Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

11.284

P-Index

This Author published in this journals

All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL MANAJEMEN BISNIS Jurnal Literasiologi Jurnal Tekno Kompak Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Jurnal Abdidas Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian UNDIKMA Journal of Electrical Engineering and Computer (JEECOM) Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Silva Samalas: Journal of Forestry and Plant Science Jurnal Elkasista ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Inovasi, Evaluasi dan Pengembangan Pembelajaran (JIEPP) Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An Jurnal Ilmiah Pendidikan Dasar (JIPDAS) Journal of Engineering, Technology and Computing (JETCom) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Management, Economic and Accounting (JMEA) Insand Comtech : Information Science and Computer Technology Journal International Journal of Engineering Continuity Valid Jurnal Pengabdian Journal of Economics and Management Scienties Prosiding Seminar Nasional CORISINDO Media Pendidikan Matematika

Hairani Hairani

Universitas Bumigora, Mataram, Indonesia

Author-ID : 2377224

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 95 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4 5

Pelatihan Pengenalan Data Science untuk Meningkatkan Kemampuan dalam Pengolahan Data Hairani Hairani; Ahmad Zuli Amrullah
Jurnal Abdidas Vol. 1 No. 3 (2020): Vol 1 No 3 July Pages 88-182
Publisher : Universitas Pahlawan Tuanku Tambusai

Data science merupakan gabungan ilmu komputer, statistika, dan pengetahuan domain bisnis untuk ekstraksi tumpukan data yang besar menjadi pengetahuan sehingga mendapatkan pattern atau pola-pola yang dapat memudahkan pengambil keputusan. Adapun orang menggeluti bidang data science disebut data scientist. Profesi data scientist akhir-akhir ini menjadi profesi yang sangat seksi di abad 21. Sumber daya manusia yang berprofesi sebagai data scientist di Indonesia sangat sedikit bila dibandingkan ketersedian lapangan kerja dibidang data science. Dengan kata lain, ketersediaan lapangan kerja data science berbanding terbalik dengan ketersediaan SDM yang berprofesi sebagai data scientist, dimana jumlah SDM data scientist sangat sedikit dibandingkan dengan jumlah lapangan kerja yang berlimpah. Salah satu solusi yang ditawarkan adalah mengadakan pelatihan dan workshop untuk pengenalan data science untuk meningkatkan SDM bidang data science khususnya di Universitas Bumigora. Metode pelaksanaan yang digunakan adalah penyampaian materi tentang data science dan simulasi penggunaan metode data science dalam kasus real menggunakan Google Colab. Berdasarkan hasil pelatihan dan workshop yang telah dilaksanakan, dapat meningkatkan pemahaman dan kemampuan para peserta untuk menggunakan metode-metode yang ada pada data science untuk mengolah data menjadi sebuah pengetahuan.

Aplikasi Pembelajaran Percakapan Bahasa Arab dan Inggris Berbasis Android Hairani Hairani; Muhammad Zulkarnaen Haris; Muhammad Arfa; Muhammad Innuddin
Jurnal SASAK : Desain Visual dan Komunikasi Vol 4 No 1 (2022): SASAK
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/sasak.v4i1.1884

Nurul Haramain Islamic Boarding School NW Narmada is a private educational institution that applies English and Arabic as daily languages for students in the boarding school environment. However, students who have just entered the Islamic boarding school are given up to six months to learn English and Arabic. Until now, Nurul Haramain Islamic Boarding School NW Narmada in teaching Arabic and English to students still uses the conventional learning process without using the help of learning media. Most of the students did not listen to the teacher delivering the subject matter, difficulties in understanding the material, and the lack of interest of the students in Arabic and English conversations. Therefore, this study aims to develop an application for learning conversational English and Arabic based on Android for class 1 students of Mts Nurul Haramain. The application development stages use the Luther Sutopo model which consists of the Concept, Design, Material Collecting, Assembly, Testing, and Distrubution stages. The application that has been developed was tested on 30 respondents related to application functionality, where the results of application functionality were 78% in the Good category, so that it can help class 1 students of Mts Islamic boarding school Nurul Haramain learn English and Arabic conversation with the correct pronunciation.

