bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Process Combination for Aluminum/Plastic Hybrid Components
Authors: Albert André , Zorn Wolfgang , Layer Markus , Drossel Welf-Guntram , Landgrebe Dirk , Kroll Lothar , Nendel Wolfgang
Published: August 2020

The research on lightweight construction increasingly gains in importance, especially for the automotive industry. New lightweight components ensure the necessary stability of car body parts on the one hand. On the other hand they are supposed to allow a low priced production. Hence, aluminum or magnesium alloys have quite a large share in production engineering. During the last years, research mainly addressed metal/plastic compounds. Weight reduction as well as the capability of producing complex structures are only some of the benefits of this technology. Furthermore, additional functionality can be integrated or functional tasks can be distributed: The metal ensures stiffness and realizes the technical connection to the car body by means of welding, while the plastic enables the insertion of special elements for the joining or assembly process. This paper presents two approaches of realizing a combined process to produce aluminum/plastic-hybrid structures. In a first approach, an active tool is presented to realize the sheet based process. The second approach focusses on the tube-based process and presents the topical state of research within the Federal Cluster of Excellence EXC 1075 “Merge Technologies for Multifunctional Lightweight Structures”.
Impregnability and Performance of rCF-nonwovens with epoxy resin
Authors: Mankiewicz Jasmin , Cleve Ernst , Heber Michael , Gutmann Jochen Stefan
Published: August 2020

Currently, the carbon fiber key market increases intensely. In consequence of production, 10–30 % of cost-intensive carbon fiber waste is accumulated by blending fabrics, prepregs which are out of specification and end-of-life products. Because of a landfill ban for carbon fibers, environmental aspects and a cost reduction potential, there is a stronger focus on carbon fiber recycling. Through new recycling methods, the carbon fiber is regained from polymer matrix, but looses its woven structure.One possibility to re-use chopped recycled fibers is through a fiber mat. Carbon fiber nonwovens can be fabricated by a wet-laid process, for example. For recycled fibers without a specific fiber length and sizing, a challenge lies in separating them in dispersion to get homogenous nonwovens and interlink the fibers in the nonwoven. For the first step, stirring and surfactants improve the separation, hydroxyethylcellulose ensures the bonding. Then the nonwoven can be impregnated with thermosets by resin transfer molding (RTM). The University of Applied Science Niederrhein is the first to investigate the whole process chain from handling recycled carbon fibers in order to attain the finished composite. Optical impregnability is inspected and material properties (tensile strength and Young’s modulus) are analyzed and compared to virgin fibers to get information on performance. A density range from 1.18 (5% CF) to 1.4 g/cm³ (40% CF) is very attractive for lightweight constructions as well.
eHealth Literacy of Medical and Health Science Students and Factors Affecting eHealth Literacy in an Ethiopian University: Cross-Sectional Study (Preprint)
Authors: Mengestie Nebyu Demeke , Yilma Tesfahun Melese , Kedir Miftah Abdela , Paulos Genet Kiflemariam
Published: August 2020

BACKGROUND

eHealth literacy is individual’s ability to look for, understand, evaluate and apply health information from electronic sources. Supporting the health system with eHealth literacy could help in lowering healthcare costs and in ensuring health equity. Given its importance, eHealth literacy level in Ethiopia hasn’t been studied. Especially, among medical and health science students who are important parties in the health system. Understanding their level of eHealth literacy facilitates the educational system, practice of healthcare and use of eHealth technologies.

OBJECTIVE

This study aims to determine eHealth literacy level and identify its associated factors among medical and health science students in the University of Gondar.

METHODS

An institution based cross-sectional study was conducted from March to May 2019 among undergraduate medical and health science students in the University of Gondar. Stratified multi-stage sampling was used. The eHealth literacy scale was used to measure eHealth literacy. Data were entered using Epi Info 7 and analysis was done using SPSS 23. Binary logistic regression model was fitted to measure the association between eHealth literacy and the independent variables.

RESULTS

A total of 801 students participated in this study accounting for 94.6% of response rate. The majority (60%) were male and (68%) previously lived in urban area. The mean eHealth literacy score on the eHealth literacy scale was 28.7. 60% of the participants possessed high eHealth literacy. Using health specific websites (AOR=2.84, 95% CI: 1.86, 4.33), having higher Internet efficacy (AOR=2.26, 95% CI: 1.56, 3.26), perceived usefulness of the Internet (AOR=3.33, 95% CI: 1.95, 5.69), medical app use (AOR=1.70, 95% CI: 1.13, 2.55), being female (AOR=1.55, 95% CI: 1.08, 2.22), and being health informatics student (AOR=2.02, 95% CI: 1.149, 3.148) contribute to high eHealth literacy level.

