bluuebunny/crossref_metadata_2025_split huggingface dataset:
Text Classification on Islamic Jurisprudence using Machine Learning Techniques
Authors: Jamal K , Kurniawan R , Batubara A S , Nazri M Z A , Lestari F , Papilo P 
Published: July 2020

Abstract
Indonesia, which is the world’s most populous Muslim-majority nation, Islamic education is not an alternative to secular education but compulsory to any Muslims learn the way of life. The two primary sources of the religion of Islam is the Quran and Hadith. These two are where the majority of the teachings come from. However, when looking for guidance, a Muslim often refers to Islamic scholars to interpret the verses and hadith for them and educate themselves on a topic. However, due to many circumstances, people could not meet and ask a scholar personally regarding Islamic jurisprudence. Chatbot is one of the solutions. Chatbot offers users new opportunities to improve the learning and engagement process, reducing the typical cost of face-to-face consultation. However, to train a chatbot on Islamic Jurisprudence, a text classifier is needed to build a strong knowledge base for the chatbot. This study adopted a standard methodology for building a text classification model. This study used 600 Islamic jurisprudence text data obtained from the books and the web written by an influential Islamic scholar named Ustadz Abdul Somad (UAS). Machine learning algorithms such as Bayesian Network and Naïve Bayes, were employed to classify the text data. Based on the experimental testing results, the Naïve Bayes algorithm is more accurate in all evaluation models, 84.25% using training set, and 76.54% using 10-Fold cross-validation. Meanwhile, the Bayesian Network algorithm is faster in terms of time taken on all evaluation models. Thus, it can be concluded that the text classification model using Naïve Bayes and String to Word Vector filter have the potential to be used effectively but still has plenty of room for improvement.
Optimizationof Municipal Solid Waste Transporter in Batam City using Genetic Algorithm
Authors: Purnajaya A R , Hanggara F D 
Published: July 2020

Abstract
Municipal Solid Waste is an endless problem because as long as life is still running, waste will always be produced. Batamcity shows that there is poor scheduling of transportation of garbage from community settlements to temporary collection points (TPS) to the final disposal site (TPA) of Batam City. The study aims to optimize the number of waste transporters by applying a genetic algorithm. In this study, genetic algorithm can optimize the number of Batam City TPS transporters based on the number of TPS per district, the volume of TPS, and the distance between TPS to TPA. The number of waste transporters available in Batam City in dump trucks, arm rolls, pickups, and motorized pedicabs are 54, 44, 100, and 40 respectively. The result determines the optimization numberof waste transporters with a genetic algorithm succeeded in making the volume gap as small as possible, particularly 15.96% with the target volume of waste transportation and reducing 26.38% of the average gap in the actual volume of waste transportation. In addition, the non-transportable wastevolume optimally by using dump trucks, arm rolls, pickups, and motorized pedicabs is 58.92, 120.21, 346.31, and 380.74 ton respectively.
Developing PC-Based Driving Simulator System for Driver Behavior Analysis Research
Authors: Iqbal Mohammad , Sari Karmila , Ade Sekarwati Kemal , Kemala Putri Dian 
Published: July 2020

Abstract
Driving simulator is one of important system for driving behavior studies. It offers a replicable virtual driving environment and it has possibility to create a various scenarios to carry out in real environment. The problem in development of this simulator is how to reduce a prices in certain point areas have made it possible to create low-cost driving simulators, but with the features that are usually found only in expensive driving simulators. This article is presented a low cost PC-based driving simulator. The aim is to use the simulator as a platform for studies of the driver’s behavior. The complete simulator is using three 27” LED screen, handle by a standard PC, typically used for computer games. As Input we using steering, pedal and shifter G29, with force feedback. For software we using city car home edition as tools to measure validity user and developing simulator to driver’s behavior study.
Implementation of Modified Cheapest Insertion Heuristic on Generating Medan City Tourism Route
Authors: Rachmawati Dian , Wilyanto  
Published: July 2020

