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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 25 Documents
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Search results for , issue "Vol 5 No 1 (2021): Februari 2021" : 25 Documents clear
Identifikasi Citra Beras Menggunakan Algoritma Multi-SVM Dan Neural Network Pada Segmentasi K-Means Nurfalah, Ridan; Dwiza Riana; Anton
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2721

Abstract

Indonesia is a country with high rice needs because it is a staple food for more than 90% of populations. High demand requires high stock so imports are carried out in accordance with Permendagri Number 19/M-DAG/PER/3/2014 which explains rice import standards. There are many types of rice imported into Indonesia with various quality, color and import requirements such as for health or price stabilization. In terms of colors, imported white rice is the most consumed rice by Indonesians. One example is jasmine rice from Thailand. Meanwhile, in terms of imports, both for health and stabilizing the price of japonica rice (Japan) and Basmati (Pakistan) are the most imported to Indonesia. But there are still many who are not familiar with those three rices. In this research, the three types of rice were identified by comparing the Multi-SVM algorithm and Neural Network algorithm. Image acquisition is done using a flatbed scanner which produces 90 images divided into 63 training images and 27 testing images. K-Means becomes an image segmentation method and image binary converts. Feature extraction using morphological features with the regionprop method combined with the Gray Level Co-Occence Matrix (GLCM) produces 9 features that can produce 96.296% accuracy for Multi-SVM and 88.89% Neural Network
Hate Speech Classification on Twitter Using Support Vector Machine Oryza Habibie Rahman; Gunawan Abdillah; Agus Komarudin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2700

Abstract

Nowadays social media has become a place for peoples to express their opinions, there are many ways that can be done to express both positive and negative opinions. Hate speech is one of the problems that we find quite a lot in cyberspace, that things can be detrimental to many parties. Twitter as one of social media, can be used as a source of analysis about people's behavior in cyberspace. Many of our society that unconsciously act of hate speech on social media, therefore this study finds out how people's behavior patterns in cyberspace and the main issue of hate speech on a particular topic and time period by classify it into five classes, namely ethnicity, religion, race, inter-groups and neutral using Support Vector Machine. In this study also compares three kernel that common to use and the result is the system can classify hate speech by using RBF kernel and got the highest result with 93% accuracy on 700 data train and 300 data test.
Acquisition of Digital evidence Android-Based Viber Messenger App Imam Riadi; Rusydi Umar; Syahib, Muhammad Irwan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2626

Abstract

Viber is one of the most popular social media in the Instant Messenger application category that can be used to send text messages, make voice calls, send picture messages and video messages to other users. As many as 260 million people around the world have used this application. Increasing the number of viber users certainly brings positive and negative impacts, one of the negative impacts of this application is the use of digital forensic crime. This research simulates and removes digital crime evidence from the viber application on Android smartphones using the National Institute of Standards Technology (NIST) method, which is a method that has work guidelines on forensic policy and process standards to ensure each investigator follows the workflow the same so that their work is documented and the results can be accounted for. This study uses three forensic tools, MOBILedit Forensic Express, Belkasoft and Autopsy. The results in this study show that MOBILedit Forensic Express gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. Belkasoft gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. For Autopsy does not give the expected results in the extraction process, in other words the Autopsy application gives zero results. It can be concluded that MOBILedit Forensic Express and Belkasoft have a good performance compared to Autopsy and thus this research has been completed and succeeded in accordance with the expected goals.
Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik Andono, Pulung Nurtantio; Rachmawanto, Eko Hari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2615

Abstract

Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.
Penerapan Blockchain dengan Integrasi Smart Contract pada Sistem Crowdfunding Aprialim, Fiqar; Adnan; Paundu, Ady Wahyudi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2613

