Ethnomathematics studies the relationship between mathematics and culture. Indonesia has many traditional cultures. One of them is traditional cloth. The traditional cloth from East Nusa Tenggara ...(NTT) province is called tenun ikat. Since the motif of tenun ikat consists of symmetrical and repeated patterns, it can be generated using Frieze groups. The Frieze groups are the plane symmetry groups of patterns whose subgroups of translations are isomorphic to Z. There are seven groups in the Frieze groups, i.e., F_1, F_2, F_3, F_4, F_5, F_6, and F_7. Translation, reflection, rotation, and glide reflection are the transformation used in the Frieze groups. In this paper, Frieze groups are used to generate digital tenun ikat motifs from the basic pattern. Since one piece of original tenun ikat may consist of some basic patterns, the basic patterns are identified first, and then each of them is generated into the desired pattern, according to the suitable Frieze groups. Furthermore, a GUI Matlab program is developed to generate the Frieze groups. Three motifs of tenun ikat are presented to demonstrate the implementation of Frieze groups. With the Frieze group, users can generate other patterns from a basic pattern, so users can generate new motifs of tenun ikat without leaving the cultural characteristics of NTT province.
One of the best-known clustering methods is the fuzzy c-means clustering algorithm, besides k-means and hierarchical clustering. Since FCM treats all data features as equally important, it may obtain ...a poor clustering result. To solve the problem, feature selection with feature weighting is needed. Besides feature selection by assigning feature weights, there is also feature selection by assigning feature weights and eliminating the unrelated feature(s). THE Feature-reduction FCM (FRFCM) clustering algorithm can improve the FCM clustering result by weighting the features and discarding the unrelated feature(s) during the clustering process. Basketball is one of the famous sports, both international and national. There are five players in basketball, each with a different position. A player can generally be in guard, forward, or center position. Those three general positions need different characteristics of players’ physical conditions. In this paper, FRFCM is used to select the related physical feature(s) for basketball players, consisting of height, weight, age, and body mass index. to determine the basketball players’ position. The result shows that FRFCM can be applied to determine the basketball players’ position, where the most related physical feature is the player’s height. FRFCM gets one incorrect player’s position, so the error rate is 0.0435. As a comparison, FCM gets five incorrect player’s positions, with an error rate of 0.2174. This method can help the coach decide the basketball new player’s position.
The characteristics and background of the church congregation and the acceptance of technology in online worship services were one of the parameter to determine user attitudes or behavior in ...accepting technology. The objective of this research to is to find out the acceptance of church congregation's technology toward the online worship services using the Unified Theory of Acceptance and Use of The Technology 2 (UTAUT 2), and answers of respondents from various backgrounds and ages were grouped using k-Means clustering. Data were collected through a questionnaire with a total of 220 respondents. From the UTAUT 2 method using multiple linear regression, it was found that the Habit variable on the use of online worship has a high influence toward the Behavioral Intention variable, and the Habit variable on the use of online worship has a high influence toward the Use Behavior variable. Furthermore, clustering with k-Means was carried out to see the age group that was satisfied with online worship services, based on four clusters obtained using the Elbow method and six age groups. The results of the k-Means clustering of all UTAUT 2 variables, the age group 12-16 years and 26-35 years are the most satisfied group and accept online worship services.
The Samsung Galaxy Z Flip 3 is one of the gadgets that are currently popular among the public because of its unique shape and features. Youtube is one of the social media that can be accessed and ...enjoyed by the public, one of which is gadget review content on the GadgetIn channel. Youtube can provide information, whether people accept or are interested in this new gadget or not. This study aims to determine the sentiment of a gadget producer. Based on the results of the analysis and testing that has been carried out on the Youtube comments of the Samsung Galaxy Z Flip 3 gadget with a total of 9,597 comments, more users gave positive opinions in the design aspect and negative opinions on the price, specifications and brand image aspects. By using the CRISP-DM model and comparing the Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) classification methods, it is proven that the SVM classification model shows the best results. The average accuracy of SVM is 96.43% seen from four aspects, namely the design aspect of 94.40%, the price aspect of 97.44%, the specification aspect of 96.22%, and the brand image aspect of 97.63%.
This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the ...time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.
Dalam data mining, pendekatan K-Means Clustering adalah metode yang digunakan untuk mengelompokkan data menjadi kumpulan data. Dalam sistem analisis, pendekatan data mining berdasarkan algoritma ...K-Means dapat digunakan untuk pengelompokan prestasi murid. Dalam penelitian ini data nilai siswa kelas X-XII Bahasa SMAN 1 Tengaran tahun 2014-2017, dari semester satu sampai lima dikelompokkan berdasar nilai rapor. Clustering digunakan dalam pembangunan program analitik ini untuk menilai dampak data murid terhadap kecenderungan keberhasilan murid di setiap kelompok yang dapat dibuktikan dengan kelulusan murid yang menduduki top rank serta dari hasil wawancara guru pengajar maupun wali kelas serta data nilai yang diperoleh dari Dapodik. Hasil dari penelitian ini membuktikan bahwa teknik clustering K-Means dapat dimanfaatkan oleh pengajar untuk mengkategorikan murid berdasarkan nilai mata pelajaran dan absensi, serta menggunakannya untuk menganalisis prestasi murid dengan mengelompokkan dari kategori prestasi rendah, rata-rata, dan tinggi. Selanjutnya, dengan metode Simple Additive Weighting (SAW) dicari top rank dari cluster tinggi untuk menemukan murid unggulan.
