User-Centered Design-Based Approach in Scheduling Management Application Design and Development

The process of manually making and setting course schedules using Microsoft Excel is ineffective, time-consuming, and still prone to errors. This research develops a website-based scheduling management application with a case study at SMK Pariwisata Margarana so that it can solve scheduling problems manually. The UserCentered Design (UCD) method is applied in the application prototype design stage. Open interviews, field observations, simulations, and questionnaires were used as research data collection methods. Three iterations were carried out at the prototype design stage to fulfill all user needs. The high-fidelity prototype in the last iteration is then implemented into an application. Application quality is measured using ISO/IEC 25010 with five characteristics. The test results on usability characteristics show that the scheduling management application obtains an average usability score of 91.2%. The appropriateness recognizability sub-characteristic obtained the highest usability score of 93.53%. UCD can help produce an application that can meet all the user’s needs when implemented in the application design phase

Simulasi Antrian Jaringan Multi Server Menggunakan Metode Open Jackson

AbstrakAntrian paket data pada jaringan komputer memiliki model antrian jaringan, dimana proses transmisi yang rumit sehingga tidak dapat diselesaikan secara analitik. Pemodelan dan simulasi diperlukan untuk menyelesaikan masalah antrian jaringan. Model antrian dalam penelitian ini adalah jaringan terbuka dengan analisis paket data menggunakan model server tunggal. Waktu pelayanan paket memiliki distribusi Eksponensial dan distribusi Erlang yang digunakan sebagai pembanding. Jika waktu pelayanan paket data menggunakan distribusi Eksponensial maka model menjadi M/M/1, sedangkan waktu pelayanan paket data menggunakan distribusi Erlang dengan parameter m dan µ, maka model menjadi M/E[m]/1. Penelitian ini menggunakan metode open Jackson untuk melakukan simulasi antrian jaringan guna menghitung nilai karakteristik jaringan. Pengujian sistem simulasi menggunakan packet switching network pada server jaringan komputer Jurusan Ilmu Komputer Universitas Udayana untuk mengetahui performansi sistem yang menggunakan distribusi waktu pelayanan berbeda. Hasil pengujian menunjukkan bahwa waktu pelayanan distribusi Eksponensial memiliki karakteristik yang lebih baik dari distribusi Erlang pada parameter m-Erlang ≥ 2.  Kata kunci— antrian jaringan, distribusi,sistem performansi, multi server.  AbstractQueue data packet at computer network having a network queueing model, with complicated transmission process so that it can not be solved analytically. Modeling and simulation are needed to resolve the issue queue network. Queueing model in this research is an open network with the analysis of data packet using a single server model. Service time packet has Exponential distribution and Erlang used as comparison. If the service time of data packet using the Exponential distribution, then the model become M/M/1, whereas the service time using the Erlang distribution with parameter m and µ, then the model becomes M/E[m]/1. This research uses an open Jackson method to perform queueing network simulations to calculate the characteristics of network queueing system. Examination of simulation system uses data packets on a computer network server of Department Computer Science University of Udayana to determine system performance using with different service time distribution. The result of examination indicate that service time of Exponential distribution has better characteristic then Erlang distribution at parameter m-Erlang ≥2. Keywords— queueing network, distribution, system performance, multiple serve

CASE BASED REASONING (CBR) FOR OBESITY LEVEL ESTIMATION USING K-MEANS INDEXING METHOD

As many as 600 million of the 1.9 billion adults who are overweight are obese. Obesity that is not treated immediately will be a risk factor for increasing cardiovascular, metabolic, degenerative diseases, and even death at a young age. Case Based Reasoning (CBR) can be used to estimate a person's obesity level using previous cases. The old case with the highest similarity will be the solution for the new case. Indexing methods such as the K-Means Algorithm are needed so that the search for similar cases does not involve all cases on a case base so that it can shorten the computation time at the retrieve stage and still produce optimal solutions. Cosine similarity is used to find relevant clusters of new cases and Euclidean distance similarity is used to calculate similarity between cases. Random subsampling method was used to validate the CBR system. The test results with K=2 indicate that the CBR is better than the CBR-K-Means, each of which produces an average accuracy of 88.365% and 88.270% at a threshold of 0.8. CBR-K-Means produces an average computation time at the retrieve stage of 33.55 seconds and is faster than the CBR of 35.5 seconds

SIMULASI ANTRIAN JARINGAN MULTI SERVER MENGGUNAKAN METODE OPEN JACKSON DENGAN PELAYANAN TUNGGAL SETIAP NODE

The increasing traffic flow of packets on the network computers that are not offset by an increase in service will cause a queue of data packets until the availability of facilities resource. Queues and packet transmission process is very complicated on a computer network that can not be solved analytically, and therefore used modeling and simulation to analyze the queuing system. Queuing model discussed in this study is an open network of queues where the packets of data arriving from the outside and from within the system which passes through several nodes to complete the job. The arrival of data packets follow a Poisson distribution with Exponential service time and Erlang distribution are used as a comparison. Analysis of the data packets are using a single server model where, if the service time of data packets using the Exponential distribution, then the model become M/M/1, whereas the service time using the Erlang distribution with parameters m and μ, then the model becomes M/E[m]/1. The process of queuing networks in general will be served by many servers. This research use an open Jackson method to perform queueing network simulations to calculate the characteristics of the network with parameters as follow: the average waiting time of data packets in queue on a network system, the average waiting time of data packets on a node in a network system, the average number of packets in a network system , the average service time of data packets in a network system, and the probability of a busy network node system. Examination of simulation system use data packets on a computer network server of Department Computer Science University of Udayana to determine system performance using with different service time distribution. The result of examination indicate that service time of Exponential distribution has better characteristic then Erlang distribution at parameter m-Erlang � 2

A COMPARISON OF DIFFERENT KERNEL FUNCTIONS OF SVM CLASSIFICATION METHOD FOR SPAM DETECTION

Today, the use of e-mail, especially for formal online communication, is still often done. There is one common problem faced by e-mail users, which is the frequent receiving of spam messages. Spam messages are generally in the form of advertising or promotional messages in bulk to everyone. Of course this will cause inconvenience for people who receive the SPAM message. SPAM e-mails can be interpreted as junk messages or junk mail. So that spam has the nature of sending electronic messages repeatedly to the owner of the e-mail. This is abuse of the messaging system. One way to solve the spam problem is to identify spam messages for automatic message filtering. Several machine learning based methods are used to classify spam messages. In this study, a comparison was made between several kernel functions (i.e., linear, degree 1 polynomial, degree 2 polynomial, degree 3 polynomial, and RBF) of the SVM method to get the best SVM model in identifying spam messages. The evaluation results based on the Kaggle 1100 dataset showed that the best model were the SVM model with a linear kernel function and a degree 1 polynomial, where both models returned Precision = 0.99, Recall = 0.99, and F1-Score = 0.98. On the other hand, the RBF kernel produced lower performance in terms of Precision, Recall, and F1-Score of 0.95, 0.95, and 0.94, respectively