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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 813 Documents

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Improving Accuracy using The ASERLU layer in CNN-BiLSTM Architecture on Sentiment Analysis Sandi Hermawan; Rilla Mandala
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

There have been 350,000 tweets generated by the interaction of social networks with different cultures and educational backgrounds in the last ten years. Various sentiments are expressed in the user comments, from support to hatred. The sentiments regarded the United States General Election in 2020. This dataset has 3,000 data gotten from previous research. We augment it becomes 15,000 data to facilitate training and increase the required data. Sentiment detection is carried out using the CNN-BiLSTM architecture. It is chosen because CNN can filter essential words, and BiLSTM can remember memory in two directions. By utilizing both, the training process becomes maximum. However, this method has disadvantages in the activation. The drawback of the existing activation method, i.e., "Zero-hard Rectifier" and "ReLU Dropout" problem to become the cause of training stopped in the ReLU activation, and the exponential function cannot be set become the activation function still rigid towards output value in the SERLU activation. To overcome this problem, we propose a novel activation method to repair activation in CNN-BiLSTM architecture. It is namely the ASERLU activation function. It can adjust positive value output, negative value output, and exponential value by the setter variables. So, it adapts more conveniently to the output value and becomes a flexible activation function because it can be increased and decreased as needed. It is the first research applied in architecture. Compared with ReLU and SERLU, our proposed method gives higher accuracy based on the experiment results.

Pengembangan Aplikasi Asisten Pintar Pembuka Al Qur’an 30 Juz dengan Perintah Voice Command Amarul Akbar; Shofiyah; Nur Hayatin; Ilyas Nuryasin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Many developers of digital Qur'an applications today still use tap to scrolling to run applications, although the features are interesting. This makes it less effective and efficient in opening the Qur'an. As is the case during the taklim assembly, some da'i are very interactive with jama'ah, asking to open certain surahs and verses so that there are some who have difficulty in searching. Therefore, the need for the Qur'anic application with voice command command to facilitate users. This research is the development of the Qur'an application with voice recognition feature. Using the waterfall method in development, voice command with google speech API as a voice command of surah and verse calling in the Qur'an application 30 juz. Conducted 10 randomized experiments with calls in the form of play or open surahs and certain verses give a 90% accuracy result. Commands can be given when online or offline. Then the use of google speech API can be very useful for use in the development of other applications.

Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN) Mawaddah Harahap; Em Manuel Laia; Lilis Suryani Sitanggang; Melda Sinaga; Daniel Franci Sihombing; Amir Mahmud Husein
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The Coronavirus (COVID-19) pandemic has resulted in the worldwide death rate continuing to increase significantly, identification using medical imaging such as X-rays and computed tomography plays an important role in helping medical personnel diagnose positive negative COVID-19 patients, several works have proven the learning approach in-depth using a Convolutional Neural Network (CNN) produces good accuracy for COVID detection based on chest X-Ray images, in this study we propose different transfer learning architectures VGG19, MobileNetV2, InceptionResNetV2 and ResNet (ResNet101V2, ResNet152V2 and ResNet50V2) to analyze their performance, testing conducted in the Google Colab work environment as a platform for creating Python-based applications and all datasets are stored on the Google Drive application, the preprocessing stages are carried out before training and testing, the datasets are grouped into theNormal and COVID folders then combined m become a set of data by dividing them into training sets of 352 images, testing 110 images and validating 88 images, then the detection results are labeled with the number 1 means COVID and the number 0 for NORMAL. Based on the test results, the ResNet50V2 model has a better accuracy rate than other models with an accuracy level of about 0.95 (95%) Precision 0.96, Recall 0.973, F1-Score 0.966, and Support of 74, then InceptionResNetV2, VGG19, and MobileNetV2, so that ResNet50V2-based CNNs can be used as initial identification for the classification of a patientinfected with COVID or NORMAL.

