Proxies Jurnal Informatika最新文献_第6页

Penentuan Kekerabatan Hewan Berdasarkan Struktur Protein IGF2 Menggunakan Metode K-Means dan N-Gram 基于IGF2蛋白结构的动物凝聚力使用了k -手段和n克

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i2.13808

Ruth Ema Febrita, Maghfirotul Amaniyah

引用次数: 0

Penentuan Kriteria Penerima Beasiswa Berprestasi Menggunakan Metode Analytical Hierarchy Process 决定成就奖学金获得者使用分析程序

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i2.12893

Anti Nada Nafisa, Erika Nia Devina Br Purba, Nurul Adawiyah Putri, Debi Yandra Niska

{"title":"Penentuan Kriteria Penerima Beasiswa Berprestasi Menggunakan Metode Analytical Hierarchy Process","authors":"Anti Nada Nafisa, Erika Nia Devina Br Purba, Nurul Adawiyah Putri, Debi Yandra Niska","doi":"10.31294/inf.v9i2.12893","DOIUrl":"https://doi.org/10.31294/inf.v9i2.12893","url":null,"abstract":"Economic problems in Indonesia are an obstacle to the continuity of education in Indonesia, thus creating a scholarship program. Scholarships are intended for underprivileged students and outstanding students. To determine scholarship recipients, a selection is carried out where there are several predetermined criteria, taking into account the most influencing aspects in the selection of scholarship recipients. To get the best criteria in determining scholarship recipients, it can be done using a Decision Support System with the Analytical Hierarchy Process method where the main input is human perception. The criteria used in this study are Grades, Student Achievements, and Social Studies which are considered the most influential in the selection of outstanding scholarships. Each criterion will be given a weight, which is done by comparing the criteria with each other. The results of this study indicate that the criteria that greatly influence the determination of scholarship recipients are in terms of the value obtained by students, with a percentage of 64.8%, Student Achievement with a percentage of 22.9% and Social Studies with a percentage of 12.2%","PeriodicalId":32029,"journal":{"name":"Proxies Jurnal Informatika","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76286808","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}

引用次数: 2

Perbandingan Model NBC, SVM, dan C4.5 dalam Mengukur Kinerja Karyawan Berprestasi Pasca Pandemi Covid-19 NBC模型，SVM，和C4 - 5在Covid-19大流行后的绩效评估中进行了比较

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i2.13772

Galih Galih, Mindit Eriyadi

{"title":"Perbandingan Model NBC, SVM, dan C4.5 dalam Mengukur Kinerja Karyawan Berprestasi Pasca Pandemi Covid-19","authors":"Galih Galih, Mindit Eriyadi","doi":"10.31294/inf.v9i2.13772","DOIUrl":"https://doi.org/10.31294/inf.v9i2.13772","url":null,"abstract":"Classifying employee performance appraisals is one way to improve the quality of workers. Employee performance appraisal is very important in determining good employees in a company. The process of appraisal of employee performance is only assessed manually in the absence of an application or system. The algorithm applied to employee performance utilizes the Naïve Bayes Classifier algorithm because it refers to previous research, there are several research findings. Using 310 employee data divided into 5 groups, namely Very High Performance, High Performance, Standard Performance, Low Performance and Ineffective Performance, this test uses the RapidMiner tool version 7.2.0 naïve Bayes Classifier algorithm model resulting in an accuracy rate of 84.52%, the C4.5 algorithm produces an accuracy rate of 74.19% and while using the Support Vector Machine algorithm produces an accuracy rate of 56.13%. If using the WEKA tools version 3.8.0 The Naïve Bayes Classifier algorithm model produces an accuracy rate of 81.93%, the C4.5 algorithm produces an accuracy rate of 75.80% and while using the Support Vector Machine algorithm produces an accuracy rate of 60.32%.","PeriodicalId":32029,"journal":{"name":"Proxies Jurnal Informatika","volume":"38 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80899011","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}

引用次数: 1

Citra Digital Untuk Klasifikasi Kualitas Udang Windu Menggunakan Algoritma GLCM dan K-Nearest Neighbor Windu对虾质量分类的数字图像使用GLCM算法和K-Nearest算法

