Miracle-dz/UniFAR-FTUs huggingface dataset:
Penerapan Jaringan Syaraf Tiruan Backpropagation Untuk Prediksi Nilai UN Siswa SMPN 2 Cihaurbeuti

Abstrak Ujian Nasional merupakan alat evaluasi yang dikeluarkan pemerintah untuk mengukur tingkat pencapaian tujuan pendidikan yang telah ditetapkan. Saat ini nilai ujian nasioanal dijadikan sebagai salah satu tolak ukur keberhasilan dan kualitas seorang siswa. Setiap sekolah menginginkan menjadi sekolah unggul dan berbagai upaya dilakukan untuk mencapai posisi tersebut. Salah satunya adalah menstabilkan nilai raport yang didapat siswa selama pembelajaran mulai dari semester awal hingga semester akhir agar mendapatkan nilai yang cukup memuaskan untuk nilai ujian nasional. Penelitian ini berbentuk studi kasus pada SMPN 2 Cihaurbeuti dengan variable penelitian adalah nilai raport dan nilai UN 2015/2016 dan 2016/2017 untuk prediksi nilai UN 2017/2018. Hasil penelitian menunjukkan bahwa nilai Mean Square Error (MSE) terkecil pada mata pelajaran B. Indonesia sebesar 0.011279, B.Inggris sebesar -0.019804, Matematika diperoleh sebesar -0.06416 dan IPA sebesar -0.0075304 dengan kombinasi parameter pelatihan berupa 2.000 epoch dan learning rate sebesar 0,1. Jaringan saraf tiruan backpropagation yang dihasilkan dapat melakukan prediksi nilai ujian nasional dengan ketepatan lebih tinggi dibanding dari perhitungan nilai rata-rata. Kata Kunci: backpropogation, jaringan syaraf tiruan, ujian nasional Abstract National Exam is a government issued an evaluation tool to measure the level of achievement of educational goals that have been set. Currently nasioanal exam scores be used as one measure of the success and quality of a student or school with only the value of some of the subjects examination. Every school wants to be superior schools and efforts were made to reach that position. One is to stabilize the value of students' report cards obtained during the semester learning from the beginning to the end of the semester in order to obtain a satisfactory value for the national exam . This research case studies form the SMP 2 Cihaurbeuti the study variables is the value of report cards and the value of the 2015/2016 and 2016/2017 UN predictive value for UN 2017/2018. The results showed that the Mean Square Error (MSE), the smallest on the subjects B. Indonesia amounted to 0.011279, -0.019804 by B. English , Mathematics obtained at -0.06416 and IPA for -0.0075304 with a combination of parameters such as 2,000 epochs of training and learning rate of 0,1. backpropagation neural network produced reliable enough to predict national exam scores with accuracy higher than from the calculation of the average value. Key Word: artificial neural network, backpropogation, national exam
Convolutional Neural Network for Retrieval of Supervised Footwear Images

With the progress of science, the traditional image retrieval technology no longer applies in terms of accuracy and retrieval speed. The emergence of big data and GPU has laid a solid foundation for the development of deep learning. However, the emergence of big data also means that the data is noisy and imperfect problems are more obvious, so the data processing and research is necessary. The experimental results show that the targeted method can also improve the image retrieval results under noisy and uncertain data.
Reviewing Cluster Based Collaborative Filtering Approaches

: With regard to rapid development of Internet technology and the increasing volume of data and information, the need for systems that can guide users toward their desired items and services may be felt more than ever. Recommender systems, as one of these systems, are one of information filtering systems predicting the items that may be more interesting for user within a large set of items on the basis of user’s interests. Collaborative filtering, as one of the most successful techniques in recommender systems, offers some suggestions to users on the basis of similarities in behavioral and functional patterns of users showing similar preferences and behavioral patterns with current user. Since collaborative filtering recommendations are based on similarity of users or items, all data should be compared with each other in order to calculate this similarity. Due to large amount of data in dataset, too much time is required for this calculation, and in these systems, scalability problem is observed. Therefore, in order to calculate the similarities between data easier and quicker and also to improve the scalability of dataset, it is better to group data, and each data should be compared with data in the same group. Clustering technique, as a model based method, is a promising way to improve the scalability of collaborative filtering by reducing the quest for the neighborhoods between clusters instead of using whole dataset. It recommends better and accurate recommendations to users. In this paper, by reviewing some recent approaches in which clustering has been used and applied to improve scalability , the effects of various kinds of clustering algorithms (partitional clustering such as hard and fuzzy, evolutionary based clustering such as genetic, memetic , ant colony and also hybrid methods) on increasing the quality and accuracy of recommendations have been examined.
A Toolbox for Nonlinear Regression in R: The Package nlstools

Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls() has a prominent position. Unlike linear regression fitting of nonlinear models relies on non-trivial assumptions and therefore users are required to carefully ensure and validate the entire modeling. Parameter estimation is carried out using some variant of the least- squares criterion involving an iterative process that ideally leads to the determination of the optimal parameter estimates. Therefore, users need to have a clear understanding of the model and its parameterization in the context of the application and data considered, an a priori idea about plausible values for parameter estimates, knowledge of model diagnostics procedures available for checking crucial assumptions, and, finally, an under- standing of the limitations in the validity of the underlying hypotheses of the fitted model and its implication for the precision of parameter estimates. Current nonlinear regression modules lack dedicated diagnostic functionality. So there is a need to provide users with an extended toolbox of functions enabling a careful evaluation of nonlinear regression fits. To this end, we introduce a unified diagnostic framework with the R package nlstools. In this paper, the various features of the package are presented and exemplified using a worked example from pulmonary medicine.
Android Based Patient Health Monitoring and Abnormalities Detection using Mobile Network

Electrocardiogram (ECG) signal, peripheral oxygen saturation (SPO2) and blood pressure are among the most used physiological signals in patient monitoring. A portable medical terminal monitor all these signals and connect the user with the medical center which is an unparalleled convenient way for patients and doctors to save lives. In this paper, Android based medical terminal is developed. This medical terminal not only has the feature of monitoring of several physiological signals and constant detection of eventual abnormalities, but also has the ability to send recorded data to the cloud server for further processing and storage. Furthermore, if any abnormality is detected then it will store data are uploaded to the cloud server. Those data are sent with the patient location obtained by GPS. Moreover, doctor at the medical center can request the terminal to upload data if needed and send some reminds as well as suggestions. The performance of the developed software in heartbeats detection is more than 99%. The sensibility of abnormal beat detection is 85.9% and 100% for abnormal SPO2 and blood pressure, all competitive for a low computing capacity mobile phone. This paper highlights the features of the developed terminal, to combine multiple physiological signals, real-time abnormalities detection, networking and its portability. Keywords—Android, Bluetooth, physilogical monitoring
Limits of Thread-Level Parallelism in Non-numerical Programs

Chip multiprocessors (CMPs), which recently became available with the advance of LSI technology, can outperform current superscalar processors by exploiting thread-level parallelism (TLP). However, the effectiveness of CMPs unfortunately depends greatly on their applications. In particular, they have so far not brought any significant benefit to non-numerical programs. This study explores what techniques are required to extract large amounts of TLP in non-numerical programs. We focus particularly on three techniques: thread partitioning with various control structure levels, speculative thread execution, and speculative register communication. We evaluate these techniques by examining the upper bound of the TLP, using trace-driven simulations. Our results are as follows. First, little TLP can be extracted without both of the speculations in any of the partitioning levels. Second, with the speculations, available TLP is still limited in conventional function-level and loop-level partitioning. However, it increases considerably with basic block-level partitioning. Finally, in basic block-level partitioning, focusing on control-equivalence instead of post-domination can significantly reduce the compile time, with a modest degradation of TLP.
Technologies for Reusing Text from the Web

