bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation model of students learning outcome using k-means algorithm
Authors: Henderi  , Sunarya Abas , Zakaria  , Nurmika Sri Linggawati , Asmainah Nur 
Published: April 2020

Abstract
The progress of students learning outcomes have to evaluated and make monitor. Student learning outcomes were indicated by score of the exam. Educational institutions can determine the ability of students’ knowledge and competences based on student exam scores. For this reason, the goals of this research is develops an evaluation model of student learning outcomes. The model was developed using k-means algorithm and it has functions to analyze the student learning outcomes. The model was tested by conducted using 50 data exam of students, rapid manner software, and has implemented by the Python 3.0 programming language. The testing the evaluation model of student learning outcomes that were developed resulted: there are 3 clusters of student learning outcomes that are good, satisfying, and lacking. The tested results also showed that 40% of students had good learning outcomes, 44% of students were satisfactory, and 16% of other students had poor learning outcomes. The results of testing the student learning evaluation model can be used by the parties concerned to make decisions related to work programs or formulate steps that need to be taken to improve student learning outcomes.
Analysis of Knowledge Management System in Grabb Mobile Application Based On Usability
Authors: Thoyyibah T. , Leslie Hendric Spits Warnars Harco , Trisetyarso Agung , abdurachman Edi , Lumban Gaol Ford , Soewito Benfano 
Published: April 2020

Abstract
Grabb mobile application is a tool that is often used by people to travel. Each will travel then grabb will be ordered and pickup in front of the house and delivered to the destination location according to the address written on the application. Much knowledge can be disseminated through this Grabb application. Among them Grabbb car when going to go using a car, grabbike, service and others. The method in this study uses the adoption of a knowledge management system life cycle (KMSLC). Where the stages used are consists of Analysis of Existing Infrastructure Evaluation, Knowledge Capture, Implement the KM system, Evaluation. The steps taken are system analysis, knowledge mapping in the application, the results of implementation and evaluation using usability with 10 respondents through the adoption of Software Usability Measurement Inventory (SUMI). The results of SUMI’s Usability are effectiveness with a value of 90, efficiency 70 and satisfaction 70. By looking at these results, the mobile grabb application is included in the good category because the value of usabililty is above average.

A post-publication change was made to this article on 20 Apr 2020 as the previously published article was a duplicate.
Factors for the implementation of electronic certification on small traditional boats and fishing vessels in Indonesia.
Authors: Saputra Diaz , Trisetyarso Agung 
Published: April 2020

Abstract
On September 2019, Directorate General of Sea Transportation, the government who in charge for the safe operation of ships in Indonesia, has introduced a new model of ship certification form. It is an electronic certificate that is used for traditional boat and fishing vessel of less than 7 gross tonnage. This electronic certificate is for the first time being applied to a ship in Indonesia. This marks a new era for ship certification in Indonesia. The conventional paper format of certification had been applied before it was changed to electronic certification.
Indonesia is implementing electronic certificates on traditional vessels and fishing vessels of less than 7 gross tonnage due to several reasons. The first is the need for durable form material due to lifetime validation of certificates. The second due to problems related to misuse of previous type of conventional paper format certificates that caused difficulties in determining the number of this type of vessels in Indonesia.
Implementation of Fuzzy Mamdani For Recommended Tourist Locations In Madura - Indonesia
Authors: Mala Sari Rochman Eka , Pratama Ifan , Husni  , Rachmad Aeri 
Published: April 2020

Abstract
As a developing country, Indonesia is currently actively developing itself in all its main fields of tourism. Diversity of tourism in Indonesia, one of which is located on Madura Island which has approximately 60 attractions spread across four districts namely Bangkalan, Sampang, Pamekasan and Sumenep. However, the current condition, there are many attractions that are not explored in the outside community so that many tourists do not know which attractions are revised. This study focuses on how to make a decision support system that provides tourist attractions for tourists. The method used is Fuzzy Mamdani which can produce the best decision by determining the Min-Max value and finding the fuzzy value based on its center. This research succeeded in making a system that provides tourist attraction recommendations for tourists by using fuzzy mamdani based on three criteria, namely the number of visitors, the distance of the tourist location to the city center, and the review of visitors on the internet.

