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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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Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest Rian Oktafiani; Arief Hermawan; Donny Avianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5574

Abstract

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.
Comparison of Machine Learning Algorithms in Detecting Tea Leaf Diseases Ihsan, Candra Nur; Agustina, Nova; Naseer, Muchammad; Gusdevi, Harya; Rusdi, Jack Febrian; Hadhiwibowo, Ari; Abdullah, Fahmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5587

Abstract

Tea is one of the top ten export products sent from Indonesia to foreign countries. However, in recent years, the amount of tea leaf exports from Indonesia has decreased, although the value of the export impacts the country’s economic structure. In addition to market competition, Indonesia must maintain tea leaf production so that the increase in export decline is not significant or even increases tea leaf export production. To improve production quality and reduce production costs, early detection of tea leaf diseases is necessary. This study aims to classify tea leaf images for early detection of tea leaf disease so that appropriate treatment can be carried out early. This study compares machine learning algorithms to determine the best algorithm for detecting tea leaf diseases. The algorithms tested as performance comparisons in classifying tea leaf diseases are random forest (RF), support vector classifier (SVC), extra tree classifier (ETC), decision tree (DT), XGBoost classifier (XGB), and convolutional neural algorithms. Network (CNN). As a result, the average accuracy performance generated by ETC produces a higher value than other algorithms, i.e., getting an average accuracy performance of 77.47%. Another algorithm, SVC, has an average accuracy of 76.57%, RF of 76.12%, DT of 65.31%, XGB of 71.62%, and the lowest is CNN of 59.08%. ETC has been proven to be the most superior machine learning algorithm for detecting tea leaf diseases in this study.
Convolutional Neural Networks for Classification Motives and the Effect of Image Dimensions Siti Aisyah; Rini Astuti; Fadhil M Basysyar; Odi Nurdiawan; Irfan Ali
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5623

Abstract

Although Indonesian batik patterns vary by location, they usually depict local customs and cultures. Each batik has a unique quality and, to correctly identify the batik designs, you need to understand the design patterns. However, many people struggle to identify and categorize these kinds of motivation because they don't have the requisite knowledge, understanding, or access to sufficient information. This study used photo data to classify batik patterns into 15 different groups. Batik Kawung, Megamendung, Lasem, Pole, Machete, Gills, Nutmeg, Karaswasih, Cendrawasih, Geblek Renteng, Bali, Betawi, and Dayak are all included in this category. 1,350 images were used in the research. Google supports the collection of data. To provide the highest level of precision and to evaluate how image dimensions affect the classification of batik designs, this study employs convolutional neural networks (CNNs). The results of this study show that Multi-Layer Perceptron (MLP) is a well-liked deep learning method for data classification, especially in domains where picture classification is involved. The size of the images utilized affects the accuracy of computational neural network (CNN) algorithms. The results showed that the test using training data comparisons of 60%, 30% and 10% resulted in a 01.89% loss of 1.18% and a 100% improvement in accuracy.
Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods Anik Vega Vitianingsih; Ullum, Choirul; Maukar, Anastasia Lidya; Yasin, Verdi; Wati, Seftin Fitri Ana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.4520

Abstract

Along with the increase in population and the acceleration of economic expansion, there has been a concomitant increase in the urgent requirement for additional property that can serve as a venue for a wide variety of community activities. It is not uncommon for large cities, which are the epicenter of urbanization, such as the city of Surabaya, to experience a sharp increase in the demand for land. One of the regions that has excellent accessibility is the Sidoarjo Regency, which is comparable to the City of Surabaya in this regard. The goal of this research is to use Web-GIS to conduct an analysis of spatial data to identify the land functions that are most suitable for use in residential areas. The Analytic Hierarchy Process (AHP) and the Weighted Product Model (WPM) are two of the methodologies that are included in the spatial data modeling method that uses multi-criteria decision making (MCDM). The parameters of the characteristics that are used are derived from data such as the distance to the city center, the distance to the market, the distance to the hospital, the distance to public transportation, the slope, the type of soil, and the amount of rainfall. The results of the spatial data modeling categorize the suitability of new residential land into categories of land that is not suitable for residential use and land that is acceptable for residential use. A K value of 0.27 is the result of the comparison test that was run between the two MCDM approaches using Cohen's Kappa coefficients.
Perbandingan Algoritma Genetika dan Recursive Feature Elimination pada High Dimensional Data Pristyanto, Yoga; Wirantanu, Dipa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5375

