Journal of System and Computer Engineering (JSCE)最新文献

{"title":"Simulasi Rancang Bangun Alat Pemberi Pakan Ayam Dan Monitoring Suhu Kandang","authors":"Bayu Kusumo","doi":"10.61628/jsce.v5i2.1278","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1278","url":null,"abstract":"Dalam bidang peternakan di Indonesia menggunakan cara manual yaitu peternak harus memasukkan pakan kedalam wadah pakan. Peternak juga harus melakukan pengecekan suhu yang ada dikandang tersebut dan hal tersebut pastinya membutuhkan banyak tenaga dan waktu untuk pengelolaan peternakan. Penerapan IoT pada peternakan ayam dapat diimplementasikan untuk membantu para peternak melakukan pemantuan dan memberikan pakan otomatis. Tentu hal ini akan membantu aktivitas dalam beternak dengan menggunakan alat pemberi pakan dan pengontrol suhu otomatis yang dapat dimonitoring dimanapun berada hanya dengan smartphone. Sistem otomatis terdiri dari sistem otomatis yang memberikan udara ideal dan sistem pakan ayam. Penentuan spesifikasi alat dan pembuatan yang bertujuan untuk mencari bentuk. Pengujian pengiriman data pada aplikasi Telegram telah dilakukan sebanyak 10 kali percobaan. Pengiriman data memiliki persentase keberhasilan 100% dan delay rata-rata sebesar 17.40 detik.Pengujian pengiriman data uji waktu makan pada sistem pakan otomatis yang dilakukan sebanyak 2 kali. Pengujian ini bertujuan untuk membuka katup pada tempat makan pada saat waktu makan pagi dan sore. Pada fitur sistem pakan otomatis ini dapat disesuaikan secara kebutuhan pada pakan ternak, dengan maksud banyaknya pemberian pakan perhari bisa diatur dengan mudah. Pengiriman data uji waktu makan memiliki persentase keberhasilan sebesar 100%.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"32 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Klasifikasi Bentuk Bingkai (Frame) Kacamata Menggunakan CNN dengan Arsitektur Inception V3 dan Augmented Reality Berbasis Android","authors":"Mochamad Wisuda Sardjono, V. Ramadhan, Margi Cahyanti, Ericks Rachmat Swedia","doi":"10.61628/jsce.v5i2.1292","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1292","url":null,"abstract":"Kacamata tidak hanya menjadi salah satu jenis alat bantu penglihatan bagi penderita penyakit mata, tetapi juga menjadi salah satu bagian pada dunia fashion yang semakin popular. Pemilihan desain bingkai kacamata dapat mempengaruhi penampilan seseorang dalam berbusana sehingga untuk menentukan pilihan harus memperhatikan dua aspek penting, yaitu gaya dan kenyamanan, serta dapat mengubah kesan pada wajah seseorang. Dalam merancang bingkai kacamata perlu berdasarkan ilmu pengukuran tubuh manusia, karena masing-masing ukuran dan bentuk pada organ manusia berbeda satu sama lain. Sehingga dengan keberagaman bentuk wajah manusia tersebut menjadi sangat penting dalam membuat pemilihan bingkai kacamata dan tantangan dalam melakukan penelitian untuk membangun aplikasi untuk merekomendasikan bingkai kacamata sesuai dengan bentuk wajah. Dengan era teknologi saat ini, dimungkinkan menerapkan kecerdasan buatan (AI) dan Machine Learning (ML) menjadi solusi terbaik menjawab tantangan tersebut. Beberapa penelitian telah mencoba mengklasifikasikan bentuk wajah dengan menggunakan ML, dengan hasil terbaik menggunakan arsitektur Inception V3. Dalam penelitian ini dikembangkan aplikasi berbasis Unity 3D yang menggabungkan Augmented Reality (AR) dengan ML untuk merekomendasikan bentuk bingkai kacamata berdasarkan bentuk wajah. Hasil pelatihan model Inception V3 menunjukkan peningkatan kinerja seiring waktu. Namun, perlu mengatasi overfitting pada data validasi. Pada pengujian data test, model mencapai akurasi sekitar 78.6%, menunjukkan kemampuan prediksi yang baik. Teknologi ini memiliki potensi untuk membantu konsumen membuat keputusan yang lebih tepat dalam pemilihan kacamata.