Abstract
Arranging the sequence of places to visit is the common problem that tourists or foreigners encountered when visiting a new site, especially for foreigners that visit Medan City. This kind of case similar to Traveling Salesman Problem cases. Therefore we could use one of the modified TSP algorithm to solve it, which is Modified Cheapest Insertion Heuristics. The Modified Cheapest Insertion is a method to insert the cheapest weight starting from 2 points of place until all of the sites are being connected from the starting point until the endpoint. The strength of Modified Cheapest Insertion Heuristics is the calculation will remain stable even for a large number of inputs. In this research, all of the desired-to-visit places are being connected as a route.
Chatbot Implementation with Semantic Technology for Drugs Information Searching System
Authors: Amalia A , Sipahutar P Y C , Elviwani E , Purnamasari F 
Published: July 2020

Abstract
This research aims to design medicine information based on the ontology scheme and to display the relationship between information through chatbot as search media. The diversity of information does not guarantee to gather a relevant search result with user’s needs. A conventional search engine would be substantially beneficial in information searching yet could not able to comprehend meaning of found information and its relationship with other information. However a machine could understand the relationship and the meaning of information through the implementation of semantic technology, which is ontology. Ontology is implemented to represent semantic metadata information. The information designed with the ontology model would be very assistive in searching for data which is relevant to users’ need. The result of implementing ontology as a knowledge base in this research able to display the information of a disease and its relationship with medicine used as the medication for such a particular disease. The evaluation of our system shows that 90% of queries from users generate a valid response from the chatbot. Therefore, this research could be used as a reference in search of medical information and describe its relationship with a particular disease or related to medicine.
Network Fault Effectiveness and Implementation at Service Industry in Indonesia
Authors: Lubis Fahrurrozi , Lubis Muharman 
Published: July 2020

Abstract
The efficiency of network administrators have been decreased concurrently due to complexity and heterogeneous of network built in certain environment of company. Actually, large heterogeneous network has created a crisis and conflict for many organizations. Available network tools and solutions are not only expensive but also difficult to install, configure, manage and maintain. This paper discusses the implementation of network fault in the service industry to match with the expectations of stakeholders to be able to achieve the higher efficiency and to carry out proactive processes although the practical truth is often the opposite way.
Internet Provider Service Value Delivery Index Problem: Case Study of the NetHost
Authors: Lubis Fahrurrozi , Lubis Muharman 
Published: July 2020

Abstract
Common problems in service delivery is might related to the issue of timing, marketing cost, user experience, generating lead, identifying prospect, forecasting demand, value proposition, security, privacy, identity management and many more. Unfortunately, company often decided on the wrong context and concept lead to misrepresentation and misinterpretation on the requirement for the service improvement. Thus, the direct intervention through certain criteria in the characteristics and attribute of service delivery should be conducted with judgment assessment. It is done in order to identify the target and examine the policy used to support the outcomes. Thus, this study explore the model used for particular company, which in this case NetHost to provide insights on how the assessment can be fruitful in the long term investment for intangible aspects of service delivery.
Comparison of Text Data Compression Using Yamamoto Recursive Codes and Punctured Elias Codes
Authors: Hardi S M , Putra M R , Tarigan J T , Jaya I 
Published: July 2020

Abstract
This research aims to study the efficiency of two variants of variable-length codes (i.e., Yamamoto Recursive codes and Punctured Elias codes) in compressing texts. The parameter being examined are the ratio of compression, the space savings, and the bit rate. As a benchmark, all of the original (uncompressed) texts are assumed to be encoded in American Standard Codes for Information Interchange (ASCII). The overall result shows that the Punctured Elias codes are consistently more efficient to compress texts than Yamamoto Recursive codes.
Analysis of Deep Learning Cyclical order for Prediction of Fresh Milk Production in Sumatera
Authors: Saefullah Asep , Hendri Muhammad , Lindawati Sri , Badaruddin Muliati , Hutahaean Jeperson 
Published: July 2020

