scholarly journals Estimasi Tujuan Penumpang Menggunakan Predictive Model dengan Data Smart Card

2019 ◽  
Vol 2 (2) ◽  
pp. 99-110
Author(s):  
Ekky Alam ◽  
Inkreswari Hardini ◽  
Goklas Panjaitan ◽  
Sita Rosida
Keyword(s):  
Predictive Model ◽  
Public Transportation ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Performance Indicator ◽  
K Nearest Neighbor ◽  
Fundamental Information ◽  
And Performance ◽  
Public Transportation Planning

Bus Rapid Transit (BRT) is one of the main choices of public transportation that supports mobility of Jakarta community. As one of the main choices of public transportation, BRT should provide good service and always improve its performance. Needs for moving or mobility will cause a problem if the moving itself is heading at the same area and at the same time. That will cause some problems which are often faced in urban areas such as traffic and delay. To overcome those problems there needs to be a strategy to build good public transportation planning, besides need to know individual travel patterns to overcome problems and improve BRT service. In case to realize those plans needs to be built origin-destination (O-D) matrix. O-D matrix is a matrix that each cell is an amount of trip from the source(row) to the destination (column). O-D matrix is beneficial for analysis, design and public transportation management. O-D matrix also provides useful information like amount of trip between 2 different locations, that can be utilized as fundamental information for decision making for three levels of strategic management (long term planning), tactic (service adjustment and network development), and operational (scheduling, passenger statistic, and performance indicator). To build O-D matrix is required a predictive model that can be measured to predict passenger destination. The predictive model will be build using classification algorithms such as Decision Tree and K-Nearest Neighbor (KNN).

Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

2018 ◽  
Vol 8 (5) ◽  
pp. 84
Author(s):  
Vineet Kumar Gupta ◽  
Sriram Yadav
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Travel Demand ◽  
Choice Model ◽  
Nearest Neighbor Search ◽  
Hill Climbing ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Bus Routing

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Based on mobility patterns, propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. It also leverages the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips. GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Ola, Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. Now, study a new kind of query – Modified k-Nearest Neighbor Search with Hill Climbing (MkNNHC), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an MkNNHC query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then introduce a filter refinement framework for processing MkNNHC queries using the proposed indexes. Experiments on real datasets demonstrate the efficiency and scalability of our approaches.

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

Determinants of rental strategy: short-term vs long-term rental strategy

2020 ◽  
Vol 13 (12) ◽  
pp. 3873-3894
Author(s):  
Sina Shokoohyar ◽  
Ahmad Sobhani ◽  
Anae Sobhani
Keyword(s):  
Nearest Neighbor ◽  
Performance Metrics ◽  
Sharing Economy ◽  
Real Estate Market ◽  
Support Vector ◽  
Attractive Alternative ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Content Type

Purpose Short-term rental option enabled via accommodation sharing platforms is an attractive alternative to conventional long-term rental. The purpose of this study is to compare rental strategies (short-term vs long-term) and explore the main determinants for strategy selection. Design/methodology/approach Using logistic regression, this study predicts the rental strategy with the highest rate of return for a given property in the City of Philadelphia. The modeling result is then compared with the applied machine learning methods, including random forest, k-nearest neighbor, support vector machine, naïve Bayes and neural networks. The best model is finally selected based on different performance metrics that determine the prediction strength of underlying models. Findings By analyzing 2,163 properties, the results show that properties with more bedrooms, closer to the historic attractions, in neighborhoods with lower minority rates and higher nightlife vibe are more likely to have a higher return if they are rented out through short-term rental contract. Additionally, the property location is found out to have a significant impact on the selection of the rental strategy, which emphasizes the widely known term of “location, location, location” in the real estate market. Originality/value The findings of this study contribute to the literature by determining the neighborhood and property characteristics that make a property more suitable for the short-term rental vs the long-term one. This contribution is extremely important as it facilitates differentiating the short-term rentals from the long-term rentals and would help better understanding the supply-side in the sharing economy-based accommodation market.

scholarly journals K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment

2022 ◽  
Vol 25 (1) ◽  
pp. 521
Author(s):  
Omar Freddy Chamorro-Atalaya ◽  
Guillermo Morales Romero ◽  
Adrián Quispe Andía ◽  
Beatriz Caycho Salas ◽  
Elizabeth Katerin Auqui Ramos ◽  
...  
Keyword(s):  
Predictive Model ◽  
Engineering Students ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Operational Characteristic ◽  
K Nearest Neighbor ◽  
Administrative Service

The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.

