Ektraksi Fitur Menggunakan Discrete Wavelet Transform dan Full Neighbor Local Binary Pattern Untuk Klasifikasi Mammogram

Saat ini pendeteksian kanker payudara dengan citra mammogram telah banyak dilakukan dengan memanfaatkan pengolahan citra digital. Tahapan dari proses pendeteksian tersebut terdiri dari preprocessing, ektraksi fitur, seleksi fitur dan klasifikasi. Tahapan yang memegang peranan penting untuk menghasilkan sistem deteksi yang akurat adalah tahap ekstraksi fitur. Terdapat beberapa penelitian yang telah dilakukan sebelumnya dengan mengkombinasikan berbagai metode untuk ekstraksi fitur, salah satu yang menghasilkan akurasi terbaik adalah kombinasi wavelet dan local binary pattern.. Saat ini pengembangan algoritma local binary pattern telah banyak dilakukan, salah satunya adalah neighbor local binary pattern (NLBP). Metode tersebut memiliki perbedaan pada arah dan distribusi relasi spasial dari pixel. Meski menghasilkan akurasi yang baik, metode NLBP tersebut memiliki beberapa kelemahan yang sama dengan local binary pattern tradisional, yakni varian terhadap rotasi dan pada proses thresholding pixel sensitif terhadap noise. Pada penelitian ini penulis mengusulkan sebuah metode ektraksi fitur baru yang didasarkan pada neighbor local binary pattern (NLBP). Metode ini memiliki perbedaan pada arah dan distribusi relasi spasial dari pixel, dimana perbandingan antar pixel pada proses trhesholding tidak hanya dengan tetangga di bagian kanan saja melainkan dengan semua tetangga yang ada pada sisi horizontal, vertical dan diagonal sehingga metode tersebut disebut full neighbor local binary pattern (FNLBP). Metode ini nantinya akan dikombinasikan dengan discrete wavelet transform untuk ektraksi fitur dari citra mammogram dengan classifier adalah Backpropagation Neural Network (BPNN). Berdasar ujicoba yang telah dilakukan metode usulan mendapatkan rata-rata akurasi yang lebih baik daripada metode local binary pattern tradisional baik yang dikombinasi dengan discrete wavelet transform ataupun tidak. Performa metode usulan full neighbor local binary pattern dapat menghasilkan akurasi yang tinggi yakni 92.70% pada saat menggunakan discrete wavelet transform dengan seleksi fitur f-test dengan significant level 0.9 dan 0.7, sedangkan akurasi terendah yang didapat adalah saat digunakan metode seleksi fitur t-test dengan nilai significant level 0.5 pada kombinasi discrete wavelet transform dan full neighbor local binary pattern yakni 77.08%. ==================================================================================== Currently the detection of breast cancer with a mammogram image has much to do with utilizing digital image processing. Stages of the detection process consists of preprocessing, feature extraction, feature selection and classification. Stages which plays an important role to produce an accurate detection system is the feature extraction stage. There are several studies that have been done before by combining various methods for the extraction of features, the one that produces the best accuracy is a combination of wavelet and local binary pattern. Currently the development of local binary pattern algorithms have been done, one of which is a neighbor of local binary pattern (NLBP). Such methods have differences on the direction and distribution of spatial relationships of pixels. Although it provides good accuracy, NLBP methods have some disadvantages similar to traditional local binary pattern, which is a variant of the rotation and the thresholding of the pixels are sensitive to noise. In this study, the authors propose a new feature extraction method based on local neighbor binary pattern (NLBP). This method has a difference in the direction and distribution relationships spatial pixel, where a comparison between the pixels of the process trhesholding not only with neighbors on the right side only, but with all the neighbors were there on the side of the horizontal, vertical and diagonal so the method called full neighbor local binary pattern (FNLBP). This method will be combined with discrete wavelet transform to extract the features of the mammogram image with a classifier is Backpropagation Neural Network (BPNN). Based on experiments the result of proposed method in an average accuracy is better than traditional methods of local binary pattern which combined with discrete wavelet transform or not. The performance of the proposed method of full neighbor local binary pattern can produce high accuracy that is 92.70%, this accuracy is reached when using discrete wavelet transform with selection feature method is f-test and the significant level is 0.9 and 0.7, while the lowest accuracy obtained are currently when use t-test feature selections method with a value of significant level is 0.5 on a combination of discrete wavelet transform and full local neighbor binary pattern that is 77.08%