Abstract
Milk is one of the most preferred and most easily absorbed nutrients. This drink naturally contains important nutrients, such as vitamins A, D, B12, protein, calcium, magnesium, phosphorus, and zinc, and others. Milk is also beneficial for children and adults. Starting from maintaining healthy bones and teeth, as a source of energy, and so on. In Indonesia, especially on the island of Sumatra, fresh milk production is carried out by cattle and goat farmers in cooperation with milk companies. This research have a purpose is to predict the production of fresh milk on the island of Sumatra so that the local government, as well as cattle and goat breeders on the island of Sumatra, have benchmarks to further increase the production of fresh milk in their respective regions in the future. The method that will be used in this research is Deep Learning Cyclical order which is the development of ANN. The research data used were data on the production of fresh milk on the island of Sumatra in 2009-2018 sourced from the Indonesian Statistics Agency. This research will be analyzed using 3 network architecture models, namely 4-5-1, 4-10-1 and 4-5-10-1 with the best network model chosen is 4-5-10-1 with an accuracy level of 90% and MSE value of 0.0157179042. Based on this best model a prediction of fresh milk production in Sumatra will be carried out in 2019-2020.
An Online Collaborative Mind Mapping Feature on Student-Centered E-Learning Environment
Authors: Hakim H , Santoso H B , Junus K 
Published: July 2020

Abstract
The Faculty of Computer Science of Universitas Indonesia has implemented online learning facilitated by a learning management system called Student-Centered E-Learning Environment (SCeLE). One of its important features is the online discussion board that enables students to construct knowledge collaboratively. Connecting and comparing several ideas in an online discussion forum are challenging for both students and teachers, especially when the discussion runs very fast with long messages. This study proposes an online collaborative mind mapping feature integrated to the discussion forum. A prototype was developed using the Go programming language and Gin web framework. The usability testing was employed to gather data about user experience in using the application. The participants of the study were all students of the Faculty of Computer Science of Universitas Indonesia. The result of the study shows that the application benefits most of the users. The participants acknowledged that the features help them follow the discussion and it supports the collaborative work. However, in terms of the design and the user experience, more works are required to improve the feature needs.
Improving The Performance of K-Nearest Neighbor Algorithm by Reducing The Attributes of Dataset Using Gain Ratio
Authors: Hasdyna N , Sianipar B , Zamzami E M 
Published: July 2020

Abstract
Data that has many attributes or higher dimensions will affect the performance of the K-NN classification algorithm. In this study, the Gain Ratio implemented for selecting and reducing the dataset attributes to form a new dataset for the classification process is carried out with the K-NN. The dataset used in this study are the Breast Cancer Coimbra dataset and Hepatitis C Virus dataset obtained from the UCI Machine Learning Repository. The results showed that the Breast Cancer Coimbra dataset, Gain Ratio can improve the performance of K-NN with average value 0.535596 TPR, TNR = 1, NPV = 0.608279, FNR = 1, FOR = 0.391721, Accuracy = 72.85%. In Hepatitis C Virus dataset also managed to improve the performance of K-NN with average value TPR = 0.665596, TNR = 0,876667, NPV=0,738279, FNR=0,88, FOR=0,521721, and Accuracy=86,25%.
Decision Support System to Determine Uang Kuliah Tunggal (UKT) by Combining Naïve Bayes Classifier and Fuzzy-TOPSIS
Authors: Mauladi  , Utomo P E P , Hutabarat B F , Putra R A 
Published: July 2020

