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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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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Clustering Analysis and Mapping of ISPA Disease Spread Patterns in Bireuen District Mutammimul Ula; Tsania Asha Fadilah Daulay; Richki Hardi; Sujacka Retno; Angga Pratama; Ilham Sahputra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4936

Abstract

ISPA disease can be detected by analyzing the regional distribution map of the disease. Early detection of ARI is very important for effective prevention. The study conducted in Bireuen Regency used data from 2019 to 2021, sourced from dr. Fauziah Bireuen Hospital, revealed that there was an increase in ARI cases from an average of 13.18 to 59.24 per year. The aims of the study were to identify ARI clusters, analyze disease patterns using Spatial Pattern Analysis and Flexible Shaped Spatial Scanning Statistics. The methodology involves collecting patient data for each ARI case and processing it using DBSCAN to obtain cluster points on the map. Spatial Pattern Analysis is used to analyze these clusters and identify hotspot points on the map. The analysis resulted in four clusters: Cluster 1 (6 subdistrict), Cluster 2 (4 subdistrict), Cluster 3 (1 subdistrict), and Cluster 4 (6 subdistrict). The study identified 6 hotspots in 2019, 5 hotspots in 2020, and 6 hotspots in 2021. Each ARI disease clustering map shows the distribution of ARI cases and identifies areas prone to the disease. These findings provide valuable insights for targeted interventions and preventive actions in identified high-risk areas of ISPA.
Modified Q-Learning Algorithm for Mobile Robot Real-Time Path Planning using Reduced States Hidayat; Buono, Agus; Priandana, Karlisa; Wahjuni, Sri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4949

Abstract

Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method requires extensive computational cost especially when there are considerable grids to be computed. This study modified the original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original Q-Learning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method, that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However, the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate parameter value of 0.2 when the discount factor value is 0.9.
Secure Cybersecurity Information Sharing for Sectoral Organizations Using Ethereum Blockchain and IPFS Tony Haryanto; Kalamullah Ramli
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4956

Abstract

The COVID-19 pandemic has resulted in increased cross-sector cyber-attacks. Passive and reactive cybersecurity techniques relying solely on technology are insufficient to combat sophisticated attacks, necessitating proactive and collaborative security measures to minimize attacks. Cybersecurity Information Sharing (CIS) enhances security via proactive and collaborative cybersecurity information exchange, but its implementation via cloud services faces threats from man in the middle (MITM) and distributed denial of service (DDoS) attacks, as well as a vulnerability in cloud storage involving centralized data control. These threats and vulnerabilities result in a lack of user confidence in the confidentiality, integrity, and availability of information. This paper proposes Secure Cybersecurity Information Sharing (SCIS) to secure Cybersecurity Information in sectoral organizations using the private interplanetary file system (IPFS) network and the private Ethereum Blockchain network. Private Ethereum Blockchain enables secure and transparent transaction logging, while Private IPFS network provides decentralized storage, addressing vulnerabilities in centralized storage systems. The outcomes of the tests reveal that the suggested SCIS system offers cybersecurity information availability, confidentiality, and integrity. SCIS provides a high level of security to protect cybersecurity information exchanged between sectoral organizations using the Private Ethereum Blockchain network and the Private IPFS network so that organizations can safely share and utilize information.
Use of Plant Health Level Based on Random Forest Algorithm for Agricultural Drone Target Points Try Kusuma Wardana; Yandra Arkeman; Karlisa Priandana; Farohaji Kurniawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4959

