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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 25 Documents

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Search results for , issue "Vol 5 No 2 (2021): April 2021" : 25 Documents clear

Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia Primandani Arsi; Rizki Wahyudi; Retno Waluyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

President Joko Widodo decided to move the capital city of the country outside Java. The relocation of the capital city is contained in the 2020-2024 National Medium-Term Development Plan. Community response to this has been mixed through national television and social media, especially Twitter. The tendency of Twitter users to respond to the government discourse can be seen with sentiment analysis. Sentiment analysis is one of the areas of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions. In this study, the Feature Selection PSO algorithm in the classification of the SVM model is proposed to improve the resulting accuracy in the sentiment analysis of moving capital cities. Experiments on the data of 1,319 tweets (457 positive sentiments and 862 negative sentiments) indicate an increase in accuracy by 2.09% from 79.06% to 81.15%, with the classification category is “Good Classification”.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah Rima Dias Ramadhani; Afandi Nur Aziz Thohari; Condro Kartiko; Apri Junaidi; Tri Ginanjar Laksana; Novanda Alim Setya Nugraha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

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.

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.

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.

Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik Faried Zamachsari; Niken Puspitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The rapid development of information technology coupled with an increase in public activity in electronic financial transactions has provided convenience but has been accompanied by the occurrence of fraudulent financial transactions. The purpose of this research is how to find the best model to be implemented in the banking payment system in detecting fraudulent electronic financial transactions so as to prevent losses for customers and banks. Fraud detection uses machine learning with ensemble and deep learning with SMOTE. Financial transaction data is taken from bank payment simulations built with the concept of Multi Agent-Based Simulation (MABS) by banks in Spain. To build the best model, not only pay attention to the accuracy value, but the value of precision is a value that needs attention. A precision score is very important for fraud prevention. Fraud detection gets the best results without the SMOTE process by using deep learning with an accuracy score of 99.602% and precision score of 90.574%. By adding SMOTE, it will increase the accuracy score and precision score with the best model produced in the Extra Trees Classification with an accuracy score of 99.835% and precision score of 99.786%.

Klasifikasi Sentimen pada Twitter Terhadap WHO Terkait Covid-19 Menggunakan SVM, N-Gram, PSO Noor Hafidz; Dewi Yanti Liliana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

On March 2020 World Health Organization (WHO) has declared Covid-19 as global pandemic. As special agency of United Nation who responsible for international public healthy, WHO has done various actions to reduce this pandemic spreading rate. However, the handling of Covid-19 by WHO is not free from a number of controversies that gave rise to criticism and public opinion on the Twitter platform. In this research, a machine learning based classifier model has been made to determine the opinion or sentiment of the tweet. The dataset used is a set of tweets containing the phrase WHO and Covid-19 in period of March 1st until May 6th 2020 consisting of 4000 tweets with positive sentiments and 4000 tweets with negative sentiments. The proposed classifier model combined Support Vector Machine (SVM), N-Gram and Particle Swarm Optimization (PSO). The classifier model performance is evaluated using the value of Accuracy, Precision, Recall, and Area Under ROC Curve (AUC). Based on experiments conducted, the combination of SVM, N-gram (bigram), and PSO produced a pretty good performance in classifying tweet sentiment with values of Accuracy 0,755, Precision 0,719, Recall 0,837, and AUC 0,844.

Evaluasi Parameter RAW Berdasarkan Multirate Pada IEEE 802.11ah: Simulasi Kinerja Optimum Jaringan IoT Haris Mustaqin; Teuku Yuliar Arif; Rizal Munadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

IEEE 802.11ah WLAN is a technology standard for IoT networks because it can provide a higher transmission range and data rate than WPAN and LPWAN. To manage channel access up to 8191 at the MAC layer IEEE 802.11ah a Restricted Access Window scheme was introduced. Generally, evaluation and optimization of RAW parameters are only based on constant data rates without taking into account the mutirate support for PHY AP and STA IoT IEEE 802.11ah. This study uses an open source-based NS-3 network simulator. Simulation analysis is run by calculating the value of throughput, delay, packet loss, and energy consumption of each node. Based on testing the effect of the number of slots on throughput, it shows that the resulting throughput values fluctuate with stable dominance, depending on the number of slots used. The effect of the number of slots on packet loss shows that the packet loss value is low for each slot because more packets can be accommodated in the RAW slot queue. The effect of the number of slots on energy consumption decreases at some data rates and some lower energy consumption values, thereby saving energy consumption.

Prediksi Jumlah Produksi Akibat Penyebaran Covid-19 Menggunakan Metode Fuzzy Takagi-Sugeno Khofifah Putriyani; Tenia Wahyuningrum; Yogo Dwi Prasetyo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Global Bakery is a food company engaged in bread production that is having difficulty determining how much bread will be produced in the event of a pandemic. This study aims to help predict the amount of bread that will be produced during a pandemic. With the benefit of making it easier for companies to determine the amount of bread to be produced. Data obtained from Global Bakery and the official website of Covid-19 Bekasi Regency from March 20, 2020 to April 20, 2020. The author uses the Fuzzy Takagi-Sugeno method to predict the amount of bread that must be produced by Global Bakery during a pandemic with the following stages: fuzzification, rule formation, calculating ɑ-predicate and zi value, then calculating defuzification. Then an evaluation is carried out using the Mean Absolute Percentage Error (MAPE). This study uses Matlab's GUI tools in implementing the Predictor program. The Fuzzy Takagi-Sugeno method is able to predict the amount of bread production at Global Bakery with optimal results, where if the sales are 180 pieces, the remaining sales are 289, and the number of positive cases of Covid-19 is 6 people with the actual production number of 469 pieces, then The prediction results obtained were 347 units. The results of the calculations that have been done obtained the results of accuracy with a good category, namely with a MAPE value of 18.6%.

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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

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