scholarly journals Implementasi Sistem Untuk Prediksi Harga Emas

2018 ◽  
Vol 8 (2) ◽  
pp. 90
Author(s):  
Bayu Pratama Nugroho
Keyword(s):  
Exchange Rate ◽  
Interest Rates ◽  
Nearest Neighbor ◽  
Precious Metals ◽  
Gold Price ◽  
Economic Situation ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Political Situation

Gold is one of the investment commodities whose value continues to increase from year to year. The rise in gold prices will encourage investors to choose to invest in gold rather than the capital market. Investment in gold gives better results for the long term and with better purchasing power, so gold investment is an effective solution considering the value of money annually eroded by inflation. Such a state of economic instability is what drives many people, organizations and companies to invest in gold precious metals. Factors influencing the rise or fall of gold price according to Riefiyono (2010) are change of exchange rate (dollar exchange rate to rupiah), world political situation, domestic economic situation, and interest rate.   The method used in this case is K-Nearest Neighbor (KNN) is a method that uses In the training phase, this algorithm only retains feature vectors and sample training data classification. In the classification phase, the same features are calculated for testing data (whose classification is unknown).   The results obtained are successfully made an application for the prediction of gold prices by utilizing the method of Nearest Neighbor Retrieval. This application can help users in knowing the gold price prediction results are expensive or cheap with views in terms of economic situation, interest rates, political situation, and changes in exchange rates.

scholarly journals The Organization’s Sustainable Work Stress and Maladjustment Management Plan by Predicting Early Retirement through Big Data Analysis: Focused on the Case of South Korea

2021 ◽  
Vol 14 (1) ◽  
pp. 434
Author(s):  
Hyunjung Ham ◽  
Eunbee Kim ◽  
Daeyeon Cho
Keyword(s):  
Big Data ◽  
Predictive Model ◽  
Work Stress ◽  
Early Retirement ◽  
Data Analytics ◽  
Nearest Neighbor ◽  
Big Data Analytics ◽  
Management Plan ◽  
Big Data Analysis ◽  
K Nearest Neighbor

The purpose of this study is to verify the predictive model of early retirees’ responses to work stress and maladjustment to the company by utilizing big data analytics and to extract the reasons for early retirement from the personnel information. Company A’s personnel information of employees working in the company for 10 years was used, K-Nearest Neighbor (K-NN) algorithm was used to verify the predictive model of early retirees, and Decision Tree Analysis algorithm was used to extract the causing factors. According to the analysis results, first, the verification of the predictive model of early retirees based on the personnel information data showed 98% accuracy. Second, among the personnel information items, the ranking of items with high relevance for early retirement was the distance between the company and the residence (first place), the recent promotion history (second place), and whether or not to have the license (third place) out of a total of 18 items. The results of the analysis conducted in this study suggest that HRD intervention is required in the provision of problem-solving solutions involved in the HRM field, which is expected to be effective as a basic diagnostic tool for HR diagnosis involving HRD and HRM. In addition, this study may provide a detailed analysis of early retirement due to work stress and maladjustment of young people.

scholarly journals Dampak Program Pengembangan Pelatihan terhadap Kinerja Perawat RSIJ Cempaka Putih dengan Model Pendekatan System Dynamics

2019 ◽  
Vol 1 (2) ◽  
pp. 116-125
Author(s):  
Budi Hartono ◽  
Nisa Afriza
Keyword(s):  
System Dynamics ◽  
Performance Appraisal ◽  
Performance Indicator ◽  
Development Program ◽  
Nurse Training ◽  
Training Development ◽  
Competence Training ◽  
Training Motivation ◽  
And Performance

This study aims to see how much the nurse development program of the Jakarta Cempaka Putih Islamic Hospital has the potential to influence the performance of nurses at the Cempaka Putih Islamic Hospital. The research method used in this research is to use the Dynamic System Approach (System Dynamics). The results of modeling using historical data show that the training development program in the long term shows a decrease so that it has an impact on the performance of nurses which also shows decreased behavior while the results of modeling based on simulations show a significant increase in nurse performance if an increase in the policy target hours of the nurse training development program is based on target hours from HPMI. In conclusion, the performance of nurses at RSIJ Cempaka Putih actually shows decreased behavior, which is different from the performance reported based on the Key Performance Indicator (KPI) and Performance Appraisal that the dominant nurse's performance is up to standard. Keywords: Performance, Competence, Training Motivation, System Dynamics

scholarly journals Common Sport Movement Recognition from Wearable Inertial Sensor

