Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

7.902

P-Index

This Author published in this journals

All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Computing and Modeling J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Journal Sensi: Strategic of Education in Information System JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology (JIfoTech) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Nasional Teknik Elektro dan Teknologi Informasi IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Informatika: Jurnal Pengembangan IT Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi

Sri Yulianto Joko Prasetyo

Universitas Kristen Satya Wacana

Author-ID : 947998

Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

Published : 93 Documents Claim Missing Document

Claim Missing Document

Articles

4 5 6 7 8

Penerapan Algoritma Random Forest dalam Menganalisa Perubahan Suhu Permukaan Wilayah Kota Salatiga Triloka Mahesti; Kristoko Dwi Hartomo; Sri Yulianto Joko Prasetyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4603

The population increase in Salatiga city is growing rapidly from 2010 to 2020. This change affects the area with vegetation cover, increasing building density and increasing land surface temperatures. The rising of land surface temperature can affect climate change, air quality, human health quality and energy usage. The purpose of this research is to find out the effect of the area with built-up land and area with vegetation cover to land surface temperature by exploring the values of NDVI, NDBI, LST and Albedo. This research shows that the NDVI value has decreased while the NDBI, LST and Albedo values have increased from 2014 to 2021. The values of NDVI, NDBI and Albedo are the components used as validation of the value of the land surface temperature (LST) change in the study area. The results of the correlation between indices show that the highest correlation occurs between NDVI and NDBI with a value of -0.979 which has a negative correlation because vegetation density is always inversely proportional to the density of built up land. The classification results show that there are 7 villages in Salatiga City with high temperature increases, the villages name are Cebongan, Mangunsari, Ledok, Kutowinangun Kidul, Gendongan, Salatiga and Kalicacing. The results of the accuracy and kappa values in the Random Forest algorithm are quite accurate with an accuracy value of 90% and a kappa value of 73%. The usability test in this study was carried out by distributing questionnaires to city planning department in Salatiga City who had a recapitulation result of 3.62 with the criteria "quite useful". From these results, this research is in accordance with its objectives, the result can be used as one of the city government's recommendations for policy making, especially in Salatiga city planning department.

Implementasi Algoritma Neural Network Dengan Optimisasi Hyperparameter Untuk Prediksi Harga Saham Muhammad Rizky Pribadi; Evi Maria; Suryasatria Trihadaru; Sri Yulianto Joko Prasetyo; Sutarto Wijono
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 4 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i4.3658

Stocks are one of the investment instruments that are currently in demand because they have a greater return value than saving in a bank. It's just that investing in stocks has the risk of decreasing the value of the stock price, which can make investors lose money. Mining stocks are currently the prima donna of investors, because their value continues to rise. However, buying shares at the right time is still an obstacle, therefore a stock price prediction is needed that can help investors determine the right time to buy mining shares. The use of machine learning can be done to predict stock prices. The data used in this research is PTBA stock price data from 2017 – 2022. In this study, the Neural Network algorithm is used with hyperparameter optimization. In this study, the RMSE value was 30.634. A small RMSE value indicates that the Neural Network algorithm can be used to predict PTBA's stock price.