Sugar Production Forecasting System in PTPN XI Semboro Jember using Autoregressive Integrated Moving Average (ARIMA) Method

There is a lot of entrepreneurial competition in the production of goods or services in the world, especially in Indonesia, especially the production of staple goods, namely sugar. The problem that is often faced at Sugar Factory PTPN XI Semboro Jember is the lack of management that is neatly organized and efficient, which makes this company less working optimally. Often there is a lack and excess of sugar production which makes the sugar does not have the maximum value, the sugar has been damaged, and sales at a reduced price because the sugar is not as efficient as the initial product. From these various problems, it can reduce profits from the company. From these problems it can be concluded that the company needs a system that can organize the management of the company, and is able to forecast production in the future. In this research will make a forecasting system using the method of Autoregressive Integrated Moving Average (ARIMA), where this method is divided into three methods, namely the Autoregressive (AR) method, the Moving Average (MA) method, and the Autoregressive Integrated Moving Average (ARIMA) method, which preceded by checking stationary data, and modeling the Autoregressive Integrated Moving Average (ARIMA) method. Forecasting is done using production data for the previous 12 years from the company. The system is made to facilitate management that is less organized and displays predictions for the next production period. The results of this forecasting system are to determine the amount of production each year needed in this company. From the results of the ARIMA method modeling, the right ARIMA method is obtained by the ARIMA / AR (1,0,0), ARIMA / MA (0,0,1), and ARIMA (1,0,1) methods. The test results found that the average value of Mean Absolute Percentage Error (MAPE) in the Autoregressive (AR) method was 17%, the Moving Average (MA) method was 19%, and the Autoregressive Integrated Moving Average (ARIMA) method was 15%

Decision Support System Scheme Using Forward Chaining And Simple Multi Attribute Rating Technique For Best Quality Cocoa Beans Selection

Cocoa is a crop plantation originating from the tropical forests of Central America and northern part of South America. In general, cocoa grouped into three types namely Forastero, Criollo, and Trinitario which is the result of a cross between Forastero with Criollo. Cocoa (Theobroma cacao L.) is one of the comodity that has an important role in the Indonesian economy. The Indonesian's processing directorate, and the programs related to the 2015-2019 development are the Increased Production and Productivity of Sustainable Plantation Crops. This program is conducted to increase the production, productivity of cocoa and other plantation crops. One of the focus activities is Inventory of postharvest data of plantation. In the selection of cocoa beans based on the best quality, Indonesian Coffee and Cocoa Research Center is often missed so that there are some cocoa beans that should not pass the quality but still processed into processed products. In that case we proposed a new scheme for Decision Support System by using Forward Chaining method and Simple Multi Attribute Rating Technique (SMART). The combination of these two methods proved to be able to do a very good selection of cocoa beans. Where the selection is done with two stages proven can really filter the cocoa beans are good for health

PENGGUNAAN BUKU PANDUAN PERTOLONGAN PERTAMA RAMAH ANAK TERHADAP KETERAMPILAN MENANGANI LUKA DALAM RANGKA MEWUJUDKAN SEKOLAH SEHAT

