scholarly journals Penilaian Esai Pendek Otomatis dengan Pencocokan Kata Kunci Frasa Nomina

Techno Com ◽  
2021 ◽  
Vol 20 (4) ◽  
pp. 489-498
Author(s):  
Nurul Chamidah ◽  
Mayanda Mega Santoni ◽  
Helena Nurramdhani Irmanda ◽  
Ria Astriratma
Keyword(s):  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Pos Tagging ◽  
Part Of Speech

Pembelajaran daring menjadi suatu kebutuhan dalam pengajaran baik dalam memberikan materi maupun ujian. Ujian dalam bentuk soal objektif kurang dapat mengukur kemampuan pemahaman seseorang dan soal esai dianggap lebih baik untuk mengevaluasi hasil pembelajaran. Namun, jawaban berbentuk esai memerlukan waktu yang lebih banyak untuk dilakukan penilaian serta hasil penilaiannya dapat inkonsisten. Maka dari itu, diperlukan suatu sistem penilaian esai otomatis yang dapat menilai esai dengan lebih cepat dan konsisten. Penelitian ini dilakukan untuk menganalisis performa penilain esai otomatis dengan mengekstrak kata kunci dari frasa nomina dalam jawaban berbentuk esai pendek. Penilaian esai dilakukan dengan mencocokkan kata kunci yang diekstrak dari jawaban uji dan jawaban referensi. Jawaban uji dan referensi diproses dengan case folding, Part of Speech (POS) Tagging, ekstraksi frasa nomina, dan stemming. Kata kunci unik jawaban uji dan jawaban referensi yang diperoleh dari proses tersebut selanjutnya dicocokkan dan kemudian dinilai berdasarkan kecocokan tersebut. Hasil evaluasi penelitian ini menunjukkan Mean Absolute Error (MAE) dari nilai yang diperoleh dengan mencocokkan kata kunci dengan nilai uji yang diberikan manusia sebesar 18% dan Pearson Correlation sebesar 0.83 yang menunjukkan korelasi antara nilai sistem dan nilai uji sangat baik.

Application of multivariate data analysis in the construction of predictive model for the chemical properties of coke

2013 ◽  
Vol 2 (1) ◽  
pp. 67-74
Author(s):  
Marcin Sajdak ◽  
Łukasz Smędowski
Keyword(s):  
Chemical Composition ◽  
Multivariate Data Analysis ◽  
Goodness Of Fit ◽  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Chemical Properties ◽  
Absolute Error ◽  
Quality Parameters ◽  
Regression Analyses ◽  
The One

ABSTRACT The aim of this work was to develop a statistical model which can predict values describing chemical composition of cokes performed in industrial scale. This model was developed on the basis of data that were taken from the production system used in the one of Polish coking plant. Elaborated equation include quality parameters of initial coals that form coal blends as well as contribution of additions such as coke and petrochemical coke. These equations allow to predict chemical composition of coke, e.g. contributions of: sulphur, ash, phosphorus and chlorine within the coke. A model was elaborated with use of STATISTICA 10 program and it is based on factor and multiply regression analyses. These analyses were chosen from among few kinds of regression analyses. They allowed to develop prediction model with the required goodness of fit between calculated and actual values. Goodness of fit was elaborated with: • residuals analyses, • residues normality and predicted normality • mean absolute error • Pearson correlation confidence

scholarly journals Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans

2021 ◽  
Vol 7 (9) ◽  
pp. 189
Author(s):  
Fares Bougourzi ◽  
Cosimo Distante ◽  
Abdelkrim Ouafi ◽  
Fadi Dornaika ◽  
Abdenour Hadid ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Ct Scans ◽  
Rt Pcr ◽  
X Ray ◽  
Fast Solution ◽  
Patient State ◽  
Polymerase Chain ◽  
Tomography Scan

COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state.

scholarly journals Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 389
Author(s):  
Asaad M. Armanuos ◽  
Nadhir Al-Ansari ◽  
Zaher Mundher Yaseen
Keyword(s):  
Linear Regression ◽  
Correlation Coefficient ◽  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Similarity Index ◽  
Absolute Error ◽  
Skill Score ◽  
Environmental Data ◽  
Climatic Data ◽  
Precipitation Data

The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with rPearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.

scholarly journals Correlation of Ultrasonographic Estimation of Fetal Weight with Actual Birth Weight as Seen in a Private Specialist Hospital in South East Nigeria

