Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

12.278

P-Index

This Author published in this journals

All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database Indonesian Journal of Innovation Multidisipliner Research Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Indonesian Journal of Innovation Multidisipliner Research Jurnal Komtika (Komputasi dan Informatika)

Elvia Budianita

Universitas Islam Negeri Sultan Syarif Kasim Riau

Author-ID : 2584034

Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

Published : 121 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4 5

SISTEM PENENTUAN PENCERAMAH MASJID PARIPURNA KOTA PEKANBARU MENGGUNAKAN ALGORITMA PENGKLASTERAN K-MEANS Silfia Silfia; Rahmad Kurniawan; Nazruddin Safaat Harahap; Elvia Budianita; Fadhilah Syafria; Iwan Iskandar
JURNAL TEKNIK INFORMATIKA Vol 14, No 2 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v14i2.23750

There are 903 mosques in Pekanbaru City, Riau Province. In 2016, the Pekanbaru City Government formed a Paripurna Mosque program which the Pekanbaru Paripurna Mosque Management Agency manages. Each mosque holds religious activities which a preacher fills. The mosque has a regular schedule of lectures with a short transition period for each type of religious activity held. Based on observations, the mosque management did not get complete information regarding the profile of the preacher. Furthermore, many preachers have canceled lecture schedules due to distance issues and the suitability of the lecturer's profile with the congregations. Therefore, a recommendation system using the K-means algorithm is necessary based on coordinate points, location access, and appropriate types of religious activities for the Pekanbaru Paripurna Mosque. This study also employed one hot encoding technique for non-numeric data. Based on the experimental testing results on the five clusters, the silhouette coefficient value is 0.945. Based on the results, it can be concluded that the system for determining the preachers of the Pekanbaru City Paripurna Mosque has the potential to be used.

Penerapan Metode Learning Vector Quantization2 (LVQ 2) Untuk Menentukan Gangguan Kehamilan Trimester I Elvia Budianita Budianita
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 15, No 2 (2018): JUNI 2018
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v15i2.4861

Trimester I adalah masa dimana 3 bulan pertama kehamilan yakni 0 sampai 12 minggu awal kehamilan. Pada masa ini tubuh ibu akan banyak mengalami perubahan seiring berkembangnya janin. Pada ibu-ibu hamil pada fase trimester I terkadang ditemukan beberapa gangguan kehamilan yaitu, Abortus, Anemia Kehamilan, Hiperemesis Gravidarum tingkat I, Hiperemesis Gravidarum tingkat II, Kehamilan Ektopik, dan Mola hidatidosa. Untuk membantu pasien dalam mengenali gangguan kehamilan pada trimester I ini maka peneliti berinisiatif merancang suatu sistem yang menerapkan konsep jaringan syaraf tiruan dengan metode LVQ 2 (Learning Vector Quantization) dalam mengenali gangguan kehamilan trimester I berdasarkan gejala gangguan kehamilan trimester I. Ada 41 gejala penyakit, dan 6 penyakit sebagai data masukan. Sistem akan mengklasifikasikan penyakit dengan proses pembelajaran dan pengujian ke dalam 6 jenis penyakit, berdasarkan pengujian metode LVQ2 cukup baik di terapkan dalam pengenalan pola gejala gangguan kehamilan, di buktikan dari hasil pengujian yang di lakukan menggunakan window 0.1, 0.3, 0.5, dan 0, data latih 90 dan data uji 18 didapat akurasi terbaik 100% dan rata-rata akurasi 97.68% dengan nilai parameter pembelajaran algoritma learning rate = 0.02, 0.04, 0.06, pengurangan learning rate = 0.1, minimal learning rate = 0.01 dan nilai window (ε) =0.1, 0.3, 0.5, dan 0. Nilai w juga mempengaruhi akurasi. Kata Kunci: Gangguan Kehamilan Trimester I, Learning Vector Quantization 2, Window

Diagnosa Penyakit Kejiwaan Menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization2 (LVQ 2) Elvia Budianita
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 13, No 2 (2016): JUNI 2016
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v13i2.1769

In diagnosing mental illness takes a relatively long time is one week to one month and could only be carried out by experts which is psychological doctors, because it must diagnosing accordance with existed procedures. Based on the above the problem can be formulated how to diagnose psychiatric illness by applying neural network Learning Vector Quantuzation 2 (LVQ 2). LVQ2 is the development of basic LVQ. This study aims to help doctors diagnose phychiatric illness by applying neural network Learning Vector Quantization 2 and can distinguish between types of mental illness. In this research using the data input 14 symptoms and 4 psychiatric diseases as output is used as the target of Schizophrenia, Organic Mental Disorders, Mental disorders and behavior due to substance users, and feeling the atmosphere Disorders (affective or mood disorders). Based on test results using 132 training data and 30 test data and parameter with a value of learning rate = 0.025, a reduction in the minimum learning rate = 0.1 learning rate = 0:01, and window = 0.4 which is done LVQ2 test result accuracy is as high as 90%. Thus LVQ2 can be applied to the classification of mental illness.

Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi Berbasis Web Elvia Budianita; Jasril Jasril; Lestari Handayani
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 12, No 2 (2015): Juni 2015
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v12i2.1005

Salah satu cara untuk mengenali daging sapi dan babi di bidang informatika adalah menggunakan pengolahan citra. Pada penelitian ini akan dibuat suatu sistem pengolahan citra untuk membedakan daging sapi dan babi menggunakan metode HSV, GLCM, dan klasifikasi K-Nearest Neighbour (K-NN). Tahapan analisa yang dilakukan adalah Data acquisition dengan menggunakan kamera handphone dilakukan pemotretan terhadap data daging sapi dan babi sehingga diperoleh citra digital daging sapi dan babi dalam format jpg. Gambar (citra) diambil dari daging babi segar, daging sapi segar, daging sapi yang telah membusuk, dan daging campuran (oplosan). Preprocessing dilakukan peningkatan kualitas citra yaitu dengan melakukan pencerahan citra dan peregangan kontras. Ekstraksi fitur menggunakan histogram model warna HSV untuk fitur warna, dan metode orde dua untuk ekstraksi fitur tekstur. Klasifikasi citra daging sapi dan babi menggunakan K-NN dengan dua tahapan yaitu tahap training dan testing. Pengujian terdiri dari 4 pengujian yaitu pengujian tanpa background dengan akurasi keberhasilan 88,75%, pengujan dengan background sebesar 73,375%, pengujian campuran sebesar 88,75% dan pengujian berjarak sebesar 50% . Pengujian dengan akurasi pengujian tertinggi terdapat pada pengujian tanpa background dan pengujian campuran sebesar 88,75%. Pada pengujian campuran dinyatakan berhasil apabila hasil klasifikasinya adalah daging campuran (oplosan) dan daging babi segar. Sedangkan nilai K yang paling baik pada semua pengujian adalah K=5 dengan total akurasi keberhasilan 78,75%. Hasil klasifikasi dipengaruhi oleh nilai ciri, jarak terdekat, nilai K, dan yang paling mempengaruhi adalah background

Penerapan Learning Vector Quantization (LVQ) untuk Klasifikasi Status Gizi Anak Elvia Budianita; Widodo Prijodiprodjo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 7, No 2 (2013): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.3354

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.

KLASIFIKASI CITRA SATELIT MULTITEMPORAL DAERAH BENCANA ALAM DENGAN METODE BACKPROPAGATION NEURAL NETWORK Lestari Handayani; Mohd. Ridho Zarkasih Rahim; M Irsyad; Elvia Budianita
Prosiding Semnastek PROSIDING SEMNASTEK 2018
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Bencana alam menimbulkan dampak negatif yang menyebabkan perubahan bentuk fisik pada suatu daerah yang mempengaruhi kehidupan masyarakat baik secara sosial maupun ekonomi. Dibutuhkan identifikasi citra satelit multitemporal pada daerah bencana alam untuk mendapatkan data dan informasi akibat dari dampak bencana alam. Dengan menggunakan metode jaringan syaraf tiruan propagasi balik akan dilakukan klasifikasi lahan hijau, pemukiman dan perairan. Data ini akan memberikan informasi perubahan akibat dampak bencana alam dengan membandingkan informasi dari citra sebelum dan sesudah bencana. Jaringan syaraf tiruan dengan algoritma backpropagation yang meliputi 3 proses penting yaitu create learning data, data training dan classification. Data berupa citra satelit yang diperoleh pada website: http://geospasial.bnpb.go.id dan www.bbc.co.uk. Hasil pengujian menunjukkan bahwa jaringan syaraf tiruan yang dibangun dapat melakukan pengenalan pola dan proses klasifikasi dengan sangat baik dan tingkat keakuratannya (overall accuracy) sebesar 98,2%