First Aid in Accidents is an effort to help and temporary care for accident victims before getting more perfect help from a doctor or paramedic. This study aims to test the effectiveness of the use of child-friendly first aid manuals on students' skills in dealing with injuries in realizing healthy schools. The book's effectiveness will determine the success of using the first aid book in creating a healthy school. The higher the level of effectiveness, the more functionality and usefulness the module. From the results of the study, it was found that the student's skills improved after using this first-aid manual. Thus, this child-friendly first aid book is very suitable as a supporting book for sports education teachers in order to realize healthy schools. This study's results can motivate teachers always to practice student skills in first aid so students can help those around them.Pertolongan Pertama Pada Kecelakaan Merupakan suatu upaya pertolongan dan perawatan sementara terhadap korban kecelakaan sebelum mendapat pertolongan yang lebih sempurna dari dokter atau paramedic. Penelitian ini bertujuan untuk menguji efektivitas penggunaan buku panduan pertolongan pertama ramah anak terhadap keterampilan siswa menangani luka dalam mewujudkan sekolah sehat. Tingkat efektivitas dari buku tersebut akan menentukan keberhasilan penggunaan buku pertolongan pertama dalam mewujudkan sekolah sehat. Semakin tinggi tingkat efektivitas, maka semakin meningkat fungsionalitas dan kegunaan modul tersebut. Dari hasil penelitian didapatkan bahwa keterampilan siswa meningkat setelah menggunakan buku panduan pertolongan pertama ini. Dengan demikian, buku pertolongan pertama ramah anak ini sangat layak dijadikan buku pendukung bagi guru Pendidikan olahraga dalam rangka mewujjudkan sekolah sehat. Hasil penelitian ini dapat memberikan motivasi kepada guru untuk selalu melatih keterampilan siswa dalam pertolongan pertama sehingga siswa dapat menolong orang-orang di sekitarnya

Perancangan User Interface dan User Experience Aplikasi E-Commerce Kain Batik pada UMKM Rezti’s Batik Menggunakan Pendekatan Design Thinking

Rezti’s Batik merupakan UMKM yang bergerak pada produksi dan penjualan kain batik serta sebagai tempat edukasi batik yang berada di Kecamatan Ambulu, Kabupaten Jember yang melakukan secara konvensional dan melakukan penjualan online melalui media sosial instagram dan whatsapp. Namun, penjualan yang dilakukan secara online dirasa masih kurang berjalan secara efektif dan efisien. Sehingga dibutuhkan solusi untuk mengatasi permasalahan yang dialami yaitu aplikasi penjualan kain batik. Dalam merancang aplikasi, diperlukan penerapan User Interface (UI) dan User Experience (UX) yang terstruktur agar sesuai dengan kebutuhan dari pengguna dan memberikan kenyamanan bagi pengguna. Pendekatan design thinking digunakan dalam merancang UI/UX aplikasi penjualan karena mempunyai proses yang berkesinambungan untuk dapat menciptakan solusi yang sesuai dengan kebutuhan pengguna. Dari perancangan dan solusi yang telah dihasilkan, akan dievaluasi dan validasi menggunakan penilaian dari ketentuan ISO 9241-11 dengan menerapkan pengujian usability System Usability Scale (SUS) dan User Experience Questionnaire (UEQ). Berdasarkan hasil pengujian yang telah didapatkan, dapat diketahui bahwa aplikasi penjualan kain batik yang dirancang mempunyai tingkat usability yang tinggi dan baik dari masing-masing nilai aspek User Experience (UX) Attribute. AbstractRezti's Batik is an MSME engaged in the production and fabric sale of batik as well as a place for batik education located in Ambulu District, Jember Regency, which conducts conventional sales and sells online through Instagram and WhatsApp social media. However, online sales are currently not effective and efficient. A solution is needed to solve the problems experienced by batik fabric sales applications. When designing applications, it is necessary to implement a structured User Interface (UI) and User Experience (UX) to suit the needs of the user and provide comfort for the user. The design thinking approach is used in designing UI/UX sales applications because it has an irrational process to be able to create solutions that suit user needs. The designs and solutions that have been produced, will be evaluated and validated using an assessment of the provisions of ISO 9241-11 by implementing the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) usability tests. Based on the test results that have been obtained, it can be seen that the designed batik cloth sales application has a high and good level of usability from each aspect of the User Experience (UX) Attribute value

Penguatan Kompetensi Computational Thinking dalam Pembelajaran IPA Melalui Perancangan Pembelajaran Argumentasi Konstruktivis