Texts from the web can be reused individually or in large quantities. The former is called text reuse and the latter language reuse. We first present a comprehensive overview of the different ways in which text and language is reused today, and how exactly information retrieval technologies can be applied in this respect. The remainder of the thesis then deals with specific retrieval tasks. In general, our contributions consist of models and algorithms, their evaluation, and for that purpose, large-scale corpus construction. The thesis divides into two parts. The first part introduces technologies for text reuse detection, and our contributions are as follows: (1) A unified view of projecting-based and embedding-based fingerprinting for near-duplicate detection and the first time evaluation of fingerprint algorithms on Wikipedia revision histories as a new, large-scale corpus of near-duplicates. (2) A new retrieval model for the quantification of cross-language text similarity, which gets by without parallel corpora. We have evaluated the model in comparison to other models on many different pairs of languages. (3) An evaluation framework for text reuse and particularly plagiarism detectors, which consists of tailored detection performance measures and a large-scale corpus of automatically generated and manually written plagiarism cases. The latter have been obtained via crowdsourcing. This framework has been successfully applied to evaluate many different state-of-the-art plagiarism detection approaches within three international evaluation competitions. The second part introduces technologies that solve three retrieval tasks based on language reuse, and our contributions are as follows: (4) A new model for the comparison of textual and non-textual web items across media, which exploits web comments as a source of information about the topic of an item. In this connection, we identify web comments as a largely neglected information source and introduce the rationale of comment retrieval. (5) Two new algorithms for query segmentation, which exploit web n-grams and Wikipedia as a means of discerning the user intent of a keyword query. Moreover, we crowdsource a new corpus for the evaluation of query segmentation which surpasses existing corpora by two orders of magnitude. (6) A new writing assistance tool called Netspeak, which is a search engine for commonly used language. Netspeak indexes the web in the form of web n-grams as a source of writing examples and implements a wildcard query processor on top of it.
FACE RECOGNITION BASED MOBILE USING FISHERFACE AND DISTANCE CLASSIFIER

Dalam era pembangunan yang semakin canggih, sistem pengenalan yang memiliki akurasi tinggi dan proses cepat sangat penting untuk mengidentifikasi pribadi. Demikian pula, perkembangan teknologi mobile seperti smartphone dan tablet memungkinkan setiap orang untuk dengan mudah melakukan berbagai kegiatan. Oleh karena itu sistem berbasis mobile akan ditambahkan sehingga sistem pengenalan wajah dapat dengan mudah diterapkan untuk perangkat mobile.Penelitian ini menggunakan metode fisherface untuk memproses fitur dari gambar wajah dan menggunakan metode ini seperti Euclidian Distance, Squared Euclidean Distance, Canberra Jarak dan Jarak Curtis Bray. Database yang digunakan dalam penelitian ini adalah dasar dari Yale, UB (Universitas Bern), dan ORL (Olivetty Penelitian Laborary) . suatu masukan dari sistem ini adalah pengujian citra wajah dari database. Sebagai output, gambar wajah diakui oleh sistem. Di sini, masukan gambar wajah akan diklasifikasikan berdasarkan pendekatan database yang telah melatih dan disimpan ke dalam database.Hasil penelitian ini menunjukkan bahwa sistem yang telah diuji dengan menggunakan metode Fisherface untuk mengambil fitur dari gambar wajah dan menggunakan Euclidian Distance memperoleh akurasi rata-rata 91,7%, Squared Euclidean Distance memperoleh akurasi rata-rata 94%, Canberra Jarak memperoleh akurasi 75,9%, dan Bray Curtis Jarak mendapatkan akurasi 81%.
PENGEMBANGAN BAHAN AJAR BERBASIS WEB MENGGUNAKAN EXE (E-LEARNING XHTML EDITOR) POKOK BAHASAN BANGUN RUANG KUBUS DAN BALOK KELAS VIII SMPN 3 KUNINGAN