PERBANDINGAN BERBAGAI METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN COVID DI INDONESIA Nadia Renatha Yuwono; Sri Yulianto
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 1 No 2 (2022): IT-Explore Juni 2022
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Abstrak – Wabah Covid 2019 adalah penyakit menular serta dapat menyerang organ pernapasan yang sangat mematikan di Negara Tiongkok. Masyarakat Indonesia yang terjangkit virus Covid 2019 ini perlu dilakukan peramalan untuk mengetahui jumlah kasus masyarakat yang terjangkit wabah Covid 2019 pada bulan berikutnya. Dengan menggunakan Metode Single Exponential Smoothing, Double Exponential Smoothing, dan Triple Exponential Smoothing ini kita gunakan pada aplikasi RStudio untuk mengetahui nilai parameter α, β, dan γ kita dapat mengetahui perbandingan dari ketiga metode tersebut. Dari ketiga metode tersebut akan menggunakan parameter nilai α, β, dan γ. Dari ketiga metode tersebut dicari nilai SSE yang terkecil. Dengan mengetahui nilai SSE yang terkecil maka akan di dapatkan hasil peramalan yang lebih akurat. Data yang saya gunakan berjumlah 30 periode. Dengan menggunakan 30 periode kita mendapatkan nilai SSE terkecil 33042318. Dengan nilai tersebut kita mendapatkan nilai coefficient 1179.6161 atau masyarakat yang terjangkit wabah covid 2019 pada hari berikutnya berjumlah 1741 orang. Dengan dilakukannya penelitian ini diharapkan untuk setiap masyarakat dapat menjaga kesehatannya dengan cara menjaga kesehatan, kebersihan, serta mengkonsumsi makanan yang sehat dan bergizi sehingga dapat terhindar dari virus covid-19. Dengan menggunakan Metode Single, Double, Triple Exponential Smoothing kita dapat meramalkan kasus covid-19 di Indonesia selama beberapa bulan kedepan. Abstract – The 2019 Covid outbreak is an infectious disease and can attack the respiratory organs which is very deadly in China. For the Indonesian people who have been infected with the 2019 Covid virus, forecasting needs to be done to find out the number of community cases infected with the 2019 Covid outbreak in the following month. By using the Single Exponential Smoothing, Double Exponential Smoothing, and Triple Exponential Smoothing methods, we use the RStudio application to determine the value of the parameters α, β, and γ we can find out the comparison of the three methods. Of the three methods will use the parameter values α, β, and γ. From the three methods, the smallest SSE value is sought. By knowing the smallest SSE value, more accurate forecasting results will be obtained. The data that I use is 30 periods. By using 30 periods, we get the smallest SSE value of 33042318. With this value we get a coefficient value of 1179.6161 or the people who were infected with the 2019 covid outbreak on the next day amounted to 1741 people. With this research, it is hoped that every community can maintain their health by maintaining health, hygiene, and consuming healthy and nutritious food so that they can avoid the Covid-19 virus. By using the Single, Double, Triple Exponential Smoothing method, we can predict COVID-19 cases in Indonesia over the next few months.

ANALISA PERBANDINGAN ALGORITMA K-MEANS, K-MEDOIDS, DAN X-MEANS UNTUK PENGELOMPOKKAN KINERJA PEGAWAI Gideon Bartolomeus Kaligis; Sri Yulianto
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 1 No 3 (2022): IT-Explore Oktober 2022
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Kinerja pegawai menjadi rangkuman dalam hal kualitas, kuantitas, jam kerja dan juga kerja sama untuk mencapai suatu tujuan yang telah ditetapkan oleh instansi atau perusahaan, namun dalam Sekretariat DPRD Provinsi Sulawesi Utara belum adanya metode untuk menentukan pengelompokkan kinerja pegawai. Untuk mengatasi permasalahan ini, diperlukan adanya pengelompokan kinerja pegawai di Sekretariat DPRD Provinsi Sulawesi Utara, sehingga bisa menentukan kinerja pegawai yang berkualitas. Tujuan dari penelitian ini dengan melakukan perbandingan metode-metode clustering untuk mendapatkan metode yang lebih baik dalam pengelompokkan cluster terhadap kinerja pegawai di Sekretariat DPRD Provinsi Sulawesi Utara. Metode pengelompokkan data kinerja pegawai yang dibuat menggunakan metode clustering k-means, k-medoids, x-means dengan menggunakan lima atribut, yaitu: orientasi pelayanan, integritas, komitmen, disiplin, dan kerjasama, kemudian diolah dengan bantuan rapidminer, sehingga membagi data menjadi dua cluster yang dikategorikan sebagai nilai tinggi dan rendah. Pada tahap berikutnya mencari nilai davies bouldin index memakai bantuan rapidminer pada setiap metode yang dipakai untuk melakukan perbandingan serta menentukan metode yang lebih optimal dalam clustering. Hasil nilai yang diperoleh dari metode davies bouldin index di setiap algoritma, yaitu: k-means sebesar -0.377, k-medoids sebesar -0.930, dan x-means sebesar -0.497, maka algoritma terbaik untuk pengelompokkan data kinerja pegawai dalam penelitian ini adalah algoritma k-means, karena memiliki nilai DBI yang terkecil.