CONCLUSIONS

The level of eHealth literacy in this study was moderate.Being female, using specific reputable health websites, using smartphone medical applications and Internet efficacy determine eHealth literacy significantly. Tailored eHealth literacy content such as health specific website use and Internet use to raise efficacy should be given to students.
Exploring the Interdisciplinary Nature of Precision Medicine：Network Analysis and Visualization (Preprint)
Authors: Xu Xin , Hu Jiming , Lyu Xiaoguang , Huang He , Cheng Xingyu
Published: August 2020

BACKGROUND

Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear.

OBJECTIVE

The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis.

METHODS

A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities.

RESULTS

The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications.

CONCLUSIONS

This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine.
Leveraging Genetic Reports and Electronic Health Records for the Prediction of Primary Cancers: Algorithm Development and Validation Study (Preprint)
Authors: Zong Nansu , Ngo Victoria , Stone Daniel J , Wen Andrew , Zhao Yiqing , Yu Yue , Liu Sijia , Huang Ming , Wang Chen , Jiang Guoqian
Published: August 2020

BACKGROUND

Precision oncology has the potential to leverage clinical and genomic data in advancing disease prevention, diagnosis, and treatment. A key research area focuses on the early detection of primary cancers and potential prediction of cancers of unknown primary in order to facilitate optimal treatment decisions.

OBJECTIVE

This study presents a methodology to harmonize phenotypic and genetic data features to classify primary cancer types and predict cancers of unknown primaries.

METHODS

We extracted genetic data elements from oncology genetic reports of 1011 patients with cancer and their corresponding phenotypical data from Mayo Clinic’s electronic health records. We modeled both genetic and electronic health record data with HL7 Fast Healthcare Interoperability Resources. The semantic web Resource Description Framework was employed to generate the network-based data representation (ie, patient-phenotypic-genetic network). Based on the Resource Description Framework data graph, Node2vec graph-embedding algorithm was applied to generate features. Multiple machine learning and deep learning backbone models were compared for cancer prediction performance.

RESULTS

With 6 machine learning tasks designed in the experiment, we demonstrated the proposed method achieved favorable results in classifying primary cancer types (area under the receiver operating characteristic curve [AUROC] 96.56% for all 9 cancer predictions on average based on the cross-validation) and predicting unknown primaries (AUROC 80.77% for all 8 cancer predictions on average for real-patient validation). To demonstrate the interpretability, 17 phenotypic and genetic features that contributed the most to the prediction of each cancer were identified and validated based on a literature review.

CONCLUSIONS

Accurate prediction of cancer types can be achieved with existing electronic health record data with satisfactory precision. The integration of genetic reports improves prediction, illustrating the translational values of incorporating genetic tests early at the diagnosis stage for patients with cancer.
Novel Graph-Based Model With Biaffine Attention for Family History Extraction From Clinical Text: Modeling Study (Preprint)
Authors: Zhan Kecheng , Peng Weihua , Xiong Ying , Fu Huhao , Chen Qingcai , Wang Xiaolong , Tang Buzhou
Published: August 2020

BACKGROUND

Family history information, including information on family members, side of the family of family members, living status of family members, and observations of family members, plays an important role in disease diagnosis and treatment. Family member information extraction aims to extract family history information from semistructured/unstructured text in electronic health records (EHRs), which is a challenging task regarding named entity recognition (NER) and relation extraction (RE), where named entities refer to family members, living status, and observations, and relations refer to relations between family members and living status, and relations between family members and observations.

OBJECTIVE

This study aimed to introduce the system we developed for the 2019 n2c2/OHNLP track on family history extraction, which can jointly extract entities and relations about family history information from clinical text.

METHODS

We proposed a novel graph-based model with biaffine attention for family history extraction from clinical text. In this model, we first designed a graph to represent family history information, that is, representing NER and RE regarding family history in a unified way, and then introduced a biaffine attention mechanism to extract family history information in clinical text. Convolution neural network (CNN)-Bidirectional Long Short Term Memory network (BiLSTM) and Bidirectional Encoder Representation from Transformers (BERT) were used to encode the input sentence, and a biaffine classifier was used to extract family history information. In addition, we developed a postprocessing module to adjust the results. A system based on the proposed method was developed for the 2019 n2c2/OHNLP shared task track on family history information extraction.