Abstract

Chemical residues from the use of pesticides in agriculture can impact human health through environmental and food pollution. To lessen the negative effects of excessive pesticide use, pesticides must be applied to plants by dose. The dose of pesticide application can be based on a plant health level, which is the result of drone Normalized Difference Vegetation Index (NDVI) image analysis. Drones can also be used for spraying pesticides. Analysis of plant health levels was carried out using the Random Forest (RF) algorithm. The results of the classification plant health levels will be used to design spray drone flight routes. The objective of this research is to classify plant health levels of rice based on NDVI imagery using the RF algorithm and to compile a database of spray drone target points. The results of this study indicate that the classification of plant health levels using the RF algorithm produces an accuracy value of 98% and a Kappa value of 0.96. As a result, the model developed and the algorithm employed is quite effective at classifying the level of plant health. Furthermore, spray drone target points based on plant health levels can be generated. Optimally the spray distance between rows is 2 m.
Triangular Fuzzy Numbers-Based MADM for Selecting Pregnant Mothers at Risk of Stunting Wiwien Hadikurniawati; Kristoko Dwi Hartomo; Irwan Sembiring; Hindriyanto Dwi Purnomo; Ade Iriani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4966

Abstract

Stunting is caused by a lack of proper nutrition before and after birth. This research paper identifies and measures the risk of stunting during pregnancy and make recommendations for ranking pregnant women at risk. These aims to provide appropriate treatment and action to reduce mothers giving birth to children at risk of stunting. To make the optimal choice, the selection procedure for pregnant women at risk of giving birth to stunted children considers a variety of factors, including maternal age, maternal nutrition, arms circumference, hemoglobin, parity, birth interval, height, baby weight, and body mass index (BMI). Decision-maker’s expectation to reduce uncertainty and imprecision are represented linguistically by triangular fuzzy numbers. The triangular fuzzy numbers arithmetic approach is used to determine the selection process output. The ranking is determined from the alternative with the most parameter values to the alternative with the fewest parameters. Based on the results of the calculation, it was determined that PM (Pregnant Mother) had the highest score and was ranked first. That pregnant mother was declared as pregnant mother who had the lowest risk of giving birth to stunted baby
Game Design for Mobile App-Based IoT Introduction Education in STEM Learning Indra Puja Laksana; Evi Dwi Wahyuni; Christian Sri Kusuma Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.5007

Abstract

STEM education has received considerable attention in recent years. However, developing valid and reliable assessments in interdisciplinary learning in STEM has been a challenge. Therefore, many students ranging from junior high school to university students are only familiar with the Internet of Things (IoT) from social media but do not know its concept and function in STEM learning. This is also supported by the absence of educational applications about IoT. This research aims to introduce IoT by using mobile applications. This research refers to the multimedia development method according. The data collection method in this study was carried out by means of observation and interviews randomly to high school students to university students. This data collection was carried out using the experimental method of application testing to analyze user needs from several aspects such as features, images, and fonts. This research is also supported by the existence of literature studies derived from several journals. The results show that the functions in the application can operate as expected. Based on the survey results of the application, 75.37% of respondents rated this application in the very good category and gave positive responses so that this application could be well received by users
Real-Time Detection of Face Mask Using Convolutional Neural Network Imam Husni Al Amin; Deva Ega Marinda; Edy Winarno; Dewi Handayani U.N; Veronica Lusiana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.5036

Abstract

Masks are a simple barrier that can help us prevent transmission and spread of disease from other people who enter the body, avoid exposure to air pollution, and protect the face from the adverse effects of sunlight. However, many people are still ignorant about the importance of wearing masks for health. This study aims to detect whether or not to use masks in real-time by proposing a deep learning model to reduce illness and death caused by air pollution. The convolutional Neural Network (CNN) method was used in this research to detect facial recognition using a mask and not using a mask. The public dataset used in this research consists of 1300 images with 650 data using masks and 650 data without masks. The results of this study show that the proposed CNN method works well in detecting masked and non-masked faces in real time. The proposed method obtains an accuracy value of 97.5% at epoch 50. Previous research on mask detection using the Eigenface method yielded an accuracy of 88.89%, and another study using the Viola-Jones method yielded an accuracy of 95.5%. It can be concluded that this research can increase the accuracy value of previous studies. So, this research is feasible to be applied to the detection of mask use in real time.
Requirement Elicitation Modeling Using Knowledge Acquisition in Automated Specification Method Aminudin Aminudin; Hafizh Salsabila Pradana; Ilyas Nuryasin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4464