Tsunami Vulnerability and Risk Assessment in Banyuwangi District using machine learning and Landsat 8 image data Gallen cakra adhi wibowo; Sri Yulianto Joko Prasetyo; Irwan Sembiring
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2677

The tsunami is a disaster that often occurs in Indonesia, there are no valid indicators to assess and monitor coastal areas based on functional land use and based on land cover which refers to the biophysical characteristics of the earth's surface. One of the recommended methods is the vegetation index. Vegetation index is a method from LULC that can be used to provide information on how severe the impact of the tsunami was on the area.In this study, an increase in the vegetation index was carried out using machine learning. The purpose of this study was to develop a tsunami vulnerability assessment model using the Vegetation Index extracted from Landsat 8 satellite imagery optimized with KNN, Random Forest and SVM. The stages of study, are: 1)extraction Landsat 8 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction of vegetation indices using KNN, Random Forest, and SVM algorithms. 3) accuracy testing using the MSE, RMSE, and MAE,4) spatial prediction using the Kriging function and 5) tsunami modelling vulnerability indicators. The results of this study indicate that the NDVI interpolation value is 0 - 0.1 which is defined as vegetation density, biomass growth, and moderate to low vegetation health. the NDWI value is 0.02 - 0.08 and the MNDWI value is 0.02 - 0.09 which is interpreted as the presence of surface water along the coast. MSAVI is a value of 0.1 – 0 which is defined as the absence of vegetation. The NDBI interpolation value is -0.05 - (-0.08) which is interpreted as the existence of built-up land with social and economic activities. From the results of research on the 10 areas studied, there are 3 areas with conditions that have a high level of tsunami vulnerability. 2 areas with medium vulnerability and 5 areas with low vulnerability to tsunami.

Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction Florentina Tatrin Kurniati; Daniel HF Manongga; Eko Sediyono; Sri Yulianto Joko Prasetyo; Roy Rudolf Huizen
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26683

In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause problems in recognizing and distinguishing objects accurately. The purpose of this research is to develop a classification method so that objects can be accurately identified. The proposed classification model uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree, SVM, and Naive Bayes classification methods. The test results show that the voting method and Combined Classifier obtain quite good results with each of them, ensemble voting with an accuracy value of 92.4%, 78.6% precision, 95.2% recall, and 86.1% F1-score. While the combined classifier with an accuracy value of 99.3%, a precision of 97.6%, a recall of 100%, and a 98.8% F1-score. Based on the test results, it can be concluded that the use of the Combined Classifier and voting methods is proven to increase the accuracy value. The contribution of this research increases the effectiveness of the Ensemble Learning method, especially the voting ensemble method and the Combined Classifier in increasing the accuracy of object classification in image processing.

KLASIFIKASI WILAYAH RISIKO BENCANA BANJIR DI KOTA SEMARANG DENGAN PERHITUNGAN INDEKS VEGETASI Adenia Kusuma Dayanthi; Sri Yulianto Joko Prasetyo; Charitas Fibriani
Jurnal Tanah dan Sumberdaya Lahan Vol. 10 No. 2 (2023)
Publisher : Departemen Tanah, Fakultas Pertanian, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jtsl.2023.010.2.29

Land use in an area is influenced by population growth and activities. Changes in land use continuously will cause environmental changes that often trigger an increase in natural disasters. In this study, the assessment was carried out using the calculation of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Adjusted Vegetation Index (SAVI). The data used came from Landsat OLI 8 imagery data from 2020 to 2023. The results of this study showed that in the range of 2020 to 2023 the changes in the three calculations of the vegetation index were not significant. From the data obtained, the classification for calculations in the rainy and dry seasons was the same, the NDVI vegetation index obtained high vegetation, the SAVI vegetation index obtained forested vegetation, and the NDWI vegetation index obtained high wetness. Overall the assessment of the vegetation index obtained good results, and it can be concluded that not all areas in Semarang City are at risk of flooding, even during the rainy season.

