TAPPS: Model Aplikasi Manajemen Tugas Akhir di Perguruan Tinggi Swasta Menggunakan Unified Modeling Language (UML)

Manajemen tugas akhir yang efektif dan efisien sangat penting bagi universitas untuk mendorong siswa menyelesaikan studi mereka tepat waktu. Penelitian ini bertujuan untuk menganalisis model Unified Modeling Language (UML) untuk model aplikasi manajemen tugas akhir di perguruan tinggi swasta. Metode untuk mendukung analisis model aplikasi manajemen tugas akhir di perguruan tinggi yang akan dibangun dilakukan dengan metode wawancara, observasi, dan studi pustaka.  Berdasarkan hasil model UML, adapun fitur pada aplikasi manajemen tugas akhir di perguruan tinggi antara lain manajemen pengguna, manajemen tugas akhir, manajemen bimbingan tugas akhir, manajemen sidang tugas akhir, manajemen kelas tugas akhir dan manajemen laporan capaian tugas akhir. Pada penelitian ini aplikasi manajemen tugas akhir diberi nama TAPPS untuk mempermudah dalam mengingat nama aplikasi ini. Kata Kunci: UML, tugas akhir, manajemen, perguruan tinggi swast

Rancang Bangun Aplikasi Monitoring Pemberian Obat Bagi Pasien

Dalam melakukan pengontrolan obat secara langsung pada tiap pasien, perawat akan menghabiskan banyak waktu. Selain itu, kemungkinan terjadi kesalahan umumnya sering terjadi. Untuk menghindari terjadinya kesalahan tersebut telah dirancang dan dibuat perangkat yang digunakan untuk mengatur dan melakukan monitoring pemberian obat pada pasien. Adapun metode pengumpulan data yang digunakan dalam penelitian ini adalah dengan menggunakan 3 (tiga) cara yaitu observasi, wawancara, dan studi pustaka. Metode pengembangan sistem yang penulis gunakan adalah metode Rapid Application Development (RAD). Sebagai hasil, aplikasi ini terdiri dari beberapa class dalam sistem antara lain, perawat, pasien, obat, resep dokter dan history. Aplikasi ini diharapkan dapat membantu perawat dalam mengontrol pemberian obat pasien

Implementasi Algoritma Nazief-adriani Pada Fitur Tebak Kata Di Web Edukasi Bahasa Indonesia

Salah satu aturan atau tata bahasa Bahasa Indonesia yang harus diketahui adalah mengenai pengenalan kata dasar dan kata berimbuhan. Dalam bidang komputasi, salah satu algoritma yang dikembangkan dan berhubungan dengan aspek kata dasar dan kata berimbuhan Bahasa Indonesia adalah algoritma Nazief-Adriani. Metodologi penelitian terdiri dari 5 tahap yaitu pengumpulan data, analisis, desain, implementasi dan penulisan laporan. Implementasi algoritma Nazief-Adriani dilakukan pada fitur permainan tebak kata yang digunakan untuk menebak kata dasar dari imbuhan yang ada. Sebagai hasil penelitian, algoritma Nazief-Adriani berhasil diimplementasikan dan diuji coba untuk beberapa kata imbuhan (pelatihan, pendidikan dan menghasilkan) pada permainan tebak kata dasar di Web Edukasi Bahasa Indonesia dengan baik namun kekurangan dari algoritma Nazief-Adriani adalah belum mampunya untuk mendeteksi dan menghapus afiks-infiks atau imbuhan tengah/sisipan

Determining the Number of Batik Motif Object based on Hierarchical Symmetry Detection Approach

In certain conditions, symmetry can be used to describe objects in the batik motif efficiently. Symmetry can be defined based on three linear transformations of dimension n in Euclidian space in the form of translation and rotation. This concept is useful for detecting objects and recognising batik motifs. In this study, we conducted a study of the symmetry effect to determine the number of batik motif objects in an image using symmetry algorithm through a hierarchical approach. The process focuses on determining the intersection line of the batik motif object. Furthermore, by utilising intersection line information for bilateral and rotational symmetry, the number of objects carried out recursively is determined. The results obtained are numbers of batik motif objects through symmetry detection. This information will be used as a reference for batik motif detection. Based on the experimental results, there are some errors caused by the axis of the symmetry line that is not appropriate due to the characteristics of batik motifs. The problem is solved by adding several rules to detect symmetry line and to determine the number of objects. The additional rules increase the average accuracy of the number of object detection from 66.21% to 86.19% (19.99% increase)

Hospital quality classification based on quality indicator data during the COVID-19 pandemic

This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.</p

Random Adjustment - Based Chaotic Metaheuristic Algorithms for Image Contrast Enhancement

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem\u27s characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment

Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%

Implementation of deep learning predictor (LSTM) algorithm for human mobility prediction

The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction the accuracy result, however, only few researchers have addressed the issue of human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result, the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of epoch and batch_size is 100 and 20 respectively

Konsep dan Struktur Penulisan Karya Ilmiah

Buku ini adalah buku ajar yang mata kuliah penulisan ilmiah. Bahasan pokok dari buku ini antara lain Konsep Penelitian, Jenis-Jenis Penelitian, Peranan Penelitian, Struktur Laporan Karya Ilmiah, Judul dan Identitas Penulis, Strategi Menulis Abstrak, Strategi Menulis Pendahuluan, Strategi Menulis Metodologi, Strategi Menulis Hasil dan Pembahasan, Strategi Menulis Kesimpulan dan Daftar Pustaka, Prinsip Dasar Menulis, Kesalahan Penulisan, Sistematika Artikel Ilmiah, Jenis Publikasi Ilmiah dan Jurnal Internasional