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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

48 49 50 51 52

Telegram Bot as a Data Collection Tool for Progress Reports in Area Mapping Progress Monitoring System Apriela Trirahma
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

In the Area Mapping Project (Preparation for the 2020 Population Census), there is monitoring and collecting data process on the progress of activities in the field. There are weaknesses in data collection on the progress of activities in the field; The manual recapitulation of progress reporting makes the progress data not displayed in real-time, the SMS Gateway is often interrupted, and progress data collection through the monitoring website is less effective if reported directly by field officers. Telegram Bot is used as a data collection tool for field progress reports on Area Mapping activities to overcome these weaknesses. This study aims to prove that Telegram Bot can be used as a real-time data collection tool, has good performance, and is acceptable to users. Telegram Bot is integrated with Monitoring Website into one system and database in this research. This system uses PHP, Yii2, and MySQL, and communication between the web server and Telegram Server uses the webhook method. Based on the Black Box test results, all functions in this system run as expected. The average bot response time was 7.72 seconds for images and 2.25 seconds for text data in the performance test. In the usability test, Telegram Bot obtained a SUS Score of 66 and an NPS of 12.195. These results show that Telegram Bot can be used as a real-time data collection tool, has good performance, and is well accepted by users.

Monitoring dan Kendali Tegangan Jaringan Listrik Fase-tiga melalui Smartphone Arief Goeritno; Febby Hendryan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This paper describes the creation of a minimum system for monitoring and controlling the voltage on a three-phase electrical network. Making a minimum system based on the result of previous research that has been implemented in the forms of device assembly, programming, and performance measurement. The research objectives are (i) assembling the hardware and programming based on Arduino software version 1.8.10 and (ii) measuring the minimum system performance. The research method for achieving the objective of assembling a minimum system is carried out through integrated wiring as an effort to get the hardware achievement, while for programming is an effort to get the software achievement. The implementation of the research method for measuring the performance as an effort to get the achievements of hardware and software is carried out by giving the orders to activate the paths of each phase. The result of the assembly is the integration of the Arduino UNO R3 module, Ethernet Shield type of W5100, MikroTik RouterBoard, relay modules, and Android smartphone, while the results of the programming are compiling and uploading the syntax to the Arduino module and making applications in the .apk format for a smartphone. Performance measurements are carried out by activating conditions for the three phases of phase-R, phase-S, and/or phase-T. The conclusion can be obtained, that the manufacture of a minimum system is appropriate for the fulfillment with respect to the presence of an electronic device for monitoring and controlling the voltage on a three-phase electrical network.

Studi Literatur Human Activity Recognition (HAR) Menggunakan Sensor Inersia Humaira Nur Pradani; Faizal Mahananto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Human activity recognition (HAR) is one of the topics that is being widely researched because of its diverse implementation in various fields such as health, construction, and UI / UX. As MEMS (Micro Electro Mechanical Systems) evolves, HAR data acquisition can be done more easily and efficiently using inertial sensors. Inertial sensor data processing for HAR requires a series of processes and a variety of techniques. This literature study aims to summarize the various approaches that have been used in existing research in building the HAR model. Published articles are collected from ScienceDirect, IEEE Xplore, and MDPI over the past five years (2017-2021). From the 38 studies identified, information extracted are the overview of the areas of HAR implementation, data acquisition, public datasets, pre-process methods, feature extraction approaches, feature selection methods, classification models, training scenarios, model performance, and research challenges in this topic. The analysis showed that there is still room to improve the performance of the HAR model. Therefore, future research on the topic of HAR using inertial sensors can focus on extracting and selecting more optimal features, considering the robustness level of the model, increasing the complexity of classified activities, and balancing accuracy with computation time.

Garbage Classification Using Ensemble DenseNet169 Ulfah Nur Oktaviana; Yufis Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Garbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to 3 million tons of waste. Waste management efforts have been carried out by the government, including by making waste sorting regulations. This sorting is known as 3R (reduce, reuse, recycle), but most people do not sort their waste properly. In this study, a model was developed that can sort out 6 types of waste including: cardboard, glass, metal, paper, plastic, trash. The model was built using the transfer learning method with a pretrained model DenseNet169. Where the optimal results are shown for the classes that have been oversampling previously with an accuracy of 91%, an increase of 1% compared to the model that has an unbalanced data distribution. The next model optimization is done by applying the ensemble method to the four models that have been oversampled on the training dataset with the same architecture. This method shows an increase of 3% to 5% while the final accuracy on the test of dataset is 96%.

Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik Nurdi Afrianto; Dhomas Hatta Fudholi; Septia Rani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Stock market is one economic driver. It has roles in growth and development of a country. Stock is an attractive investment due to the huge profit. Many people buy and sell their stock. Stock investors try to choose the good investment company to get profits with small risk. Therefore, stock investors need to be careful and must evaluate a company. With machine learning technology, stock prediction problems can be solved. Deep learning is a subset of machine learning with own network. Deep learning has good performance in managing large amounts of data. This study used stock price history data and public sentiment data on a company. The method used in this research is Bidirectional Long-Short Term Memory (BiLSTM). The features used were closing price and compound score value of the public sentiment. Four scenarios were used in finding the best predictive model. The four scenarios use the same test data with different lengths of training data window. From the modelling, predictions with the model built using BiLSTM resulted in the smallest MSE value of 0.094 and the smallest RMSE value of 0.306.

