Along with the development of increasingly modern times, so that all activities require gadgets, including laptops. However, it is often found among prospective laptop buyers who are still confused in determining a laptop to suit their needs, for that purpose the purpose of this study is to help people who want to buy a laptop when choosing or who are looking for a laptop to get the right one for their needs. To achieve this goal, a decision support system is needed. The Decision Support System that will be used is the SAW (Simple Additive Weighting) method because this method can filter out several existing alternatives and based on predetermined criteria so that later you get the best alternative. By using this SAW method, a matrix normalization process is needed, the weight value of each attribute, and finally a ranking process that will determine the optimal alternative. The results obtained in this study are to be able to provide laptop advice to prospective buyers based on the specifications of the prospective buyers' needs and with a 100% accuracy level based on calculations from the decision support system.

Laptop selection decision support system according to buyer criteria with the simple additive weighting method

Companies in supporting activities that can achieve competitive advantage require a description of the right activities or commonly called business processes. To evaluate existing business processes so as to improve business productivity performance, every company needs to conduct business process analysis so that it is easy to understand the ongoing business processes and can improve them if needed. An important stage of business process analysis is modeling. The purpose of writing this journal is to identify business processes at PT Datacomm Diangraha and make modeling of ongoing business processes (As-Is Model). The Business Process Model Notation (BPMN) method which is a technique or method for understanding, designing and analyzing a business process is used in this research. This study uses observation, interviews and literature review to obtain data. The results obtained are the business processes at PT Datacomm consisting of the contract making process, the work design submission process, the process of paying service fees in stages (30%, 60%, 10%), providing a guarantee period, and customer service and process improvement.

Analysis and development of company business processes using business process model notation (case study of PT Datacomm Diangraha)

The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.

/pdf/credit-risk-assessment-in-p2p-lending-using-lightgbm-and-1my3ygl9.pdf

Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization

The development of distribution and market segmentation has become the company's background in improving business processes. The purpose of this research is to analyze the business processes of beverage companies using Business Process Management (BPM) modeling and improvised based on six core element management. In the analysis process, it is found that there is no stock forecasting system in forecasting sales stock that must be fulfilled. The results of the study show that the Business Process Management model is improved with the addition of a stock forecasting system, so that business processes become more controlled with the presence of a product stock inventory forecasting system in the company.

Improvement business process model and notation on the drink distribution industries using six core element

Logistics plays a role in the smooth transaction between companies because it is a facilitator of buying and selling goods and services to fulfill the supply orders of consumer companies. This study aims to analyze how the impact of improved Logistic Service Quality (LSQ) for quality of goods delivery services by using LSQ dimensions from previous research. Sample data is obtained through the dissemination of questionnaires which are then processed quantitatively with convergent validity and reliability tests. Data processing with a sample count of 61 respondents. The results of this study show that there is the main dimension of logistic service quality in improving the quality of service, namely ordering condition, time, and information quality. Each comparison factor is tested for consistency using the Analytical Hierarchy Process (AHP), each of the main criteria has a consistency value of less than 0.1 so that the main criteria tested have a consistent comparison matrix and can be the basis of decision making for companies in choosing alternative criteria priorities.

/pdf/improved-logistics-service-quality-for-goods-quality-1zmi1bw8rv.pdf

Improved logistics service quality for goods quality delivery services of companies using analytical hierarchy process

Peer-to-peer (P2P) Lending is a type of financial innovation that offers loans without intermediaries to individuals and companies. In the P2P lending system, there is a risk of default on the loan which causes the company to lose. Many studies have to reduce the risk of default by developing a classification model of prediction of default that focuses on increasing accuracy. However, the big problem with prediction is data imbalance and low performance classification algorithms. The purpose of this study is to improve the accuracy of default risk prediction by balancing the data and combining the stacking model ensemble with the meta-learner. The proposed new model consists of 3 optimization parts, the first is Synthetic Minority Oversampling Technique (SMOTE), the second is the selection of features and the third is stacking ensemble learning. The SMOTE method is used to balance the data, the feature selection LightGBM and stacking ensemble learning (LGBFS-StackingXGBoost) to optimize machine learning accuracy. A new model of stacking ensemble learning by combining three base-learner algorithms namely KNN, SVM and Random Forest into the XGBoost meta-learner algorithm. The model was tested using two datasets, namely the online P2P lending dataset and the lending club loan data analysis dataset. The evaluation results show that LGBFS-StackingXGBoost is the best model for both datasets. In the online P2P lending dataset, it received an accuracy of 99,982% and in the lending club loan data analysis dataset, it received an accuracy of 91,434%. This study shows that the accuracy of the prediction model can be improved using the LGBFS-StackingXGBoost method. 

New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning

Practical classroom learning in the multimedia department requires props, where props range from damage. To address this need, learning media are made by applying Augmented Reality. Learning media presents actual images without holding and seeing the objects in real terms so that there is no damage to the props. This research was conducted to create learning media for students of SMK Negeri 1 Banyuwangi majoring in multimedia as an Android-based teaching aid. Stages of research using the development method in the form of Multimedia Model Life Cycle (MDLC). The concept stage analyzes and applies the Augmented Reality (AR) method, the design stage performs application planning according to the needs of learning media. The data collection stage conducted interviews with teachers and students while the stages of making learning media used Blender, Unity and Visual Studio software. At the trial stage of the application by making a guidebook, it was carried out for students at SMK Negeri 1 Banyuwangi. For the stages of distributing learning media using the Likert scale method through distributing questionnaires. The results of the application trials and questionnaire distribution, the responses of students about learning media, the results show the interpretation of respondents by combining a value of 72.22%, which means students accept this learning media. The results of this research can create learning media for multimedia majors that can reduce the risk of damage to props and provide cool and fun learning media.

Augmented reality development using multimedia development life cycle (MDLC) method in learning media

This study aims to predict whether the patient deserves to be inpatient or outpatient by comparing several machine learning techniques, namely, logistic regression, decision tree, neural network, random forest, gradient boosting. The research method uses three stages of research, namely data collection, data preprocessing, and data modeling. Implementation of program code using google colab and python programming language. The dataset used as the research sample is Electronic Health Record Predicting data. Based on the accuracy results generated in this study, the use of the Neural Network machine learning algorithm to predict hospitalization decisions for patients has proven to be a machine learning algorithm that has the highest accuracy rate reaching 74, 47% compared to other comparison machine learning algorithms, namely logistic regression, decision tree, neural network, random forest, gradient boosting.

Dwika Ananda Agustina Pertiwi

Papers

Improved logistics service quality for goods quality delivery services of companies using analytical hierarchy process

New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning

Augmented reality development using multimedia development life cycle (MDLC) method in learning media

Prediction of hospital intensive patients using neural network algorithm

Information system operational audit at garment company using ITIL V3 framework