Prediksi dan visualisasi penyakit COVID-19 menggunakan kombinasi Prophet dan GeoPandas Ardito Laksono Suryoputro; Sri Yulianto Joko Prasetyo
AITI Vol 20 No 2 (2023)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v20i2.135-149

Covid-19 is spreading very rapidly. Indonesia is one of the countries with the highest cases in Southeast Asia. The purpose of this research is to use machine learning models with the help of tools such as Prophet to predict the trend of the Covid-19 outbreak in Indonesia. Obtained data will be visualized using a Geographic Information System (GIS) with Geopandas, which is used to visualize the spread of Covid-19 in Indonesia. Predictions with three tuning methods using Prophet with trend flexibility and holiday effects scored the best, with 0.68 for RMSLE and 1070 for MAE. Based on the use of Geopandas for Covid-19 cases in Indonesia, Geopandas can be used to visualize geospatial data effectively.

Klasifikasi Wilayah Rawan Banjir di Tomohon Menggunakan Citra Satelit Landsat 8 OLI Gabriel Kenisa Meqfaden Baali; Kristoko Dwi Hartomo; Sri Yulianto Joko Prasetyo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 4: November 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i4.7396

Natural disasters often occur unexpectedly, resulting in both material and nonmaterial losses. Floods are among natural disasters that often occurs in several regions in Indonesia, one of which is Tomohon. Tomohon is a city located in the highlands, so it is expected to have a low flood risk level. Nevertheless, in reality, flood still occurs in Tomohon, which then causes material and nonmaterial losses. The data used in this research were the satellite imagery of the Landsat 8 onboard operational land imager (OLI) accessed through the United States Geographical Survey (USGS). The land covers in Tomohon were classified using the supervised classification method with the minimum distance classification (MDC) algorithm. This method provided the advantage of classifying land covers by utilizing training data in Tomohon, achieving an accuracy rate of 99.56%. In addition, the calculations of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI) were also utilized to determine the level of vegetation and surface soil moisture in Tomohon using the Quantum GIS (QGIS) application. Upon examining the land covers and calculating the index, weighting was once more performed in accordance with criteria. It was done to facilitate the classification of the area into three flood risk classifications: high, medium, and low. The results showed that green spaces in Tomohon are still greater than residential areas. However, NDVI, NDWI, and SAVI calculations indicated that some densely populated areas are susceptible to flood. These areas include Tomohon Selatan and Tomohon Tengah Subdistricts, which have a high level of flood risk and the Tomohon Timur Subdistrict, which has a medium level of flood risk.

Prediksi Tingkat Kesembuhan Pasien Covid-19 Berdasarkan Riwayat Vaksin Menggunakan Metode Naïve Bayes Gudiato, Candra; Prasetyo, Sri Yulianto Joko; Purnomo, Hindriyanto Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Covid-19 has shocked the world since it first appeared at the end of December 2019. At the beginning of 2022, the global community is more prepared to face the COVID-19 pandemic, especially with the mass vaccination program in countries around the world, including Indonesia. The next issue is how effective the vaccine is in dealing with the COVID-19 virus. The main parameter used is to see the recovery rate of patients affected by COVID-19 based on the history of vaccine doses that have been received by the patient. In this study using data mining techniques, namely using the Naïve Bayes algorithm. The test results show the accuracy of the Naïve Bayes algorithm is 98.14%. The prediction results show that the recovery rate of patients who have received the vaccine, either dose 1, dose 2, or dose 3 (booster) is higher than those who have not been vaccinated at all (dose 0). The results of this study are expected to provide an overview to the public and the government about the benefits of vaccination in dealing with the Covid-19 virus.

