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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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Optimasi Business Process Improvement Berbantuan Metode FLASH dengan Integrasi API Trello Hilmi Aziz Bukhori Bukhori; Bayu Rahayudi; Widhy Hayuhardhika Nugraha Putra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The emergence of the COVID-19 case has a major impact on all sectors. At this critical time, customer satisfaction can be done by optimizing the use of resources by implementing BPI. The BPI method will conduct a review regarding the resources owned and will be adjusted to the current conditions. BPI is closely related to changes in project management. One of the optimal methods used in project management is FLASH, where the project duration will be in the form of a more flexible time interval. In this research, the system also uses Trello as a project management application. The purpose of this research is to design a scheme for calculating the duration of the project, as well as mapping project management from the data tasks that are owned using the FLASH method. System testing on the fuzzy system algorithm is implemented on the AOA network. Based on the calculation on the test object carried out, the probability of completing the project on time is 76%. This amount is obtained from the average delay factor for each task. With these values, scheduling using the FLASH method obtained the fastest duration is 19 days and the latest is 30 days.

Keamanan Jaringan dengan Cowrie Honeypot dan Snort Inline-Mode sebagai Intrusion Prevention System Tati Ernawati; Fikri Faiz Fadhlur Rachmat
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Computer network systems have been designing to share resources. Sharing resources process, data security, and confidentiality are main issues in anticipating misuse of the access to information by unauthorized parties. The solution to anticipating these problems is the availability of a security system capable of handling various intruders who threaten the system and protect network resources. This study builds and analyzes the performance of computer network security using cowrie honeypot and snort inline-mode as an Intrusion Prevention System (IPS). The development process goes through the stages of analysis, design, implementation, and monitoring. The content analysis method has been using to explore the problems and requirements of the system built. The security system was build by configuring the IP address and network system devices (server, remote admin, client attacker). The test has been carrying out on 3 test parameters (confidentiality, availability, and integrity), comparison testing method has been using to test the integrity parameters. The test results indicate that the system functionality test for user needs have fulfilled, the results of the confidentiality test (83.3%), availability (93.3%), and the integrity of the inline-mode snort show faster response time (0.069 seconds on average) and more CPU resource usage efficient (0.04% average) than the cowrie honeypot. IPS snort inline-mode overall integrity parameter testing is more recommended for used network security systems than cowrie honeypots.

Pendeteksian Septoria pada Tanaman Tomat dengan Metode Deep Learning berbasis Raspberry Pi Kahlil Muchtar; Chairuman; Yudha Nurdin; Afdhal Afdhal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.

Pabrikasi Unit Kontrol Berbasis Web pada Smarthome System untuk Pengoperasian Pintu Gerbang Lucky Hardian; Arief Goeritno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

A control unit based-on web in smarthome system has been designed and constructed. The control unit can be integrated into the smarthome system platform for gate operation via a smartphone. The research objectives, namely (i) integration of hardware and availability of raw files for applications, (ii) programming for control unit, and (iii) availability of control unit and implementation of validation tests. The result of integration is a form of successful hardware handshaking. The programming result is a form of successful software handshaking, including (i) a flowchart-based algorithm, while the syntax structure is based on the Arduino IDE, (ii) the comfiling and uploading stages of the syntax structure to the Arduino UNO R3 module, including the process online control based on RemoteXY version 4.5.1 via a web browser, and (iii) uploading files from personal computers and smartphones based on Android. The availability of the control unit physical building for the validation test process is the achievement of handshaking in hardware and software, in the form of performance measurement of the control unit with 3 (three) kinds of observations, namely (i) “open”, (ii) “close”, or (iii) lock/unlock state. In general, it is concluded that the web-based control unit on the smarthome system for gate operation can function and perform according to plan.

Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi Gde Agung Brahmana Suryanegara; Adiwijaya; Mahendra Dwifebri Purbolaksono
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Diabetes is a disease caused by high blood sugar in the body or beyond normal limits. Diabetics in Indonesia have experienced a significant increase, Basic Health Research states that diabetics in Indonesia were 6.9% to 8.5% increased from 2013 to 2018 with an estimated number of sufferers more than 16 million people. Therefore, it is necessary to have a technology that can detect diabetes with good performance, accurate level of analysis, so that diabetes can be treated early to reduce the number of sufferers, disabilities, and deaths. The different scale values for each attribute in Gula Karya Medika’s data can complicate the classification process, for this reason the researcher uses two data normalization methods, namely min-max normalization, z-score normalization, and a method without data normalization with Random Forest (RF) as a classification method. Random Forest (RF) as a classification method has been tested in several previous studies. Moreover, this method is able to produce good performance with high accuracy. Based on the research results, the best accuracy is model 1 (Min-max normalization-RF) of 95.45%, followed by model 2 (Z-score normalization-RF) of 95%, and model 3 (without data normalization-RF) of 92%. From these results, it can be concluded that model 1 (Min-max normalization-RF) is better than the other two data normalization models and is able to increase the performance of classification Random Forest by 95.45%.