2019 ◽  
Vol 2019 ◽  
pp. 1-4
Author(s):  
Chisolum Ogechukwu Okafor ◽  
Charles Ikechukwu Okafor ◽  
Ikechukwu Innocent Mbachu ◽  
Izuchukwu Christian Obionwu ◽  
Michael Echeta Aronu
Keyword(s):  
Birth Weight ◽  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Fetal Weight ◽  
Percentage Error ◽  
Mean Error ◽  
The Mean ◽  
Route Of Delivery ◽  
Private Specialist

Background. Ultrasound estimation of fetal weight at term provides vital information for the skilled birth attendants to make decisions on the possible best route of delivery of the fetus. This is more pertinent in a setting where women book late for antenatal care. Aim and Objectives. The study evaluated the accuracy of estimation of fetal weight with ultrasound machine at term. Methods. This was a cross sectional study conducted at a private specialist hospital in Nigeria. A coded questionnaire was used to retrieve relevant information which included the last menstrual period, gestational age, parity, and birth weight. Other information obtained includes Ultrasound-delivery interval, maternal weight, and route of delivery. The ultrasound was used to estimate the fetal weight. The actual birth weight was determined using a digital baby weighing scale. The data were inputted into Microsoft excel and analyzed using STATA version 14. Statistical significance was considered at p-values less than 0.05. Measures of accuracy evaluated in the statistical analysis included mean error, mean absolute error, mean percentage error, and mean absolute percentage error. Pearson correlation was done between the estimated ultrasound fetal weight and the actual birth weight. The proportion of estimates within ±10% of actual birth weight was also determined. Result.A total of 170 pregnant women participated in the study. The mean maternal age was 30.77 years ± 5.54. The mean birth weight was 3.47 kg ± 0.47, while the mean estimated ultrasound weight was 3.43 kg ± 0.8. There was positive correlation between the ultrasound estimated weight and the actual birth weight. The mean ultrasound scan to delivery interval was 0.8 days (with range of 0–2 days). The study recorded a mean error of estimation of 41.17 grams and mean absolute error of 258.22 grams. The mean percentage error was 0.65%, while the mean absolute error of estimation was 7.56%. About 72.54% of the estimated weights were within 10% of the actual birth weight. Conclusion. The ultrasound estimated fetal weight correlated with the actual birth weight. Ultrasound estimation of fetal weight should be done when indicated to aid the clinician in making decisions concerning routes of delivery.

scholarly journals Pencocokan Berbasis Kata Kunci pada Penilaian Esai Pendek Otomatis Berbahasa Indonesia

Techno Com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-27
Author(s):  
Nurul Chamidah ◽  
Mayanda Mega Santoni
Keyword(s):  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Absolute Error

Evaluasi dalam pengajaran dapat dilakukan melalui ujian. Ujian berupa esai dapat digunakan untuk mengevaluasi pemahaman sesuai konteks dan memiliki jawaban referensi. Sayangnya, jawaban dari esai ini membutuhkan waktu yang lebih banyak untuk dievaluasi dan dapat terjadi inkonsistensi dalam melakukan penilaian. Penelitian ini dilakukan untuk menganalisis performa untuk penilaian esai pendek otomatis berbahasa Indonesia untuk mengevaluasi jawaban yang berbentuk esai pendek. Sehingga, penilaian terhadap jawaban esai lebih konsisten dan dapat digunakan sebagai alternatif untuk penilaian dalam ujian online. Penilaian esai dilakukan dengan menghitung kecocokan antara jawaban uji dengan jawaban referensi, yakni dengan meilhat kata kunci dari masing-masing jawaban. Kata kunci diperoleh dengan melakukan praproses pada teks yakni dengan case folding, pembuangan stopword, stemming, dan tokenisasi. Setelah mendapatkan kata kunci untuk jawaban uji dan jawaban referensi, pada tahap keyword matching dilakukan pencocokan jawaban uji terhadap jawaban referensi. Hasil kecocokan antara jawaban uji dan referensi selanjutnya dihitung menjadi nilai pada tahapan grading. Nilai yang diperoleh dari grading selanjutnya dibandingkan dengan nilai uji sebagai evaluasi performa dengan menghitung Mean Absolute Error (MAE) dan Pearson Correlation. Hasil dari penelitian ini menunjukkan MAE untuk keseluruhan jawaban uji sebesar 0.25 dan korelasi antara nilai uji dengan nilai hasil grading sebesar 0.79.

scholarly journals An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 174
Author(s):  
Dionisis Margaris ◽  
Dimitris Spiliotopoulos ◽  
Gregory Karagiorgos ◽  
Costas Vassilakis
Keyword(s):  
Collaborative Filtering ◽  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Mean Square ◽  
Error Metrics ◽  
Target User ◽  
The Mean ◽  
Rating Prediction ◽  
Sparsity Problem

Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.

scholarly journals Implementasi Metode Item-Based Collaborative Filtering dalam Pemberian Rekomendasi Calon Pembeli Aksesoris Smartphone

2019 ◽  
Vol 9 (1) ◽  
pp. 17-27
Author(s):  
Bondan Prasetyo ◽  
Hanny Haryanto ◽  
Setia Astuti ◽  
Erna Zuni Astuti ◽  
Yuniarsi Rahayu
Keyword(s):  
Collaborative Filtering ◽  
Hybrid Method ◽  
Mean Absolute Error ◽  
Pearson Correlation ◽  
Weighted Average ◽  
Absolute Error ◽  
Content Based Filtering

Flazzstore merupakan sebuah toko yang bergerak dibidang penjualan casing smartphone. Terdapat banyak produk yang berbeda-beda dengan banyak tema yang berbeda pula, hal ini membuat beberapa user kesulitan dalam menentukan pilihan mengenai produk yang akan dipilih. Perlunya sebuah sistem rekomendasi yang mampu memberikan rekomendasi produk kepada user, untuk memudahkan user dalam memilih produk yang akan dibelinya. Penelitian ini menggunakan metode Item-Based Collaborative Filtering, metode ini mencari similarity/kesamaan item dengan item lainnya. Sistem akan mencari rating tiap item dan menghitung nilai similarity menggunakan persamaan pearson correlation-based similarity. Kemudian nilai dari hasil perhitungan similarity akan digunakan untuk menghitung nilai prediksi tiap produk dengan menggunakan persamaan weighted average of deviation. Sebelum direkomendasikan kepada pelanggan dari hasil prediksi tersebut dihitung nilai Mean Absolute Error (MAE) dihitung selisih antara nilai rating sebenarnya dengan prediksi, dan kemudian diurutkan mulai dari terkecil ke terbesar untuk direkomendasikan kepada user. Hasil dari penelitian menunjukkan kecilnya nilai rata-rata MAE 0,572039 namun untuk proses eksekusi, waktu yang dibutuhkan cukup lama yaitu 6,4 detik. Penelitian berikutnya dapat mengombinasikan pendekatan metode content based filtering dan collaborative filtering atau disebut dengan Item Based Clustering Hybrid Method (ICHM) supaya hasil yang diperoleh lebih baik dan dapat mempersingkat waktu yang dibutuhkan.

Lexical Rule and Lexicon Effect for Part of Speech Tagging Bahasa Madura

2018 ◽  
Vol 18 (1) ◽  
pp. 65-72
Author(s):  
Nindian Puspa Dewi ◽  
Ubaidi Ubaidi
Keyword(s):  
Text Processing ◽  
Part Of Speech Tagging ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Speech Tagging ◽  
Bahasa Indonesia

POS Tagging adalah dasar untuk pengembangan Text Processing suatu bahasa. Dalam penelitian ini kita meneliti pengaruh penggunaan lexicon dan perubahan morfologi kata dalam penentuan tagset yang tepat untuk suatu kata. Aturan dengan pendekatan morfologi kata seperti awalan, akhiran, dan sisipan biasa disebut sebagai lexical rule. Penelitian ini menerapkan lexical rule hasil learner dengan menggunakan algoritma Brill Tagger. Bahasa Madura adalah bahasa daerah yang digunakan di Pulau Madura dan beberapa pulau lainnya di Jawa Timur. Objek penelitian ini menggunakan Bahasa Madura yang memiliki banyak sekali variasi afiksasi dibandingkan dengan Bahasa Indonesia. Pada penelitian ini, lexicon selain digunakan untuk pencarian kata dasar Bahasa Madura juga digunakan sebagai salah satu tahap pemberian POS Tagging. Hasil ujicoba dengan menggunakan lexicon mencapai akurasi yaitu 86.61% sedangkan jika tidak menggunakan lexicon hanya mencapai akurasi 28.95 %. Dari sini dapat disimpulkan bahwa ternyata lexicon sangat berpengaruh terhadap POS Tagging.

Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Fahad Layth Malallah ◽  
Baraa T. Shareef ◽  
Mustafah Ghanem Saeed ◽  
Khaled N. Yasen
Keyword(s):  
Body Temperature ◽  
Embedded System ◽  
Core Temperature ◽  
Integrated Circuit ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Thermal Sensor ◽  
Human Face ◽  
Body Contact ◽  
Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

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