Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Biometrika Nirsentuh Berbasis Pengenalan Pembuluh Darah pada Telapak Tangan Menggunakan Wavelet dan Local Line Binary Pattern
No full textTo support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.Untuk mendukung roadmap hidup berdampingan bersama Covid-19, diperlukan biometrika nirsentuh sebagai teknologi verifikasi identitas individu dalam aktivitas keseharian seperti sistem kontrol, pencatatan kehadiran di kantor/sekolah/instansi dan hak akses masuk ke suatu ruangan. Salah satu contoh biometrika nirsentuh adalah biometrika berbasis pembuluh darah pada telapak tangan. Karena bersifat nirsentuh, sistem biometrika ini tidak membutuhkan kontak langsung antara pengguna dengan alat sensor sehingga memberikan beberapa kelebihan dalam aspek kenyamanan saat akuisisi dan lebih higienis. Pada sistem pengenalan palm vein, pola palm vein dapat dianggap sebagai fitur tekstur. Oleh karena itu, penelitian ini mengusulkan sistem biometrika nirsentuh berbasis pengenalan pembuluh darah pada telapak tangan dengan menggunakan metode Local Line Binary Pattern untuk ekstraksi fitur tekstur citra palm vein hasil dekomposisi Transformasi Wavelet 2D, sehingga dihasilkan deskriptor tekstur yang berukuran kecil dan sesuai dengan karakteristik tekstur vein yang tipis. Metode ekstraksi fitur tekstur yang diusulkan tersebut telah diuji menggunakan metode klasifikasi Fuzzy k-NN pada 600 citra telapak tangan dengan akurasi CRR yang diperoleh mencapai 95.0% dengan waktu komputasi 0.057 detik
Prediksi Waktu Tanam Cabai Rawit Berdasarkan Kondisi Lingkungan Berbasis Internet of Things (IoT) Menggunakan Metode Neural Network
No full textIn Indonesian cuisine, the red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of the chili prices. Farmers often struggle with the critical decision of when to plant Tabasco peppers to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advances in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study focuses on the development of an IoT-based solution aimed at helping farmers in precisely determining the optimal planting time for Tabasco pepper. It uses five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours) and groundwater usage data (m3) to make data-driven planting decisions. The urgent need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Using historical data from 2019, obtained from the Jakarta Provincial Government Open Data DKI, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The result is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.Cabai rawit merah adalah salah satu bahan masakan yang sering digunakan oleh masyarakat Indonesia. Karena kebutuhannya yang tinggi harga cabai menjadi fluktuatif, untuk itu diperlukan sistem yang mampu memberikan prediksi waktu tanam yang baik untuk cabai, dengan berkembangnya teknologi Internet of Things saat ini banyak dikembangkan sistem yang dapat memberikan data dan informasi yang dibutuhkan dalam pengambilan keputusan menentukan waktu tanam yang baik untuk cabai rawit berdasarkan kondisi lingkungan. Dari sensor yang digunakan penulis mengambil 5 kriteria dalam menanam cabai rawit antara lain temperatur rata-rata (°C), kelembapan rata-rata (%), curah hujan (mm), lamanya penyinaran matahari (jam), serta data penggunaan air tanah (m3), penulis mengembangkan prototype sistem prediksi waktu tanam berbasis IoT serta sebuah model prediksi menggunakan algoritme jaringan syaraf tiruan (neural network) dengan dataset yang diambil dari data historis selama tahun 2019 yang didapat dari Data Terbuka Pemerintah Provinsi DKI Jakarta serta data iklim dari Badan Meteorologi, Klimatologi dan Geofisika (BMKG) dengan hasil akurasi sebesar 91,26%, dapat memberikan prediksi waktu tanam yang baik untuk membantu petani dalam memulai penanaman cabai rawit
Comparative Analysis of Forensic Software on Android-based MiChat using ACPO and DFRWS Framework
Get PDFInstant Messaging (IM) is a popular and widely used communication application. MiChat is a multi-platform instant chat service with several features that can attract various segments of the population to use it as a tool for committing cybercrimes. A forensic framework and several forensic tools are needed to carry out physical evidence investigation procedures. This study focuses on analyzing and comparing the forensic tools used during the research, based on defined digital evidence parameters and applying a specific mobile forensic framework. The results show that Final Mobile Forensic has the highest ability to obtain digital evidence and can recover deleted data, while Oxygen Forensic Detective has advantages in terms of audio, images, and video but cannot recover data. The best framework is DFRWS, which has the most complete stages so that it can support the investigation process. The best digital evidence is text chat and contacts, which can be used to support valid legal claims.Instant Messaging (IM) is a popular and widely used communication application. MiChat is a multi-platform instant chat service with several features that can