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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 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 Mathematics Social Sciences Other

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Journal : Journal of Applied Business and Technology

 1 
COSINE SIMILARITY FOR ESSAY ANSWER DETECTION Laurensius Rendi Setiawan; Dewi Nasien
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; Dewi Nasien
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.
Employee Attendance System Using Rapid Application Development Method Based on Location Based Service Nazara, Elvin Meiwati; Nasien, Dewi
Journal of Applied Business and Technology Vol. 5 No. 2 (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.v5i2.148

Abstract

Employee attendance is the main benchmark in assessing employee performance and discipline, as well as providing important data for company management. The use of information technology, such as computers, has helped companies make decisions effectively. However, some companies face obstacles in the employee attendance process, especially in manual or finger print situations, and absence management. By utilizing GPS technology or similar LBS technology to detect employee presence. In this context, mobile technology, especially smartphones, has an important role in providing fast and accurate access to information. The RAD method is used in making application prototypes with repeated iterations, enabling fast development and efficient improvements. The aim of this research is to apply the LBS method to support absenteeism. This application allows employees to take attendance on time via their smartphones, with attendance data directly stored in the company's servers and databases. In addition, the Global Positioning System (GPS) feature allows tracking the location of employees who are on external service. It is hoped that the results of this research can help companies minimize the problem of employee absenteeism, increase the efficiency of work processes, and provide faster and more accurate access to information. The RAD and LBS methods have proven their effectiveness in overcoming absenteeism problems and speeding up work flow. Apart from that, this research also underlines the importance of employee discipline in achieving company goals.
Increasing Trust in AI with Explainable Artificial Intelligence (XAI): A Literature Review Nasien, Dewi; Adiya, M. Hasmil; Anggara, Devi Willeam; Baharum, Zirawani; Yacob, Azliza; Rahmadhani, Ummi Sri
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.
Automated Waste Classification Using YOLOv11 A Deep Learning Approach for Sustainable Recycling Nasien, Dewi; Adiya, M. Hasmil; Farkhan, Mochammad; Rahmadhani, Ummi Sri; Samah, Azurah A.
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.
Optimization of Body Mass Index Classification Using Machine Learning Approach for Early Detection of Obesity Risk Nasien, Dewi; Owen, Steven; Fenly, Fenly; Johanes, Johanes; Lombu, Frendly; Leo, Leo; Baharum, Zirawani
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.
Co-Authors Adiya, M. Hasmil Agus Joko Purwanto Agus Setiawan ahmad kamal, ahmad Ahmad Mulyadi Alberta Akbar Marunduri Alexander Cia Alin Meisya Putri Alyauma Hajjah Amalia Sapriati Andi Andi Andrean Leo Winata Anggara, Devi Willeam Angriawan, Sherkhing Anwar Senen Baharum, Zirawani Cici Oktaviani Dahliyusmanto, Dahliyusmanto Deny Deny Deny Jollyta Desnelita, Yenny Devi Willieam Anggara Diah Anugrah Dipuja Diniya Diniya Erlin Erma Yunita Farkhan, Mochammad Fenly, Fenly Feri Candra Firman Afriadi Fitri Indriani Fitriani, Mike Gusman, Taufik Gustientiedina Habibollah Haron Ihsan, M. Nurul Iis Afrianty Iis Afrianty Imran B. Mu’azam Jack Billie Chandra Jerry Go Jesi Alexander Alim Johan Johan Johanes Johanes, Johanes Kevin Charles Lo Laurensius Rendi Setiawan Leo, Leo Lina Warlina Lombu, Frendly M. Siddik Mahbubah, Khoiro Mahmud Dwi Sulistiyo Marlim, Yulvia Nora Mestika Sekarwinahyu Mike Fitriani Mochammad Farkhan Muhammad Rakha Muhammad Ridha Mukhsin Mukhsin Nazara, Elvin Meiwati Neni Hermita Neni Hermita Nopendri Nopendri Nor Fatihah Ismail Nursalim Oraple, Ezri Trivena Owen, Steven Pamungkas, Dwi Putra Yansen, Eka Rahmadhani, Ummi Sri Rahmadian Yuliendi, Rangga Ramalia Noratama Putri Ria Asrina Marza Rianda, Gilang Ricalvin Darwin Richard M.C Richardo Prawinata See Rio Asikin Rio Rio Juan Hendri Butar-Butar Rokhima, Nur Roni Sanjaya Ryan Charles Wijaya Ryan Syahputra Ryan Syahputra Salama A. Mostafa Samah, Azurah A. Sardius, Sardius Sirait, Andrio Pratama Sirvan Sirvan Sri Tatminingsih Sukabul, Ahmad Suliana Supriati, Amelia Suroyo Suroyo Tavip, Achmad Tommy Tanu Wijaya Wicaksono, Mahfuzan Hadi Wilda Susanti Yacob, Azliza Yuli Astuti Yulianti, Deni Yusnita Rahayu Zetra Hainul Putra Zeva Adi Fianto Zirawani Baharum