The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3 Okfalisa Okfalisa; Elvia Budianita; Musa Irfan; Hidayati Rusnedy; Saktioto Saktioto
IT Journal Research and Development Vol. 5 No. 2 (2021)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2021.vol5(2).5681

The addiction of children to gadgets has a massive influence on their social growth. Thus, it is essential to note earlier on the addiction of children to such technologies. This study employed the learning vector quantization series 3 to classify the severity of gadget addiction due to the nature of this algorithm as one of the supervised artificial neural network methods. By analyzing the literature and interviewing child psychologists, this study highlighted 34 signs of schizophrenia with 2 level classifications. In order to obtain a sample of training and test data, 135 questionnaires were administered to parents as the target respondents. The learning rate parameter (α) used for classification is 0.1, 0.2, 0.3 with window (Ɛ) is 0.2, 0.3, 0.4, and the epsilon values (m) are 0.1, 0.2, 0.3. The confusion matrix revealed that the highest performance of this classification was found in the value of 0.2 learning rate, 0.01 learning rate reduction, window 0.3, and 80:20 of ratio data simulation. This outcome demonstrated the beneficial consequences of Learning Vector Quantization (LVQ) series 3 in the detection of children's gadget addiction.

Implementation of Backpropagation Neural Network to Detect Suspected Lung Disease Fadhilah Syafria; Boni Iqbal; Elvia Budianita; Iis Afrianty
Indonesian Journal of Artificial Intelligence and Data Mining Vol 1, No 1 (2018): March 2018
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Many People were less concerned with lung health, it caused people identified as suffering from lung diseases. Early symptoms that often appear was cough that took a long time and could be the beginning of more severe disease. Therefore it was necessary to create application that could detect suspected person contracted lung disease. The applications were made by using artificial neural network with Backpropagation with initial input data, symptoms by patients of lung diseases. The symptoms were 22, and kind of lung diseases as a diagnosis were asthma, pneumonia, pulmonary tuberculosis and lung cancer. It used medical records of lung disease as much as 110 data. Network training uses 3 different architectures [input neurons ; hidden neurons ; output neurons], liked [22; 22 ; 2], [22 ; 33 ; 2] and [22 ; 43 ; 2]. Testing with 2 training data sharing and test data, namely comparison 90:10 and 80:20. The Parameters values were used namely learning rate 0.1, 0.3, 0.5, 0.7 and 0.9. The number of epoch was used, that is 15 epoch, 25 epoch and 35 epoch. Based on the tests performed, it was obtained an accuracy system on the 90:10 data comparison of 82% and the 80:20 data ratio of 82% as well. Thus, backpropagation method could be applied in detecting suspected lung diseases.

Membangun Sistem Penjadwalan Ruang Laboratorium dengan Algoritma Modified BiDirectional A M ridwan; Elvia Budianita
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2014: SNTIKI 6
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (378.25 KB)

Sistem penjadwalan ruang laboratorium merupakan sistem terkomputerisasi yang berfungsi untukmenyusun jadwal kelas-kelas praktikum yang akan menggunakan laboratorium. Pada saat ini, penyusunanjadwal laboratorium jurusan Teknik Informatika UIN Suska masih dilakukan secara manual sehingga kepalalaboratorium harus mengumpulkan sendiri data yang dibutuhkan dalam penyusunan jadwal. Teknik analisis datapada sistem ini menggunakan metode pembangunan perangkat lunak secara waterfall. Proses penyusunanjadwal dilakukan dengan metode MBDA (Modified Bidirectional A*) dengan penentuan bobot berdasarkankategori sisa waktu terbuang, kelas yang berulang, dan status dosen yang telah terjadwal. Pada MBDA* metodepencarian heuristik dilakukan dan setiap kandidat solusi akan disimpan kedalam struktur data graph yangmemiliki bobot. Algoritma MBDA akan menelusuri simpul tersebut dan mencari solusi terbaik berdasarkan totalbobot terendah. Berdasarkan pengujian terhadap 10 kasus secara acak, seluruh kasus menghasilkan jadwalyang bebas bentrokan waktu pengajar ataupun mahasiswa dan sesuai dengan waktu kosong yangdiajukan(100%).Kata kunci: Penjadwalan, pencarian heuristic, euclidean distance, MBDA.