CV. XYZ merupakan perusahaan distribusi alat teknik pertanian di kota Sragen yang harus memenuhi kebutuhan konsumennya dan menentukan strategi yang paling tepat guna memaksimalkan penjualan. ...Perusahaan memiliki permasalahan dengan pengaturan persediaan barang karena kurang memahami pola pelanggan ketika membeli barang yang dibeli dalam waktu bersamaan, sehingga penelitian ini dilakukan agar perusahaan bisa memperoleh informasi dan bisa mengambil keputusan tentang pembaharuan stok persediaan barang yang lebih tepat dan sesuai dengan kebutuhan. Penelitian ini dilakukan dengan memanfaatkan salah satu pendekatan data mining berupa metode asosiasi dengan algoritma apriori. Penggunaan metode asosiasi dilakukan untuk melihat hubungan antar barang misal konsumen membeli barang A maka juga membeli barang B, sedangkan analisis dengan algoritma apriori digunakan untuk menentukan nilai support dan nilai confidence. Hasil dari penelitian ini adalah jika membeli pompa air dan selang, maka nilai support serta confidence yang didapat sebesar 30,28% dan 96,36%, pompa submersible dan tali kuralon dengan nilai support 34,28% dan nilai confidence sebesar 90,9%. Pola yang lain adalah tali kuralon dan pompa submersible dengan nilai support 34,28% dan nilai confidence sebesar 100%, serta selang dan pompa air dengan nilai support 30%, nilai confidence sebesar 88,33%.
PT. Bintang Selatan Agung merupakan perusahaan yang bergerak dalam bidang kontraktor jalan dan rental alat berat. Dalam mendukung proses bisnis, perusahaan memerlukan suatu teknologi berupa sistem ...yang dapat mendukung suatu perusahaan. Tujuan penelitian ini adalah untuk mengetahui bagaimana proses bisnis dapat berjalan dengan menerapkan Knowledge Management System (KMS). Metodologi yang digunakan dalam melakukan penelitian ini didasarkan pada metode KMS dengan tahapan Knowledge Management System Life Cycle (KMSLC), yang terdiri dari evaluasi infrastruktur, analisis dan desain sistem, sistem penyebaran/penerapan, dan evaluasi. Dalam penelitian didapatkan suatu hasil berupa struktur organisasi, alur proses sistem manajemen pengetahuan, dan penerapan/penyebaran sistem yang mendukung perusahaan. sistem manajemen pengetahuan ini dibangun berbasis web menggunakan bahasa pemrograman Native PHP dengan PhpMyAdmin sebagai framework database. Hasil analisis knowledge management pada perusahaan didapatkan bahwa terdapat penerapan KMS berbasis web yang berfungsi menjaga dan melindungi data dan informasi supaya tidak rusak dan hilang. Perusahaan telah menerapkan transfer pengetahuan explicit melalui sistem database add user, input data pegawai, dan sistem absen karyawan. Selain itu, perusahaan masih membutuhkan peningkatan KM berupa halaman yang dapat menambahkan pengetahuan tacit dan menampilkan pengetahuan tacit untuk menampung pengetahuan dari pegawai, sehingga kinerja karyawan dapat dimaksimalkan.
Paper-based documents or printed documents such as recommendation letters, academic transcripts, and diplomas are prone to forgery. Several methods have been used to protect them, such as ...watermarking, security holograms, or using paper with specific security features. This paper presents a document authentication system that utilizes QR code and ECDSA as the digital signature algorithm to protect this kind of document from counterfeiting. A digital signature is a well-known technique in modern cryptography used for providing data integrity and authentication. The idea proposed herein is to put a QR code in the printed documents where the QR code includes a digital signature. The signature can later be authenticated using the proposed system by uploading the document for authentication or scanning the document's QR code. The proposed system is particularly developed for digital signature generation and verification of students' final project approval documents as the case study. In traditional settings, the approval form is typically signed directly by the student's advisor dan co-advisor using handwritten signatures. However, using the conventional handwritten signature, the signature on the approval form can be falsified. Therefore, a digital signature generation and verification system is implemented herein to avoid handwritten signature falsification. The advisors can use this system to sign the approval form using a digital signature instead of a handwritten one. The signature is stored in a QR code and is generated using ECDSA with SHA-256 as the hash function. The proposed system is evaluated using documents (i.e., approval forms) with genuine and forged QR codes. The evaluation results showed that the system could verify the authenticity of the approval forms, which contain genuine QR codes. The approval forms that contained forged QR codes were correctly identified.
This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs ...atmospheric correction using DOS1 algorithm combined with
-NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (
., NDWI, NDVI, SAVI), slope, and distance. The result of the experiment compared to other classification algorithms demonstrates good results for the proposed model. It has the smallest MSEs of 0.0002 for MNDWI, 0.0002 for SAVI, 0.0006 for NDVI, 0.0003 for NDWI, and 0.0003 for NDBI. The experiment also shows that the accuracy rate for the prediction model is about 93.62%.
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