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"51 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141805036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Perbandingan Kinerja Model Yolov7 dalam Deteksi Kuku Diabetes","authors":"Nur Inda","doi":"10.61628/jsce.v5i2.1334","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1334","url":null,"abstract":"abstrak \u0000Diabetes mellitus (DM) penyakit degenerative dan tidak menular yang dapat dilihat dari warna kuku jari tangan. Dalam menganalisis warna mata manusia memiliki keterbatasan dalam pengenalan warna dan analisis tekstur sedangkan komputer mampu mengklasifikasi jutaan warna dan sedikit perubahan tekstur untuk mengenali perubahan warna kuku individu untuk mencega gejala awal diabetes menggunakan metode YOLOv7 untuk mewakili model satu tahap untuk mendeteksi objek menggunakan Convolutional Neural Network (CNN). \u0000Penelitian ini dilaksanakan di Puskesmas Polewali. Pengambilan sampel dilakukan dengan mengambil rekam medis dan melakukan wawancara ke Dokter terkait. Data sampel yang diambil dari beberapa data pasien diabetes mellitus dan beberapa tenaga kerja pada Puskesmas Polewali untuk data sampel kuku sehat. \u0000Hasil pengujian model YOLOv7 dengan epoch 100 didapatkan hasil accuracy 81%, precision 82,4%, recall 95,5% dan F1-Score 88,5%. Pengujian model YOLOv7 dengan epoch 200 didapatkan hasil accuracy 90%, precision 93,3%, recall 93,3% dan F1-Score 93,3%. Pengujian model YOLOv7-x dengan epoch 100 didapatkan hasil accuracy 71,4%, precision 72,3%, recall 82,9% dan F1-Score 77,2%. Pengujian model YOLOv7-x dengan epoch 200 didapatkan hasil accuracy 63,3%, precision 60,4%, recall 90,6% dan F1-Score 72,5%. Pengujian model YOLOv7-tiny dengan epoch 100 didapatkan hasil accuracy 91,4%, precision 95,6%, recall 93,5% dan F1-Score 94,5%. Pengujian model YOLOv7-tiny dengan epoch 200 didapatkan hasil accuracy 94,6%, precision 93%, recall 100% dan F1-Score 96,4%. Hasil pengujian perbandingan model YOLOv7 dalam mendeteksi kuku diabetes, disimpulkan bahwa model ideal yang dapat digunakan adalah model YOLOv7-tiny dengan nilai epoch 200. \u0000  \u0000Kata kunci: Confussion Matrix, CNN, Diabetes Mellitus, Kuku, YOLOv7","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"109 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perancangan Aplikasi Kepuasan Mahasiswa Terhadap Dosen Fakultas Teknik Universitas Islam Makassar Menggunakan Metode Simple Additive Weighting","authors":"Nursuci Putri Husain, Syarifuddin Baco","doi":"10.61628/jsce.v5i2.1211","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1211","url":null,"abstract":"Fakultas Teknik Universitas Islam Makassar merupakan salah satu fakultas pada perguruan tinggi swasta di Kota Makassar. Saat ini pengukuran kepuasan mahasiswa terhadap dosen dengan cara mengisi kuesioner melalui google form. Kekurangan dalam mengisi google form karena dapat menggunakan gmail bebas sehingga bisa terjadi manipulasi data. Sehingga dibuat Aplikasi web yang memudahkan akses dan partisipasi mahasiswa dalam memberikan penilaian terhadap kinerja dosen. Tujuan penelitian merancang aplikasi kepuasan mahasiswa terhadap dosen berbasis web guna meningkatkan partisipasi mahasiswa dan menghasilkan penilaian yang lebih representatif. Penelitian ini menggunakan pendekatan Reseach and Development dengan metode Waterfall sebagai metode pengembangan aplikasi dari fase analisis kebutuhan, desain, implementasi, pengujian dan pemeliharaan sedangkan metode Simple Additive Weighting berfungsi dalam proses pengambilan keputusan. Hasil penelitian aplikasi dimana mahasiswa login untuk masuk kehalaman dashboard yang menampilkan list dosen. Dihalaman list dosen terdapat list pertanyaan dalam menilai kinerja dosen sedangkan dihalaman admin ada menu yang menampilkan hasil perhitungan SAW dengan skala 20 - 100 keterangan tidak baik, kurang baik, cukup baik, baik dan sangat baik. Kesimpulan aplikasi mampu memberikan solusi dalam pemetaan, pemahaman dan penilaian kinerja kepuasan mahasiswa terhadap dosen dalam meningkatkan kualitas pembelajaran dengan tampilan interface yang ramah pengguna.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Klasifikasi Liver Cirrhosis Menggunakan Teknik Ensemble: Studi Perbandingan Model Boosted Tree, Bagged Tree, dan Rusboosted Tree","authors":"Mardewi Mardewi, Supriyadi La Wungo","doi":"10.61628/jsce.v5i2.1302","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1302","url":null,"abstract":"Penyakit liver cirrhosis, sebagai penyakit hati kronis yang signifikan, menunjukkan peningkatan prevalensi global yang memerlukan pendekatan pencegahan yang lebih efektif. Dalam upaya meningkatkan deteksi dini dan manajemen pasien, penelitian ini mengusulkan pengembangan model prediksi risiko liver cirrhosis menggunakan teknologi machine learning, khususnya dengan membandingkan kinerja tiga model ensemble tree: Ensemble Boosted Tree, Ensemble Bagged Tree, dan Ensemble RUSBoosted Tree. Dengan memanfaatkan data klinis dan laboratorium dari pasien dewasa dengan riwayat atau risiko cirrhosis, penelitian ini menghasilkan temuan bahwa Ensemble Bagged Tree mencapai akurasi tertinggi sebesar 71%, diikuti oleh Ensemble Boosted Tree (67.2%) dan Ensemble RUSBoosted Tree (66%). Analisis variabel klinis dan laboratorium memberikan wawasan lebih lanjut tentang kontribusi faktor-faktor yang paling signifikan dalam prediksi risiko. Hasil penelitian ini memberikan landasan untuk pengembangan alat prediksi risiko liver cirrhosis yang lebih canggih, mendukung visi pencegahan yang lebih personalisasi dan efektif dalam manajemen penyakit hati.