attract various segments of the population to use it as a tool for committing cybercrimes. A forensic framework and several forensic tools are needed to carry out physical evidence investigation procedures. This study focuses on analyzing and comparing the forensic tools used during the research, based on defined digital evidence parameters and applying a specific mobile forensic framework. The results show that Final Mobile Forensic has the highest ability to obtain digital evidence and can recover deleted data, while Oxygen Forensic Detective has advantages in terms of audio, images, and video but cannot recover data. The best framework is DFRWS, which has the most complete stages so that it can support the investigation process. The best digital evidence is text chat and contacts, which can be used to support valid legal claims
Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices
No full textCrude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil.
Crude oil price fluctuations affect the business cycle due to affecting the ups and downs of the growth of the economy, which one of the indicators of the economic business cycle phenomenon. The importance of oil price prediction requires a model that can predict future oil prices quickly, easily, and accurately so that it can be used as a reference in determining future policies. Machine learning is an accurate method that can be used in predicting and makes it easier to predict because there is no need to program computers manually. ARIMA is a machine learning algorithm while ARIMA that uses a seasonal component is called SARIMA. Based on background, research purpose is modeling crude oil price forecasting by ARIMA and SARIMA. Forecasting is done on daily crude oil price data taken from Yahoo Finance from January 27, 2020 to January 25, 2023. The evaluation results show the RMSE value of ARIMA and SARIMA is 1.905. The forecast result of 7 days ahead with ARIMA is 86.230003 while SARIMA is 86.260002. The research results are expected to be helpful for policy makers to adopt policies and make the right decisions in the use of crude oil
Optimization Fuzzy Geographically Weighted Clustering with Gravitational Search Algorithm for Factors Analysis Associated with Stunting
Get PDFStunting 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.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
Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm
Get PDFWriting Mandarin characters is considered the most challenging component for beginners due to the rules and character formations. This paper explores the potential of a machine learning-based digital learning tool to write Mandarin characters. It also conducts a comparative study between MobileNetV2 and MobileNetV3, exploring different configurations. The research follows the Multimedia Development Life Cycle (MDLC) method to create both application and machine learning models. Participants from higher education institutions that offer Mandarin courses in Batam, Indonesia, participated in a User Acceptance Test (UAT). Data were collected through questionnaires and analyzed using the System Usability Scale (SUS) methods. The results show positive user acceptance, with an SUS score of 77.92%, indicating a high level of acceptability. MobileNetV3Small was also preferred for recognizing user handwriting, due to comparable accuracy size, rapid inference time and smallest model size. Although the application was well received, several participants provided constructive feedback, suggesting potential improvements. 
Decomposing Monolithic to Microservices: Keyword Extraction and BFS Combination Method to Cluster Monolithic’s Classes
Get PDFAbstract
Microservices architecture is widely used because of the ease of maintaining its microservices, as opposed to encapsulating functionality in a monolithic, which may negatively impact the development process when the application continues to grow. The migration process from a monolithic architecture to microservices became necessary, but it often relies on the architect's intuition only, which may cost many resources. A method to assist developers in decomposing monolithic into microservices is proposed to address that problem. Unlike the existing methods that often rely on non-source code artifacts which may lead into inaccurate decomposition if the artifacts do not reflect the latest condition of the monolith, the proposed method relies on analyzing the application source code to produce a grouping recommendation for building microservices. By using specific keyword extraction followed by Breadth First Search traversal with certain rules, the proposed method decomposed the monolith's component into several cluster whose majority of cluster’s members have uniform business domain. Based on the experiment, the proposed method got an 0.81 accuracy mean in grouping monolithic's components with similar business domain, higher than the existing decomposition method's score. Further research is recommended to be done to increase the availability of the proposed method.