2020 ◽  
Vol 8 (5) ◽  
pp. 1285-1292
Keyword(s):  
Nearest Neighbor ◽  
Inertial Sensor ◽  
Window Size ◽  
Evaluation Process ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Total Energy Consumption ◽  
Movement Recognition ◽  
The Common ◽  
And Performance

Common sport movements are the fundamental movements in all kind of sports. There are lots of researches done on classifying sports movements but very few are focused on common sport movement which is the focus of this project. The main aim is to develop an automated algorithm that can detect the common sport movements into walking based and jumping based movement from the wearable inertial sensor. The inertial sensor signals obtained from ten subjects were processed and grouped into walking-based and jumping-based movements. Time-domain features were extracted from the signals. Finally, the classification and performance evaluation process is done by using three different classification models (Support Vector Machine (SVM), k Nearest Neighbor (k-NN) and Decision Tree) with fixed window size of 1.28 seconds at the first stage. At the second stage, the best model from the first stage was used to determine the best window size in extracting the features that represent the walking and jumping based movement. As a result, SVM algorithm with window size of 2 seconds produced the highest overall accuracy of 95.4 % which proved to be the best classification algorithm to classify the common sport movements into walking-based and jumping-based movements. It is hoped that the outcome from this project can be used as a part of developing the overall automated sport movement recognition which is useful for the analyst, coach or player to analyse the performance of the player as well as predicting total energy consumption in preventing the injury among the player

scholarly journals Cloud-based architecture for face identification with deep learning using convolutional neural network

2021 ◽  
Vol 23 (2) ◽  
pp. 811
Author(s):  
Aditya Herlambang ◽  
Putu Wira Buana ◽  
I Nyoman Piarsa
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Face Identification ◽  
K Nearest Neighbor ◽  
Non Invasive ◽  
The Face ◽  
And Performance ◽  
University Of Manchester

The use of a face as a biometric to identify a person in order to keep the system safe from an unauthorized person has advantages over other biometric characteristics. The face as a biometric has more structure and a wider area than other biometrics, while can be retrieved in a non-invasive manner. We proposed a cloud-based architecture for face identification with deep learning using convolutional neural network. Face identification in this study used a cloud-based engine with four stages, namely face detection with histogram of oriented gradients (HOG), image enhancement, feature extraction using convolutional neural network, and classification using k-nearest neighbor (KNN), SVM, as well as random forest algorithm. This study conducted a classification experiment with cloud-based architecture using three different datasets, namely Faces94, Faces96 and University of Manchester Institute of Science and Technology (UMIST) face dataset. The results from this study are with the proposed cloud-based architecture, the best accuracy is obtained by KNN algorithm with an accuracy of 99% on Faces94 dataset, 99% accuracy on Faces96 dataset, 97% on UMIST face dataset, and performance of the three algorithms decreased in UMIST face dataset with facial variations from various angles from left to right profile.

scholarly journals Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure

2018 ◽  
Vol 13 (1) ◽  
pp. 155798831881429 ◽  
Author(s):  
DeAngelo McKinley ◽  
Pamela Moye-Dickerson ◽  
Shondria Davis ◽  
Ayman Akil
Keyword(s):  
Heart Failure ◽  
African American ◽  
Predictive Model ◽  
Nearest Neighbor ◽  
African American Men ◽  
Intervention Group ◽  
Admission Diagnosis ◽  
Control Group ◽  
K Nearest Neighbor ◽  
The Impact

Heart failure (HF) is responsible for more 30-day readmissions than any other condition. Minorities, particularly African American males (AAM), are at much higher risk for readmission than the general population. In this study, demographic, social, and clinical data were collected from the electronic medical records of 132 AAM patients (control and intervention) admitted with a primary or secondary admission diagnosis of HF. Both groups received guideline-directed therapy for HF. Additionally the intervention group received a pharmacist-led intervention. Data collected from these patients were used to develop and validate a predictive model to evaluate the impact of the pharmacist-led intervention, and identify predictors of readmission in this population. After propensity score matching, the intervention was determined to have a significant impact on readmission, as a significantly smaller proportion of patients in the intervention group were readmitted as compared to the control group (11.5% vs. 42.9%; p = .03). A predictive model for 30-day readmission was developed using K-nearest neighbor (KNN) classification algorithm. The model was able to correctly classify about 71% patients with an AUROC of 0.70. Additionally, the model provided a set of key patient attributes predictive of readmission status. Among these predictive attributes was whether or not a patient received the intervention. A relative risk analysis identified that patients who received the intervention are less likely to be readmitted within 30 days. This study demonstrated the benefit of a pharmacist-led intervention for AAM with HF. Such interventions have the potential to improve quality of life for this patient population.

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