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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Gita Gayatri, Nyoman Ayu Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science Dept. of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350367492

Prediction of software defects is an important aspect of software quality assurance, aiming to identify defective modules to improve reliability and reduce costs in the maintenance process. A systematic literature review (SLR) on software defect prediction is presented in this study, focusing on identifying commonly used datasets, methods, and frameworks in this domain between 2014 and 2023. The review follows established SLR guidelines to synthesize data from primary studies, providing insight into research trends as well as methodological advances. The results show a significant reliance on public datasets such as the NASA Metrics Data program (MDP) and the PROMISE repository. Various machine-learning techniques have been used, with Neural networks, support vector machines, and random forests becoming the most popular techniques. This review aims to guide future research by highlighting key trends and gaps in the existing literature. © 2024 IEEE.

关键词： Software quality

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Machine Learning-Based Crop Recommendation for IoT-Enabled Smart Agriculture 24

Machine Learning-Based Crop Recommendation for IoT-Enabled S...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Sonata, Ilvico Computer Science Department Binus Graduate Program Bina Nusantara University Indonesia Jakarta Indonesia

ISBN: (纸本)9798400717161

Human food needs are increasing all the time. This condition is in line with the increasing world population and exacerbated by climate change. Agricultural products are one of the sources of food to meet food needs. The use of technology is one of the keys to success in increasing agricultural production. One of the technologies used in agricultural technology is the Internet of Things (IoT). IoT in agricultural technology can be used to monitor soil and weather conditions to ensure that these conditions are in accordance with the needs of agricultural crops. The use of machine learning technology can be combined with IoT to help predict the types of crops that are suitable for planting based on soil and weather conditions. The results of the experiment show that the machine learning model can be used to predict the right types of plants to plant based on soil and weather conditions obtained through IoT. This method can be used to increase agricultural yields. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Smart agriculture

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean Technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Virality Prediction on Twitter Using Combined CNN and BERT Models 4

Virality Prediction on Twitter Using Combined CNN and BERT M...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Benedecas, Hilarius Ryan Auxilio Girsang, Abba Suganda Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331506490

Nowadays, social media serves as a platform for sharing and receiving information. One indicator of the importance of information is its virality. Virality is an interesting topic in the world of Natural Language Processing (NLP), including the prediction of virality and identifying features that influence it. Research in this area has utilized Bidirectional Encoder Representations from Transformers (BERT) models and yielded promising results. Previous studies aimed to predict virality based on specific features. This research aims to predict virality by combining the abilities of Convolutional Neural Network (CNN) and BERT models. The BERT model transforms textual data into numerical and adds new features, while the CNN model excels in numerical classification. Data for this study is sourced from Twitter and in Indonesian language. The proposed model demonstrates improvements compared to the model from previous research. Specifically, it shows a 7.1% increase in precision, a 9.9% increase in recall, a 3.8% increase in accuracy, and an 8.1% increase in F1-Score compared to the previous model. These findings contribute to future research on similar topics, particularly in leveraging multiple models for prediction tasks. Overall, the study provides insights into effectively utilizing the strengths of various models and combining them in prediction tasks. © 2024 IEEE.

关键词： Prediction models

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E-commerce Security Issues for SMEs: A Systematic Literature Review 7th

E-commerce Security Issues for SMEs: A Systematic Literature...

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7th International Conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Gunawan, Zaldy Abdurachman, Edi Soewito, Benfano Gaol, Ford Lumban Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9789819950140

Significant advancements in information technology have altered existing markets, transforming them from physical to online electronic markets (e-commerce). The practice of conducting business online by smaller and medium-sized companies has received encouragement and assistance from governments all around the world, particularly those of developing nations. With the evolution of e-commerce, businesses must find a better way to implement e-commerce while maintaining its security. The purpose of this article is to carry out a Systematic Literature Review (SLR) in order to determine the types of vulnerabilities that are present in e-commerce platforms used by SMEs. As a result, in the conclusion, security issues and recommended solutions related to e-commerce systems for SMEs will be presented. These outcomes can serve as a foundation for further study on e-commerce security issues, such as solutions and recommendations for providing a secure e-commerce system for SMEs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic commerce

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Identification of Clustering BRISK Keypoint Feature in Grocery Product Using the Elbow Method

Identification of Clustering BRISK Keypoint Feature in Groce...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Rahmania, Rissa Anggreainy, Maria Susan Kartowisastro, Iman Herwidiana Budiharto, Widodo Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