Fitria Fauziah. NIM 58451019. Pengembangan Bahan Ajar Berbasis Web Menggunakan eXe (e-learning XHTML editor) Pokok Bahasan Bangun Ruang Kubus dan Balok Kelas VIII SMPN 3 Kuningan. Skripsi. Cirebon: Fakultas Tarbiyah, Tadris Matematika, Institut Agama Islam Negeri, Juni 2012. Proses pembelajaran yang terpaku pada buku cetak sebagai satu-satunya sumber belajar bagi siswamenyebabkan mereka merasa jenuh dan sulit dalam belajar, terutama bagi mereka yang mempunyai minat yang sangat rendah untuk membaca. Sebagai seorang pendidik, guru harus mampu menciptakan suasana belajar yang menyenangkan saat kegiatan belajar salah satunya yaitu dengan mengembangkan bahan ajar berbasis web dengan menggunakan aplikasi eXe (e-learning XHTML editor). Bahan ajar ini dapat didesain semenarik mungkin yang disesuaikan dengan kebutuhan akademis siswa, tentunya dengan harapan dapat mewujudkan proses pembelajaran yang lebih menarik. Adapun tujuan dari penelitain ini adalah(1) mengembangkan bahan ajar berbasis web menggunakan eXe pada pokok bahasan bangun ruang kubus dan balok, (2) melihat respon siswa terhadap pembelajaran matematika dengan menggunakan bahan ajar berbasis web menggunakan eXe dan (3) mengkaji apakah terjadi peningkatan secara signifikan rata-rata hasil belajar matematika siswa antara yang menggunakan bahan ajar berbasis web menggunakan eXe dengan hasil belajar matematika siswa yang tidak menggunakan bahan ajar berbasis web menggunakan eXe. Metode yang digunakan pada penelitian ini yaitu metode eksperimen dengan menggunakan bahan ajar berbasis web menggunakan eXe. Instrumen yang digunakan yaitu tes dan angket baik itu angket ahli maupun angket sisiwa. Tes digunakan untuk mengukur hasil belajar matematika siswa sedangkan angket ahli digunakan untuk mengetahui kualitas media yang digunakan. Sedangkan angket siswa digunakan untuk mengetahui respon siswa terhadap media. Penelitian ini dilakukan di SMPN 3 Kuningan dengan subjek penelitian adalah siswa kelas VIII semester II tahun ajaran 2011-2012 dengan jumlah 81 siswa. Hasil yang diperoleh dari penelitian ini yakni hasil evaluasi media diperoleh dari segi materi, media sudah termasuk kriteria Sangat Baik dengan skor 83% dan dari segi media juga sudah termasuk kriteria Sangat Baik dengan skor 97,5%. Untuk respon siswa terhadap media yang dibuat termasuk kriteria Sangat Baik dengan skor sebesar 84%. Sedangkan hasil belajar matematika siswa dengan analisis Uji Independent T-tes diperoleh nilai thitung sebesar 8,816Dengan menggunakan taraf signifikasi sebesar 5% diperoleh nilai ttabel sebesar 1,9908. Karena 8,816 > 1,9908 maka H0 ditolak. Ini artinya bahwa peningkatan hasil belajar siswa yang menggunakan bahan ajar berbasis web menggunakan eXe (e-learning XHTML editor) lebih tinggi dibanding dengan siswa yang tidak menggunakan bahan ajar berbasis web menggunakan eXe (e-learning XHTML editor). Kata kunci: Pengembangan, Bahan Ajar, eXe
INTERIOR DESIGN USING AUGMENTED REALITY

: Augmented Reality (AR) technology is used in various products and fields. It is often used in the application that simulates an arrangement of furniture . This paper presents an application of Augmented Reality technology for interior design. Augmented Reality offers many advantages for digital architectural design and construction fields. AR is also being considered as a new design approach for interior design. In an AR environment, the virtual furniture can be displayed and modified in real-time on the screen, allowing the user to have an interactive experience with the virtual furniture in a real-world environment. Nowadays, there are lot of people familiar with computer system and communication technologies. Hence this application helps to explore the prospect of new form of Virtual Reality (VR) application called Augmented Reality. This application is based on Augmented Reality (AR) for Interior Design, which supports real time tracking with Identification Markers. Also the system uses FAST corner detection and local window matching algorithms to establish 3D maps.
Design of two-storey final settling tanks using mathematical models

A mathematical model is applied to the design of two-storey final settling tanks. Computations show that the flow and suspended solids (SS) concentration fields for the upper and bottom tanks are similar. The flow field has the ‘two-layer’ structure, observed in real and laboratory settling tanks, consisting of a bottom current and a free surface return current with approximately equal heights. The SS concentration field is governed by the flow field (and vice versa). The SS concentration profiles are uniform in the major part of the tanks. The hydraulic and SS removal efficiencies improve with decreasing flow rate. In both tanks the outlet SS concentrations are lower than the maximum permissible value (20 mg l �1 ), with the upper tank showing a better performance than the bottom tank.
Dynamic Search Models and Applications

