http://www.jurnal.iaii.or.id/index.php/RESTI/issue/feed Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 2019-08-20T10:14:35+00:00 Yuhefizar ephi.lintau@gmail.com Open Journal Systems <p><span class="" lang="id"><span class="">Ikatan Ahli Informatika Indonesia (IAII) merupakan lembaga/organisasi profesi di bidang informatika yang merangkul semua insan informatika untuk berperan aktif dalam mengembangkan profesi informatika di Indonesia. Dalam panduan Dikti, terkait pengelola Jurnal merupakan salah satu wewenang organisasi profesi dengan penilaian 4. Oleh karena itu Jurnal ini hadir dalam rangka mengemban tugas tersebut.</span></span></p> <p><span id="result_box" class="" lang="id"><span class="">Jurnal </span>RESTI&nbsp; (Rekayasa Sistem dan Teknologi Informasi) adalah sebuah jurnal <em>blind peer-review</em> <span class="">yang didedikasikan</span> untuk publikasi hasil penelitian yang berkualitas dalam&nbsp; bidang Rekayasa Sistem dan Teknologi Informasi namun tak terbatas secara implisit. Semua publikasi di junal RESTI bersifat akses terbuka yang memungkinkan <span class="">artikel </span>tersedia secara bebas online tanpa berlangganan apapun.</span></p> <div class="itanywhere-activator" style="left: 0px; top: 49px; display: none;" title="Google Translator Anywhere">&nbsp;</div> <div class="itanywhere-activator" style="left: 0px; top: 45px; display: none;" title="Google Translator Anywhere">&nbsp;</div> http://www.jurnal.iaii.or.id/index.php/RESTI/article/view/887 Prediksi IHSG dengan Backpropagation Neural Network 2019-08-20T10:14:25+00:00 Andy Santoso andy@student.umn.ac.id Seng Hansun hansun@umn.ac.id <p>IDX Composite is a combination of all common stock and preferred stock which registered on Bursa Efek Indonesia (BEI). IDX Composite is often used by investor to predict the stock price to get profit. But, to predict the stock price is not easy, hence it yields a high risk to investor. This study offers the usage of backpropagation algorithm to minimize the risk. Backpropagation is a supervised algorithm and will be made in Python programming language, in this case, backpropagation will use and learn the past 5 days data to predict the outcome. Also, this study shows that backpropagation have a high accuracy which reflects in Mean Square Error Testing value of 320.49865083640924 to predict IDX Composite using 0.3 learning rate and 3000 epoch.</p> 2019-08-12T15:42:54+00:00 ##submission.copyrightStatement## http://www.jurnal.iaii.or.id/index.php/RESTI/article/view/971 Sistem Rekomendasi Buku untuk Perpustakaan Perguruan Tinggi Berbasis Association Rule 2019-08-20T10:14:28+00:00 Laras Dewi Adistia ldadistia@gmail.com Tubagus Mohammad Akhriza akhriza@stimata.ac.id Singgih Jatmiko singgih@staff.gunadarma.ac.id <p><em>One of the services in the university library is an information system to find the availability of library collections and the location of each collection shelf. But not many of these systems provide a mechanism that can recommend visitors not only about the books they want, but also other related books that may be needed. This study uses association rule mining techniques that are applied to library transaction data to identify relationships between books (titles) that attract visitors' attention. Relationships are built on interesting measurements between the titles, namely support and confidence, where support determines the combination of the most frequently borrowed book titles, while confidence produces the possibility that the title of the book will be borrowed along with other books. The pattern of book titles association with high confidence indicates that the titles are very related so it is recommended for visitors to consider borrowing along with the book they are looking for. In addition, the system can also recommend the procurement of new books and rack configurations to improve the visitor's experience when searching for books on the site. In the experiment, the precision of recommendations generated from the system reached 70%. Web applications were developed to help understand the effectiveness of the recommendation system based on association rules.</em></p> 2019-08-12T15:26:40+00:00 ##submission.copyrightStatement## http://www.jurnal.iaii.or.id/index.php/RESTI/article/view/908 Aplikasi “GIZIe” Untuk Mengetahui Status Gizi Balita Menggunakan Metode Forward Chaining 2019-08-20T10:14:30+00:00 Fiby Nur Afiana fiby@amikompurwokerto.ac.id Ika Romadoni Yunita ikarom@amikompurwokerto.ac.id <p><em>Based on the results of Nutritional Status Monitoring (PSG) conducted by the Ministry of Health in several regions in Indonesia there are sufferers of malnutrition from the age of 0 - 59 months. Banyumas Regency itself entered into the red zone the highest number of cases of malnutrition with 65 cases. For the Baturraden area with an area of ​​45.35 km2 and having 12 villages, there is a Puskesmas which only has 1 general practitioner, 3 nurses, 6 midwives and 1 nutritionist. Based on the Recaputulation of the Agency for Health and Human Resource Development, the Health Center I Baturraden is considered still not meeting the standard number of Health Human Resources. In addition, the lack of public awareness and economic reasons further worsened the condition of community nutrition. Seeing the cases that occur requires a system that can diagnose early conditions of malnutrition in children that can be accessed directly by everyone by utilizing expert system information technology. The purpose of this study is to design and build a mobile phone (android) application called ‘Gizie’ by using the forward chaining method for Puskesamas 1 Baturraden Banyumas Regency which can help medical personnel and parents diagnose early malnutrition in children. The research method used is the research and development method (R &amp; D) to produce certain products, and test the effectiveness of these products.</em></p> <p><em>&nbsp;</em></p> 2019-08-12T15:05:56+00:00 ##submission.copyrightStatement## http://www.jurnal.iaii.or.id/index.php/RESTI/article/view/943 Sistem Pemeringkat Otomatis Berbasis Kata Sifat 2019-08-20T10:14:32+00:00 Faisal Rahutomo faisal@polinema.ac.id Diana Mayangsari Ramadhani diana.mayangsari@gmail.com Inggrid Yanuar Risca Pratiwi inggrid_yanuar@polinema.ac.id <p><em>This paper exposes a novel method has been developed during these 2 years. The method is named as “adjective based automatic </em><em>rating</em><em> system”. This method is developed to utilize the abundant availability of text on the internet for quality and performance </em><em>rating</em><em> purpose. The text is processed in such a way and leave only the adjectives. Semantic analysis is done by two knowledge: adjectives of performance definition and Indonesian adjectives database with its synonym-antonym relation. This research proposes several formula steps, therefore the method output is a </em><em>rating</em><em> score that can be tunned its scale. The experiment results have been gathered for several objects: tourism, courier service, and organization performance. With detail information in tourism object experiment, this paper cites the other experiment results as well. This paper </em><em>also </em><em>provides availability information of the method as Python library.</em> <em>The results show a high correlation score, always more than 0.9. The results also show acceptable error scores, never more than 45%.</em></p> 2019-08-09T23:41:58+00:00 ##submission.copyrightStatement## http://www.jurnal.iaii.or.id/index.php/RESTI/article/view/1013 Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana 2019-08-20T10:14:35+00:00 Ika Oktavianti ikaoktavianti86@gmail.com Ermatita Ermatita ermatitaz@yahoo.com Dian Palupi Rini dprini@unsri.ac.id <p><em>Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.</em></p> <p><em>&nbsp;</em></p> 2019-08-09T23:08:05+00:00 ##submission.copyrightStatement##