Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
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<![CDATA[Klasifikasi Kualitas Biji Kopi Menggunakan MultilayerPerceptron Berbasis Fitur Warna LCH ]]>
<![CDATA[Ilhamsyah Ilhamsyah]]>;
<![CDATA[Aviv Yuniar Rahman]]>;
<![CDATA[Istiadi Istiadi]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3438
<![CDATA[Coffee is one of Indonesia's foreign exchange earners and plays an important role in the development of the plantation industry. In previous studies, coffee bean quality research has been carried out using the ANN method using color features. RGB and GLCM. However, the results carried out in the study only had an accuracy value of up to 47%. Therefore, this study aims to improve the performance of coffee bean quality classification using four machine learning methods and 7 color features. From the results obtained, it shows that MultilayerPerceptron is better starting with RGB color with an accuracy of 38% split ratio 90:10. HSV has an accuracy of 57% split ratio 90:10. CMYK has an accuracy of 63% split ratio 90:10. LAB has a 58% curation split ratio of 90:10. The YUV type has an accuracy of 58% split ratio 90:10. Furthermore, the HSI color type has an accuracy of 42% split ratio 90:10. The HCL color type has an accuracy of 65% split ratio 90:10 and LCH has an accuracy of 78% split ratio 90:10. In testing, it can be concluded that the MultilayerPerceptron method is better than other methods for the coffee bean classification process.]]>
<![CDATA[Implementasi Raised Cosine Filter Pada Sistem Penyiaran Televisi Digital Satelit 2 (DVB-S2) ]]>
<![CDATA[Rio Setiawan]]>;
<![CDATA[Emy Haryatmi]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3442
<![CDATA[The development of digital video broadcasting is still continue recently and was done by many parties. One of the project regarding this research was DVB project. There was three areas in digital video broadcasting. One of them was Digital Video Broadcasting Satellite Second Generation (DVB-S2). The development of this project is not focus only in video broadcasting but also focus in applications and mutlimedia services. The objective of this research was to implement raised cosine filter in DVB-S2 using matlab simulink in order to optimize SNR and BER value. Parameters used in this project was QPSK mode and LDPC with 50 iteration. Those parameters was chosen to maintain originality of data that sent in noisy channel. The result showed that by implementing raised cosine filter could optimized BER value of the system. The higher SNR value would give the lower BER value. In static video, the best SNR value when using a filter is 0.9 dB with a BER value of 0.000004810 while for dynamic video the SNR is 0.9 with a BER value of 0.00001030.]]>
<![CDATA[Sistem Pendistribusian Air Bersih Metode Prabayar Terkendali Mikrokontroler Berbasis IoT ]]>
<![CDATA[Efrizon]]>;
<![CDATA[Muhammad Irmansyah]]>;
<![CDATA[Anggara Nasution]]>;
<![CDATA[Era Madona]]>;
<![CDATA[Anggi Lifya Rani]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3485
<![CDATA[A number of problems sometimes often arise regarding the flow of clean water from Regional Drinking Water Companies (PDAMs) to customers, such as the flow of water stops suddenly or there is no water at all, so it is necessary to manufacture a prototype system for monitoring the distribution of clean water with a microcontroller-controlled prepaid method. IoT based. The distribution of PDAM water that is channeled to consumers can be monitored online through the Internet network. The objectives of this research are (a) to make a prototype (prototype) of a prepaid clean water distribution system controlled by a microcontroller based on IoT, (b) to program an Arduino IDE-assisted system, and (c) to measure system performance. The research method starts from making a prototype physical form of clean water distribution assisted by a microcontroller, programming the microcontroller and Wi-Fi module, and measuring system performance. The results of measuring system performance are indicated by an error in the ultrasonic sensor reading HC-SR04 that occurs when the water level is low and too high with a maximum measured water level of 95%. The error when measuring the waterflow sensor at the water level is lower than 49% which is influenced by the water speed from the low pressure pump when the water level is below that value. The accuracy level of the waterflow sensor is 96.96% which is based on the sensor measurement results which are compared to the measurement results with a measuring cup. The system can monitor data readings from the waterflow sensor by using the NodeMCU ESP8266 on a web server from Thinkspeak via the smartphone screen. Overall the tool can function well]]>
<![CDATA[Analisis Algoritma Shi-Tomasi Dalam Pengujian Citra Senyum Pada Wajah Manusia ]]>
<![CDATA[Ardi wijaya]]>;
<![CDATA[Puji Rahayu]]>;
<![CDATA[Rozali Toyib]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3496
<![CDATA[Problems in image processing to obtain the best smile are strongly influenced by the quality, background, position, and lighting, so it is very necessary to have an analysis by utilizing existing image processing algorithms to get a system that can make the best smile selection, then the Shi-Tomasi Algorithm is used. the algorithm that is commonly used to detect the corners of the smile region in facial images. The Shi-Tomasi angle calculation processes the image effectively from a target image in the edge detection ballistic test, then a corner point check is carried out on the estimation of translational parameters with a recreation test on the translational component to identify the cause of damage to the image, it is necessary to find the edge points to identify objects with remove noise in the image. The results of the test with the shi-Tomasi algorithm were used to detect a good smile from 20 samples of human facial images with each sample having 5 different smile images, with test data totaling 100 smile images, the success of the Shi-Tomasi Algorithm in detecting a good smile reached an accuracy value of 95% using the Confusion Matrix, Precision, Recall and Accuracy Methods.]]>