Implementasi Learning Vektor Quantization (LVQ) dalam Mengidentifikasi Citra Daging Babi dan Daging Sapi Jasril Jasril; Meiky Surya Cahyana; Lestari Handayani; Elvia Budianita
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2015: SNTIKI 7
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.668 KB)

Widespread circulation of adulterated meat and based on the word of Allah which confirms the prohibition of pork to eat, it needs to be made of a system that can distinguish between beef and pork to avoid cheating merchants and keep halal meat we eat. This study makes a system for identifying the image of beef and pork and meat adulterated with the color feature extraction HSV (Hue, Saturation, Value) and texture feature extraction GLCM (Grey Level Co-occurent Matrix) using classification LVQ (Learning Vector Quantization). A result of image identification adulterated meat pig is considered as a pork class. Image data on the image of the study consisted of 107 primary and 13 secondary image. Identification testing conducted on the distribution of training data and test data are different. Accuracy of the highest success with an average of 94.81% on the distribution of the 80 training data and test data 20 and the accuracy of the lowest success with an average of 82.22% on the distribution of training data and test data 50 50 with Learning Rate of 0.01, 0.05, 0.09. More increase the distribution of training data and more decrease division of the test data, so more increase the accuracy of success in identifying the image.Keywords: beef, GLCM, HSV, Learning Rate, LVQ, pork

Co-Authors Abdul Halim Adzhima, Fauzan Afrianti, Liza Afriyanti, Iis Agnesti, Syafira Agung Syaiful Rahman Agustian, Surya Agustina, Auliyah Aji Pangestu Adek Akbar, Lionita Asa Akhyar, Amany Al Rasyid, Nabila Alfaiza, Raihan Zia Alfarabi.B, Alif Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Ammar Muhammad Anggi Pranata Aprilia, Tasya Aprima, Muhammad Dzaky Arif Pratama Budiman Azhima, Mohd Berliana, Trisia Intan Boni Iqbal buhfi arides hanyodi Chely Aulia Misrun Damayanti, Elok Desra Rizki Riyandi Dicky Abimanyu Dodi Efendi doli fancius silalahi Dwitama, Raja Zaidaan Putera Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eka Suryani Indra Septiawati Elin Haerani Elin Haerani Elin Haerani Elin Haerani Ellin Haerani Fadhilah Syafria Fahrozi, Aqshol Al Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Yanto Fikri Utri Amri Fikry Utri Amri Fitri Astuti Fitri Insani Fitri Insani Fitri Insani Fitri Insani Fitri, Anisa Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Habibi, M. Ilham Hara Novina Putri Hariansyah, Jul Hasibuan, Ilham Habibi Ibnu Afdhal Ichsan Permana Putra Ihda Syurfi Ihlal Hanafi Harahap Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Iis Afrianty Ikhsanul Hamdi Indah Wulandari Isra Almahsa, Muhammad Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril jasril jasril jasril Jeki Dwi Arisandi Khair, Nada Tsawaabul Kurnia Gusti, Siska Lestari Handayani Lestari Handayani Lili Rahmawati Lola Oktavia M Fikry M Ikhsan Maulana M ridwan Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Mawadda Warohma Mazdavilaya, T Kaisyarendika Megawati Megawati Meiky Surya Cahyana Mhd. Kadarman Mohd. Ridho Zarkasih Rahim Muhammad Affandes Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Irsyad Muhammad Rizky Ramadhan Mulyati, Sabar Mulyono, Makmur Musa Irfan Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Neni Sari Putri Juana Novi Yanti Novi Yanti Novriyanto Novriyanto Nur Iza Nuradha Liza Utami Nurafni Syahfitri Nurfadilah, Nova Siska Okfalisa Okfalisa Pasiolo, Lugas Permata, Rizkiya Indah Pizaini Pizaini Putri, Widya Maulida Rahmad Abdillah Rahmad Kurniawan Ramadani, Repi Ramadhan, Aweldri Ramadhani, Astrid Ramadhani, Siti Reni Susanti Reski Mai Candra Reski Mai Candra Rinaldi Syarfianto Robby Azhar Roni Salambue Rusnedy, Hidayati Said Nurfan Hidayad Tillah Saktioto Saktioto Sephia Pratista Silfia Silfia Siska Kurnia Gusti Siti Sri Rahayu Suwanto Sanjaya Syahputra, Armadani Ulti Desi Arni, Ulti Desi Wahyuni, Ayu Sri Widodo Prijodiprodjo Wiranti, Lusi Diah Yeni Fariati Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri Zulkarnain Zulkarnain

Title

Found 121 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 121 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 121 Documents
Search