Abstract

Uang Kuliah Tunggal (UKT) is a portion of the single tuition fee that is borne by each undergraduate student at a state university in Indonesia. UKT is the number of fees that have to be paid by students in each semester. Basically, UKT is implemented to impose tuition fees according to the income and the condition of students’ families. However, there is an issue in regard to the inappropriate classification of UKT. This issue is caused by several factors such as manual method is still used in determining UKT Classes and there is likely an element of subjectivity in determining UKT Classes of new students. Based on these issues, a decision support system that can determine UKT Class of new students is needed. The Naïve Bayes Classifier (NBC) method is used to classify data into eight UKT Classes. Whereas Fuzzy-TOPSIS is used in the optimization selection process of UKT Class 1 to 8. This method was chosen for its capability in choosing the best alternative out of several possibilities, in this case, the intended alternative is the most suitable new student to be selected in UKT Class based on six predetermined criteria. Research results show that the NBC Model can identify UKT groups of students with mean values of precision and recall testing are 77.8% and 77.8% and the model accuracy is 77.8% as well. In optimization selection process of UKT using Fuzzy-TOPSIS results obtained the percentage of UKT 1 group recommendations was 5.33%, UKT 2 was 5.33%, UKT 3 was 10.22%, UKT 4 was 24.89%, UKT 5 was 24.89%, UKT 6 was 10.22%, UKT 7 was 10.22% and UKT 8 was 8.00%. Based on the results above, it can be concluded that the combination of Naïve Bayes Classifier and Fuzzy-TOPSIS can be implemented for determining the UKT Classes. Then the recommendations of the UKT Classes can be considered in determining the UKT Classes of students for the Decision-maker.
A Meta-Review of the Smartphone as the Measurement Device
Authors: Hantono B S , Nugroho L E , Santosa P I , Musaddiq R 
Published: July 2020

Abstract
With the development of increasingly affordable smartphone technology that is owned by almost everyone, making some activities can be done only by using this device. Smartphone computing capability that is quite powerful, accompanied by various sensors embedded in it, allows the smartphone to be used as a measurement device in various fields of research. This article tries to review the use of smartphones as a measure of one’s mentality by conducting a meta-review of several review articles that have been done before. This article tries to look at research trends that measure a person’s mental state by using sensors found on smartphones and also to help researchers decide on topics to explore.
RETRACTED: Performance of news clustering using ant colony optimization
Authors: Jenson T , Girsang A S 
Published: July 2020

Abstract
Humans are basically curious creatures. We always look for all the latest information through books or news. News usually contains tags or categories to make it easier to find similar news. It can be done manually by a human or with a machine to make it easier. Data mining techniques such as clustering and classification can be used to help the categorization process. Clustering is a technique that can be used to grouped news based on their similarity. There is some algorithm that can be used to clustering data. One of them is Ant Colony Optimization (ACO). ACO is a metaheuristic algorithm that mimics how ants bring food to the colony. ACO also can be used to solve the clustering problem. In this paper, we will use the ACO version for clustering to cluster news and find the performance of the said method. Then we compare the result with ground truth.
Web Based Application for Controlling Data Quality in Phenotype Prediction of Indonesian Rice Genomes
Authors: Nababan E B , Nurhasanah R , Huzaifah A S 
Published: July 2020

Abstract
Single Nucleotide Polymorphism (SNP) is a form of Deoxyribonucleic Acid (DNA) variation that can be used in predicting phenotypes. Data quality control is a crucial stage in the process of detecting phenotypes using SNP data. In this study, we built a web-based application to carry out the SNP data quality control function. Raw SNP data in string type are filtered by calculating the missing rate, minor allele frequency, and Hardy-Weinberg Equilibrium values. The result is SNP data that has been filtered in numeric form, namely the value 1 represents dominant homozygous, 2 represents heterozygous and 3 represents homozygous recessive. SNP encoding in numerical form aims to make SNP data can be processed into machine learning for the further phenotype prediction step.
Bigdata Clustering using X-means method with Euclidean Distance
Authors: Zendrato N , Dhany H W , Siagian N A , Izhari F 
Published: July 2020