Dynamic search is an information retrieval task that involves a sequence of queries for a complex information need (e.g. searching for one-week tour plans in Italy). It is characterized by rich user-system interactions and temporal dependency between queries and between consecutive user behaviors. Dynamic search is a trial-and-error process, which matches well with the family of Reinforcement Learning (RL) algorithms: the RL algorithms learn from repeated, varied attempts which are continued until success. The learner/agent learns from its dynamic interactions with the world. These similarities inspire us to model dynamic search using RL frameworks. In particular, we model dynamic search as a dual-agent stochastic game, one of the standard variants of Partially Observable Markov Decision Processes (POMDPs), where the user agent and the search engine agent work together to jointly maximize their long-term rewards. In the framework, users’ search statuses, such as exploitation, exploration, struggle, etc. are modeled as hidden states, which can only be estimated through user interaction data. In each search iteration, one search algorithm is picked from a set of candidates to maximize a reward function. Our work provides a general framework to model dynamic search. It enables the use of Reinforcement Learning algorithms for optimizing retrieval results. The experiments on the Text REtrieval Conference (TREC) 2011–2014 Session Track datasets show a statistically significant improvement over the state-of-the-art dynamic search algorithms. The design of states, actions, and rewards is quite flexible when using POMDPs in dynamic search. In the thesis, we also examine all available design choices from
Parameter estimation of the systemic arterial bed for control of a total artificial heart

A method of estimating the parameters of a model of the systemic arterial bed for control of an electrically actuated pusher-plate-type total artificial heart was developed and tested. The parameters, which have a physical interpretation, are estimated on the basis of noninvasive measurement of exclusively aortic flow and pressure during systole. Performance of the estimation algorithm was tested by model-to-model experiments. Results demonstrate the ability of the estimator to converge quickly and track time-varying parameters. <<ETX>>
Discrete ray-tracing high resolution 3D grids

This article suggests a new approach to discrete Ray-Tracing in which a two step three-dimensional DDA and an octree are used. A verpy important problem regarding this kind of Ray Tracing, also known as Raster Ray Tracing, is the amount of memory required to store the 3D raster grid which will contain the discretized scene to be visualized. Since the resolution of this grid is huge because the voxel is assumed to be approximately the size of a pixel on the screen, it is limited by the maximum amount of memory of todays´ machines. Although using case in most scenes, an important memory saving is achieved. This memory saving helps using discrete Ray Tracing in normal workstations. It is shown that the special octree presented here doesn´t slow down Ray Tracing significantly and even competes with normal discrete Ray-Tracing. It is also shown that in critical cases this octree will not occupy much more space than a normal 3D grid. Another important problem is the bottleneck caused by the three-dimensional DDA in such huge resolution ones. The process can be divided in two steps where optimal times of three-dimensional DDA are achieved. This is shown through the comparative tests with the single step process.
Space-Time Block Coded Spatial Modulation Based on Golden Code

In this paper, a new Space-Time Block Coded Spatial Modulation (SM) scheme based on the Golden Code, called the Golden Coded Spatial Modulation (GC-SM), is proposed and analyzed. This scheme still keeps some main benefits of the Golden Code by satisfying the non-vanishing Space Time Block Code (STBC) criteria. In the signal constellation domain, the GC-SM spectral efficiency is twice that of the STBC-SM. In addition, simulation and theoretical results show that the GC-SM performance surpasses several SM schemes at the same spectral efficiency and antenna configuration. Furthermore, we study the impact of channel spatial correlation on the GC-SM performance. Finally, the GC-SM detection complexity is studied and compared with the existing SM schemes. DOI: 10.32913/rd-ict.vol2. no15.702
Patch dynamics: macroscopic simulation of multiscale systems

We describe a numerical algorithm to perform time integration of a partial differential equation (PDE) which is not available in closed from; instead we are given a more microscopic description of the system behaviour. The proposed methods mimic a macroscopic finite difference (FD) or finite volume (FV) scheme for the unavailable PDE; they extract a macroscopic time derivative (for FD schemes) or flux (for FV schemes) from appropriately initialized, but otherwise unconstrained simulations with the given microscopic model. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)