Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)
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Sink position analysis of energy efficiency in Wireless Sensor Network (WSN) using routing Stable Election Protocol (SEP)
Full text linkWireless Sensor Network (WSN) is a wireless network that involves sensors in the network. The sensor node on the WSN will collect data information from the environment around the sensor. However, each sensor node has storage capacity, processing power, communication range, and battery life limitations. The use of energy consumption from these factors is the main problem because each sensor node uses its power consumption from the battery. Stable Election Protocol (SEP) is a type of routing protocol on WSN that uses the clustering method. SEP has a function to extend the time interval before the first node dies. This research was carried out on the SEP protocol with alive node parameters, total initial energy, and stability. This study indicates that on a network that uses 100 nodes with sink positions (0, 100), two nodes are still alive and several nodes that are still alive in several sink positions that use 200 nodes. For networks where there is still a lot of energy remaining in the sink position (0, 100) with the network using 100 nodes and for networks using 200 nodes, the remaining energy is mainly in the sink position (100, 100). The highest stability period is in the sink position (50, 50) for networks using 100 nodes, and for networks using 200 nodes, the highest stability period is in the sink position (100, 50)
Sensitivity analysis of the SMARTER and MOORA methods in decision making of achieving students
Full text linkEvaluation of student learning in Islamic boarding schools is still limited to the results of exams conducted in writing which can lead to the determination of student achievement using simple criteria, resulting in less than optimal results. In addition, the importance of selecting criteria to suit the learning characteristics of the Islamic boarding school students. The purpose of this study is to assist the process of evaluating student learning based on the value of the criteria, sub-criteria, and priorities. The method used is Rank Order Centroid in assigning weight values to the criteria applied to the Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER) and Multi-Objective Optimization By Ratio Analysis (MOORA) methods. This study produces the same alternative value in the first rank. To calculate the accuracy is done by using sensitivity analysis according to the results of preference values in each method. Based on the sensitivity analysis shows that in the first sensitivity calculation the lowest value is obtained. The sensitivity value of the SMARTER method on the first sensitivity is 0.0714. While the first sensitivity value of the MOORA method is 0.0076. So the best method is owned by the MOORA method because it has the lowest sensitivity value.Evaluasi pembelajaran santri di pondok pesantren masih terbatas pada hasil ujian yang dilakukan secara tertulis yang dapat menyebabkan penentuan prestasi santri menggunakan kriteria secara sederhana sehingga menimbulkan hasil yang kurang maksimal. Selain itu pentingnya melakukan pemilihan kriteria agar sesuai dengan karakter pembelajaran santri pondok pesantren. Tujuan Penelitian ini adalah membantu proses evaluasi pembelajaran santri berdasarkan nilai kriteria, sub kriteria dan prioritas. Adapun metode yang digunakan adalah Rank Order Centroid dalam pemberian nilai bobot kriteria yang diterapkan pada metode Simple Multy Attribute Rating Technique Exploiting Ranks (SMARTER) dan Multi-Objective Optimization By Ratio Analysis (MOORA). Penelitian ini menghasilkan nilai alternatif yang sama pada peringkat pertama. Untuk menghitung akurasi dilakukan dengan menggunakan analisis sensitivitas sesuai hasil nilai preferensi pada masing-masing metode. Berdasarkan analisis sensitivitas menunjukkan bahwa pada perhitungan sensitivitas pertama diperoleh nilai terendah. Nilai sensitivitas metode SMARTER pada sensitivitas pertama adalah 0,0714. Sedangkan nilai sesnsitivitas pertama metode MOORA sebesar 0,0076. Sehingga metode terbaik dimiliki oleh metode MOORA karena memiliki nilai sensitivitas terendah
Implementasi Intrusion Prevention System (IPS) Untuk Menganalisis Triad CIA Terhadap Serangan Keamanan Jaringan Pada Web Server