Abstract
Centroid is the central point of data in the grouping process, it is necessary to analyze the centroid in determining the initial value in the initial clustering process. So it is used as a cluster center point in the X-Means algorithm clustering process. Determine cluster center points or centroid, measure the performance of the X-Means algorithm with range cluster parameters by measuring distances between centroid for a fast and efficient way to group unstructured data, and to speed up the model construction process and divide several centroid in half to match the data as a test tool for the analysis of the X-Means method. From testing using the X-Means algorithm with the determination of the number of Centroid clusters carried out by modifying the X-Means method to do some determination of the centroid to get the results of 11 iterations. From the results of these tests produce good cluster members the level of similarity of data with other data and in determining the number of clusters, using the modification of the Euclidean distance method, get better results of the similarity level of each member compared to randomly determining the number of clusters with several iterations.
Embedded System for Detecting Cigarette Smoke Indoors using STM32 Microcontroller
Authors: Nasution T H , Zarlis M 
Published: July 2020

Abstract
In everyday life, smokers are often found around us. As we know, a lot of losses caused by cigarette smoke and cigarette smoke itself can also disturb the people around us. In order not to disturb the health of others, there are restrictions in certain rooms for smoking by giving a sign that smoking is prohibited. But some people sometimes ignore the ban. In this study, our system was embedded to give warnings to people who smoke in rooms that cannot smoke. The system will sound the siren if smoke is detected. This system is designed using a STM32 microcontroller and MQ-2 sensor to detect cigarette smoke. From the results of testing the system will work well by directly giving a signal when there is cigarette smoke.
Security of Image File with Tiny Encryption Algorithm And Modified Significant Bit Pseudo Random Number Generator
Authors: Hardi S M , Syah Ramadhani R , Muisa Zamzami E. , Tarigan JT , Jaya I 
Published: July 2020

Abstract
One of the negative impacts of the development of the era of digital technology on communication media today is the damage and loss of information data. Access to communication media is a means of delivering information that is vulnerable to manipulation. Therefore, cryptography is used to keep the information confidential. Cryptography is an art in encoding messages into other forms so that the message cannot be read by unauthorized parties. Tiny Encryption Algorithm (TEA) algorithm is a symmetric cryptographic algorithm in which the key is divided into four subsections and uses 128 key bits and operates 64 bits of data blocks and then divided into two blocks into 32 bits during the encryption process. In order to make the encryption stronger, it is combined with steganography where the steganography method used is Least Significant Bit (LSB) which has the function of inserting data into the last bit of other data. Linear Congruential Generator (LCG) is a random number generator in the LSB method where its function is as a flow of inputting data that has been encrypted randomly into another data as a cover so that the combination of these methods becomes Modified Least Significant Bit Pseudo Random Number Generator. The combination of cryptography and steganography is able to create a security in sending a confidential message to be safer.
Exploring the Pattern of Voters’ Characteristics: Partial Least Square Analysis
Authors: Lubis Muharman , Ridho Lubis Arif , Almaarif Ahmad 
Published: July 2020

Abstract
Understanding of characteristics can be assessed either by public decision-makers, who have been the primary concern of political actors or by voters themselves, who have participated directly in the process of election. Therefore, this study focused on the voters’ self-evaluation to look out on the voter decisions with survey questionnaire have been used as the tools to collect the data independently in relation to create formative measurement model. In addition, major influences to the public also involves the role of emotion, political socialization and tolerance to the diversity of media views. This study investigated gender-based patterns of 790 samples using PLS as a data analysis tool, which found interesting result, which suggested that Social Norm (SNorm) have strongest effect to both PCon and PBen with 0.179 and 0.066 respectively followed by Technology Solution (TSol) with 0.057 and 0.022 as well Legal Regulation (LReg) with 0.043 and 0.020 accordingly. Thus, in the developed model, the researcher recommend that social factors can lead to determination of demand and participation in the voting to have public confidence and good behaviour in the protective manner.