PENGELOMPOKAN PROFIL PEKERJAAN ALUMNI MENGGUNAKAN ALGORITMA K-MEANS

Tracer Study adalah salah satu pelacakan jejak kepada alumni yang umum dilakukan program studi di perguruan tinggi sebagai upaya dalam memperbaiki kualitas penyelenggaraan pendidikan. Terdapat beberapa kuesioner yang ditujukan kepada alumni, namun tanggapan sebagai umpan balik yang diberikan alumni masih terbilang cukup rendah. Penelitian ini bertujuan mengoptimalkan program tracer study yang dilakukan dengan cara mengelompokkan profil pekerjaan alumni agar dapat disesuaikan dengan kebutuhan penyebaran kuesioner. Metode yang digunakan dalam pengelompokkan profil pekerjaan alumni adalah clustering yang dalam penelitian ini menggunakan algoritma K-Means. Hasil dari penelitian ini adalah cluster-cluster profil pekerjaan alumni yang setiap anggota dalam cluster yang sama memiliki kriteria pekerjaan yang mirip.------------- Tracer Study is one of methods used in university to track their alumnus’ traces as an approach to improve the quality of their education management. There exist a few questionnaires aimed at the alumnus, but responses the alumnus given are still quite lacking. This research focused on optimizing tracer study program by separating alumnus’ work profiles into parts so it could suit distribution of the questionnaire. Method used to group the alumnus work profiles is clustering with the help of K Means algorithm. The aforementioned research resulting in clusters of alumnus’ work profiles in which each member of the same cluster has similar work characteristics

Pengenalan Emosi untuk Evaluasi User Experience Pada Aplikasi Google Form Dengan Metode K-Nearest Neighbor

Electroencephalography (EEG) merupakan alat untuk merekam aktivitas gelombang otak yang dapat dimanfaatkan untuk memvalidasi tingkat kebergunaan dengan pendekatan user experience (UX) dalam mendesain antarmuka aplikasi. Proses pengujian UX secara umum termasuk mengamati perubahan emosi seseorang yang sedang berinteraksi dengan aplikasi dimana hasilnya tidak bisa langsung disimpulkan. Klasifikasi emosi yang diteliti yaitu keadaan senang dan sedih. Emosi senang mempresentasikan kemudahan yang dirasakan saat berinteraksi dengan aplikasi, sedangkan emosi sedih merepresentasikan adanya perasaan bingung atau frustasi.Sinyal yang digunakan sinyal beta berjenis attention dengan stimulus pengerjaan task pada aplikasi google form. Penelitian ini mengaplikasikan metode K-Nearest Neighbor sebagai klasifikasi dengan ekstraksi fitur orde pertama. Responden yang digunakan sebanyak 30 dengan 3 kali perulangan berjumlah 90 data. Sebanyak 20 responden 3 kali perulangan berjumlah 60 data dijadikan sebagai data training dan 10 responden dengan 3 kali perulangan berjumlah 30 data dijadikan sebagai data uji.Pada penelitian ini didapatkan hasil pengujian emosi seseorang dalam keadaan senang dan sedih. Pengujian yang dilakukan dengan Confusion Matrix untuk menentukan tingkat akurasi. Nilai akurasi tertinggi yang didapat pada pengujian sinyal beta berjenis attention sebesar 73,3%

Wavelet Based Feature Extraction for The Indonesian CV Syllables Sound

This paper proposes the combined methods of Wavelet Transform (WT) and Euclidean Distance (ED) to estimate the expected value of the possibly feature vector of Indonesian syllables. This research aims to find the best properties in effectiveness and efficiency on performing feature extraction of each syllable sound to be applied in the speech recognition systems. This proposed approach which is the state-of-the-art of the previous study consist of three main phase. In the first phase, the speech signal is segmented and normalized. In the second phase, the signal is transformed into frequency domain by using the WT. In the third phase, to estimate the expected feature vector, the ED algorithm is used. Th e result shows the list of features of each syllables can be used for the next research, and some recommendations on the most effective and efficient WT to be used in performing syllable sound recognition

An approach based on tunicate swarm algorithm to solve partitional clustering problem