Gradient Boosting Machine, Random Forest dan Light GBM untuk Klasifikasi Kacang Kering Indrawata Wardhana; Musi Ariawijaya; Vandri Ahmad Isnaini; Rahmi Putri Wirman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Bean seed classification is critical in determining the quality of beans. Previously, the same dataset was tested using the MLP, SVM, KNN, and DT algorithms, with SVM producing the best results. The purpose of this study is to determine the most effective model through the use of the BoxCox transformation selection feature and the random forest (RF) algorithm, as well as the gradient boosting machine (GBM), light GBM, and repeated k-folds evaluation model. The bean dataset is available on the UCI Repository website. The BoxCox transformation and repeated k-folds improved the classification prediction's accuracy. The model is used in the optimal training phase for a random forest with decision tree parameters 50 and depth 10, a gradient boosting machine model with a learning rate of 1, and a light gradient boosting machine model with a learning rate of 0.5 and estimator of 500. The best training accuracy results are obtained with light GBM. which is 99 percent accurate, but only 91 percent accurate in terms of validation. According research, the Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira beans classes provided accuracy values of 91 percent, 100 percent, 92 percent, 92 percent, 95 percent, 94 percent, and 84 percent, respectively.

Sistem Pengukuran Suhu Tubuh Menggunakan AMG8833 Dan Kinect Sebagai Pencegahan Penularan Covid-19 Wiwin Lovita; Aulia; Junaldi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The purpose of this study is to create an effective and safe body temperature measurement system to prevent the transmission of covid-19 using the AMG8833 and Kinect. The method of sending data uses the Internet of Thing (IoT) and face tracking with 3D form as face identification using a kinect type xbox 360 using an arduino uno and a buzzer connected to the AMG8833. AMG8833 has an infrared detector which is arranged in an 8x8 array and reads body temperature non-contact by detecting infrared energy from the body. kinect recognizes facial features based on the distance of the kinect position coordinates on the face. AMG8833 and kinect as input, Arduino uno as AMG8833 data processing and buzzer gives a sound signal if the temperature is above 37.10 0C. Body temperature measurement data was carried out 3 times, namely at a distance of 5,10 and 15cm. Measurement data from this body temperature measuring instrument are compared with a thermogun average error value of 0.11% with a difference between the maximum and minimum average body temperatures of 0.04%. It is hoped that body temperature measurements can be as a precaution against the spread of covid-19.

Design of Smart Farm Irrigation Monitoring System Using IoT and LoRA Kurniawan D. Irianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Agriculture is an essential part of society in Indonesia because most of the population lives off of farming. Water and irrigation are the most critical and central factors in the agricultural system. The uneven distribution of irrigation water can be a problem for farmers. In addition, most of the current irrigation systems are still operated manually, for example, irrigation gates. The gate still works manually and requires human labor to run it. This study aims to design a smart farm irrigation system using internet of things and LoRa communication technology. LoRa can transmit information up to a range of several kilometers without an internet connection. It will be advantageous when the farm's location is deep in the forest, and there is no GSM signal for internet access. This study indicates that this system brings benefits for farmers in running agriculture. Farmers' work time is shorter. They can use the remaining time to do other businesses to increase their income. The irrigation monitoring process becomes easier because they do not need to come to the farm location. In fact, they can use smartphones to monitor it.

Analisis Spasial Untuk Klasifikasi Pengembangan Tempat Penampungan Sementara Menggunakan Metode Jaringan Syaraf Tiruan Luqman Hakim; Anik Vega Vitianingsih; Gita Indah Marthasari; Kresna Arief Nugraha; Anastasia Lidya Maukar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Garbage is a problem that needs an in-depth study in urban areas because the development of an area has consequences on increasing population density, facilities and infrastructure, public services, and other aspects that impact increasing the volume of waste. The distribution of temporary waste shelters (TPS) in each area is still insufficient to accommodate the volume of waste, and its availability is inadequate. The purpose of this study is to model spatial data through spatial analysis using artificial intelligence methods in classifying the development of integrated temporary shelter locations (TPST) and regional integrated temporary shelters (TPST Regions) by utilizing Web-based technology (Geographical Information System (Web-GIS). The Artificial Neural Network method with the Backpropagation algorithm is used for the spatial analysis process based on the parameters of the population, the amount of organic and inorganic waste, the amount of industrial waste, and the volume of the TPST and Regional TPST capacity. The spatial analysis results using the Artificial Neural Network method obtained an accuracy value of 7171.02%. The results of this study can be the basis for Department of Environment and Cleanliness policies for the development of TPST and TPST areas with information coverage at the village level.

Pengenalan Emosi Pembicara Menggunakan Convolutional Neural Networks Rendi Nurcahyo; Mohammad Iqbal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Recognition of the speaker's emotions is an important but challenging component of Human-Computer Interaction (HCI). The need for the recognition of the speaker's emotions is also increasing related to the need for digitizing the company's operational processes related to the implementation of industry 4.0. The use of Deep Learning methods is currently increasing, especially for processing unstructured data such as data from voice signals. This study tries to apply the Deep Learning method to classify the speaker's emotions using an open dataset from SAVEE which contains seven classes of voice emotions in English. The dataset will be trained using the CNN model. The final accuracy of the model is 88% on the training data and 52% on the test data, which means the model is overfitting. This is due to the imbalance of emotion classes in the dataset, which makes the model tend to predict classes with more labels. In addition, the lack of heterogeneity of the dataset makes the character of the emotion class more different from the others so that it can reduce the bias in the model so as not to overfit the model. Further development of this research can be done, such as over-sampling the existing dataset by adding other data sources, then performing data augmentation to get the data character of each emotion class and setting hyperparameter values to get better accuracy values.

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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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Abstract

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