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Sparepart Maintenance Menggunakan Algoritma Machine Learning Extreme Gradiant Boosting Regressor","authors":"Syahrul Usman","doi":"10.61628/jsce.v5i2.1418","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1418","url":null,"abstract":"Spare parts are components that make up a single object that has a specific function. In car vehicles, spare parts have the function of maintaining the performance and function of the vehicle. Predictive Spare Part Maintenance is an effort to improve operational efficiency, customer service, and reduce vehicle downtime through the application of analysis and machine learning algorithms to predict spare part replacement times. A machine learning approach can be used to predict maintenance times for car spare parts, where one of the algorithms that can be used is XGBoost Regressor. Through this approach, this research aims to improve service planning by predicting spare part replacement times based on certain indicators, With the implementation of this research, it is hoped that it can increase operational efficiency in automotive after-sales services, increase customer satisfaction, reduce vehicle downtime, and improve overall service planning and most importantly can provide preventive maintenance information to customers. This research provides prediction results with R2-Score values ​​as follows: train data: 93%, Valid: 90%, Test: 90%","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"20 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prototipe Sistem Manajemen Tangki Pintar Berbasis Internet of Things (IoT)","authors":"Ircham Hidayat, M. Marlina, Nurani Nurani","doi":"10.61628/jsce.v5i2.1239","DOIUrl":"https://doi.org/10.61628/jsce.v5i2.1239","url":null,"abstract":"Tujuan penelitian merancang prototipe sistem manajemen tangki pintar yang bertujuan untuk membantu pengelola SPBU mengukur kadar air dan memantau kandungan BBM di tangki SPBU secara otomatis dan realtime sehingga kualitas BBM tetap terjaga, informasi dapat dipantau melalui sistem berbasis web dan aplikasi mobile dengan teknologi Internet of Things. Desain sistem IoT terdiri dari mikrokontroler Arduino Uno R4, sensor ultrasonik HC-SR04 untuk mengukur isi level tangki, sensor konduktivitas untuk mengukur kadar air yang terkandung didalam bahan bakar sedangkan sensor suhu untuk mengukur suhu didalam tangki dan selenoid valve untuk mengeluarkan air yang ada di dalam tangki. Hasil pengujian akurasi sensor ultrasonik yang digunakan cukup baik untuk mengukur isi tangki dengan average error 1,3%, sedangkan pengukuran sensor conductivity memiliki average error 0,292% dengan proses validasi menggunakan metode centrifuge, sensor suhu memiliki akurasi 1,6 %. Katup selenoid bekerja dengan baik yang diaktifkan melalui aplikasi pemantauan berbasis web dan aplikasi seluler.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"57 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Sistem Tracking Berbasis GPS pada Alat Pendeteksi Alkohol Terintegrasi dengan Arduino untuk Pengemudi Mobil Penumpang Umum","authors":"Abdurrahman K Jusuf, M. Agus","doi":"10.61628/jsce.v4i2.871","DOIUrl":"https://doi.org/10.61628/jsce.v4i2.871","url":null,"abstract":"Regarding the problems faced by managers of the transportation business, namely cases of vehicle theft which still often occur, therefore an idea emerged on how owners can monitor their vehicles effectively, then accidents that usually occur due to reckless public transportation drivers due to influence of liquor. Based on the background described above, the authors aim to conduct research by creating a GPS tracking tool that is integrated with Arduino, which is able to monitor the location of the driver/vehicle and is able to reduce vehicle embezzlement, and the system to be designed is able to detect whether the driver of the vehicle is in a state of drink alcohol or not. Research has been carried out on 2 people, namely for the