RESULTS

Our system ranked first in the challenge, and the F1 scores of the best system on the NER subtask and RE subtask were 0.8745 and 0.6810, respectively. After the challenge, we further fine tuned the parameters and improved the F1 scores of the two subtasks to 0.8823 and 0.7048, respectively.

CONCLUSIONS

The experimental results showed that the system based on the proposed method can extract family history information from clinical text effectively.
Intent to Adopt Video-Based Integrated Mental Health Care and the Characteristics of its Supporters: Mixed Methods Study Among General Practitioners Applying Diffusion of Innovations Theory (Preprint)
Authors: Haun Markus W , Stephan Isabella , Wensing Michel , Hartmann Mechthild , Hoffmann Mariell , Friederich Hans-Christoph
Published: August 2020

BACKGROUND

Most people with common mental disorders, including those with severe mental illness, are treated in general practice. Video-based integrated care models featuring mental health specialist video consultations (MHSVC) facilitate the involvement of specialist mental health care. However, the potential uptake by general practitioners (GPs) is unclear.

OBJECTIVE

This mixed method preimplementation study aims to assess GPs’ intent to adopt MHSVC in their practice, identify predictors for early intent to adopt (quantitative strand), and characterize GPs with early intent to adopt based on the Diffusion of Innovations Theory (DOI) theory (qualitative strand).

METHODS

Applying a convergent parallel design, we conducted a survey of 177 GPs and followed it up with focus groups and individual interviews for a sample of 5 early adopters and 1 nonadopter. We identified predictors for intent to adopt through a cumulative logit model for ordinal multicategory responses for data with a proportional odds structure. A total of 2 coders independently analyzed the qualitative data, deriving common characteristics across the 5 early adopters. We interpreted the qualitative findings accounting for the generalized adopter categories of DOI.

RESULTS

This study found that about one in two GPs (87/176, 49.4%) assumed that patients would benefit from an MHSVC service model, about one in three GPs (62/176, 35.2%) intended to adopt such a model, the availability of a designated room was the only significant predictor of intent to adopt in GPs (β=2.03, SE 0.345, P<.001), supporting GPs expected to save time and took a solution-focused perspective on the practical implementation of MHSVC, and characteristics of supporting and nonsupporting GPs in the context of MHSVC corresponded well with the generalized adopter categories conceptualized in the DOI.

CONCLUSIONS

A significant proportion of GPs may function as early adopters and key stakeholders to facilitate the spread of MHSVC. Indeed, our findings correspond well with increasing utilization rates of telehealth in primary care and specialist health care services (eg, mental health facilities and community-based, federally qualified health centers in the United States). Future work should focus on specific measures to foster the intention to adopt among hesitant GPs.
Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network
Authors: Abidin Zaenal , Harjoko Agus
Published: August 2020

Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15,  for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.Keywords— facial expression, fisherface method, PCA, LDA, backpropagation neural network.
Rancang Bangun Fault Tolerance pada Sistem Database untuk Aplikasi Point Of Sale
Authors: Oei Standy , Ashari Ahmad
Published: August 2020

Abstract— A fault tolerant system is a system that has the ability to be able to continue its jobs properly although there is a fault in hardware or software of the system. A system that has a fault tolerant capability is usually critical or important system. For example is at Point Of Sale application. Point Of Sale Application has evolved from offline-based toward online-based. With an online-based, the existence of a central database server must be guaranteed free from failure.To get a central database server that is free from failure is impossible. Something that can be cultivated is to create a system that can tolerate a failure in the central database server. This is done with the help of a second central database server (slave), which is useful as a replication of the central database server (master). To ensure data from these two central database servers are the same, then used the concept of reading and writing "read one/write all". And to manage all the processes needed in implementing a fault tolerant application, we need the help from a coordinator.The result obtained in this research is a design of fault tolerant architecture that can be applied to various types of information system applications include Point Of Sale application. By using this fault tolerant architecture that has been built, Point Of Sale application can still run the transaction process even if there is a failure in accessing data in one central database server (master/slave). Keywords— fault tolerance, database system, point of sale application
Sistem Pendukung Keputusan Seleksi Anggota Paduan Suara Dewasa Menggunakan Metode Fuzzy Mamdani
Authors: Jayanti Sherly , Hartati Sri
Published: August 2020