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i2.13686

Najirah Umar, Fiqri Haikal, M. Razak

{"title":"Citra Digital Untuk Klasifikasi Kualitas Udang Windu Menggunakan Algoritma GLCM dan K-Nearest Neighbor","authors":"Najirah Umar, Fiqri Haikal, M. Razak","doi":"10.31294/inf.v9i2.13686","DOIUrl":"https://doi.org/10.31294/inf.v9i2.13686","url":null,"abstract":"Shrimp is a food that is easily damaged based on direct observation of the shrimp sorting process carried out by distributors or fishermen to select shrimp based on quality still using the manual method and sometimes the sorting results are still not in accordance with the quality of the shrimp and the quality indicators are only seen from the physical such as the weight or size of the shrimp, so that good quality shrimp can be mixed with less good quality, therefore contamination will occur which causes good quality shrimp to rot quickly. This final project aims to build an image processing system that applies the Gray-level Co-occurrence Matrix (GLCM) and K-nearest Neighbor (K-NN) algorithms to detect the quality level of Windu shrimp. The first process in this research is to perform image acquisition. That is, collecting several digital images of each quality of shrimp to use as an object. In addition, a pre-processing process is also carried out, namely changing the image to grayscale. Then the feature extraction process uses the Gray Level Co-occurrence Matrix (GLCM) method to obtain feature data from all digital images and classify them using the K-Nearest Neighbor (K-NN) method. The test results give an accuracy of 10 samples, it was found that as much as 80% got the results of quality classification information in accordance with the system. And this system is able to provide decision solutions in determining the quality classification of Windu Shrimp, while based on the results of blackbox testing, this system produces a percentage of application ease of use as much as 92%.","PeriodicalId":32029,"journal":{"name":"Proxies Jurnal Informatika","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86929038","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}

引用次数: 0

Penerapan Algoritma CNN Untuk Mengetahui Sentimen Masyarakat Terhadap Kebijakan Vaksin Covid-19 CNN算法的应用，以了解公众对Covid-19疫苗政策的看法

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i2.13257

Fany Alifian Irawan, Dwi Anindyani Rochmah

{"title":"Penerapan Algoritma CNN Untuk Mengetahui Sentimen Masyarakat Terhadap Kebijakan Vaksin Covid-19","authors":"Fany Alifian Irawan, Dwi Anindyani Rochmah","doi":"10.31294/inf.v9i2.13257","DOIUrl":"https://doi.org/10.31294/inf.v9i2.13257","url":null,"abstract":"Jejaring sosial Twitter merupakan wadah bagi netizen dari seluruh dunia untuk bertukar pendapat dan argumen, beragam topik diangkat oleh netizen terutama permasalahan yang sedang hangat diperbincangkan atau menjadi perdebatan di khalayak umum. Salah satu topik yang hangat dibicarakan netizen Indonesia yaitu mengenai Vaksin Covid-19 yang merupakan salah satu kebijakan pemerintah Indonesia dalam upaya menanggulangi pandemic Covid-19. Seperti kebijakan lainnya yang tak luput menimbulkan pro-kontra, kebijakan vaksin ini juga menjadi perbincangan pada jejaring Twitter. Atas dasar itu untuk mendapatkan informasi yang terdapat pada komentar netizen di jejaring sosial Twitter, maka diperlukan analisis sentimen dengan tujuan mengetahui sebagian respon masyarakat Indonesia mengenai kebijakan vaksin sehingga dapat menjadi bahan pertimbangan pihak terkait dalam mengevaluasi kebijakan sehingga menjadi lebih baik. Analisa sentimen dilakukan dengan mengambil data komentar Twitter seputar vaksin yang dibuat menjadi dataset dengan dua polaritas sentimen positif dan negatif dengan nilai masing-masing sentimen sebesar 650 data. Dataset digunakan untuk menganalisa sentimen serta digunakan pada tahap pengujian tingkat akurasi algoritma. Berdasarkan hasil pengujian, algoritma Convolutional Neural Network memperoleh rata-rata nilai akurasi sebesar 98.66%, dengan algoritma pembanding yaitu Naïve Bayes yang memperoleh rata-rata nilai akurasi sebesar 94.66%. Hasil dari penelitian dapat disimpulkan bahwa kebijakan vaksinasi ini mendapatkan respon yang positif berdasarkan data komentar Twitter yang berjumlah 1424 baris, sebanyak 950 komentar berpolaritas positif dengan persentase 66.7% dan 33.3% sisanya sejumlah 474 komentar berpolaritas negatif. Selain itu berdasarkan data Wordcloud diketahui sebagian besar komentar bermuatan negatif berisi dengan kata-kata yang menyiratkan efek samping dari vaksinasi terutama jenis vaksinasi booster.","PeriodicalId":32029,"journal":{"name":"Proxies Jurnal Informatika","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89245603","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}