Argumentasi merupakan keterampilan kritis yang perlu dibangun pada siswa usia SD, dan perlu dikembangkan pada siswa usia sekolah menengah. Secara teoritis, siswa dengan usia muda seharusnya mampu memahami dan mem­bangun argumen, akan tetapi berdasarkan bukti empiris belum mendukung harapan tersebut. Kondisi ini juga terjadi pada siswa di SD sekitar desa jelbuk. Pada pengabdian kepada masyarakat ini dirancang dan diimplementasikan penguatan kompetensi computational thinking (CT) dalam pembelajaran IPA melalui perancangan pembelajaran argumentasi konstruktivis. Penguatan kompetensi CT pada pengabdian ini dilakukan menggunakan konsep CT-Argumentasi. CT menyediakan proses yang diperlukan untuk merumuskan argumen, sedangkan argumen memanfaatkan dan menerapkan keterampil­an CT melalui penalaran logis. CT yang diberikan kepada siswa dalam peng­abd­ian kepada masyarakat ini mengacu pada 4 tahapan yaitu: decompos­ition, pattern recognition, abstraction, dan algorithm. Hasil dari pengabdian kepada masyarakat ini dapat meningkatkan pemahaman siswa terhadap materi sebesar 40%. Pengabdian ini juga mengenalkan CT kepada siswa dan guru, dan dapat meningkatkan keterampilan menyelesaikan permasalahan dengan CT dibuktikan dengan rata-rata 30% siswa yang hadir angkat tangan dan dapat menjawab dengan benar, ketika diberi pertanyaan dengan permasalahan terbuka yang diambil dari contoh soal di situs web resmi Bebras Indonesia.&nbsp

EKTRAKSI FITUR MENGGUNAKAN DISCRETE WAVELET TRANSFORM DAN FULL NEIGHBOUR LOCAL BINARY PATTERN UNTUK KLASIFIKASI MAMMOGRAM

[Id]Local binary pattern adalah sebuah kode biner yang menggambarkan pola tekstur lokal. Hal ini dibangun dengan lingkungan batas dengan nilai abu-abu dari pusatnya. Local binary pattern tradisional memiliki beberapa kelemahan yakni varian terhadap rotasi dan pada saat proses thresholding pixel sensitif terhadap noise. Pada penelitian ini diusulkan sebuah metode ektraksi fitur baru untuk mengatasi masalah tersebut, metode tersebut disebut full neighbour local binary pattern (fnlbp). Metode ini nantinya akan dikombinasikan dengan discrete wavelet transform untuk ektraksi fitur dari citra mammogram dengan metode klasifikasi adalah Backpropagation Neural Network (BPNN). Berdasar ujicoba yang telah dilakukan metode usulan mendapatkan rata-rata akurasi yang lebih baik daripada metode local binary pattern tradisional baik yang dikombinasi dengan discrete wavelet transform ataupun tidak. Performa metode usulan full neighbour local binary pattern dapat menghasilkan akurasi yang sempurna yakni 100% baik pada saat menggunakan discrete wavelet transform ataupun tidak, sedangkan akurasi terendah yang didapat adalah 90.49%.Kata Kunci: Ekstraksi fitur, local binary pattern, wavelet, klasifikasi mammogram.[En]Traditional local binary pattern have some disadvantages which is a variant of the rotation and during the thresholding process the pixel is sensitive to noise. At this study the authors proposed a new method of features extraction to solve that problem and this method called full neighbor local binary pattern (fnlbp). This method will be combined with discrete wavelet transform to extract the features of the mammogram image and the classification method is Backpro- pagation Neural Network (BPNN). Based on experiments the result of proposed method in an average accuracy is better than traditional methods of local binary pattern which combined with discrete wavelet transform or not. The performance of the proposed method of full neighbor local binary pattern can produce perfect accuracy that is 100%, this accuracy is reached when using discrete wavelet transform or not, while the lowest accuracy obtained is 90.49%

KLASIFIKASI KATEGORI DOKUMEN BERITA BERBAHASA INDONESIA DENGAN METODE KATEGORISASI MULTI-LABEL BERBASIS DOMAIN SPECIFIC ONTOLOGY