AbstrakKebijaksanaan dalam mengambil sebuah keputusan pada permasalahan tertentu bukan lah hal yang mudah, karena perlu dilakukan pertimbangan yang diharapkan dapat membantu memberikan alasan keputusan tertentu harus diambil. Begitu juga penanganan masalah menentukan seseorang untuk menjadi anggota paduan suara dewasa pada Sanggar Bina Vokalia Menteng Palangka Raya.Seseorang yang akan bergabung pada sebuah tim paduan suara yang akan mengikuti kegiatan atau event tertentu, maka hal yang perlu diperlukan adalah seperti kualitas usia, pengalaman, kedisiplinan, intonasi, artikulasi dan wilayah nada dari seseorang tersebut. Sistem Pendukung Keputusan Seleksi Anggota Paduan Suara Kategori Dewasa  sangat tepat diterapkan untuk penanganan masalah  yang membutuhkan penyelesaian mandiri dari komputer untuk pemrosesan data peserta yang mengikuti seleksi dengan perhitungan efisien dan akurat. Dengan menggunakan penalaran Logika Fuzzy Mamdani dalam pemrosesan data input dan output, serta informasi pendukung berupa ranking sangat mendukung dalam pengambilan keputusan untuk menentukan seseorang untuk menjadi anggota paduan suara dewasa. Kata kunci— Sistem Pendukung Keputusan, Paduan Suara, Logika Fuzzy, Mamdani  AbstractWisdom in taking a decision on a particular issue is in fact not an easy thing, because consideration needs to be done that is expected to help justify a particular decision should be taken. So is handling the problem of determining a person to become a member of the adult choir at Sanggar Bina Vokalia Menteng Palangkaraya.Someone who will join in a choir team who will follow a particular activity or event, then it is necessary to like the quality of the age, experience, discipline, intonation, articulation and tone areas of a person is.Decision Support System Selection Category Adult Choir members are very appropriate to be applied to the handling problems that require independent completion of the computer for processing the data that follows the selection of participants with efficient and accurate calculations. By using Mamdani Fuzzy Logic reasoning in data processing input and output, and supporting information in the form of ranking is very supportive in the decision to determine a person to become a member of the adult choir. Keywords— Decision Support System, Chorus, Fuzzy's logic, Mamdani
Reduksi Parameter Quality-Of-Service Menggunakan Rough-Set-Theory Dalam Simulasi Routing Algoritma Dijkstra
Authors: Santyadi Gede Saindra , Pulungan Reza
Published: August 2020

AbstrakProses pemilihan jalur/rute terbaik dalam proses routing melibatkan bobot-bobot yang terbentuk dalam topologi jaringan. Terdapat banyak parameter yang menunjang munculnya bobot tersebut. Kesalahan dalam pemilihan parameter dapat mengganggu proses kumunikasi data yang terlihat pada communication-overhead (CO) yang muncul dalam jaringan. Proses reduksi parameter dalam teori Rough Set dapat memilih parameter mana yang dianggap penting sesuai dengan nilai quality of service. Sehingga dari sekian banyak parameter, akan terpilih parameter yang akan dijadikan bobot dalam topologi. Hal ini akan mengurangi beban CO yang akan mengoptimalkan proses routing dalam jaringan.  Kata kunci—Quality of Service, reduksi parameter, teori rough set, routing, algoritma Dijkstra AbstractPath selection process/best route in the routing process involves weights that are formed in the network topology. There are many parameters that support the emergence of these weights. Errors in the selection process parameters can interfere with communication data seen in communication-overhead (CO) which appears in the network. Reduction process on Rough Set theory can select the parameters which are considered important parameters in accordance with the quality of service (QoS). So of the many parameters, will be selected parameters to be used as weights in the topology. This will reduce the burden of CO that will optimize the process of routing in the network. Keywords—Quality of Service, parameter’s reduction, rough set theory, routing, Dijkstra algorithm
Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification
Authors: Rodiyansyah Sandi Fajar , Winarko Edi
Published: August 2020

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92%  dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58%  by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine
Optimasi Cluster Pada Fuzzy C-Means Menggunakan Algoritma Genetika Untuk Menentukan Nilai Akhir
Authors: Mas`udia Putri Elfa , Wardoyo Retantyo
Published: August 2020