Abstract

The existing crowdfunding platforms still operate using centralized system. While centralized system can operate well, it requires a third party intermediary in order to operate and thus does not completely provide data security and transparency of crowdfunding activities. In addition, the existence of a third party intermediary in a crowdfunding activity also causes the existing processing costs to be expensive. Therefore, the crowdfunding system needs to be built in a decentralized manner so that it eliminates the need for third parties as intermediaries in the crowdfunding process. This study proposes a prototype of decentralized crowdfunding system using Ethereum blockchain and smart contract technology. The result of system functionality test using black box testing method shows that all functionality of the crowdfunding system can run properly while operate in decentralized architecture.
Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window Dwi Kartini; Friska Abadi; Triando Hamonangan Saragih
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2602

Abstract

The water level in the reservoir is an important factor in the operation of a hydroelectric turbine to control water overflow so that there is no excessive degradation. This water control has an influence on the performance and production of hydroelectric energy. The daily reservoir water level (tpaw) recording of PLTA Riam Kanan is carried out through a daily direct measurement and observation process on the reservoir measuring board which is recapitulated every month in excel form. This time series historical data continues to grow every day to become a data warehouse that is still useless if only stored. Extracting knowledge from the data warehouse can be done using one of the artificial neural network data mining techniques, namely backpropagation to predict the next day's tpaw. Historical data for the tpaw time series is presented with a sliding window concept approach based on the window sizes used, namely 7, 14, 21 and 28. Some backpropagation network testing is carried out using a combination of the number of window sizes against the comparison of the amount of training data and test data on the network. The prediction results obtained with the smallest mean squared error (mse) in network testing is 0.000577 as a high accuracy value of the prediction results. The network architecture with the smallest mse using 28 input layers, 10 hidden layers and 1 output layer can be a knowledge that can help the hydropower plant as an alternative in making turbine operation decisions based on the predicted results of reservoir water level.
Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation Putu, Ni Luh Putu Merawati; Ahmad Zuli Amrullah; Ismarmiaty
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2587

Abstract

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.
Classroom Attendance Based on Smiling Face Patterns and Nearby Wifi with Deep Learning Miftakhurrokhmat; Rajagede, Rian Adam; Rahmadi, Ridho
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2575

Abstract

Students' attendance in class is often mandatory in education and becomes a benchmark for assessing students. Sometimes there are still fraudulent practices by students to achieve minimum attendance. From the administrative perspective, a paper-based presence system is potentially wasteful and extends the administrative stage because it requires manual recapitulation. This study aims to design a class attendance application based on facial pattern recognition, smile, and closest Wi-Fi. The method used in this research is a deep learning approach with CNN based architecture, FaceNet, to recognize faces. In addition to facial images, the system will also validate the attendance with location and time data. Location data is obtained from matching SSID from the database, and time data is taken when the user sends attendance data through API. This attendance system consists of three applications: web, mobile, and services installed on a mini-computer, which are integrated to sending attendance data to the academic system automatically. As confirmation, students are required to smile selfies to strengthen the validity of their presence. The testing model's accuracy results are 92.6%, while for live testing accuracy the model obtained 66.7%.
Peramalan Data Indeks Harga Konsumen Berbasis Time Series Multivariate Menggunakan Deep Learning Zahara, Soffa; Sugianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2562

Abstract

Multivariate Time Series based forecasting is a type of forecasting that has more than one criterion changes from time to time that it can forecast based on historical patterns of data sequences. The Consumer Price Index (CPI) issued regularly every month by the Statistics Indonesia calculated based on data observations. This study is a development of previous research that only used on type of algorithm to predict CPI value resulting poor of accuracy due to lack of architecture variations testing. This study developed a CPI forecasting model with a new approach about using several types of deep learning algorithms, namely LSTM, Bidirectional LSTM, and Multilayer Perceptron with architectural variations of the number of neurons and epochs. Furthermore, this study adapt ADDIE model of Research and Development method. Based on the results, the best accuracy is obtained from the LSTM Bidirectional with 10 neurons and 2000 epoch resulting 3,519 of RMSE value. Meanwhile, based on the average RMSE value for the whole test, LSTM gets the smallest average of RMSE followed Bidirectional LSTM and Multilayer Perceptron with the RMSE value 4,334, 5,630, 6,304 respectively.
Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning Alvi Hasanah, Novrindah; Nanik Suciati; Diana Purwitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2927

Abstract

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.

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