Abstract

Errors often occur during the requirements elicitation stage, causing failure of the software development process as a whole, so that the built system cannot be used optimally; these data are obtained from survey data from several large companies involved in technology development. To overcome this problem, this study tries to apply the elicitation requirements using the KAOS method in the case study of the SMM reseller ordering system to obtain system requirements that are in accordance with the goals and objectives of each existing stakeholder. Based on the elicitation of system requirements, functional requirements are generated that include automatic orders, automatic payments, manage product sales, manage orders, manage payment methods, manage problem orders, manage customer data, manage company information, automatic email notifications, and sales statistics information. The results of this study are a table of functional requirements that have been declared valid and in accordance with the goals and requirements of each stakeholder after evaluating and validating the results for each stakeholder involved.
Implementation of Enhanced Spray Routing Protocol for VDTN On Surabaya Smart City Scenario Agussalim; Agung Brastama Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4494

Abstract

The application of smart-city, which promises better city management in helping to improve people's quality of life, is still inhibited due to the high cost of infrastructure investment. In several Smart Cities, it takes at least $ 30 - 40 billion to convert a conventional town into a smart city, including for data collection infrastructure. Alternatively, low-power wide-area networks (LPWANs) could be considered, but they need more bandwidth to serve data transmission in a smart city. Vehicle Delay Tolerant Network (VDTN) is one part of DTN that employs vehicles as a communication infrastructure that allows communication in challenging conditions and could make it an alternative network for Data Collection in a Smart City. This paper proposes a Surabaya Smart City scenario with VDTN as data collection. The scenario consists of 40 wireless sensors and 50 to 200 vehicles (car and bus) with five Road Side Units that forward data from the sensor to the monitoring server. Furthermore, to increase the VDTN performance, we improve our proposed routing protocol, Spray and Hop Distance (SNHD), with two sprays method (Adaptive and Simple) and data collection support from multiple sources and destinations. The evaluation was carried out using a simulation-based comparison with an increase in the number of vehicles to determine the impact of vehicle density on data collection performance in terms of delivery probability, latency average, and overhead ratio. Based on the simulation results, the simple spray method in SNHD and A-SNHD outperformed the well-known VDTN routing protocol, i.e., Epidemic and Spray and Wait. Furthermore, when the number of cars increases from 50 to 200, the performance of VDTN does not increase significantly as the density of the network increases. It means that VDTN only requires a small number of vehicles to be used as a low-cost alternative network for smart cities.
Solution to Scalability and Sparsity Problems in Collaborative Filtering using K-Means Clustering and Weight Point Rank (WP-Rank Mohamad Fahmi Hafidz; Sri Lestari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4543

Abstract

Collaborative filtering is a method that can be used in recommendation systems. Collaborative Filtering works by analyzing rating data patterns. It is also used to make predictions of interest to users. This process begins with collecting data and analyzing large amounts of information on the behavior, activities, and tendencies of users. The results of the analysis are used to predict what users like based on similarities with other users. In addition, collaborative filtering is able to produce recommendations of better quality than recommendation systems based on content and demographics. However, collaborative filtering still faces scalability and sparsity problems. It are because the data is always evolving so that it becomes big data, besides that there are many data with incomplete conditions or many vacancies are found. Therefore, the purpose of this study proposed a clustering and ranking-based approach. The cluster algorithm used K-Means. Meanwhile, the WP-Rank method was used for ranking based. The experimental results showed that the running time was faster with an average execution time of 0.15 seconds by clustering. Furthermore, it was able to improve the quality of the recommendations, as indicated by an increase in the value of NDCG at k=22, the average value of NDCG was 0.82, so the recommendations produced were higher quality and more appropriate to the interests of the users.

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