Klasifikasi Kualitas Biji Kopi Menggunakan MultilayerPerceptron Berbasis Fitur Warna LCH Ilhamsyah Ilhamsyah; Aviv Yuniar Rahman; Istiadi Istiadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Coffee is one of Indonesia's foreign exchange earners and plays an important role in the development of the plantation industry. In previous studies, coffee bean quality research has been carried out using the ANN method using color features. RGB and GLCM. However, the results carried out in the study only had an accuracy value of up to 47%. Therefore, this study aims to improve the performance of coffee bean quality classification using four machine learning methods and 7 color features. From the results obtained, it shows that MultilayerPerceptron is better starting with RGB color with an accuracy of 38% split ratio 90:10. HSV has an accuracy of 57% split ratio 90:10. CMYK has an accuracy of 63% split ratio 90:10. LAB has a 58% curation split ratio of 90:10. The YUV type has an accuracy of 58% split ratio 90:10. Furthermore, the HSI color type has an accuracy of 42% split ratio 90:10. The HCL color type has an accuracy of 65% split ratio 90:10 and LCH has an accuracy of 78% split ratio 90:10. In testing, it can be concluded that the MultilayerPerceptron method is better than other methods for the coffee bean classification process.

Implementasi Raised Cosine Filter Pada Sistem Penyiaran Televisi Digital Satelit 2 (DVB-S2) Rio Setiawan; Emy Haryatmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The development of digital video broadcasting is still continue recently and was done by many parties. One of the project regarding this research was DVB project. There was three areas in digital video broadcasting. One of them was Digital Video Broadcasting Satellite Second Generation (DVB-S2). The development of this project is not focus only in video broadcasting but also focus in applications and mutlimedia services. The objective of this research was to implement raised cosine filter in DVB-S2 using matlab simulink in order to optimize SNR and BER value. Parameters used in this project was QPSK mode and LDPC with 50 iteration. Those parameters was chosen to maintain originality of data that sent in noisy channel. The result showed that by implementing raised cosine filter could optimized BER value of the system. The higher SNR value would give the lower BER value. In static video, the best SNR value when using a filter is 0.9 dB with a BER value of 0.000004810 while for dynamic video the SNR is 0.9 with a BER value of 0.00001030.

Sistem Pendistribusian Air Bersih Metode Prabayar Terkendali Mikrokontroler Berbasis IoT Efrizon; Muhammad Irmansyah; Anggara Nasution; Era Madona; Anggi Lifya Rani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

A number of problems sometimes often arise regarding the flow of clean water from Regional Drinking Water Companies (PDAMs) to customers, such as the flow of water stops suddenly or there is no water at all, so it is necessary to manufacture a prototype system for monitoring the distribution of clean water with a microcontroller-controlled prepaid method. IoT based. The distribution of PDAM water that is channeled to consumers can be monitored online through the Internet network. The objectives of this research are (a) to make a prototype (prototype) of a prepaid clean water distribution system controlled by a microcontroller based on IoT, (b) to program an Arduino IDE-assisted system, and (c) to measure system performance. The research method starts from making a prototype physical form of clean water distribution assisted by a microcontroller, programming the microcontroller and Wi-Fi module, and measuring system performance. The results of measuring system performance are indicated by an error in the ultrasonic sensor reading HC-SR04 that occurs when the water level is low and too high with a maximum measured water level of 95%. The error when measuring the waterflow sensor at the water level is lower than 49% which is influenced by the water speed from the low pressure pump when the water level is below that value. The accuracy level of the waterflow sensor is 96.96% which is based on the sensor measurement results which are compared to the measurement results with a measuring cup. The system can monitor data readings from the waterflow sensor by using the NodeMCU ESP8266 on a web server from Thinkspeak via the smartphone screen. Overall the tool can function well