AbstrakNilai akhir mahasiswa dapat ditentukan dengan berbagai cara, beberapa diantaranya menggunakan range nilai, standart deviasi, dll. Dalam penelitian ini akan ditawarkan sebuah metode baru untuk menentukan nilai akhir mahasiswa menggunakan clustering dalam hal ini adalah Fuzzy C-Means.Fuzzy C-Means digunakan untuk mengelompokkan sejumlah data dalam beberapa cluster. Tiap data memiliki derajat keanggotaan pada masing-masing cluster antara 0-1 yang diukur melalui fungsi objektif. Pada Fuzzy C-Means ini fungsi objektif diminimumkan menggunakan iterasi yang biasanya terjebak dalam optimum lokal. Algoritma genetika diharapkan dapat menangani masalah tersebut karena algoritma genetika berbasis evolusi yaitu dapat mencari individu terbaik melalui operasi genetika (seleksi, crossover, mutasi) dan dievaluasi berdasarkan nilai fitness. Penelitian ini bertujuan untuk mengoptimasi titik pusat cluster pada Fuzzy C-Means menggunakan algoritma genetika. Hasilnya, bahwa dengan menggunakan GFS didapatkan fungsi objektif yang lebih kecil daripada menggunakan FCM, walaupun membutuhkan waktu yang relative besar. Meskipun selisih antara FCM dan GFS tidak terlalu besar namun hal tersebut berpengaruh pada anggota cluster  Kata kunci— clustering, Fuzzy C-Means, algoritma genetika AbstractThe final grade of students could be determined in various ways, some of which use a range of values, deviation standard, etc. In this study will be offered a new method for determining final grades of students by using the clustering method. In this research the clustering method that will be used is the Fuzzy C-Means (FCM).Fuzzy C-Means is used to group a number of data in multiple clusters. Each data has a degree of membership (the range value of membership degree is 0-1). Membership degree is measured through the objective function. In Fuzzy C-Means,  objective function is minimized by using iteration and is usually trapped in a local optimum. Genetic algorithm is expected to handle these problems. The operation of genetic algorithm based on evolution that is able to find the best individuals through genetic operations (selection, crossover and mutation) and evaluated based on fitness values.This research aims to optimize the cluster center point of FCM by using genetic algorithms. The result of this research shows that by combining the Genetic Algorithm with FCM could obtained a smaller objective function than using FCM, although it takes longer in execution time. Although the difference of objective function that produced by FCM and FCM-Genetic Algorithm combination is not too big each other, but it takes effect on the cluster members. Keywords— clustering, fuzzy c-means, genetic algorithm
Penerapan Algoritma Genetika Untuk Menyelesaikan Permasalahan Penjadwalan Perawat Dengan Fuzzy Fitness Function
Authors: Dwi Utami Putra Desak Made , Subanar Subanar
Published: August 2020

AbstrakPenjadwalan perawat merupakan suatu permasalahan yang sangat kompleks dan memerlukan banyak waktu dalam proses pembuatannya. Terdapat dua bentuk batasan yang harus dipenuhi dalam penjadwalan perawat yaitu batasan keras (hard constraint) dan batasan lunak (soft constraint), oleh karena itu permasalahan penjadwalan perawat sangat sulit untuk dikerjakan dengan cara konvensional. Penelitian ini bertujuan untuk mengkaji penerapan algoritma genetika dalam menyelesaikan permasalahan penjadwalan perawat yang dapat memkuenuhi seluruh batasan keras dan meminimalkan pelanggaran terhadap batasan lunak yang telah ditentukan sehingga penjadwalan yang dihasilkan tidak hanya dapat memenuhi aturan yang ditetapkan saja namun juga dapat memenuhi preferensi dari masing-masing perawat terhadap jadwal yang dihasilkan.Algoritma genetika digunakan dalam penelitian ini untuk menyelesaikan permasalahan penjadwalan perawat pada RS. BaliMed Denpasar. Dimana kromosom yang dihasilkan merupakan representasi solusi dari penjadwalan perawat yang berupa jadwal mingguan dari masing-masing perawat.Himpunan fuzzy dalam penjadwalan perawat ini digunakan untuk menentukan nilai fitness dari kromosom yang dihasilkan.Sebuah nilai fungsi keanggotaan yang tinggi menggambarkan tingkat keidealan yang lebih tinggi dari solusi yang dihasilkan. Kata Kunci— penjadwalan perawat, algoritma genetika, fuzzy fitness function. AbstractNurse scheduling is one of the problems that need more attentions to handle.  This scheduling problem is complex with some constraints involved.  There are two general constraints in the problem named, the hard constraints and the soft constraint.  This research goal is to study the application of genetic algorithm for solving the nurse scheduling problem.  However, this solving problem should satisfy all the hard constraints, and minimize the violation of the soft constraints. The good scheduling is not only satisfies the hard constraints defined, but also the preferences of each nurse.The genetic algorithm used in this research solved the nurse scheduling problem faced at BaliMed Hospital, Denpasar.  The chromosome is the representation of the weekly scheduling of each nurse.  This scheduling utilized fuzzy set to determine the fitness values of the chromosome.  High member value represents high ideal level of solution. The result of this research is that the lower penalty value achieved from a solution, the more ideal scheduling got according to the membership degree in fuzzy set.  Hopefully, this scheduling method can be an alternative solution for arranging a nurse scheduling. Keyword— nurse scheduling, genetic algorithms, fuzzy fitness function.
Case-Based Reasoning untuk Diagnosa Penyakit THT (Telinga Hidung dan Tenggorokan)
Authors: Rismawan Tedy , Hartati Sri
Published: August 2020