Abstract
Microservices architecture is widely used because of the ease of maintaining its microservices, as opposed to encapsulating functionality in a monolithic, which may negatively impact the development process when the application continues to grow. The migration process from a monolithic architecture to microservices became necessary, but it often relies on the architect's intuition only, which may cost many resources. A method to assist developers in decomposing monolithic into microservices is proposed to address that problem. Unlike the existing methods that often rely on non-source code artifacts which may lead into inaccurate decomposition if the artifacts do not reflect the latest condition of the monolith, the proposed method relies on analyzing the application source code to produce a grouping recommendation for building microservices. By using specific keyword extraction followed by Breadth First Search traversal with certain rules, the proposed method decomposed the monolith's component into several cluster whose majority of cluster’s members have uniform business domain. Based on the experiment, the proposed method got an 0.81 accuracy mean in grouping monolithic's components with similar business domain, higher than the existing decomposition method's score. Further research is recommended to be done to increase the availability of the proposed method
Requirement Elicitation Modeling Using Knowledge Acquisition in Automated Specification Method
No full textErrors often occur during the requirements elicitation stage, causing failure of the software development process as a whole, so that the built system cannot be used optimally; these data are obtained from survey data from several large companies involved in technology development. To overcome this problem, this study tries to apply the elicitation requirements using the KAOS method in the case study of the SMM reseller ordering system to obtain system requirements that are in accordance with the goals and objectives of each existing stakeholder. Based on the elicitation of system requirements, functional requirements are generated that include automatic orders, automatic payments, manage product sales, manage orders, manage payment methods, manage problem orders, manage customer data, manage company information, automatic email notifications, and sales statistics information. The results of this study are a table of functional requirements that have been declared valid and in accordance with the goals and requirements of each stakeholder after evaluating and validating the results for each stakeholder involved.
Errors often occur during the requirements elicitation stage, causing failure of the software development process as a whole so that the system built cannot be used optimally, this data is obtained from survey data from several large companies involved in technology development. To overcome this problem, this study tries to apply elicitation requirements using the KAOS method in the case study of the SMM Reseller ordering system to obtain system requirements that are in accordance with the goals and objectives of each existing stakeholder. Based on the elicitation of system requirements, functional requirements are generated which include, automatic orders, automatic payments, manage product sales, manage orders, manage payment methods, manage problem orders, manage customer data, manage company information, automatic email notifications, and sales statistics information. The results of this study are a table of functional requirements that have been declared valid and in accordance with the goals and requirements of each stakeholder after evaluating and validating the results for each stakeholder involved
Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm
Get PDFClustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, is easy to use, and is fast in clustering data. The K-means method groups the data based on k distances and randomly determines the initial centroid as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method to determine the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the sum of intragroup distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli, and cancer, in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and dataset is measured by providing a ranking proving that the proposed method on the iris, wine, and cancer datasets gets the first rank, and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first, which provides evidence that the proposed method can improve the clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method.
Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, easy to use and fast in clustering data. The K-means method groups data based on k distances and determines the initial centroid randomly as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method for determining the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the Sum of Intra-Cluster distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli and cancer in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and datasets is measured by providing a ranking proving that the proposed method on the iris, wine, cancer datasets gets the first rank and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first which provides evidence that the proposed method can improve clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method
Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network
Get PDFCrude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the Gross Domestic Product in 2021. Excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The Cascade Forward Neural Network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the Mean Absolute Percentage Error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting results for Brent crude oil for the next 12 months tend to experience a downward trend until December 2023