In smart retail, the problem in grocery product image classification is fine-grained image representation. Product variants are characterized by differences in color, text, and size. However, while the process of image classification involves quite complex stages, there is still a misclassification of grocery products. This is because there are many areas of the image being calculated, which makes the models not optimally calculate the most important area. This paper aims to identify a number of patch image formations as an important area of the product by involving Binary Robust Invariant Scalable Keypoints (BRISK) as a feature descriptor, K-Means Clustering, and the elbow method. First, we experiment with the BRISK keypoint feature for grocery products with different categories of brands. Second, the keypoint features are clustered using K-Means clustering. Lastly, the number of clusters is analyzed using the elbow method to find the optimal number. The result shows that the number of clusters from 8 products has variations of 5, 5, and 6 clusters. Finally, each cluster represents an important area in forming patches to support fine-grained grocery product image classification. ©2024 IEEE.

关键词： K-means clustering

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Implementation of Light Gradient Boosting Machine (LGBM) for Customer Services Feedback Classification 5

Implementation of Light Gradient Boosting Machine (LGBM) for...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Anggreainy, Maria Susan Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798331528553

Customer complaints are an important aspect in the business world. Every company must face complaints from its customers, whether related to unsatisfactory products, services or experiences. Customer complaints are a sign of unmet needs or expectations. A customer service person is tasked with serving questions asked by customers or consumers. The purpose of this research is to classify types of complaints to help customer service. The classification carried out is in binary form, namely disputed or not. This research compares several machine learning algorithms for classification of customer questions into appropriate categories for more efficient responses. Based on evaluation metrics, the F1 Score light gradient boosting machine (LGBM) is the best model compared to Multinomial Naive Bayes (MNB), Logistic Regression (LR), SVM (Support Vector Machine), and EXTC (Extra Tree Classifier). © 2024 IEEE.

关键词： Sales

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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Efficiency of Multi-Language Data Serialization for Hybrid Cloud-Edge HRIS 9th

Efficiency of Multi-Language Data Serialization for Hybrid C...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Winata, Andreas Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

In hybrid cloud-edge systems, data processing is distributed between cloud servers and edge servers, this makes data serialization critical for efficient data transfer between servers. This study evaluates the efficiency of multi-language data serialization of Human Resources Information System (HRIS) in a hybrid cloud-edge environment. Because lack of existing studies that specifically evaluate multi-language data serialization for hybrid cloud-edge HRIS, this study conducted a Systematic Literature Review (SLR) to get the list of current state of the art of data serialization methods and then used this result to evaluate the methods. By focusing on HRIS employee data from the employee management module, this study measures both data serialization and deserialization using performance metrics of time, data size, and CPU usage. The results indicate that no methods excel in all metrics, but FlatBuffers, Protocol Buffer, and Apache Avro can provide balanced effectivity across the metrics. The study gives valuable insight for selecting data serialization methods, architecture, and systems requirements in hybrid cloud-edge HRIS to ensure the performance efficiency of the system. © 2024 The Authors.

关键词： Cloud platforms

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Object-Driven Fuzzy Decision Model for Assessing the Employees' Work from Home Eligibility 4

Object-Driven Fuzzy Decision Model for Assessing the Employe...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Utama, Ditdit Nugeraha Immanuel, Christiawan Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

Work from Home (WFH), especially in the post-pandemic era, has emerged as an alternative work system adopted by many companies. Efficiency and effectiveness are the two main reasons why WFH is favored. In addition, the implementation of the WFH system in conducting business processes within the company allows for greater flexibility in work methods. For certain types of jobs or specific industry domains, this WFH system can be quite reliable. Workers can perform their tasks from anywhere and at any time. Also, monitoring systems enable employees to be remotely tracked by the company. However, the question remains: is an employee's work environment suitable enough to ensure the successful implementation of WFH? To answer this, a standardized model is needed to assess the level of eligibility for WFH implementation. This study aims to develop a fuzzy-based decision model to evaluate the eligibility of an employee's work environment for implementing WFH. An object-oriented approach is used to describe the design of the decision model, while fuzzy logic serves as the primary method for building the model. The model successfully simulated 24 raw employee data points, yielding a highest eligibility score of 88.61 with a similarity index of 0.78 when compared to the model proposed in previous research. © 2024 IEEE.

关键词： Personnel

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