Telkom Institute of Technology

About: Telkom Institute of Technology is a based out in . It is known for research contribution in the topics: Computer science & Network packet. The organization has 570 authors who have published 470 publications receiving 1390 citations.

Exploring e-Commerce Usability by Heuristic Evaluation as a Compelement of System Usability Scale

Abstract: The use of the System Usability Scale (SUS) questionnaire in the measurement of e-commerce usability has been widely carried out. However, the SUS score is not an adequate measure to express the level of user acceptance and satisfaction. Other evaluations are needed to complement the usability test, including assessments based on expert judgment. The proposed method consists of two stages, the heuristic evaluation stage, which involves expert judgment, and the SUS questionnaire stage based on user perceptions of the e-commerce website. Input from experts is expected to be able to show better the usability issues faced in using the website. Expert and user perspectives are combined to get user input in design improvements. We collect data from experts and users about their perceptions of the usability of Shoppee e-commerce websites. Most users agree that the Shopee site is excellent (grade B-). The results of the examination by the expert stated that the Shopee site was also excellent. Nine out of ten evaluation criteria scored above 72%. The most usability issue is the flexibility and efficiency of the system, especially problems in search engines.

DCT-based local motion blur detection

Abstract: One of the frequently encountered problems in photography is the appearance of motion blurring effect due to either object movement or camera motion associated with the speed of the camera (shutter speed) when pictures are taken. This Paper presents a novel but simple method of detecting unwanted motion blur effects that appear local within an arbitrary area on a digital image. The proposed method uses various size block-based discrete cosine transform (DCT) calculations on the distorted image. The outcome of this detection are then subsequently used to improve the quality of the image by means of pixel correlation based deblurring method applied to the specific area identified by our motion blur detector. Subjective experiment to evaluate the quality of the resulting enhanced image is then conducted and objective evaluations using several published image quality metrics are also computed. Experimental results show that the quality of the enhanced images produced by the chosen deblurring method is better when local motion blur detection is employed than those without blur detection. Out of various block sizes used in the experiment, block size of 32}32 pixels produce better perceived quality.

Perbandingan kinerja word embedding word2vec, glove, dan fasttext pada klasifikasi teks

Abstract: Karakteristik teks yang tidak terstruktur menjadi tantangan dalam ekstraksi fitur pada bidang pemrosesan teks. Penelitian ini bertujuan untuk membandingkan kinerja dari word embedding seperti Word2Vec, GloVe dan FastText dan diklasifikasikan dengan algoritma Convolutional Neural Network. Ketiga metode ini dipilih karena dapat menangkap makna semantik, sintatik, dan urutan bahkan konteks di sekitar kata jika dibandingkan dengan feature engineering tradisional seperti Bag of Words. Proses word embedding dari metode tersebut akan dibandingkan kinerjanya pada klasifikasi berita dari dataset 20 newsgroup dan Reuters Newswire. Evaluasi kinerja diukur menggunakan F-measure. Performa terbaik menunjukkan FastText unggul dibanding dua metode word embedding lainnya dengan nilai F-Measure sebesar 0.979 untuk dataset 20 Newsgroup dan 0.715 untuk Reuters. Namun, perbedaan kinerja yang tidak begitu signifikan antar ketiga word embedding tersebut menunjukkan bahwa ketiga word embedding ini memiliki kinerja yang kompetitif. Penggunaannya sangat bergantung pada dataset yang digunakan dan permasalahan yang ingin diselesaikan. Kata kunci: w ord embedding, word2vec, glove, fasttext, klasfikasi teks, convolutional neural network, cnn.

Perbandingan Metode Clustering Menggunakan Metode Single Linkage dan K - Means pada Pengelompokan Dokumen

Abstract: Penyebaran berita saat ini semakin tersebar luas semenjak perkembangan dunia internet yang semakin pesat. Perkembangan dunia internet membuat berita yang tersebar semakin beragam dan berjumlah sangat besar. Pembaca berita akan kesulitan untuk memperoleh berita yang diinginkan jika berita tersebut tidak terkelompok dengan baik. Dan jika harus dikelompokkan secara manual membutuhkan waktu yang sangat lama. Oleh sebab itu, Clustering menjadi solusi untuk mengatasi masalah tersebut. Clustering akan mengelompokkan dokumen berita berdasarkan tingkat kemiripan dari dokumen tersebut. Metode Single Linkage merupakan metode pengelompokan hierarchical clustering . Metode Single Linkage mengelompokkan dokumen didasarkan pada jarak terdekat antar dokumen. Komputasi Single Linkage merupakan komputasi yang mahal dan kompleks. Sedangkan metode K-means merupakan metode pengelompokan partitioned clustering . Metode K-means mengelompokkan dokumen didasarkan pada jarak terdekat dengan centroid -nya. K-Means merupakan metode pengelompokan yang sederhana dan dapat digunakan dengan mudah. Tetapi pada jenis data tertentu, K-means tidak dapat memberikan segementasi data dengan baik, sehingga kelompok yang terbentuk tidak murni data yang sama. Metode pengujian yang digunakan untuk mengukur kualitas cluster adalah Silhouette Coefficient dan Purity . Berdasarkan hasil pengujian yang dilakukan, dapat disimpulkan, bahwa metode Single Linkage memiliki performansi yang lebih baik dibandingkan dengan metode K-means . Nilai silhouette coefficient Single Linkage selalu lebih unggul dibandingkan dengan K-Means . Pertambahan jumlah dokumen membuat nilai silhouette coefficient single linkage semakin kecil sedangkan K-means terkadang menghasilkan nilai yang negatif. Untuk nilai purity, Single Linkage selalu bernilai 1 sedangkan K-Means tidak pernah bernilai 1. Hasil pertambahan jumlah cluster dan jumlah dokumen memberikan pengaruh terhadap nilai silhouette coefficient dan purity . Hal ini berarti single linkage selalu menghasilkan dokumen yang sama, sedangkan K-means masih bercampur dengan dokumen yang lain.

A Model for Evaluation Smart City Readiness using Structural Equation Modelling: a Citizen's Perspective

Abstract: Smart City is the use of ICT-based governance and cloud is now a trend throughout the world. Various increasingly complex city problems are expected to become increasingly more efficient by applying the concept of smart cities in city governance. However, in its implementation, many crucial problems significantly affect the success of the implementation of smart cities, including the lack of competent human resources, ICT policies and governance that have not been efficient, lack of government commitment and the lack of community participation. This study aims to model smart city readiness factors in community perspectives. The method in this study is quantitative descriptive research using a combination of the Technology Readiness Index model, the Technology Acceptance Model and the Delone McLean Model. Data collection is done by conducting surveys and interviews with 200 citizens. Data analysis using Structural Equation Modeling with the help of AMOS 23.0 software. The results showed that there were a significant relationship and relevance between the variables in the model used in the TRI, TAM and Delone McLean models. This study contributes to the relationship modelling of the factors that determine the success of applying the smart city concept. This research provides recommendations to the government and stakeholders to pay more attention to the crucial factors in the success of developing smart cities.