AbstrakCase-Based Reasoning (CBR) merupakan sistem penalaran komputer yang menggunakan pengetahuan lama untuk mengatasi masalah baru.CBR memberikan solusi terhadap kasus baru dengan melihat kasus lama yang paling mendekati kasus baru. Hal ini akan sangat bermanfaat karena dapat menghilangkan kebutuhan untuk mengekstrak model seperti yang dibutuhkan oleh sistem berbasis aturan. Penelitian ini mencoba untuk membangun suatu sistem Penalaran Berbasis Kasus untuk melakukan diagnosa penyakit THT (Telinga, Hidung dan Tenggorokan). Proses diagnosa dilakukan dengan cara memasukkan kasus baru (target case) yang berisi gejala-gejala ang akan didiagnosa ke dalam sistem, kemudian sistem akan melakukan proses indexing dengan metode backpropagation untuk memperoleh indeks dari kasus baru tersebut. Setelah memperoleh indeks, sistem selanjutnya melakukan proses perhitungan nilai similarity antara kasus baru dengan basis kasus yang memiliki indeks yang sama menggunakan metode cosine coefficient. Kasus yang diambil adalah kasus dengan nilai similarity paling tinggi. Jika suatu kasus tidak berhasil didiagnosa, maka akan dilakukan revisi kasus oleh pakar. Kasus yang berhasil direvisi akan disimpan ke dalam sistem untuk dijadikan pengetahuan baru bagi sistem. Hasil penelitian menunjukkan sistem penalaran berbasis kasus untuk mendiagnosa penyakit THT ini membantu paramedis dalam melakukan diagnosa. Hasil uji coba sistem terhadap 111 data kasus uji, terdapat 9 kasus yang memiliki nilai similarity di bawah 0.8.  Kata kunci—case-based reasoning, indexing, similarity, backpropagation, cosine coefficient Abstract Case-Based Reasoning (CBR) is a reasoning system that uses old knowledge to solve new problem. CBR provides solutions to new cases by looking at old case that comes closest to the new case. It will be very useful because it eliminates the need to extract the model as required by the rule-based systems. This studytriestoestablisha system forCBR for diagnosingdiseasesof ENT.Diagnosisprocessis done byinsertinga new casethat containsthe symptoms ofthe disease to bediagnosed, thenthe system willdo theindexingprocess with backpropagation method toobtainan indexofnewcases. Afterthat, the systemdo thecalculation of the valueof similaritybetweenthe newcasebycasebasiswhichhas thesame indexwithnew cases using cosine coefficient method. The casetaken isthe casewiththe highestsimilarityvalue. If acaseis not successfullydiagnosed, thecasewillbe revisedby theexperts and it can be used asnew knowledgefor thesystem. The results showedcase-basedreasoningsystemtodiagnosediseasesof ENTcan helpparamedicsin performingdiagnostics. The test results of 111 data test cases, obtained 9 cases that have similarity values below 0.8. Keywords—case-based reasoning, indexing, similarity, backpropagation, cosine coefficient
Design and Implementation 3D Karambol Game Application Using DirectX
Authors: Pamungkas Aji , Nugradito Azhari S
Published: August 2020

The 3D game industry in Indonesia is increasing that can be seen by many game centre are always full of customers. But most of the game centre are not using original software because an original one can be very expensive. That is not the only reason, the other reason is that not many Indonesian are developing their own game software. And these increase the use of pirated game software. In this final assignment has been designed and developed, a game application titled 3D Karambol. This application is using DirectX and capable to process karambol game -a tradisional game from Indonesia-, to transform the game objects in 3D world, to do collision detection between those objects and to interact with the users through input device such as keyboard and mouse.
Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification
Authors: Fajar Rodiyansyah Sandi , Winarko Edi
Published: August 2020