Identifikasi Citra Pap Smear RepoMedUNM dengan Menggunakan K-Means Clustering dan GLCM Dwiza Riana; Sri Rahayu; Sri Hadianti; Frieyadie Frieyadie; Muhamad Hasan; Izni Nur Karimah; Rafly Pratama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Cervical cancer’s a gynecological malignancy in women that’s very dangerous, even causes death. Prevention through early detection of Pap smear test. It was carried out by pathologists with the help of a microscope still have obstacles in observations. There’re many studies on Pap smear image processing for helping pathologists in cell identification. Availability of Pap smear image dataset is needed in cervical cancer early detection research. The purpose of this study was to segment, feature extraction and classify 180 Pap smear images of RepoMedUNM. The method used to identify Pap smear images begins with preprocessing, namely changing the color in the image to L*a*b color, segmentation using the K-means method, extraction of 6 features, namely metric, eccentricity, contrast, correlation, energy, and homogeneity, and then identified by calculating the closest distance between the training data features and the test data features with the Euclidean distance. The result of identification ThinPrep Pap smear images in 3 classes achieve average accuracy of 93.33%, Non-ThinPrep Pap smear images in 2 classes achieve 90% average accuracy and the average accuracy of the overall in the 4 classes reached 92%. These results indicate that the proposed method can identify Pap smear images well.

Analisis Algoritma Shi-Tomasi Dalam Pengujian Citra Senyum Pada Wajah Manusia Ardi wijaya; Puji Rahayu; Rozali Toyib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Problems in image processing to obtain the best smile are strongly influenced by the quality, background, position, and lighting, so it is very necessary to have an analysis by utilizing existing image processing algorithms to get a system that can make the best smile selection, then the Shi-Tomasi Algorithm is used. the algorithm that is commonly used to detect the corners of the smile region in facial images. The Shi-Tomasi angle calculation processes the image effectively from a target image in the edge detection ballistic test, then a corner point check is carried out on the estimation of translational parameters with a recreation test on the translational component to identify the cause of damage to the image, it is necessary to find the edge points to identify objects with remove noise in the image. The results of the test with the shi-Tomasi algorithm were used to detect a good smile from 20 samples of human facial images with each sample having 5 different smile images, with test data totaling 100 smile images, the success of the Shi-Tomasi Algorithm in detecting a good smile reached an accuracy value of 95% using the Confusion Matrix, Precision, Recall and Accuracy Methods.

Hate Speech Detection on Twitter in Indonesia with Feature Expansion Using GloVe Febiana Anistya; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Twitter is one of the popular social media to channel opinions in the form of criticism and suggestions. Criticism could be a form of hate speech if the criticism implies attacking something (an individual, race, or group). With the limit of 280 characters in a tweet, there is often a vocabulary mismatch due to abbreviations which can be solved with word embedding. This study utilizes feature expansion to reduce vocabulary mismatches in hate speech on Twitter containing Indonesian language by using Global Vectors (GloVe). Feature selection related to the best model is carried out using the Logistic Regression (LR), Random Forest (RF), and Artificial Neural Network (ANN) algorithms. The results show that the Random Forest model with 5.000 features and a combination of TF-IDF and Tweet corpus built with GloVe produce the best accuracy rate between the other models with an average of 88,59% accuracy score, which is 1,25% higher than the predetermined Baseline. The number of features used is proven to improve the performance of the system.

Feature Expansion Word2Vec Untuk Analisis Sentimen Kebijakan Publik di Twitter Alvi Rahmy Royyan; Erwin Budi Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Social media users, especially on Twitter, can freely express opinions or other information in the form of tweets about anything, including responding to a public policy. In a written tweet, there is a limit of 280 characters per tweet and this allows for problems such as vocabulary mismatches. Therefore, in this study, the feature expansion Word2vec method was applied to overcome when the vocabulary mismatches occur. This study develops and compares the Twitter sentiment analysis system using the feature expansion Word2vec method with the Logistic Regression (LR) and Support Vector Machine (SVM) classification algorithms and the system without the feature expansion Word2Vec method. The results of this study, the feature expansion Word2Vec method on the SVM classification algorithm succeeded in increasing the system accuracy up to 0,99% with an accuracy value of 78,99%.

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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