A post-publication change was made to this article on 20 Apr 2020 as the previously published article was a duplicate.
Working tool redesign to reduce ergonomic risk of salt evaporation field workers based on RULA and REBA assessments using esMOCA Instrument
Authors: Arendra Anis , Akhmad Sabarudin , Mu’alim  , Lumintu Ida 
Published: April 2020

Abstract
What are the ergonomic risks of Indonesian salt evaporation field workers especially in Madura? This problem has not been much noticed, though salt production from Madura is the largest contributor to national salt production. This study aims to assess the level of ergonomic risk of salt evaporation field workers in Madura. Conduct ergonomic risk analysis and reduce the level of ergonomic risk through redesigning work tools with the REBA and RULA approaches. REBA and RULA assessments are conducted with esMOCA instrument application. This research shows that five activities of salt evaporation field workers pose a high ergonomic risk with a REBA and RULA score above 6. These five activities are, chopping salt deposits on salt field, scraping salt deposits on salt field, collecting and pile up salt deposits, picking up salt to the carrying basket, and packing salt into sack. One of these five activities, picking up salt activity poses the highest ergonomic risk with REBA score 11. Efforts undertaken to reduce risk were made by replacing shovel tool with pan hoe tool, REBA score dropped to 5.
Text Categorization with Fractional Gradient Descent Support Vector Machine
Authors: Hapsari Dian Puspita , Utoyo Imam , Purnami Santi Wulan 
Published: April 2020

Abstract
Text documents on the web are an incredible resource including one example of big data, large size and so many variations that it becomes difficult for humans to choose meaningful information without the help of a computer. Text categorization job is to automatically classify text documents into standards class based on their content. The objective of this research is to implement a classifier with optimization based on the Fractional Gradient Descent in text classification. In our research, we propose using the Fractional Gradient Descent to optimize the SVM classifier so that it can increase the speed of training data. We explore a batch of different training data to compare the speed of the UCI ML text dataset training process with the SVM- SGD and SVM-FGD classifiers. This research concludes that using SVM-FGD will optimize the training time for text dataset in the activity of data classification.
TransJoin: An Algorithm to Implement Division Operator of Relational Algebra in Structured Query Language
Authors: Imamuddin A , Nahar I , Chandra S 
Published: April 2020

Abstract
Division operator is one of operators in Relational Algebra which is not implemented directly in SQL (Structured Query Language) standard. Therefore it is not be able to retrieve division query results with an SQL statement in the current relational database management systems (RDBMS). Database programmers have to create a complicated query to perform the task. It is the only relational algebra operators which is not implemented in ANSI SQL standard. This research aimed to study and design an algorithm named TransJoin (transformation and join) to implement it in SQL. TransJoin works to relation P(x, y) divided by Q(y) resulted R(x) through grouping and transforming each y attribute becomes P’(x, y’) and Q’(y’) and each x value is a single tuple with y’ is a composite value of y. Then, result R(x) is resulted of joining P’(x, y’) and Q’(y’) relations by matching y’ attribute. TransJoin was implemented in an open source RDBMS SQLLite. TransJoin tested and delivered valid results by comparing of traditional SQL queries and our proposed SQL queries with various data. The research showed that our algorithm is much more efficient by consuming 0.078 milliseconds compared contrasted the traditional query in 1,974.08 milliseconds for 9,991 tuples.
Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset
Authors: Untoro Meida Cahyo , Praseptiawan Mugi , Widianingsih Mastuti , Ashari Ilham Firman , Afriansyah Aidil , Oktafianto  
Published: April 2020

AbstractImbalanced data causes misclassification because the majority of the dominant data is in the minority data, which results in a decrease in the value of accuracy. UCI dataset is a public dataset that can be used as a dataset in machine learning. This study aims to evaluate the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods on data imbalances in MWMOTE. MWMOTE is used in resolving Imbalanced cases through weighting and grouping. This goal is achieved by evaluating the Decision Tree, K-NN, Naive Bayes, and Support Vector Machine classification methods in MWMOTE to produce more representative synthetic data and increase the accuracy value. The results obtained from this study indicate that the Decision Tree has higher evaluations of recall, precision, F-measure, and accuracy compared to K-NN, Naive Bayes, and Support Vector Machine for data that are balanced with MWMOTE.
Forward Selection Technique to Choose the Best Features in Prediction of Student Academic Performance Based on Naïve Bayes
Authors: Saifudin A , Ekawati  , Yulianti  , Desyani T 
Published: April 2020

Abstract
Supervision of academic performance is very important to ensure that students can complete their education on time. There have been many proposed applications of machine learning algorithms to predict students’ academic performance. Prediction is done by analyzing a dataset of historical academic of the student’s grade. The dataset which analyzed has many variables (features), this can increase complexity and decrease model performance because maybe not all features are relevant. We propose to implement the forward selection algorithm to select features that can improve model performance. The result shows that the performance of predictive models of students academic scores can improve with the application of feature selection.
Feature Selection Techniques to Choose the Best Features for Parkinsons Disease Predictions Based on Decision Tree
Authors: Yulianti  , Syapariyah A N , Saifudin A , Desyani T. 
Published: April 2020