Aplikasi Penentuan Penerima Beasiswa Menggunakan Algoritma C4.5 Abdurraghib Segaf Suweleh; Dyah Susilowati; Hairani Hairani
Jurnal Bumigora Information Technology (BITe) Vol 2 No 1 (2020)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Pada proses penentuan beasiswa sering muncul permasalahan mengenai tidak adanya perhitungan pasti untuk menentukan penerima beasiswa yang berhak yang mengakibatkan pelaksanaan seleksi beasiswa membutuhkan waktu yang relative lama. Implementasi aplikasi yang dapat memprediksi calon penerima beasiswa yang menggunakan teknik data mining, dapat menjadi salah satu alternative solusi untuk mengatasi permasalahan tersebut. Metode penelitian yang digunakan yaitu metode waterfall dengan tahap : analisa kebutuhan, perancangan diagram alur dan interface, implementasi menggunakan PHP dan MySQL ,dan pengujian menggunakan metode black box. Data yang digunakan untuk pengujian merupakan data mahasiswa sebanyak 125 data. Hasil yang dicapai dari pengujian tersebut yaitu diketahuinya tingkat akurasi implementasi algoritma C4.5 pada proses penentuan penerima beasiswa mencapai 92%, spesifisitas 92.3%, dan sensitifitas 91.6% . Kesimpulan dari penelitian ini adalah algoritma C4.5 berhasil diimplementasikan dalam proses klasifikasi penerima beasiswa dan fungsi – fungsi aplikasi ini sudah sesuai dengan yang diharapkan berdasarkan hasil pengujian menggunakan metode black box.

Implementation of Certainty Factor Method on Problematic Student Counseling Guidance Expert System Zilullah Nazir Hadi; Dyah Susilowati; Hairani Hairani; Muhammad Innuddin
Jurnal Bumigora Information Technology (BITe) Vol 3 No 2 (2021)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v3i2.1553

The main thing in the development of education is the quality of education. One of the determinants of the quality of education is counseling guidance. The problem so far is that most students who have problems feel embarrassed in doing counseling directly to the BK teacher and usually consult with their friends so that they cannot solve the problems they face. This makes it difficult for BK teachers to deal with student problems, so we need a system that can help and solve problems experienced by students. The purpose of this study is to design an expert system that overcomes the problems suffered by students using the certainty factor method that can provide solutions based on the types of problems suffered by the students. The expert system development methodology in this study uses the waterfall methodology which consists of needs analysis, design, coding, and testing. The result of this research is in the form of an expert system application for counseling problem students who apply a web-based certainty factor method that can make it easier for students to find out the types of problems they face based on the problems symptoms entered. This study concludes that the expert system application that was built has a good level of convenience based on the results of usability testing using the SUS (System Usability Scale) method of 76.5%.

Kombinasi Metode AHP dan TOPSIS untuk Rekomendasi Penerima Beasiswa SMK Berbasis Sistem Pendukung Keputusan M. Rasyid Ridho; Hairani Hairani; Kurniadin Abd Latif; Rifqi Hammad
Jurnal Tekno Kompak Vol 15, No 1 (2021): Februari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v15i1.905