No full textComputer networks are built to achieve the main goal of communicating with each other . During the transmission process, it is expected that information can be conveyed quickly, efficiently and safely. Network security serves to avoid damage or even data loss caused by attacker activities during the communication process. Security aspects that need to be maintained in data information are Confidentiality, Integrity and Availability. Intrusion Prevention System is a solution that can maintain network security from various attacks. The Intrusion Prevention System will act as a protector on the network by detecting and preventing suspicious traffic on nodes in a network. The Intrusion Prevention System in its implementation has several tools which are used in this study, namely Snort and IPTables. Testing is done by performing attacks on the Web Server. The attacks carried out are Port Scanning, DDoS attacks and Brute Force. The results of this study are based on the CIA Triad with the three attacks having different characteristics in terms of cause and effect. On the defense side, Port Scanning and Brute Force can be easily prevented by IPS, but in DDoS attacks there are differences in results between drop and reject rule. In a DDoS attack with an action drop rule, it can recover the web server in 160 seconds while the action reject rule can be restored at 145 seconds which normally can be recovered in a DDoS attack in 165 seconds. The IPS server can also reduce resources when there is a DDoS attack by 9.2% .Jaringan komputer dibangun untuk mencapai tujuan utama yaitu saling berkomunikasi. Selama proses transmisi yang diharapkan adalah informasi dapat tersampaikan dengan cepat, efisien dan aman. Keamanan jaringan berfungsi untuk menghindari kerusakan atau bahkan kehilangan data yang disebabkan oleh aktivitas penyerang selama proses komunikasi berlangsung. Aspek keamanan yang perlu dijaga dalam sebuah informasi data yaitu Confidentiality, Integrity dan Availability. Intrusion Prevention System merupakan solusi yang dapat menjaga keamanan jaringan dari berbagai serangan. Intrusion Prevention System akan berperan sebagai pelindung pada jaringan dengan mendeteksi sekaligus mencegah jika terdapat trafik yang mencurigakan pada node di dalam suatu jaringan. Intrusion Prevention System dalam implementasinya memiliki beberapa tools dimana yang digunakan pada penelitian ini yaitu Snort dan IPTables. Pengujian dilakukan dengan cara melakukan serangan terhadap Web Server. Serangan yang dilakukan adalah Port Scanning, DDoS attack dan Brute Force. Hasil dari penelitian ini yaitu berdasarkan Triad CIA dengan ketiga serangan memiliki karakteristik yang berbeda dilihat dari sebab dan akibatnya. Pada sisi pertahanan, serangan Port Scanning dan Brute Force dapat dicegah dengan mudah oleh IPS namun pada serangan DDoS terdapat perbedaan hasil antara rule drop dan reject. Pada serangan DDoS dengan action rule drop dapat memulihkan web server pada detik 160 sedangkan pada action rule reject dapat dipulihkan pada detik 145 yang normalnya serangan DDoS dapat terpulihkan pada detik 165. IPS server juga dapat menurunkan penggunaan resource ketika terdapat serangan DDoS sebesar 9,2%
Concurrently wireless sensor network using microcontroller for home monitoring against fire
Full text linkThis paper proposes a prototype fire monitoring system with Wireless Sensor Network (WSN) in order to detect fires in several places at once and facilitate the placement of the detection. WSN is a technology where several sensors work together to establish communication over a wireless network. This prototype fire monitoring system can be monitored through the website in real time and the detection data is stored on the cloud server. This prototype fire monitoring system uses 4 sensor nodes, each of which is placed in several places to detect fires and sends the detection results to the master node. The master node receives and processes the data sent by the sensor node to generate 16 different conditions concurrently, in the event of a fire it will send a telegram message and the condition data to the cloud server. Several attempts to send data from the sensor node to the master node were completely successful and sending data from the master node to the cloud server as well as sending notification messages have been sent properly
Customer satisfaction of urban farming application services: “e-Buruan Sae”
Full text linkPenelitian ini mengevaluasi model kepuasan pelanggan dalam mengadopsi aplikasi e-Buruan Sae berbasis Web 2.0. Aplikasi e-Buruan Sae merupakan aplikasi interaktif berbasis Web 2.0 yang digunakan oleh pemerintah dan masyarakat Kota Bandung untuk mengelola kegiatan urban farming. Penelitian ini merupakan penelitian kuantitatif korelasional dan menggunakan metode penelitian berdasarkan Design Research Methodology (DRM). Tahapan penelitian ini adalah tahap tinjauan pustaka dengan luaran tujuan penelitian, tahap analisis empiris awal dengan luaran hipotesis dan model penelitian, tahap eksperimen pengumpulan data, dan tahap analisis empiris akhir dengan luaran hasil analisis data. Penelitian ini menggunakan enam variabel untuk mengukur kepuasan pelanggan: kemudahan penggunaan, kualitas layanan, interaktivitas, kepercayaan, kepuasan pelanggan, dan pengembangan TI. Hasil pengujian empiris menunjukkan bahwa variabel kemudahan penggunaan dan pengembangan TI tidak berpengaruh terhadap variabel kepuasan pelanggan. Sebaliknya, variabel yang tersisa berpengaruh positif terhadap kepuasan pelanggan. Penelitian ini menghasilkan pengukuran untuk mengevaluasi kepuasan pelanggan pada aplikasi urban farming berbasis Web 2.0.This study evaluates the customer satisfaction