Co-Authors Adenia Kusuma Dayanthi Anna Simatauw Antar Maramba Jawa Antonius Mbay Ndapamury Ardian Ariadi Ardito Laksono Suryoputro Arit Imanuel Meha Arvira Yuniar Isnaeni Ayuningtyas, Fajar Baali, Gabriel Megfaden Kenisa Baronio, Nodas Constantine Bintang Lazuardi Bistok Hasiholan Simanjuntak Brian Laurensz Brilliananta Radix Dewana Bunga, Alex Frianco Cahyaningtyas, Christian Charitas Fibriani Christanto, Erwien Christiana Ari Setyaningrum Daniel HF Manongga Danny Manongga Devianto, Yudo Dian Widiyanto Chandra Dwi Hayati Edwin Zusrony Eko Sediyono Elvira Umar Engles Marabangkit Yoesmarlan Erik Wahyu Abdi Nugroho Evan Bagus Kristianto Evan Geraldy Suryoto Evi Maria Evi Maria Evi Maria Fabian Valerian Feibe Lawalata Florentina Tatrin Kurniati Gallen cakra adhi wibowo Gideon Bartolomeus Kaligis Gilbert Yesaya Likumahua Gudiato, Candra Haikal Nur Rachmanrachim Achaqie Haikal Nur Rachmanrachim Achaqie Hindriyanto Dwi Purnomo Ida Ayu Putu Sri Widnyani Indra Yunanto Irdha Yunianto Irwan Sembiring Isnaeni, Arvira Yuniar Joko Siswanto Joko Siswanto Josua Josen Alexander Limbong Kase, Celomitha Putri Welhelmina Kristoko Dwi Hartomo Kurnia Latifatul Nazila Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lobo, Murry Albert Agustin Lyonly Evany Tomasoa Maipauw, Musa Marsel Manongga, Daniel HF Maya Sari Merryana Lestari Mikhael Dio Eclesi Mila Chrismawati Paseleng Mira Mira Muhamad Yusup Muhammad Rizky Pribadi Muhammad Sholikhan Nadia Renatha Yuwono Nadya Inarossy Novem Berlian Uly Nugroho, Ignatius Dion Nusantara, Bandhu Otniel, Marcelinus Vito Patasik, Eva Sapan Patrick Simbolon Permatasari, Aurilia Dinda Petty, Holbed Joshua Praditya, Al-Farrel Raka Prayitno, Gunawan Priatna , Wowon Priyadi Priyadi Purwoko, Agus Qurotul Aini Ratu, Herman Huki Ravensca Matatula Raymond Elias Mauboy Riko Yudistira Rina Pratiwi Pudja I. A Rohmad Abidin, Rohmad Rony, Zahara Tussoleha Roy Rudolf Huizen Santoso, Nuke Puji Lestari Sarassati, Dwi Sinta Sebastian, Danny Septian Silvianugroho Septio, Pius Aldi Solly Aryza Sri Hartati Stanny Dewanty Rehatta Stevanus Dwi Istiavan Mau Supit, Christanti Ekkelsia Suryasatria Trihadaru Suryasatriya Trihandaru Susatyo, Yeremia Alfa Sutarto Wijono Theopillus J. H. Wellem Tirsa Ninia Lina Triloka Mahesti Triloka Mahesti Untung Rahardja Untung Rahardja Valentino Kevin Sitanayah Que Vinsensius Aprila Kore Dima Wahani, Puteri Justia Kardia Momuat Wasis Pancoro Wicaksono, Muhammad Ryqo Jallu Winarko, Edi Wiwin Sulistyo Yansen Bagas Christianto Yerik Afrianto Singgalen Yesi Arumsari Yohanes Aji Priambodo Yuliawan, Kristia

Title

Found 93 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 93 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 93 Documents
Search