[Id]Sebuah dokumen berita seringkali terkait lebih dari satu kategori, untuk itu diperlukan pemanfaatan metode kategorisasi yang tidak hanya cepat tetapi juga dapat mengelompokkan sebuah berita kedalam banyak kategori. Banyak metode yang dapat digunakan untuk mengkategorisasi dokumen berita, salah satunya adalah ontologi. Pendekatan ontologi dalam kategorisasi sebuah dokumen berita didasarkan pada kemiripan fitur yang ada di dokumen dengan fitur yang ada di ontologi. Penggunaan ontologi dalam kategorisasi yang hanya didasarkan pada kemunculan term dalam menghitung relevansi dokumen menyebabkan banyak kemunculan fitur lain yang sebenarnya sangat terkait menjadi tidak terdeteksi. Dalam? paper ini diusulkan? metode baru untuk kategorisasi dokumen berita? yang terkait dengan banyak kategori, metode ini berbasis domain specific ontology yang perhitungan relevansi dokumen terhadap ontologinya tidak hanya didasarkan pada kemunculan term tetapi juga memperhitungkan relasi antar term yang terbentuk. Uji coba dilakukan pada dokumen berita berbahasa indonesia dengan 2 kategori yaitu olahraga dan teknologi. Hasil uji coba menunjukkan nilai rata-rata akurasi yang cukup tinggi yaitu kategori olahraga adalah 93,85% sedangkan pada kategori teknologi adalah 96,32%.Kata Kunci: Dokumen berita, kategorisasi, multi-label, ontologi,? domain-spesifik.[En]A news document often related? to more than one category,? necessary for utilization? the method of categorization that is not only fast but also able to Classify a news into many categories. Many methods can be used to categorize the news documents, one of which is an ontology. Ontology approach in the categorization of a document is based on the similarity of news features in documents with features that exist in the ontology. The use of ontologies in categorization that just based on the occurance of the term in calculating the relevance of the document, led to the emergence of many other fea-tures that are actually very relevant is undetectable. This paper proposed a new method for categorizing news documents are related with many categories, the method is based on a specific domain ontology and for document relevance calculation is not only based on the occurrence of the term but also take into account the relationships between terms that are formed. Tests performed on the Indonesian language news document with? two categories: sports and technology. The trial results show the value of the average accuracy is high, that the sports category was 93,85% and the technology category is 96,32%.Keywords : News document, ?categorization, multi-label, Ontology, domain-specific

Identification of lung disease types using convolutional neural network and VGG-16 architecture

Pneumonia, tuberculosis, and Covid-19 are different lung diseases but have similar characteristics. One of the reasons for the worsening of disease in lung sufferers is a diagnosis that takes a long time. Another factor, the results of the X-ray photos look blurry and lack contracture, causing different diagnostic results of X-ray photos. This research classifies lung images into four categories: normal lungs, tuberculosis, pneumonia, and Covid-19 using the Convolutional Neural Network method and VGG-16 architecture. The results of the research with models and scenarios without pre-trained use data with a ratio of 9:1 at epoch 50, an accuracy of 94%, while the lowest results are in scenarios using data with a ratio of 8:2 at epoch 50, non-pre-trained models, accuracy by 87%

Investigation of polyurethane primer coating paint based on local palm oil with antimicrobial and anticorrotion agent formula bentonite-chitosan

Paint is a product that is really needed to coat iron or steel materials such as industrial pipes, medical equipment, and so on. The sample formulation used was the addition of chitosan bentonite filler 2, 4, 6, 8% w/w. In TGA testing, the sample underwent single decomposition and showed the best results in the Polyurethane/Bentonite/Chitosan 8:8% w/w sample where the sample began to degrade at a temperature of 416.85 (℃). The bacterial test results showed the best results in the Polyurethane/Bentonite/Chitosan 8:8% w/w sample which had the widest inhibition zone with a value of 6.9 mm for E.Coli bacteria. The sample with the composition Polyurethane/Bentonite/Chitosan 8:8% w/w was the best sample where the sample experienced the smallest corrosion rate, namely 5.08 mpy. The application of this paint is to strengthen the durability of the building so that corrosion does not occur in the Hagu Barat Laut of Lhokseumawe because it is close to sea waters which have air levels that are quite susceptible to corrosion