SMK 2 Negeri Mataram merupakan lembaga pedidikan yang terteletak dikota mataram. Setiap tahunnya pihak SMK Negeri 2 Mataram (pihak humas) melakukan seleksi beasiswa bagi siswa yang memiliki nilai akademik bagus tetapi tidak mampu secara ekonomi. Selama ini permasalahan yang dihadapi oleh pihak pengambil keputusan atau humas SMK Negeri 2 Mataram dalam seleksi penerima beasiswa adalah waktu yang dibutuhkan sangat lama karena dilakukan secara manual. Karena pihak humas SMK Negeri 2 mataram memilih siswa yang berhak mendapatkan bantuan beasiswa dengan cara verifikasi manual. Cara ini tentunya akan menyebabkan banyak terjadinya salah sasaran dalam pembagiannya, dimana yang dianggap mampu, bisa mendapatkan beasiswa tersebut, sedangkan yang dianggap tidak mampu tidak mendapatkannya. Tidak hanya itu, pihak humas juga kesulitan dalam penentuan penerima beasiswa dikarenakan antar calon penerima memiliki kesamaan nilai setiap kriteria yang digunakan. Adapun kriteria yang digunakan dalam seleksi penerima beasiswa adalah nilai rata-rata, penghasilan orang tua, tanggungan orang tua, jarak tempat tinggal, dan kehadiran. solusi yang ditawarkan pada penelitian ini adalah menggunakan konsep sistem pendukung keputusan dengan kombinasi metode AHP dan TOPSIS untuk seleksi penerima beasiswanya. Metode AHP digunakan untuk pembobotan secara otomatis dan mendapatkan bobot prioritas antar kriteria yang digunakan, untuk minimalisir terjadinya pembobotan secara subyektif. Sedangkan metode TOPSIS digunakan untuk melakukan perangkingan penerima beasiswa dengan cara mengoptimalkan solusi ideal positif dan solusi ideal negatif untuk mendapatkan penerima beasiswa yang tepat dan layak. Tahapan-tahapan dalam pengembangan sistem pendukung keputusan seleksi penerima beasiswa adalah pengumpulan data, perancangan model MADM untuk melihat hubungan antar kriteria dengan alternatifnya, Coding menggunakan Bahasa pemrograman PHP dan Mysql. Tahapan terakhir adalah Pengujian untuk validasi hasilnya dengan perhitungan manual dan sistem. Hasil penelitian yang didapatkan adalah kombinasi metode AHP-TOPSIS mampu dimplementasikan untuk mendapatkan alternatif terbaik sebagai penerima beasiswa. Adapun kesimpulan penelitian ini adalah dengan adanya sistem pendukung keputusan rekomendasi penerima beasiswa tersebut dapat memudahkan dan mempercepat pihak pengambil keputusan (pihak humas) dalam seleksi penerima beasiswa dengan transparan dan okjektif.

Recommendations of Thesis Supervisor using the Cosine Similarity Method Hairani Hairani; Mujahid Mujahid
Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v11i3.2003

During the thesis writing process, the role of the supervisor is needed so that the completion of the thesis is timely so that the quality of the thesis is maintained. One of the problems in determining the thesis supervisor for undergraduate computer science at Bumigora University is the subjectivity of the head of the study program in determining the thesis supervisor. Not only that, the selection process for supervisors is done manually so that it can take a long time and can slow down student thesis work. Students' thesis work will be late and not on time if the thesis topic is not in accordance with the competence of the lecturer. This study aims to apply the cosine similarity method to the recommendation of a thesis supervisor for undergraduate computer science at Bumigora University. The stages of this research consist of collecting thesis documents, pre-processing text (Case Folding, Tokenization, Filtering, Stemming), word weighting with TF-IDF, implementation of the cosine similarity method, and accuracy testing. The data used are 113 thesis documents which are divided as training data as many as 90 documents and testing data 23 documents. Based on the testing data on the test, the cosine similarity method can correctly recommend 21 of 23 thesis documents with an accuracy of 91.3%. Thus, the cosine similarity method can be applied to the case of selecting a thesis supervisor for undergraduate computer science at Bumigora University because it has very good accuracy.

Prediksi Penjualan Produk Unilever Menggunakan Metode Regresi Linear Anthony Anggrawan; Hairani Hairani; Nurul Azmi
Jurnal Bumigora Information Technology (BITe) Vol 4 No 2 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i2.2416

Barokah Shop is a retail store that sells various basic necessities for daily needs. Too much inventory will result in losses such as storage costs and the possibility of a decrease in the quality of goods. On the other hand, a small amount of inventory will reduce a larger profit. This study aims to build a web-based Unilever sales prediction system using a simple linear regression method. Testing the accuracy of the prediction results of sales of Unilever products using MEA and MAPE to see the level of error in the prediction results. The dataset uses Unilever product sales data for 15 months, from January 2021 to March 2022. The dataset is divided into 12 months as training data and 3 months as testing data. Prediction results in the next 3 periods of each type of product produce the same value between the system results and the results of manual linear regression calculations. Testing the error rate on the prediction results for 3 periods, namely January to March 2022, each Ax Deodorant, Bango Kecap, Buavita, Citra Lotion, Citra Soap, Clear Shampoo, Sariwangi, Sunsilk Conditioner, Vixal and Wall's Ice Cream products belong to the category of very accurate forecasting results. With the smallest MAPE value in Sunsilk Conditioner products of 1%. Thus, the linear regression method is very accurate for predicting sales of Unilever types goods.

