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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 1,046 Documents
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Modification of SqueezeNet for Devices with Limited Computational Resources Rahmadya Trias Handayanto; Herlawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4446

Abstract

In recent years, the computational approach has shifted from a statistical basis to deep neural network architectures which process the input without explicit knowledge that underlies the model. Many models with high accuracy have been proposed by training the datasets using high performance computing devices. However, only a few studies have examined its use on non-high-performance computers. In fact, most users, who are mostly researchers in certain fields (medical, geography, economics, etc.) sometimes need computers with limited computational resources to process datasets, from notebooks, personal computers, to mobile processor-based devices. This study proposes a basic model with good accuracy and can run lightly on the average computer so that it remains lightweight when used as a basis for advanced deep neural networks models, e.g., U-Net, SegNet, PSPNet, DeepLab, etc. Using several well-known basic methods as a baseline (SqueezeNet, ShuffleNet, GoogleNet, MobileNetV2, and ResNet), a model combining SqueezeNet with ResNet, termed Res-SqueezeNet, was formed. Testing results show that the proposed method has accuracy and inference time of 84.59% and 8.46 second, respectively, which has an accuracy of 2% higher than the SqueezeNet (82.53%) and is close to the accuracy of other baseline methods (from 84.93% to 0.88.01%) while still maintaining the inference speed (below nine second). In addition, residual part of the proposed method can be used to avoid vanishing gradient, hence, it can be implemented to solve more advanced problems which need a lot of layers, e.g., semantic segmentation, time-series prediction, etc.
Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting Isran K Hasan; Nurwan; Nur Falaq; Muhammad Rezky Friesta Payu
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4508

Abstract

Stunting is a significant threat to the quality of human resources in Indonesia because stunting does not only involve physical growth disorders but can also cause children to be vulnerable to disease and experience disorders of brain development and intelligence. Many factors cause stunting, not only malnutrition in pregnant women and toddlers. Grouping can be done to make it easier to see the characteristics of the factors causing stunting in Indonesia. The grouping is done based on the similarity of the characteristics of the factors causing stunting in each province. This study used Fuzzy Geographically Weighted Clustering (FGWC) with Gravitational Search Algorithm (GSA) to group and assess the best cluster using the Partition Coefficient validity index, Classification Entropy, Separation Index, Xie & Beni's Index, and IFV Index. Furthermore, a difference test was conducted to determine the dominant factor causing stunting in the formed cluster. The results showed that the FGWC-GSA gave the best clustering results on the fuzziness value of 2 with the number of clusters 2. Cluster 1 consisted of 16 provinces, and cluster 2 consisted of 18 provinces. Based on the T-test, the variables of infants who received exclusive breastfeeding had significant differences between clusters. Therefore, cluster 2 is a cluster that has dominant problems related to exclusive breastfeeding.
IoT Microcontroller Application Prototype as Data Transceiver from Network to USB Device Rieke Wijayanti; M. Syirajuddin S; Abdul Rasyid; Ahmad Wilda Y
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4555

Abstract

Telecommunications technology in the early 2000s until now has experienced a rapid increase. Starting with complex devices to microcomputer devices that are able to connect to the network. With the development of networking technology, it will leave behind non-network-support devices that can still be used. The existence of previous research on "The interface between the IoT microcontroller (ESP32) and the Max3421e USB Host" can be taken advantage of developing a device that can facilitate a non-support-network device into a support-network electronic device. So that non-support-network electronic devices do not become electronic waste and can also become a device that can be used in the present. In this study, a prototype of a data receiver from WiFi was designed, then the data that has been received is reorganized into rows of data that are ready to be sent to non-support-network electronic devices. This developed tool uses an ESP32 IoT microcontroller connected to the USB Host max3421e which has been packaged in the form of a USB host shield module using SPI protocol communication. The result obtained is that data from the network can be sent correctly to the USB Host max3421e via the ESP32 microcontroller.
Credit Scoring Model for Farmers using Random Forest Kharida Aulia Bahri; Yeni Herdiyeni; Suprehatin Suprehatin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4583

Abstract

One of the problems faced by farmers in Indonesia is capital. Based on Indonesian Central Statistics Agency survey results, the number of farmers who borrow capital from formal institutions such as banks is still small. This is because the process of applying for loans at banks is lengthy, farmers are considered high-risk and unbankable, and the rating of the agricultural sector is unattractive to banks. This study aims to determine the attributes and design a model of agricultural credit assessment. This study uses secondary data related to bank credit ratings and land productivity from banks in the Telagasari sub-district in 2018–2020 and Cipayung sub-district in 2020. Data were analyzed using random forests. The research process includes four stages: data collection, data pre-processing, model building, and model analysis and evaluation. This study produced five important variables that are relevant to farmers: planting costs, sales, land productivity, total production, and land area. The model built produces the most optimal accuracy of 83% with an AUC score of 81%. Based on the AUC performance classification, it can be concluded that the model that has been made is good at predicting the credit status of farmers because the AUC value is included in the good classification predicate.
Improved Classification of Handwritten Jawi Script Based on Main Part of Script Body Safrizal Razali; Fitri Arnia; Rusdha Muharrar; Kahlil Muchtar; Akhyar Bintang
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4600