引用次数: 1

Analisis Kinerja ResNet-50 dalam Klasifikasi Penyakit pada Daun Kopi Robusta 罗布斯塔咖啡叶分类疾病的ResNet-50表现分析

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i1.13049

Suprihanto Suprihanto, Iwan Awaludin, M. Fadhil, M. A. Z. Zulfikor

{"title":"Analisis Kinerja ResNet-50 dalam Klasifikasi Penyakit pada Daun Kopi Robusta","authors":"Suprihanto Suprihanto, Iwan Awaludin, M. Fadhil, M. A. Z. Zulfikor","doi":"10.31294/inf.v9i1.13049","DOIUrl":"https://doi.org/10.31294/inf.v9i1.13049","url":null,"abstract":"Indonesia merupakan negara agraris yang banyak ditanami tumbuhan salah satunya yaitu tanaman kopi. Dalam budidaya tanaman kopi terdapat halangan seperti hama dan cuaca ekstrim yang bisa membuat tanaman layu atau terkena penyakit. Dengan kemajuan teknologi yang pesat di masa kini, banyak sistem yang membantu para petani untuk membantu mengidentifikasi penyakit pada daun kopi. Sistem ini menggunakan teknologi salah satu arsitektur Convolutional Neural Network, yaitu ResNet-50 untuk mengidentifikasi dan mengklasifikasi penyakit pada daun kopi robusta. Dalam melatih model ResNet-50 diperlukan proses pelatihan dan validasi model yang kemudian model yang telah dilatih akan dilakukan pengujian. Pengujian model akan digunakan untuk mengukur kinerja model yang akan dihitung dengan menggunakan Confusion Matrix yang variabel output nya akan digunakan untuk menghitung Akurasi, presisi, recall, Spesifisitas, dan F1 Score. Penelitian ini akan berfokus pada perhitungan nilai kinerja akurasi dan F1 Score dari model tersebut. Penelitian dilakukan dengan dua kasus yaitu binary class dan multiclass dimana binary class untuk mengklasifikasi gambar daun kopi robusta sehat dan sakit dan multiclass untuk mengklasifikasikan gambar daun kopi robusta pada setiap jenis kategori dari daun yang berpenyakit dan sehat. Hasil dari penelitian menunjukan pada kasus binary class mencapai akurasi 92,68% dan f1-score mencapai 92,88%, sedangkan pada kasus multiclass akurasi hanya mencapai 88,98% dan f1-score mencapai 88,44%. Kedua kasus tersebut diukur menggunakan data testing dengan model yang telah dilatih.","PeriodicalId":32029,"journal":{"name":"Proxies Jurnal Informatika","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80530655","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}

引用次数: 1

Deteksi Tingkat Keparahan Cedera Panggul Menggunakan ANFIS 用ANFIS检测骨盆损伤的严重程度

Proxies Jurnal Informatika Pub Date : 2022-10-02 DOI: 10.31294/inf.v9i2.12511

Adam Fahmi Khariri, Monika Refiana Nurfadila, D. C. R. Novitasari

引用次数: 0

Classification of IGF1R ligand compounds for Identification of herbal extracts using extreme gradient boosting IGF1R配体的分类及其在草药提取物鉴定中的应用

Proxies Jurnal Informatika Pub Date : 2022-09-30 DOI: 10.26555/jifo.v16i3.a23286

Mohammad Hamim Zajuli Al Faroby, S. Amiroch, Bernadus Anggo Seno Aji, Avriono Aritonang

引用次数: 0

Revisiting the challenges and surveys in text similarity matching and detection methods 回顾了文本相似度匹配和检测方法中的挑战和调查

Proxies Jurnal Informatika Pub Date : 2022-09-30 DOI: 10.26555/jifo.v16i3.a23471

Alva Hendi Muhammad, Kusrini Kusrini, Irwan Oyong

引用次数: 0

Image processing for maturity classification of tomato using otsu and manhattan distance methods 基于otsu和曼哈顿距离方法的番茄成熟度分类图像处理

Proxies Jurnal Informatika Pub Date : 2022-09-30 DOI: 10.26555/jifo.v16i1.a21985

Anindita Septiarini, H. Hamdani, Muhammad Sofian Sauri, J. A. Widians

引用次数: 1