first person, the lowest sensor data reading was obtained, namely 22 ppm with a delay of 2 seconds for GPS readings for a distance of 3.5 km and the condition of the relay was off indicating that alcohol was not detected, and for the highest sensor data readings it was at 109 ppm with a delay of 3 seconds GPS reading for a distance of 4.2 km and the condition of the relay is on which indicates alcohol is detected. In the second person test, the lowest sensor data reading was obtained, namely 23 ppm with a 4 second GPS reading delay for a distance of 11 km and the condition of the relay was off indicating no alcohol was detected, and the highest sensor data reading was 121 ppm with a 2 second GPS reading delay for a distance 10 km and the condition of the relay is on which indicates alcohol is detected. GPS testing was carried out 10 times for each person where the longest distance tested on the first person was 42 km, and the shortest distance was 3.3 km. And for the test, the fastest delay in reading the driver's location is 2 seconds, and the longest is 5 seconds. In testing the second person, the longest distance was 12 km, and the shortest distance was 10 km. And in the test, the fastest delay in reading the driver's location point is 2 seconds, and the longest delay in reading the location point is 4 seconds.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139355781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sistem Kendali Ayunan Bayi Berbasis Mikrokontroller dan Android","authors":"A. Abdullah, Zahir Zainuddin, Andani Achmad","doi":"10.61628/jsce.v4i2.889","DOIUrl":"https://doi.org/10.61628/jsce.v4i2.889","url":null,"abstract":"This study aims to design a prototype of a microcontroller and arduino-based baby swing control system that will be very useful for housewives who have babies. This system will automatically work to move the swing when the sound of a baby and water is detected. This system is designed using a sound sensor that functions to detect the baby's voice, a water sensor to detect water when a baby is urinating, a DC motor that functions to move the swing automatically and a mobile application that functions to monitor swings. The test results show that the tool functions as desired, where when the tool detects the sound of a baby's cry, the tool automatically moves the swing, when the tool detects the presence of water, the swing will send a notification to the smartphone, this tool also successfully displays information to the smartphone in the form of a state baby in the form of video and sensor conditions on the swing. The communication distance between the smartphone and the device is 20 meters.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139355710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan Algoritma K-Modes Menggunakan Validasi Davies Bouldin Index Untuk Klasterisasi Karakter Pada Game Wild Rift","authors":"Dynesh Radixavendra Quinthara, Abdi Fauzan, M. Huda","doi":"10.61628/jsce.v4i2.802","DOIUrl":"https://doi.org/10.61628/jsce.v4i2.802","url":null,"abstract":"Bagi pemain baru pemilihan karakter merupakan hal yang sulit ketika role yang didapatkan tidak sesuai dengan yang diinginkan karena akan mempengaruhi faktor kemenangan. Dari berbagai macam jenis game ada yang disebut sebagai game MOBA (Multiplayer Online Battle Arena) pemain hanya bisa mengontrol 1 karakter. Dalam game League Of Legend : Wild Rift ada 40 lebih karakter. Masing-masing variabel pada karakter Wild-Rift mempengaruhi jalannya pertandingan penggunaan algoritma K-Modes Clustering untuk menentukan klaster. Penelitan ini menggunakan algoritma K-Modes Clustering untuk menentukan klaster berdasarkan role dari setiap karakter tanpa melihat tipe masing-masing karakter tersebut. Metode clustering dengan cara mengumpulkan data-data setiap karakter yang kemudian di proses dan dikelompokkan untuk mendapatkan data yang optimal dan menentukan karakter yg diinginkan tanpa memaksakan roleplay. Hasil dari perhitungan K-modes dievaluasi menggunakan validasi Davies Bouldin Index yang menghasilkan nilai DBI sebesar 0.8200 menunjukkan bahwa algoritma k-modes mencapai pengelompokan yang optimal. Klaster 5 merupakan klaster yang paling sering dipilih dengan karakter bertipe MarksMan.","PeriodicalId":355150,"journal":{"name":"Journal of System and Computer Engineering (JSCE)","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139355819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}