Abstract
Parkinson is a disease that is caused by nerve cell damage in the brain and incurable. Knowing about Parkinson disease is very important so that medical action can be taken to prevent Parkinson’s getting worse. The dataset that uses to analysis for Parkinson disease using machine learning algorithms has many features. The dataset with many features can increase complexity, but not all features have a positive influence on the results of the analysis. Irrelevant features can reduce model performance. This research proposes to apply feature selection to choose features that have a positive effect so that the performance of the model does not decrease. The experiment results show that the application of feature selection can lead to better model performance.
Evaluation Methods of Bird Repellent Devices in Optimizing Crop Production in Agriculture
Authors: Roihan Ahmad , Hasanudin Muhaimin , Sunandar Endang 
Published: April 2020

Abstract
Rice field environments and bird attacks are the main problems for coverage and automatic pest control. Birds are one of several pests or destructive animals that can be found in rice fields. In general, farmers use traditional equipment such as plastic ropes and scarecrow to ward off these bird attacks. This research compares several methods of bird detection through camera sensors and chooses the best method in terms of accuracy, then applies it to repel birds automatically by utilizing sound frequencies that are disliked by birds and can repel bird pests. The design of the Bird repellent device works by utilizing computer vision techniques through camera sensors to capture bird objects in each frame, then it is processed by a microcontroller. After the object is captured on the camera, the microcontroller will activate the actuator in the form of sound frequency. The research objective is to design prototypes for the process of monitoring and automatic control of bird attacks to optimize crop yields in agriculture, based on the Internet of Things.
Data warehouse for analysing music sales on a digital media store
Authors: Ranti Kiefer Stefano , Tuapattinaya Deyanara , Chang Calvin , Girsang Abba Suganda 
Published: April 2020

Abstract
Nowadays, every company knows that when making a decision that has a potential in affecting their assets, an accurately processed report is necessary in order to support the reasoning behind their decision. Generating a report for stakeholders quickly and accurately is highly required in assisting them making a data-driven decision. By developing a data warehouse, it is possible for a company to do a data-driven decision making to appeal to their customer segments. This paper proposes a data warehouse model design to analyse the sales data contained in the database. The method that was implemented for this particular data warehouse development is the nine-step methodology designed by Kimball. The results are then presented in pdf form and an interactive dashboard.
Implementation of cart Algorithm for Monitoring System and Prediction of Electric Power use of IOT-Based Household Equipment
Authors: Palupiningsih Pritasari , Prayitno Budi 
Published: April 2020

Abstract
PLN customer power consumption from the household sector is quite large, originating from the use of household appliances, such as refrigerators, televisions, dispensers, lamps and air conditioners. When users feel that their electricity usage is wasteful, they do not know in detail which household electrical appliances consume the most electricity. At this time customers still find it difficult to monitor the power usage of every household electrical appliance. So it is not known which equipment consumes large amounts of electrical energy. For this reason, a monitoring system for household electrical power usage is needed. This system can be used by PLN household sector customers to find out which household appliances use large power, so customers can manage the use of household appliances. To do this monitoring, a wattmeter device is needed that is able to measure the power use of household electronic equipment. The results of this measurement are measured current data through the current sensor. So that monitoring can be done through the system in real time, the measurement data is sent to the monitoring system database server via the internet of things (IoT) devices. Data generated from the monitoring system can be analyzed using prediction techniques, to obtain information about the length of time the availability of electrical energy has been purchased by the customer. One algorithm that can be used to predict the use of household electrical power is the CART algorithm. This research aims to build a monitoring system for power usage for each IoT -based household appliances. This research aims to build a monitoring system for power usage for each IoT-based household appliances. The second objective of this research is to apply CART algorithm to predict the power usage of household appliances.
The Innovation Development of Early Flash Flood Warning System Based on Digital Image Processing through Android Smartphone
Authors: Priantama Rio , Nugraha Nunu , Darmawan Erlan 
Published: April 2020

Abstract
Flooding is a phenomenon that almost always occurs every year in various regions throughout Indonesia with a variety of cycle variations. In the rainy season, high rainfall and narrowed watersheds and silting up are no longer able to accommodate the flow of water, causing flash floods. Flood Warning Systems operate with traditional local wisdom that is highly dependent on the availability and responsiveness of the personnel assigned to oversee the river. Therefore, the development innovation of the existing system is expected to be able to provide information and data regularly and be an early warning to flash floods in order to minimize the impact. This research was conducted to design, create and implement an image-based flash flood early warning system prototype based on an Android cellular phone so that the public is expected to be able to access information on possible flash floods through their respective cell phones. Image processing is the basis of this development innovation through the Background Substraction method which is carried out to automatically detect High Levels of Water (TMA) on the control board (Peil) via an IP Camera which is an input parameter for the system and is processed into information for interested parties. The results of analysis of the detection of water level (TMA) on the peil board carried out on the server computer, become input parameters for determining the status of the dam water level. Flood early warning application that is installed on Android smartphone devices receives data from the server computer via the internet network.
Information Retrieval Technique for Indonesian PDF Document with Modified Stemming Porter Method Using PHP
Authors: Riza Faizal , Rifai Saefulloh , Dirgantara Akmal , Sfenrianto  , Rasenda  , Herdyansyah Syarifudin 
Published: April 2020

