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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

RESTI
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Published by Ikatan Ahli Informatika Indonesia

ISSN : 25800760 EISSN : 25800760 DOI : https://doi.org/10.29207/resti.v2i3.606

Core Subject : Science, Engineering,

Computer Science & IT Engineering

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

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 1,046 Documents

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Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis Aprillian Kartino; M. Khairul Anam; Rahmaddeni; Junadhi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Covid-19 is a disease of the virus that is shaking the world and has been designated by WHO as a pandemic. This case of Covid-19 can be a place of dissemination of disinformation that can be utilized by some parties. The dissemination of information in this day and age has turned to the internet, namely social media, Twitter is one of the social media that is often used by Indonesians and the data can be analyzed. This study uses the social network analysis method, conducted to be able to find nodes that affect the ongoing interaction in the interaction network of information dissemination related to Covid-19 in Indonesia and see if the node is directly proportional to the value of its popularity. As well as to know in identifying the source of Covid-19 information, whether dominated by competent Twitter accounts in their fields. The data examined 19,939 nodes and 12,304 edges were taken from data provided by the web academic.droneemprit.id on the project "Analisis Opini Persebaran Virus Corona di Media Sosial", using the period of December 2019 to December 2020 on social media Twitter. The results showed that the @do_ra_dong account is an influential actor with the highest degree centrality of 860 and the @detikcom account is the actor with the highest popularity value of follower rank of 0.994741605. Thus actors who have a high degree of centrality value do not necessarily have a high follower rank value anyway. The study ignores if there are buzzer accounts on Twitter.

Classifying Quranic Verse Topics using Word Centrality Measure Ferdian Yulianto; Kemas Muslim Lhaksmana; Danang Triantoro Murdiansyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Muslims believe that, as the speech of Allah, The Quran is a miracle that has specialties in itself. Some of the specialties that have studied are the regularities in the number of letters, words, vocabularies, etc. In the past, the early Islamic scholars identify these regularities manually, i.e. by counting the occurrence of each vocabulary by hand. This research tackles this problem by utilizing centrality in quranic verse topic classification. The goal of this research is to analyze the effect of The Quran word centrality measure on the topic classification of The Quran verses. To achieve this objective, the method of this research is constructing the Quran word graph, then the score of centralities included as one of the features in the verse topic classification. The effect of centrality is observed along with support vector machine (SVM) and naïve Bayes classifiers by performing two scenarios (with stopword and without stopword removal). The result shows that according to the centrality measure the word “الله” (Allah) is the most central in The Quran. The performance evaluation of the classification models shows that the use of centrality improves the hamming loss score from 0.43 to 0.21 on naïve Bayes classifier with stopword removal. Finally, both of classification method has a better performance in word graph that use stopword removal.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB Muhammad Muttabi Hudaya; Siti Saadah; Hendy Irawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO Tomy Abuzairi; Nurdina Widanti; Arie Kusumaningrum; Yeni Rustina
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Pain in a baby is difficult to detect is because the method for detecting pain is self-reporting even though babies themselves still cannot describe the pain verbally, then by observing changes in behavior in the form of facial expressions. Statistically, it is also recorded that about 80% of the world's population pays less attention to pain assessment, especially for children, even though this pain gives children a bad experience so that it can interfere with pain responses in the future or psychological trauma. Based on these problems, a prototype system was made using the NVIDIA Jetson Nano Developer kit to help detect pain, especially in infants 0-12 months by using the Convolutional Neural Network (CNN) model with the PyTorch framework and the You Only Look Once (YOLO) algorithm with three detection classification is sad, neutral and sick. From the results of the study, it was found that the YOLO algorithm was able to detect the three classifications with a sad mAP value of 77.8%, neutral 76.7%, in pain 68.9%. With a precision value of 71.4%, recall 62.5% and f1-score 66.6%. The average value of Confidence is 53.57%.

Simulator Proses Pengisian dan Pemasangan Tutup Botol Terkendali PLC Berbantuan Miniatur Konveyor Anang Dwi Purnomo; Arief Goeritno; Danang Adi Nugroho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

A miniature conveyor machine assisted by embedded systems and controlled by a programmable logic controller (PLC) has been fabricated with the operating mechanism is based on the detection of two different colors. The objectives of this research are to obtain a miniature machine is controlled by the PLC system, create a ladder diagram-based program structure, and measure the performance of the embedded system. Obtaining the machine is carried out through assembly of the conveyor frame, installation of all devices, and integrated wiring. The programming for the Omron PLC system is based on providing the CX-Programmer 64 bits, establishing algorithms and compiling ladder diagrams, and compiling and uploading processes. The performance measurement includes synchronization conditions between the simulator and the control system, observations of the readings of installed sensors for activating all devices on the output side, and observing the measurement of the filling process time and the installation of bottle caps assisted by the pneumatic system. The results of the performance during the process of filling and installing the lid obtained a success rate of 75%, based on four trials, three successes, and one failure. The general conclusion is that the embedded system that has been built can be used as a simulator for the mechanism of filling liquid into bottles and installing bottle caps, and it is as an implementation of instrumentation and automation processes.