model in adopting the Web 2.0-based e-Buruan Sae application. The e-Buruan Sae application is an interactive Web 2.0-based application used by the government and the people of Bandung City to manage urban farming activities. This research is quantitative correlational research and applies a research method based on Design Research Methodology (DRM). The stages of this research are the literature review stage with the output of research objectives, the initial empirical analysis stage with the outputs of the hypothesis and research model, the experimental stage for data collection, and the final empirical analysis stage with the output of the data analysis results. This study uses six variables to measure customer satisfaction: ease of use, service quality, interactivity, trust, customer satisfaction, and IT development. The results of empirical testing show that the ease of use and IT development variables do not affect the customer satisfaction variable. In contrast, the remaining variables have a positive effect on customer satisfaction. This research produces measurements to evaluate customer satisfaction on the Web 2.0-based urban farming application. 
Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features
Full text linkThis study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than previous studies which the average is still below 99%, this study also tries to analyze the false positive rate considering that the false positive rate in attack detection is very influential for the convenience of the information security team, with the modeling proposed, the false positive rate is very small, namely 0.039%This study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than previous studies which the average is still below 99%, this study also tries to analyze the false positive rate considering that the false positive rate in attack detection is very influential for the convenience of the information security team, with the modeling proposed, the false positive rate is very small, namely 0.039
An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control
Full text linkHand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance.Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance
Requirements Engineering of Village Innovation Application Using Goal-Oriented Requirements Engineering (GORE)
Full text linkThe delay in the absorption of village funds from the central government to the village government is due to the village government's difficulty preparing village development innovation programs. The innovation tradition will grow if the cycle of transformation of knowledge and acceptable practices from one village to another, especially villages with similar conditions and problems, can run smoothly. For the process of exchanging knowledge and experiences between villages to run smoothly, it is necessary to codify best practices in a structured, documented, and disseminated manner. This research aims to design an application that functions as a medium for sharing knowledge about the use of village funds through government innovation narratives. The application is expected to become a reference for villages to carry out innovative practices by conducting replication studies and replicating acceptable practices that other villages have done. Therefore, it is necessary to have a system requirements elicitation method that can explore the village's requirements in sharing knowledge so that the resulting system is of high quality and by the objectives of being developed. There are several Goal-Oriented Requirements Engineering (GORE) methods used, such as Knowledge Acquisition in Automated Specification (KAOS) and requirements engineering based on business processes. In this research, the KAOS method was demonstrated as the elicitation activity of a village innovation system. Then the results were stated in the Goal Tree Model (GTM). Model building begins with discussions with the manager of the village innovation program to produce goals. The goals are then broken down into several sub-goals using the KAOS method. The KAOS method is used for the requirements elicitation process resulting in functional and non-functional requirements. This research is the elicitation of the requirement for the village innovation system so that it can demonstrate the initial steps in determining the requirements of the village innovation system before carrying out the design process and the system creation process. The results of this requirement elicitation can be used further in the software engineering process to produce quality and appropriate village innovation applications.The delay in the absorption of village funds from the central government to the village government is due to the village government's difficulty preparing village development innovation programs. The innovation tradition will grow if the cycle of transformation of knowledge and acceptable practices from one village to another, especially villages with similar conditions and problems, can run smoothly. For the process