AbstrakSetiap hari server Twitter menerima data tweet dengan jumlah yang sangat besar, dengan demikian, kita dapat melakukan data mining yang digunakan untuk tujuan tertentu. Salah satunya adalah untuk visualisasi kemacetan lalu lintas di sebuah kota.Naive bayes classifier adalah pendekatan yang mengacu pada teorema Bayes, dengan mengkombinasikan pengetahuan sebelumnya dengan pengetahuan baru. Sehingga merupakan salah satu algoritma klasifikasi yang sederhana namun memiliki akurasi tinggi. Untuk itu, dalam penelitian ini akan membuktikan kemampuan naive bayes classifier untuk mengklasifikasikan tweet yang berisi informasi dari kemacetan lalu lintas di Bandung.Dari hasil uji coba, aplikasi menunjukan bahwa nilai akurasi terkecil 78% dihasilkan pada pengujian dengan sampel sebanyak 100 dan menghasilkan nilai akurasi tinggi 91,60% pada pengujian dengan sampel sebanyak 13106. Hasil pengujian dengan perangkat lunak Rapid Miner 5.1 diperoleh nilai akurasi terkecil 72% dengan sampel sebanyak 100 dan nilai akurasi tertinggi 93,58% dengan sampel 13106 untuk metode naive bayesian classification. Sedangkan untuk metode support vector machine diperoleh nilai akurasi terkecil 92%  dengan sampel sebanyak 100 dan nilai akurasi tertinggi 99,11% dengan sampel sebanyak 13106. Kata kunci— Twitter, tweet, klasifikasi, naive bayesian classification, support vector machine  AbstractEvery day the Twitter server receives data tweet with a very large number, thus, we can perform data mining to be used for specific purpose. One of which is for the visualization of traffic jam in a city.Naive bayes classifier is an approach that refers to the bayes theorem, is a combination of prior knowledge with new knowledge. So that is one of the classification algorithm is simple but has a high accuracy. With this, in this research will prove the ability naive bayes classifier to classify the tweet that contains information of traffic jam in Bandung.The testing result, the program shows that the smallest value of the accuracy is 78% on testing by using a sample 100 record and generate high accuracy is 91,60% on the testing by using a sample 13106 record. The testing results with Rapid Miner 5.1 software obtained the smallest value of the accuracy is 72% by using a sample 100 records and the high accuracy is 93.58%  by using a sample 13.106 records for naive bayesian classification. And for the method of support vector machine obtained the smallest value is 92% accuracy by using a sample 100 records and the high accuracy of 99.11% by using a sample 13.106 records. Keywords—Twitter, tweet, classification, naive bayesian classification, support vector machine
Pengenalan Karakter Plat Nomor Mobil Secara Real Time
Authors: Ruslianto Ikhwan , Harjoko Agus
Published: August 2020

AbstrakPengenalan plat nomor di Indonesia biasanya digunakan pada sistem parkir yang masih dilakukan secara manual, yaitu dengan mencatat karakter plat nomor oleh petugas jaga parkir. Padahal pengenalan plat nomor tidak hanya dilakukan untuk system perparkiran tetapi dapat digunakan untuk menemukan kendaraan yang melanggar peraturan lalu lintas dijalan raya secara real time, misalnya pelaku tabrak lari pada kecelakaan maupun kendaraan yang melanggar rambu-rambu lalu lintas.Penelitian ini memberikan alternatif pengenalan karakter plat nomor mobil menggunakan metode connected component analysis dan matching sehingga dapat menyelesaikan permasalahan dengan background yang kompleks dan mobil yang bergerak dijalan raya.Metode connected component analysis berhasil melakukan proses segmentasi plat dan segmentasi karakter dengan kondisi background yang kompleks secara tepat terhadap 67 sampel citra dengan tingkat keberhasilan 95,52% untuk segmentasi plat dan 94,98% untuk segmentasi karakter dan metode template matching berhasil melakukan proses pengenalan karakter secara akurat dengan tingkat keberhasilan 87,45%. Kata kunci— real time, connected component analysis, template matching  Abstract Indonesia’s number plat recognition system are typically used in parking lots that are still done manually, by recording the license plate characters by parking guard. Though number plate recognition system is not only for parking but can be used to find vehicles that violate traffic rules highway street in real time, such as actors on the hit and run accident and the vehicles that violate traffic signs.This study provides an alternative car number plate character recognition using connected component analysis and matching so as to solve problems with complex background and a moving car on the road.Connected component analysis method successfully to the plates segmentation and character segmentation in complex background condition are appropriate to the 67 sample images with the success rate of 95.52% for the plate segmentation and 94.98% for plate character segmentation and template matching method successfully perform the character recognition process accurately with a success rate of 87.45%. Keywords— real time, connected component analysis, template matching
Penerapan Learning Vector Quantization (LVQ) untuk Klasifikasi Status Gizi Anak
Authors: Budianita Elvia , Prijodiprodjo Widodo
Published: August 2020