Abstract

Since the entry of Islam, many ancient relics in the archipelago were written using Jawi script. Due to human or natural factors, these ancient relics will be damaged or destroyed. To avoid the loss of this ancient heritage data, the data must be stored in digital documents. In order to convert digital documents into machine-readable text format, the use of Optical Character Recognition (OCR) technology is inevitable. In this research, OCR technology is implemented on isolated Jawi scripts. Freeman Chain Code (FCC) is used to extract the isolated Jawi script features. Subsequently, the FCC feature is fed into Support Vector Machine (SVM) in order to classify the character. The decision rule classification is applied to the class of SVM classification in the Jawi script form. The results of the SVM classification into 19 classes reached 81.58%, while the results for merging into 15 classes produced better results with the accuracy 84.21%. Feature extraction of dot location is divided into the top, middle, and bottom. Feature extraction of the number of dotss is done by counting the number of dots, while feature extraction of the presence of holes is carried out by detecting the presence of holes in the characters. These features are applied to the class of results from SVM classification with decision-making rules. The percentage of success in applying the decision rules to the results of the classification of incorporation into 15 classes by SVM reached 92.86%. Further research will be conducted to determine the effect of the feature of the location of the dot and the number of dots on the shape of the main part of the character.
Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture Yazid Aufar; Muhammad Helmy Abdillah; Jiki Romadoni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4622

Abstract

In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.
Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN Waluyo Nugroho Waluyo; R. Rizal Isnanto; Adian Fatchur Rochim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4626

Abstract

Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40 testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN. The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%.
Sentiment Analysis Against Political Figure’s Billboard During Pandemic Using Naïve Bayes Algorithm Ade Bastian; Ardi Mardiana; Dinda Sri Wulansari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4643

Abstract

In the midst of the Covid-19 Pandemic, many Indonesians have reacted negatively to the placement of political individuals' billboards with very huge sizes on the streets. The early political campaign that was run was thought to be contentious. On social media like Twitter, the majority of people freely share their thoughts. The purpose of this study is to investigate how the general public reacted to the placement of billboards advertising political figures during the epidemic and to categorize those responses. It is envisaged that it would also provide advice for connected parties that may be used when making judgments regarding the policy of constructing billboards for political figures during a pandemic based on the results of data analysis. Twitter users tend to be more expressive because of the character limits, which means they have sentimental or emotional values. Using the Nave Bayes Algorithm, it is possible to do sentiment analysis on the sentiment data by categorizing user comments into positive, negative, and neutral attitudes. Regarding the sentiments expressed on billboards showing political leaders during the pandemic, tweets were sorted into three categories: liked, unfavorable, and neutral. The accuracy rate from Naive Bayes categorization of political personalities during the pandemic on social media Twitter was 83.3% with a precision value of 89%, recall 83%, and f-1 score of 82%.
Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics Rini Nuraini; Rachmat Destriana; Desi Nurnaningsih; Yeni Daniarti; Allan Desi Alexander
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4673

Abstract

Sunflower is an important commodity in agriculture, besides being used as an ornamental plant, sunflower is an oil-producing plant and a source of industrial materials. In Indonesia, sunflower productivity is considered less than optimal, because knowledge and information about sunflowers are still lacking. Therefore, information is needed that can be used as an extension of knowledge about sunflowers itself, especially in Indonesia, which is a tropical region which is an area suitable for the growth of sunflowers. Sunflowers can actually be identified based on recognizable traits. However, the similar shape makes it difficult for some people to distinguish the types of sunflowers. This study aims to classify sunflower images using a first-order feature extraction algorithm using the characteristics of mean, skewness, variance, kurtosis, and entropy which are then used as input to the Multiclass SVM identification algorithm. Data points are mapped to dimensionless space using a Multiclass SVM to produce hyperplane-linear separation between each class. Based on the results of testing the accuracy of the model is able to perform classification with an average accuracy of 79%. These results show that the developed model can classify well.
A Comparative Study of CatBoost and Double Random Forest for Multi-class Classification Annisarahmi Nur Aini Aldania; Agus Mohamad Soleh; Khairil Anwar Notodiputro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4766

Abstract

Multi-class classification has its challenge compared to binary classification. The challenges mainly caused by the interactions between explanatory and responses variable are increasingly complex. Ensemble-based methods such as boosting and random forest (RF) have been proven to handle classification problems. We conducted this research to study multi-class classification using CatBoost, a method developed with gradient boosting and double random forest (DRF), RF’s development that is good to be used when the resulting RF model is underfitting. Analysis was carried out using simulation and empirical data. In the simulation study, we generate data based on the distance between classes: high, medium, and low. The empirical data used is the industrial classification code, namely KBLI. CatBoost and DRF can rightly solve the multi-class classification problem at a high distance, measured by a 100% balanced accuracy score. At a medium distance, CatBoost and DRF produce balanced accuracy scores of 99.25% and 97.54%, respectively, whereas 32.37% and 23.97% at the low distance. In empirical studies, CatBoost’s performance outperforms DRF by 4.27%. All the differences are statistically significant based on the t-test result. We also use LIME to explain individual predictions of CatBoost and learn words that contribute the most to an example class’s prediction.

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