrun Barokah; Neni Hermita; M. Jaya Adi Putra; Suroyo Suroyo; Nur Asiah; Gustimal Witri; Dewi Nasien
Unri Conference Series: Community Engagement Vol 7 (2025): Seminar Nasional Pemberdayaan Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/unricsce.7.399-406

Abstract

Characterized by rapid technological progress, education today demands teachers who are not only digitally literate but also capable of fostering computational thinking (CT) in students. However, many elementary teachers still face challenges in integrating coding and STEM principles into classroom learning. This community service program was designed to empower elementary school teachers in Bengkalis Regency through the integration of STEM-based coding to enhance their CT skills. The two-day program involved 50 teachers from 22 schools and employed a combination of conceptual sessions on deep learning and CT, unplugged coding practices using Kids First Coding and Robotics (Smart Bricks), plug-in coding practices with Scratch for Arduino (S4A) and workshops to develop STEM-based science lesson plans (RPP) showcased through microteaching. Results revealed an average post-test CT score of 79.3, with 82% of teachers achieving mastery (≥70). Teachers successfully created innovative lesson plans integrating CT into science topics such as phase changes of matter and the solar system. The microteaching sessions showed that participants could contextualize coding-STEM learning effectively. Teacher reflections emphasized the need for continuous training, sufficient facilities, and the establishment of a coding-STEM teacher community. In conclusion, integrating STEM-based coding effectively enhanced teachers’ CT skills and pedagogical competence while supporting the goals of Indonesia’s Merdeka Curriculum.
Co-Authors Adiya, M. Hasmil Agus Joko Purwanto ahmad kamal, ahmad Ahmad Mulyadi Akbar Marunduri, Alberta Alin Meisya Putri Alyauma Hajjah Amalia Sapriati Andi Andi Angriawan, Sherkhing Anwar Senen Azliza Yacob Azurah A. Samah Butar-Butar, Rio Juan Hendri Charles Lo, Kevin Charles Wijaya, Ryan Cia, Alexander Cici Oktaviani Dahliyusmanto, Dahliyusmanto Darwin, Ricalvin Deny Deny, Deny Deny Jollyta Desnelita, Yenny Devi Willeam Anggara Devi Willieam Anggara Diniya, Diniya Dipuja, Diah Anugrah Dwi Pamungkas Erlin Erma Yunita Farkhan, Mochammad Fenly Fenly Feri Candra Firman Afriadi Fitri Indriani Fitriani, Mike Frendly Lombu Go, Jerry Gusman, Taufik Gustientiedina Gustimal Witri Habibollah Haron Ihsan, M. Nurul Iis Afrianty Iis Afrianty Imran B. Mu’azam Jack Billie Chandra Jesi Alexander Alim Johan Johan Johanes Johanes Leo Leo Leo Winata, Andrean Lina Warlina M. JAYA ADI PUTRA, S.Si, M.Pd, M. JAYA ADI M.C, Richard Mahbubah, Khoiro Mahmud Dwi Sulistiyo Marlim, Yulvia Nora Mestika Sekarwinahyu Mike Fitriani Mochammad Farkhan Muhammad Rakha Muhammad Ridha Nazara, Elvin Meiwati Neni Hermita Nopendri Nopendri Nor Fatihah Ismail Nur Asiah Nurwijayanti Oraple, Ezri Trivena Pandapotan, Boris Yosua Prawinata See, Richardo Putra Yansen, Eka Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ria Asrina Marza Rianda, Gilang Rifqa Gusmida Syahrun Barokah Rio Asikin Rio Rokhima, Nur Roni Sanjaya Ryan Syahputra, Ryan Syahputra Salama A. Mostafa Sardius, Sardius Setiawan, Laurensius Rendi Siddik, M. Sirait, Andrio Pratama Sirvan, Sirvan Sri Tatminingsih Steven Owen Sukabul, Ahmad Suliana Supriati, Amelia Suroyo Suroyo Suroyo Tavip, Achmad Ummi Sri Rahmadhani Wicaksono, Mahfuzan Hadi Wijaya, Tommy Tanu Wilda Susanti Yuli Astuti Yulianti, Deni Yusnita Rahayu Zetra Hainul Putra Zeva Adi Fianto Zirawani Baharum Zirawani Baharum