Abstract
Finding relevant information from a collection of information requires a process of stemming. Stemming is the process of combining or solving each morphological variants of a word into a basic word. Based on the basic structure of the word morphology, Porter’s stemming looks appropriate to be applied in conducting basic word searches in Indonesian-language documents, but with a few modifications. For this need, an Information Retrieval Technique for Indonesian PDF Document Application Using PHP from Indonesian documents is made using the Modified Stemming Porter Method. Implementation of the application was carried out using the Php (Hypertext Preprocessor) programming language. Testing was performed on 26 pdf e-book documents are 23,197 basic words out of 28,532 total words. the experiment found 94% as the largest percentage of precision words in the document. And the results obtained 81% as the lowest percentage of the basic words that are precise in the document. The results obtained from the test are that the application can operate well in conducting stemming on e-books in Indonesian.
A Review of Implementation and Obstacles in Predictive Machine Learning Model at Educational Institutions
Authors: Ranggadara Indra , Sari Yunita Sartika , Dwiasnati Saruni , Prihandi Ifan , Sfenrianto  
Published: April 2020

Abstract
Machine learning is a system that can learn by itself by using training data and testing data testing. In a variety of machine learning research has various obstacles in implementation especially in education institution, in its application, many things need to considered for more information to enrich research in the area of machine learning by way of comparing various obstacles in implementation in similar research. So that in this study is to find similarities and anticipations made in the initial stages of pre-processing to visualisation, as well as the contribution that can give in this study explains the best alternative for researching following the experience of several researchers in conducting machine learning research.
The Strategy of Improving Project Management Using Indicator Measurement Factor Analysis (IMF) Method
Authors: Sudaryono  , Rahardja Untung , Lutfiani Ninda 
Published: April 2020

Abstract
POAC is the de facto international standard that has been universally accepted as the theory behind successful execution of project management and implementation. Many however do not possess the know-how of correctly implementing POAC in project management. Often confused with the variety of available applications, time consuming to explore and often requiring to prepare big budgets to purchase the required tools. In this study, a new method called IMF (Indicator Measurement Factor Analysis) is proposed. IMF is a simple, low cost implementation tools that facilitate effective POAC control monitoring and implementation in project management. Not only that, the IMF is simple to use and produce effective results in harbouring communication, collaboration, critical thinking, competition, and creativity of teamwork. A collection of information from various feature of IMF is discussed, and the satisfaction level of users are collected and analysed. These data can be used to accurately predict the importance of IMF in POAC implementation, and hence encourage wide acceptance among prospective users in various different fields.
Book Recommendation System Development Using User-Based Collaborative Filtering
Authors: Hikmatyar Missi , Ruuhwan  
Published: April 2020

AbstractIn general, a library has a Book Search System, both search books by title, author, publisher, and book subject. But with so many book search results displayed by the system, sometimes it makes users (library members) find it difficult to choose books that match their profile. For that, we need a system that can provide recommendations in the book search process to provide recommendations systematically based on previous user searches. This study aims to produce a book search recommendation system in a desktop-based library using the Python programming language and the MySQL database. recommendation system aims to reduce errors in getting the reference books needed. The information that comes out of the search process is right on target with user needs so that it can streamline time in searching for books. The book search recommendation system uses a user-based collaborative filtering method based on the similarity of one member to another member based on the lending pattern which is grouped based on the subject of the book being sought. This system will display the results of book search recommendations by ranking from highest to lowest and provide book title solutions to users according to their profile. The development of the book search recommendation system uses the System Development life cycle (SDLC) method to design a system with a case study research in the TasikmalayaPejuang University library. The first stage of system development is the Identification of Problems that occur in the University of Struggle library following the phenomena that occur in the field. The second stage is the analysis of software requirements, hardware, and information needed in developing a system. The third stage is the design of workflow system design recommendations for book search using Unified Modeling Language (UML), database design and designing the interface. The fourth stage is the implementation of the design results in real form using Python as a programming language and MySql as a database system. The last step is testing the book search recommendation system using the Blackbox testing method by Trial and Error.