<![CDATA[Hate Speech Detection on Twitter in Indonesia with Feature Expansion Using GloVe ]]>
<![CDATA[Febiana Anistya]]>;
<![CDATA[Erwin Budi Setiawan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3521
<![CDATA[Twitter is one of the popular social media to channel opinions in the form of criticism and suggestions. Criticism could be a form of hate speech if the criticism implies attacking something (an individual, race, or group). With the limit of 280 characters in a tweet, there is often a vocabulary mismatch due to abbreviations which can be solved with word embedding. This study utilizes feature expansion to reduce vocabulary mismatches in hate speech on Twitter containing Indonesian language by using Global Vectors (GloVe). Feature selection related to the best model is carried out using the Logistic Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) algorithms. The results show that the Random Forest model with 5.000 features and a combination of TF-IDF and Tweet corpus built with GloVe produce the best accuracy rate between the other models with an average of 88,59% accuracy score, which is 1,25% higher than the predetermined Baseline. The number of features used is proven to improve the performance of the system.]]>
<![CDATA[Pengembangan Antarmuka Portal Universitas untuk Meningkatkan Pengalaman Pengguna ]]>
<![CDATA[Ikhwan Arief]]>;
<![CDATA[Asmuliardi Muluk]]>;
<![CDATA[Ahmad Syafruddin Indrapriyatna]]>;
<![CDATA[Mahira Falevy]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3532
<![CDATA[Technology has been growing rapidly to help humans living their everyday life. Human, as the user interacts through an interface called user interface (UI), and the experience that the users are having is called user experience (UX). UI and UX are inseparable as a good user interface will result in a better user experience. Portal Unand is a web-based app that has all academic information for students. An initial survey was conducted to find out student’s thoughts on Portal Unand. Students have complaints towards Portal Unand due to its unresponsiveness, old-fashioned design, important features weren’t highlighted, etc. Hence, it reduced user experience in using Portal Unand. In this study, the redesign was done by using the design thinking. The study started from empathizing with the users until testing the prototype to the users by conducting usability testing. Usability testing was conducted by using Maze and System Usability Scale (SUS). The score of usability testing was 84 which fell into the high range. The SUS score was 83.33 which fell into grade A and acceptable category. As the new prototype managed to fulfill users’ needs and met users’ expectations, the prototype was usable and ready to be developed.]]>
<![CDATA[Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods ]]>
<![CDATA[Shoffan Saifullah]]>;
<![CDATA[Andiko Putro Suryotomo]]>;
<![CDATA[Yuhefizar]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3540
<![CDATA[This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.]]>
<![CDATA[Deteksi Kesamaan Teks Jawaban pada Sistem Test Essay Online dengan Pendekatan Neural Network ]]>
<![CDATA[I Made Suwija Putra]]>;
<![CDATA[Putu Jhonarendra]]>;
<![CDATA[Ni Kadek Dwi Rusjayanthi]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3544
<![CDATA[E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.]]>
<![CDATA[Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor ]]>
<![CDATA[Nur Ghaniaviyanto Ramadhan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3547
<![CDATA[News is information disseminated by newspapers, radio, television, the internet, and other media. According to the survey results, there are many news titles from various topics spread on the internet. This of course makes newsreaders have difficulty when they want to find the desired news topic to read. These problems can be solved by grouping or so-called classification. The classification process is carried out of course by using a computerized process. This study aims to classify several news topics in Indonesian language using the KNN classification model and word2vec to convert words into vectors which aim to facilitate the classification process. The use of KNN in this study also determines the optimal K value to be used. In addition to using the classification model, this study also uses a word embedding-based model, namely word2vec. The results obtained using the word2vec and KNN models have an accuracy of 89.2% with a value of K=7. The word2vec and KNN models are also superior to the support vector machine, logistic regression, and random forest classification models.]]>
<![CDATA[Numerical Approach of Symmetric Traveling Salesman Problem Using Simulated Annealing ]]>
<![CDATA[I Iryanto]]>;
<![CDATA[Putu Harry Gunawan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3549
<![CDATA[The aim of this paper is to elaborate the performance of Simulated Annealing (SA) algorithm for solving traveling salesmen problems. In this paper, SA algorithm is modified by using the interaction between outer and inner loop of algorithm. This algorithm produces low standard deviation and fast computational time compared with benchmark algorithms from several research papers. Here SA uses a certain probability as indicator for finding the best and worse solution. Moreover, the strategy of SA as cooling to temperature ratio is still given. Thirteen benchmark cases and thirteen square grid symmetric TSP are used to see the performance of the SA algorithm. It is shown that the SA algorithm has promising results in finding the best solution of the benchmark cases and the squared grid TSP with relative error 0 - 7.06% and 0 – 3.31%, respectively. Further, the SA algorithm also has good performance compared with the well-known metaheuristic algorithms in references.]]>