of exchanging knowledge and experiences between villages to run smoothly, it is necessary to codify best practices in a structured, documented, and disseminated manner. This research aims to design an application that functions as a medium for sharing knowledge about the use of village funds through government innovation narratives. The application is expected to become a reference for villages to carry out innovative practices by conducting replication studies and replicating acceptable practices that other villages have done. Therefore, it is necessary to have a system requirements elicitation method that can explore the village's requirements in sharing knowledge so that the resulting system is of high quality and by the objectives of being developed. There are several Goal-Oriented Requirements Engineering (GORE) methods used, such as Knowledge Acquisition in Automated Specification (KAOS) and requirements engineering based on business processes. In this research, the KAOS method was demonstrated as the elicitation activity of a village innovation system. Then the results were stated in the Goal Tree Model (GTM). Model building begins with discussions with the manager of the village innovation program to produce goals. The goals are then broken down into several sub-goals using the KAOS method. The KAOS method is used for the requirements elicitation process resulting in functional and non-functional requirements. This research is the elicitation of the requirement for the village innovation system so that it can demonstrate the initial steps in determining the requirements of the village innovation system before carrying out the design process and the system creation process. The results of this requirement elicitation can be used further in the software engineering process to produce quality and appropriate village innovation applications
Cellular Communication Propagation at Drone around Building Environment with Single Knife Edge at 10 GHz
Full text linkThe drone communication systems used a cellular network for controlling a drone from a long distance. That communication propagations between drone and base station were analyzed. The drone moved at the track around building environment. That environment used variations in building height. The communication propagation around building environment caused diffraction mechanism. Single knife edge method is used for that diffraction mechanism. The frequency of communication used 10 GHz. That frequency was influenced by atmospheric attenuation. This research was using some variations such as height of drone track location, transmitter power, and AMC (Adaptive Modulation Coding). MCS (Modulation Coding Scheme) was used AMC such as QPSK, 16 QAM, and 64 QAM. Some result was obtained at this research consist of LOS and NLOS distance, SNR, MCS probability, and percentage of drone coverage. NLOS propagation was caused by building height. The SNR value become increase when higher at drone position, such as drone was moving at 20 meters with height of flying drone 80 m and transmitter power 30 dBm obtained SNR 38.21 dBm. That SNR is affected AMC, so a higher SNR value increases AMC. The drone’s coverage 100%, with a height of flying drone 80 meters and transmitter power of 30 dBm. That condition showed more increasing coverage percentage than 64.8% for height of flying drone 20 meters and transmitter power 30 dBm. That result showed that more drone height increased of coverage percentage, probability modulation, and SNR value.The drone communication systems used a cellular network for controlling a drone from a long distance. That communication propagations between drone and base station were analyzed. The drone moved at the track around building environment. That environment used variations in building height. The communication propagation around building environment caused diffraction mechanism. Single knife edge method is used for that diffraction mechanism. The frequency of communication used 10 GHz. That frequency was influenced by atmospheric attenuation. This research was using some variations such as height of drone track location, transmitter power, and AMC (Adaptive Modulation Coding). MCS (Modulation Coding Scheme) was used AMC such as QPSK, 16 QAM, and 64 QAM. Some result was obtained at this research consist of LOS and NLOS distance, SNR, MCS probability, and percentage of drone coverage. NLOS propagation was caused by building height. The SNR value become increase when higher at drone position, such as drone was moving at 20 meters with height of flying drone 80 m and transmitter power 30 dBm obtained SNR 38.21 dBm. That SNR is affected AMC, so a higher SNR value increases AMC. The drone’s coverage 100%, with a height of flying drone 80 meters and transmitter power of 30 dBm. That condition showed more increasing coverage percentage than 64.8% for height of flying drone 20 meters and transmitter power 30 dBm. That result showed that more drone height increased of coverage percentage, probability modulation, and SNR value