Realtime Object Detection Masa Siap Panen Tanaman Sayuran Berbasis Mobile Android Dengan Deep Learning Andri Heru Saputra; Dhomas Hatta Fudholi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Determining the harvesting period can be done visually, physically, computationally, and chemically. Since the harvesting process is crucial, late harvesting will affect post-harvest and production quality. Leafy vegetables have a relatively short ready-to-harvest period. Visual recognition of the harvesting period combined with image processing can recognize harvesting vegetables' visual characteristics. This study aims to build a deep learning-based mobile model to detect real-time vegetable plant objects such as bok choy, spinach, kale, and curly kale to determine whether these vegetables are ready for harvest. Mobile-based architecture is chosen due to latency, privacy, connectivity, and power consumption reason since there is no round-trip communication to the server. In this research, we use MobileNetV3 as the base architecture. To find the best model, we experiment using different image input size. We have obtained a maximum MAP score of 0. 705510 using a 36,000 image dataset. Furthermore, after implementing the model into the Android mobile application, we analyze the best practice in using the application to capture distance. In real-time detection usage, the detection can be done with an ideal distance of 5 cm and 10 cm.

Prototype Sistem Pakan Ikan dan Pemantauan PH Berbasis Android dengan Metode PLC Maria Rosaria Oktaviani; Rizky Pradana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Abstract During the pandemic of covid-19, betta fish cultivation is one of the income alternatives. It makes the cultivation of betta fish is increasing. However, not all cultivators can successfully cultivate betta fish. Water quality and fish feed must be maintained in order for betta fish can grow perfectly and attractive. The problems that often encountered to the betta fish cultivator is about feeding and monitoring pH levels, which is still manual. This is can failure in betta fish cultivation of the cultivators are not disciplined. To minimize it, then will be made “Prototype Fish Feeding System and PH Monitoring Based Android”. This research is used PLC method: (a) Requirements Analysis, (b) Design, and (c) Implementation. Divided into 2 functions: Automatic Betta Fish Feeding System Based Android Function using Node MCU ESP8266 and Monitoring PH Levels Function using Arduino Uno. The result from this research is the user can control the open and close feed on the servo through an android application, the system can provide a distance of the feed supply to a user in real-time by notifications, the LCD always display pH value, the buzzer provides a sound when the water quality has reached the acid or alkaline index.

Optimasi Performansi Pengendalian Robot Swarm menggunakan Logika Fuzzy Tipe 2-Particle Swarm Optimazation Gita Fadila Fitriana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Robot control is currently very helpful for human work to be more effective and efficient both in completion time and in mitigating the risk of work accidents that may occur. This study determines the direction of the robot so that it does not collide with each other and reach the target. Controlling the swarm robot with the leader-follower approach uses Fuzzy Logic Type 2-Particle Swarm Optimization (PSO) to optimise the performance of the swarm robot. The Fuzzy Logic Method Type 2 measures the direction decisions of the leader robot and follower robot using a rule base of 8 rules; the leader-follower robot is given a target. Achieving targets using PSO, the PSO process looks for potential solutions with quality references to reach the target as the optimal solution. The leader-follower modelling has been modelled using kinematic equations and controlling the movement of the robot's trajectory in the form of a simulation that has been carried out. The measurement results based on robot data in an open environment are 110 data, and a square environment is 1342. The measurement results based on robot time in a four-obstacle environment have the fastest time of 10.83 seconds and the longest time environment in an oval environment of 134.9 seconds. The measurement results are based on resources in a free environment of 10.6 kb and a square environment of 49.1 kb. Fuzzy Logic Type 2-PSO has a higher time indicating a stable speed result and judging from the trajectory in avoiding obstacles, and the leader-follower robot has a faster response.

Semantic Approach for Big Five Personality Prediction on Twitter Ghina Dwi Salsabila; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Personality provides a deep insight of someone and has an important part in someone’s job performance. Predicting personality through social media has been studied on several research. The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personality on Twitter users and increase the performance of the personality prediction system. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the personality prediction system.

Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi Aviv Yuniar Rahman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 4 (2021): Agustus 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficulty distinguishing the types of lovebirds. This research is needed to improve previous study performance in classifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case, also to reduce the stage of feature extraction to speed up the computational process. Based on available comparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000, and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841, and an accuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 can improve performance and produce the best accuracy.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Yuhefizar

Contact Email jurnal.resti@gmail.com

Phone +628126777956

Journal Mail Official ephi.lintau@gmail.com

Editorial Address Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

Location , INDONESIA

Abstract

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Contact Name
Yuhefizar

Contact Email
jurnal.resti@gmail.com

Phone
+628126777956

Journal Mail Official
ephi.lintau@gmail.com

Editorial Address
Politeknik Negeri Padang, Kampus Limau Manis, Padang, Indonesia.

Location
,

INDONESIA