Abstract

The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company, so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the WDC dataset with 30 features and 569 data points, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then, a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy and the use of weighted macros on precision, recall, and f1 score, using GA selection features has slightly higher results than RFE, so it is concluded that GA selection features are better at solving problems in high-dimensional data.
Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach Baidawi, Taufik; Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5502

Abstract

The fundamental problem in this research is to explore a more profound understanding of both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modeling. This research focuses on developing Madaline and Perceptron algorithms using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The data set used in this study is a lens data set. There are four attributes: 1) patient age: young, prepresbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia, 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows that the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to the development of computing and neural networks, with potential applications extending from data processing to more accurate modeling in artificial intelligence, data analysis, and understanding complex patterns.
Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation Erwin Syahrudin; Ema Utami; Anggit Dwi Hartanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5529

Abstract

The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation skills. This research aims to introduce a superior object detection method, especially to support the sustainability and effectiveness of blind navigation. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method and distance measurement technology from OpenCV. The main challenge to address involves improving object detection accuracy and performance, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking in environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects that may involve obstacles or potential threats, thus preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution to the presentation of technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact on the facilitation of their daily lives.
RAFT: An IoT-Based Nutrition Monitoring System for Bok Choy Hydroponics Plants Himawan, Aldi; Putra, Widhy Hayuhardhika Nugraha; Setiawan, Eko
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5560

Abstract

The Internet of Things (IoT) plays a crucial role in technology advancements, especially in the agricultural sector, such as hydroponics. Manual monitoring of parameters such as nutrient levels, pH, and water levels in plants consumes farmers' time and energy and increases the risk of crop failure. This research aims to evaluate the effectiveness of using IoT RAFT (Remote Automated Farming Technology) system for farmers, particularly hydroponic bok choy farmers, to monitor and control plant nutrient levels and the development process using waterfall as a research methodology. The parameters tested in this research include the height of the bok choy, the number of leaves, and the weight of the harvest of the bok choy. We conducted this research on 14 plants for one harvest period, then we used linear regression to determine the growth rate by calculating the slope. The results show that the plant height, the number of leaves, and the harvest weight using the IoT RAFT system are 0.5897 cm/day, 0.6391 leaves/day, and 216.43 grams, respectively. We also compared the IoT RAFT system with a non-IoT bok choy growing method, and we concluded that our IoT RAFT system has a better growth rate compared to the non-IoT bok choy growing method.
IoT-Based Irrigation System Using Machine Learning for Precision Shallot Farming Rafrin, Mardhiyyah; Muh. Agus; Putri Ayu Maharani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5579

Abstract

Despite the massive production of shallots in Enrekang Regency, South Sulawesi Province, Indonesia, the cultivation method is still very conventional. Shallot cultivation is very challenging because it requires precision irrigation and pest prevention. In this research, we proposed a smart irrigation system to help farmers manage irrigation with more efficient water usage without hampering their pest prevention. The results of the system were three options: 1) no water needed, 2) water is required and is efficient for irrigation, and 3) water is required but it is not efficient for irrigation. We used Wireless Sensor Networks and IoT to collect yield parameters, designed a firebase database, and developed a mobile application and a web service embedded with a machine learning application. All applications interacted using the representational state transfer application programming interface. The proposed system architecture successfully gathered cropland data and distributed them to all applications within the system. Furthermore, we analyzed four supervised learning algorithms (decision trees, random forest, gradient boosting, and K-Nearest neighbor), and the random forest was deployed in the web service because it outperformed other algorithms with a precision of 94% and an AUC score of 0.90.
Design and Implementation of the Shortest Path Navigation in Samosir District using Branch and Bound Algorithm Chandra, Rudy; Arifin Prasetyo , Tegar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5585

Abstract

Samosir has a large area and several tourist attractions, making it difficult for visitors to explore the island. Unknowing the route can make the journey less fun and waste time. In general, tourists seek to know the fastest way to a tourist location to save time and money while on vacation. As a result, we require an application that will offer directions to the shortest path. This research aims to develop a web-based application that may display a map of the shortest travel to a tourist site. This website will display a map that marks the route from the origin point to the destination point. The Branch and Bound algorithm is used to determine the shortest path. The Python libraries OSMnx, Folium, and NetworkX modify paths and show a route map with OpenStreetMap. The error value of the distance between the branch, the bound algorithm, and Google Maps is used to obtain the RMSE accuracy value. The RMSE value is 3.02 and the MAPE value is 0.0023 indicating that the application produced already has a good implementation prototype. Furthermore, there is no significant distinction between the appearance of maps that implement OpenStreetMap and Google Maps.

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