AbstrakPenentuan klasifikasi status gizi anak yang sering dilakukan adalah berdasarkan indeks berat badan menurut tinggi badan (BB/TB). Pada Puskesmas Batupanjang, indeks antropometri tersebut dihitung secara manual untuk menilai status gizi anak sekolah dasar dengan menggunakan daftar tabel z-skor atau simpangan baku / standar deviasi (SD) WHO NCHS (National Centre for Health Statistic). Metode Learning Vektor Quantization (LVQ) dan salah satu algoritma pengembangannya yaitu LVQ3 digunakan dalam penelitian ini untuk menangani penilaian status gizi anak berdasarkan simpangan baku rujukan terhadap indeks berat badan dan tinggi badan tersebut. Variabel yang digunakan dalam penilaian status gizi anak adalah jenis kelamin, berat badan, tinggi badan, penyakit infeksi, nafsu makan, dan pekerjaan kepala keluarga (KK). Berdasarkan dari hasil penelitian dan pembahasan yang dilakukan, algoritma LVQ3 lebih baik diterapkan untuk klasifikasi status gizi anak dibandingkan dengan algoritma LVQ1. Penggunaan parameter window (ε) pada jaringan syaraf tiruan LVQ3 memberikan pengaruh positif yakni dapat meningkatkan performa dalam klasifikasi jika dibandingkan tanpa menggunakan window (LVQ1). Kata kunci— Antropometri,  Learning Vektor Quantization,  Z-skor.  AbstractThe shortest path determination of child nutrient that common uses is based on body weight index by body high level (BB/BT). In Batupanjang Puskesmas, that anthropometry index is calculated manually for assessing  the nutrition of children in elementary school by used z-score table list or deviation standard  (SD) WHO NCHS (National Centre for Health Statistic).Learning Vektor Quantization (LVQ) Method and one of its algorithm, LVQ3 is used for this research to handle appraisal of children nutrition status based on deviation standard reference for that weight and high index. The variable that used in this appraisal are genre, body weight, body high, infection disease, appetite, and father work.Based on result of this research and discuss that has been done, LVQ3 algorithm is better applied for children nutrient status classification than LVQ1 algorithm. Using of window parameter (ε) in neural network LVQ3 effect positive impact, that is can increase perform in classification than without used window (LVQ1). Keywords—Anthropometry,  Learning Vektor Quantization,  Z-score.
Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA
Authors: Rumagit Silviani E , SN Azhari
Published: August 2020

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif groups
SIM Kemiskinan Sebagai Dasar Informasi Geografis Untuk Pemetaan Prioritas Pengentasan Kemiskinan di Kabupaten Banjarnegara
Authors: Supriyanto Aji , Winarno Edy , Prasetyo Agus
Published: August 2020

Program pengentasan  kemiskinan merupakan prioritas bagi pemerintah daerah Kabupaten Banjarnegara yang harus ditangani. Penelitan ini bermaksud merancang bangun sebuah sistem informasi yang mengolah data penduduk yang dapat diolah menjadi sebuah informasi  kemiskinan yang dapat diakses  melalui web dengan menggunakan standar indikator kemiskinan menurut BPS. Metode yang digunakan dalam penelitian ini adalah dengan cara action research dan model pengembangan sistem informasi adalah secara terstruktur menggunakan waterfall. Hasil dari penelitian ini adalah dapat menyajikan  informasi yang dapat menetukan kriteria kemiskinan dengan model singgle-criteria maupun multiple-criteria sesuai kebutuhan indikator kemiskinan yang ditentukan hingga pada tingkat desa, serta memberikan informasi tentang jenis-jenis bantuan yang telah diberikan pada setiap penduduk berdasarkan nama dan alamat (by name by address). Hasil selanjutnya adalah dapat dijadikan sebagai dasar pemetaan digital (Sistem Informasi Geografis/SIG) untuk menentukan kantong kemiskinan di suatu daerah, dengan memberikan pewarnaan yang menjadi indikator tingkat kemiskinan. Kata Kunci : kemiskinan, sistem informasi, penduduk, indikator BPS, GIS.