The tunicate swarm algorithm (TSA) is a newly proposed population-based swarm optimizer for solving global optimization problems. TSA uses best solution in the population in order improve the intensification and diversification of the tunicates. Thus, the possibility of finding a better position for search agents has increased. The aim of the clustering algorithms is to distributed the data instances into some groups according to similar and dissimilar features of instances. Therefore, with a proper clustering algorithm the dataset will be separated to some groups and it’s expected that the similarities of groups will be minimum. In this work, firstly, an approach based on TSA has proposed for solving partitional clustering problem. Then, the TSA is implemented on ten different clustering problems taken from UCI Machine Learning Repository, and the clustering performance of the TSA is compared with the performances of the three well known clustering algorithms such as fuzzy c-means, k-means and k-medoids. The experimental results and comparisons show that the TSA based approach is highly competitive and robust optimizer for solving the partitional clustering problems

Fuzzy Reasoning Approach for Predicting Web Services QoS/QoE with ANFIS

Nowadays, the web service (WS) usage in information systems (IS) includes determining a feasible WS that fulfils a set of non-functional requirements of Quality of Services (QoS) and user’s needs of Quality of Experience (QoE). While most existing studies evaluate WS from one perspective, i.e., users, and are based on data-driven approach, which employs a numerical dataset to learn a reasoning model, they overlook that users express their needs in a non-numerical form. To address these issues, we propose a new fuzzy reasoning approach for predicting WS QoS/QoE with the adaptive neuro-fuzzy inference system (ANFIS) that encompasses multiple viewpoints and perspectives, and is also suitable for linguistic terms. To verify the efficiency, we implemented the proposed approach, conducted two experiments and compared them. The results show a good performance of the proposed approach for predicting WS QoS/QoE, and, consequently, it can be considered a suitable tool for predicting

Pengenalan Emosi untuk Evaluasi User Experience Pada Aplikasi Google Form Dengan Metode K-Nearest Neighbor

Electroencephalography (EEG) merupakan alat untuk merekam aktivitas gelombang otak yang dapat dimanfaatkan untuk memvalidasi tingkat kebergunaan dengan pendekatan user experience (UX) dalam mendesain antarmuka aplikasi. Proses pengujian UX secara umum termasuk mengamati perubahan emosi seseorang yang sedang berinteraksi dengan aplikasi dimana hasilnya tidak bisa langsung disimpulkan. Klasifikasi emosi yang diteliti yaitu keadaan senang dan sedih. Emosi senang mempresentasikan kemudahan yang dirasakan saat berinteraksi dengan aplikasi, sedangkan emosi sedih merepresentasikan adanya perasaan bingung atau frustasi.Sinyal yang digunakan sinyal beta berjenis attention dengan stimulus pengerjaan task pada aplikasi google form. Penelitian ini mengaplikasikan metode K-Nearest Neighbor sebagai klasifikasi dengan ekstraksi fitur orde pertama. Responden yang digunakan sebanyak 30 dengan 3 kali perulangan berjumlah 90 data. Sebanyak 20 responden 3 kali perulangan berjumlah 60 data dijadikan sebagai data training dan 10 responden dengan 3 kali perulangan berjumlah 30 data dijadikan sebagai data uji.Pada penelitian ini didapatkan hasil pengujian emosi seseorang dalam keadaan senang dan sedih. Pengujian yang dilakukan dengan Confusion Matrix untuk menentukan tingkat akurasi. Nilai akurasi tertinggi yang didapat pada pengujian sinyal beta berjenis attention sebesar 73,3%

Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection

The Minimal Learning Machine (MLM) is a nonlinear supervised approach based on learning a linear mapping between distance matrices computed in the input and output data spaces, where distances are calculated using a subset of points called reference points. Its simple formulation has attracted several recent works on extensions and applications. In this paper, we aim to address some open questions related to the MLM. First, we detail theoretical aspects that assure the interpolation and universal approximation capabilities of the MLM, which were previously only empirically verified. Second, we identify the task of selecting reference points as having major importance for the MLM's generalization capability. Several clustering-based methods for reference point selection in regression scenarios are then proposed and analyzed. Based on an extensive empirical evaluation, we conclude that the evaluated methods are both scalable and useful. Specifically, for a small number of reference points, the clustering-based methods outperformed the standard random selection of the original MLM formulation.Comment: 29 pages, Accepted to JML