Prediction of Electricity Usage with Back-propagation Neural Network Anthony Anggrawan; Hairani Hairani; M. Ade Candra
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 1 No 1 (2022): March 2022
Publisher : Universitas Bumigora Mataram-Lombok

The use of electricity has become a need that is increasing day by day. So it is not surprising that the problem of using electricity has attracted the attention of many researchers to research it. Electricity users make various efforts and ways to save on the use of electrical energy. One of them is saving electricity usage by electricity users using electrical energy-efficient equipment. That is why the previous research confirms the need for interventions to reduce the use of electrical energy. Therefore, this study aims to predict electricity use and measure the performance of the anticipated results of electricity use. This study uses the back-propagation method in predicting the use of electricity. This study concluded that the backpropagation architectural model with better performance is the six hidden layer architecture, 0.4 learning rate, and the Root Means Square Error (RMSE) value of 0.203424. Meanwhile, the training data test results get the best architectural model on hidden layer 8 with a learning rate of 0.3 with an RMSE performance value of 0.035811. The prediction results show that the prediction of electricity consumption is close to the actual data of actual electricity consumption.

Web-Based Application for Toddler Nutrition Classification Using C4.5 Algorithm Hairani Hairani; Lilik Nurhayati; Muhammad Innuddin
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 1 No 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Health is something that is important for everyone, from year to year various efforts have been developed to get better and quality health. Good nutritional status for toddlers will contribute to their health and also the growth and development of toddlers. Fulfillment of nutrition in children under five years old (toddlers) is a factor that needs to be considered in maintaining health, because toddlerhood is a period of development that is vulnerable to nutritional problems. There are more than 100 toddler data registered at the Integrated Healthcare Center in Peresak Village, Narmada District, West Lombok Regency. The book contains data on toddlers along with the results of weighing which is carried out every month. However, to classify the nutritional status of toddlers, they are still going through the process of recording in a notebook by recording the measurement results and then looking at the reference table to determine their nutritional status. This method is still conventional or manual so it takes a long time to determine the nutritional status. Therefore, the solution in this study is to develop a web-based application for the classification of the nutritional status of children under five using the C4.5 method. The stages of this research consisted of problem analysis, collection of 197 instances of nutritional status datasets obtained from Integrated Healthcare Center Presak, analysis of system requirements, use case design, implementation using the C4.5 method, and performance testing based on accuracy, sensitivity, and specificity. The results of this study are a website-based application for the classification of the nutritional status of children under five using the C4.5 method. The performance of the C4.5 method in the classification of the nutritional status of toddlers using testing data as much as 20% gets an accuracy of 95%, sensitivity of 100%, and specificity of 66.6%. Thus, the C4.5 method can be used to classify the nutritional status of children under five, because it has a very good performance.

Improvement Performance of the Random Forest Method on Unbalanced Diabetes Data Classification Using Smote-Tomek Link Hairani Hairani; Anthony Anggrawan; Dadang Priyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1069

Most of the health data contained unbalanced data that affected the performance of the classification method. Unbalanced data causes the classification method to classify the majority data more and ignore the minority class. One of the health data that has unbalanced data is Pima Indian Diabetes. Diabetes is a deadly disease caused by the body's inability to produce enough insulin. Complications of diabetes can cause heart attacks and strokes. Early diagnosis of diabetes is needed to minimize the occurrence of more severe complications. In the diabetes dataset used, there is an imbalanced data between positive and negative diabetes classes. Diabetes negative class data (500 data) is more than diabetes positive class (268), so it can affect the performance of the classification method. Therefore, this study aims to apply the Smote-Tomeklink and Random Forest methods in the classification of diabetes. The research methodology used is the collection of diabetes data obtained from Kaggle, as many as 768 data with eight input attributes and 1 output attribute as a class, pre-processing data is used to balance the dataset with Smote-Tomeklink, classification using the random forest method, and performance evaluation based on accuracy, sensitivity, precision, and F1-score. Based on the tests conducted by dividing data using 10-fold cross-validation, the Random Forest algorithm with Smote-TomekLink gets the highest accuracy, sensitivity, precision, and F1-score compared to Random Forest with Smote. The Random Forest algorithm with Smote-Tomeklink has 86.4% accuracy, 88.2% sensitivity, 82.3% precision, and 85.1% F1-score. Thus, using Smote-Tomeklink can improve the performance of the random forest method based on accuracy, sensitivity, precision, and F1-score.