Pengenalan Aktivitas Manusia pada Area Tambak Udang dengan Convolutional Neural Network M Arfan; Ahmad Nurjalal; Maman Somantri; Sudjadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Thievery is a problem that can harm theft victims. Thievery usually occurs at night when there is no supervision of goods in a location. To avoid thievery and monitor conditions in a location, CCTV (Closed-Circuit Television) cameras can be used. However, the function of CCTV camera systems is only a passive monitoring systems. In this paper, a human activity recognition is designed using CCTV cameras to produce a security system. Inputs on the recognition process are videos obtained from CCTV cameras installed in the shrimp pond. Human activity recognition that is used in this study is Convolutional Neural Network. Before the human activity recognition was carried out, the program first detected humans with the YOLO (You Only Look Once) algorithm and tracking it with the SORT (Simple Online and Realtime Tracking) algorithm. The results obtained from the human activity recognition is class labels on human objects that are tracked.

Implementasi Firebase Realtime Database pada Aplikasi FeedbackMe sebagai Penghubung Guru dan Orang Tua Khairun Nisa Meiah Ngafidin; Artika Arista; Rona Nisa Sofia Amriza
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The necessity of learning assistance for elementary student is to ensure that students can absorb the learning well. In order to keep track of the student's progress, the teacher needs to know how and what the student has done while at home. The FeedbackMe application was created to become a liaison between teachers and parents during distance learning. Firebase Realtime Database is implemented to support messages to be delivered quickly. The purpose of this study is to implement the Firebase Realtime Database into the FeedbackMe application to support remote student learning. The system development method used is the Waterfall method which is a systematic and sequential method. The results of this study indicate that all the features in the application and also the application of Firebase can run properly and correctly. Meanwhile, testing of respondents regarding user satisfaction results in the amount of 89.28% from teacher, and respondents from parents got 89.73% satisfaction.

Analisis Hybrid DSS untuk Menentukan Lokasi Wisata Terbaik Annisak Izzaty Jamhur Nisa; Radius Prawiro; Novi Trisna
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Tourism is an activity carried out by humans to a place alone or together to have fun to get rid of the burden of thoughts that were previously acquired. The Mandeh area is a leading tourist area in West Sumatra which has 10 alternative tourist attractions. With the many tourist locations in the area, tourists are confused about what places to visit in the Mande area. In this study, a combined analysis or Hybrid Decision Support System (DSS) was carried out using a combination of the Analytical Hierarchy Process (AHP) method with the Simple Additive Weighting (SAW) method. The purpose of this research is to be able to combine AHP and SAW methods in one DSS analysis and then be able to recommend the decision results to tourists in the form of the best tourist locations in the Mandeh area. With the recommendation, it can increase the interest of tourists to come and increase the opinions of tourist location owners and the surrounding community. The result of this research is to obtain a recommendation for the best tourist location decision in the Mande area of West Sumatra, namely the location of Manjunto Beach with the highest value of 0.895.

Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning Novrindah Alvi Hasanah; Nanik Suciati; Diana Purwitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June.

Forecasting Cases of Dengue Hemorrhagic Fever Using the Backpropagation, Gaussians and Support-Vector Machine Methods I Made Yudha Arya Dala; I Ketut Gede Darma Putra; Putu Wira Buana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Dengue disease has been known to the people of Indonesia since 1779. The Aedes mosquito has two types, namely Aedes aegypti and Aedes albopictus. Aedes aegypti is a mosquito that carries the dengue virus. The dengue fever cases in Bali province tend to increase from year to year, especially when approaching the rainy season. The government's preventive action is needed to tackle the spread of the dengue virus and casualties. Data mining attempts to extract known knowledge or use historical data to find regularity patterns and relationships in a set of data. In this study, data mining predicts the number of dengue cases in Bali's province. The prediction uses several database variables to predict future variables' values, which are not currently known. The process of estimating predictive values based on patterns in a data set. This forecasting aims to assist the government in predicting dengue fever cases in the coming period to prepare appropriate prevention efforts. Forecasting dengue fever cases are carried out using three methods: backpropagation, gaussians, and support-vector machine. The amount of data used was 528 sample data, from 2008 to 2018. The results obtained are that the backpropagation method is better at predicting dengue fever cases with a MAPE error rate of 0.025. Simultaneously, the gaussian method has a MAPE error rate of 0.035, and support-vector machine has a MAPE error rate of 0.060.

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Editorial Team

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Contact Name Yuhefizar

Contact Email jurnal.resti@gmail.com

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Journal Mail Official ephi.lintau@gmail.com

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

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Contact Name
Yuhefizar

Contact Email
jurnal.resti@gmail.com

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+628126777956

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ephi.lintau@gmail.com

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

Location
,

INDONESIA