Co-Authors Abdillah, Mokhammad Nurkholis Abdurraghib Segaf Suweleh Abdurraghib Segaf Suweleh Abu Tholib Adam, M. Awaludin Afrig Aminuddin Ahmad Ahmad Ahmad Fathoni Ahmad Zuli Amrullah Amelia, Bengi Amin, Farda Milanda Andi Sofyan Anas Andi, Moh syaiful Andini, Nisha Anggarawan, Anthony Anthony Anggrawan Arfa, Muhammad Ashadi, Diki Astuti, Ni Luh Budi Ayu Dasriani, Ni Gusti Candra, M. Ade Christine Eirene Christopher Michael Lauw Dadang Priyanto Dedi Aprianto Dedy Febry Rachman Dedy Febry Rahman Deny Jollyta Dian Syafitri Didik Dwi Prasetya Diki Ashadi Dirgantara, Bhintang Donny Kurniawan Dyah Susilowati Dyah Susilowaty Edddy, Syaiful Eka Setiawan, Rian Putra Fahry, Fahry Fatimatuzzahra Fatimatuzzahra Fitra Rizki Ramdhani Gede Yogi Pratama Gibran Satya Nugraha Gibran Satya Nugraha Gumangsari, Ni Made Gita Guntara, Muhammad Gusti Ayu Diah Gita Kartika Santi, I Gustiya, Sherly Dwi Guterres, Juvinal Ximenes Hadi, M Fawazi Hammad, Rifqi Hartono Wijaya Haryono Haryono Hasbullah Hasbullah Herawati, Baiq Candra Heru Kurnianto Tjahjono Hery Widijanto Hidayati, Diana Huda, Dias Nabila Husnul Madihah, Husnul I Gusti Agung Ayu Hari Triandini I Nyoman Switrayana Ida Putu Andika Ifnaldi, Ifnaldi Ilham Saifuddin Indah Puji Lestari Indradewa, Rhian Isviyanti, Isviyanti Janhasmadja, Mengas Jauhari, M. Thonthowi Jupriadi, Jupriadi Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Kandisa, Amelia Kasiyanto Kasiyanto, Kasiyanto Khairan marzuki Khairil Ihsan Khasnur Hidjah Khurniawan Eko Saputro Kurniadin Abd Latif Kurniawan Kurniawan Lalu Ganda Rady Putra Lalu Zazuli Azhar Mardedi Lilik Nurhayati lnnuddin, Muhammad M. Ade Candra M. Rasyid Ridho M.Khaerul Ihsan Maariful Huda, Muhammad Malika, Riwayati Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Mayadi, Mayadi Mayasari, Astri Melati Rosanensi Michael Lauw, Christopher Miftahul Madani Muhamad Azwar Muhamad Azwar, Muhamad Muhammad Arfa Muhammad Innuddin Muhammad Maariful Huda Muhammad Ridho Akbar Muhammad Ridho Hansyah muhammad Syahbudi, muhammad Muhammad Zulfikri Muhammad Zulfikri Muhammad Zulkarnaen Haris Mujahid Mujahid Neny Sulistianingsih Noor Akhmad Setiawan Nurhayati, Lilik Nurul Azmi Nurvianti, Nurvianti Nuzululnisa, Bq Nadila Pahrul Irfan Putu Tisna Putra Qososyi, Sayidina Ahmadal Rahman, Mochamad Farhan Caesar Rahmawati, Lela Rahmi, Agustina Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Rifqi Hammad Riosatria, Riosatria Riwayati Malika Rizki Wahyudi RR. Ella Evrita Hestiandari Saifuddin Zuhri Saifuddin, Ilham Samsul Hadi Santoso, Heroe Shudiq, Wali Ja'far Soepriyanto, Harry Sofiansyah Fadli Sri Winarni Sofya Sri Winarni Sofya Sudi Prayitno Sukron, Moh Sutarman Sutarman Syahrir, Moch. tadianta m., Winardi aries Teguh Bharata Adji Tri Widayatsih, Tri Triwijoyo, Bambang Krismono Triyanna Widiyaningtyas Umi Hanifah Vidiasari, Herlita Vidiasari, Viviana Herlita Wahyuningsih, Rr. Sri Handari Wangiyana, I Gde Adi Suryawan Widiatmoko, Dekki Wira Hendri Wiyanto, Suko Ximenes Guterres, Juvinal Yuri Ariyanto Zilullah Nazir Hadi

Title

Found 95 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 95 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 95 Documents
Search