Buletin Ilmiah Sarjana Teknik Elektro
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
Articles
295 Documents
<![CDATA[Automatic Feeding System in Pond Fish Farming Based on the Internet of Things ]]>
<![CDATA[Tomy Chandra Mahendra]]>;
<![CDATA[Sunardi Sunardi]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.5784
<![CDATA[One of the fish commodities consumed by the Indonesian people is catfish because it tastes good. Cultivation of catfish requires special attention regarding feeding because if it is not enough it can cause the fish to become cannibals, whereas if too much feed can cause disease. Therefore, it is necessary to monitor and control the provision of fish feed on a scheduled basis. This study aims to facilitate catfish farming in automatically scheduled fish feeding by utilizing the Internet of Things (IoT). This system is built using a NodeMCU micro controller which is connected to a Real Time Clock (RTC) sensor to adjust the feeding schedule. In addition, ultrasonic sensors are used to monitor feed conditions and servo motors to open and close the fish feed storage valve. This study succeeded in providing catfish feed automatically and on time according to a predetermined schedule, namely at 06.00 am, 12.00 noon, and 18.00 pm. Timing is based on the active hours of catfish. The system has also been successfully monitored and controlled remotely via the internet using the Blynk application. In addition, the system has also been able to identify the remaining feed reserves remaining in the storage container. This automatic feeding system has been operating in accordance with the purpose of the system, which is to provide fish feed according to the feeding hours of catfish so that cannibals or fish that are sick with ammonia are not found from leftover feed that is not eaten by catfish.]]>
<![CDATA[The Stability of the Fixed Wings Unmanned Aircraft Using the PID Method at KRTI 2019 ]]>
<![CDATA[Pramudya Wisnujati]]>;
<![CDATA[Son Ali Akbar]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.6478
<![CDATA[This study designed a UAV with an unmanned aircraft type with a Fixed Wings model that can perform the PID tunning process manually using the FBWA flight mode. An unmanned aircraft with the Fixed Wings type is designed to perform PID Roll tuning based on altitude and flight speed using the trial and error method increases the Proll value of 0.06 x 2 until the oscillation disappears and the final value of Proll is reduced by 50%. For the Iroll value, the increase in the value made is 0.1 x 2, increase it until it is sufficient and the final value of Iroll is reduced by 50%. For the Droll value, increase the value of 0.004 x 2 until the oscillation disappears and the final value of the Droll is reduced by 50%.]]>
<![CDATA[Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease ]]>
<![CDATA[Dwika Ananda Agustina Pertiwi]]>;
<![CDATA[Pipit Riski Setyorini]]>;
<![CDATA[Much Aziz Muslim]]>;
<![CDATA[Endang Sugiharti]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.7548
<![CDATA[This study aims to improve the accuracy of the classification algorithm for diagnosing chronic kidney disease. There are several models of data mining. In classification, the Support Vector Machine (SVM) algorithm is widely used by researchers worldwide. The data used is a chronic kidney disease dataset taken from the UCI machine learning repository. This data consists of 25 attributes and 11 numeric data attributes, and 14 negative attributes. To call continuously, discrete data is used. Meanwhile, data is selected using Correlation-based Feature Selection (CFS) to reduce irrelevant and redundant data. The research results by applying discretization and feature selection based on correlation for classification in the SVM algorithm with 10-fold cross-validation show an increase in accuracy of 0.5%. The classification of the vector machine support algorithm in the diagnosis of chronic kidney disease produces an accuracy of 99.25%, and after applying discretization and correlation-based feature selection, produces an accuracy of 99.75%. Implementation of discretion and correlation-based feature selection to optimize support vector machine for diagnosis of chronic kidney disease has increased accuracy by 0.5%. The proposed method is feasible as a method of diagnosing chronic kidney disease.]]>
<![CDATA[EM-IOT: IoT-Based Battery Monitoring System and Location Tracking on Electric Vehicles ]]>
<![CDATA[Tasya Thifali Salsabila]]>;
<![CDATA[Haikal Faiz Ramadhan]]>;
<![CDATA[Iftitah Imawati]]>;
<![CDATA[Dwi Ana Ratna Wati]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.8053
<![CDATA[As the community increasingly embraces electric vehicles over time, there is a growing necessity for electric vehicle users to determine the real-time status of their batteries and their locations. This enables them to estimate when and where their electric vehicle batteries will need to be recharged. The battery condition can be used to estimate the remaining distance that the electric vehicle can travel while monitoring and observing the location of the electric vehicle is required for security and can also be used as a suggestion for electric vehicle users to carry out charging by adding information on the location of the nearest charging station. The EM-IOT system is designed to be accessible via an Android smartphone with an easy-to-understand and attractive user interface. The wireless system is designed with the ES12E module and the Thingspeak platform. Testing is done to ensure the system can work properly. The test results of battery monitoring and location tracking on the EM-IOT application have been able to display data with an average error not exceeding 5%. The data on the application is updated every 15 seconds. The EM-IOT system can function properly to monitor battery voltage, battery percentage, remaining distance, and the current location of the system vehicle.]]>
<![CDATA[Distance Estimation on Ultrasonic Sensor Using Kalman Filter ]]>
<![CDATA[Haryo Setiawan]]>;
<![CDATA[Alfian Ma'arif]]>;
<![CDATA[Hamzah M Marhoon]]>;
<![CDATA[Abdel-Nasser Sharkawy]]>;
<![CDATA[Abdullah Çakan]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.8089
<![CDATA[This research discusses about the distance estimation on ultrasonic sensor using Kalman Filter method. Accuracy level problem on ultrasonic sensor will be increased using Kalman Filter. Kalman Filter consists of two parts which are prediction and update. This research applies Kalman Filter method using Arduino Uno and Ultrasonic sensor HC-SR04. The test result compares the sensor data before and after Kalman Filter is applied. The test result of sensor value after given Kalman Filter depends on the value of noise sensor covariance matrix (R) and process noise covariance (Q). The best value of R and Q is 100 and 0.01. If the distance value between R and Q is too small, the filtering result will be invisible. In contrast, if the distance value between R and Q is too far, the filtering result could remove the original measured sensor data. In conclusion, applying Kalman Filter method in Ultrasonic sensor could estimate and increase the accuracy of sensor value up to 7%.]]>
<![CDATA[Automatic Roof Prototype On Aviary With Telegram Based Monitoring ]]>
<![CDATA[Muhammad Irsyad Hidayatullah]]>;
<![CDATA[Indah Sulistiyowati]]>;
<![CDATA[Dwi Hadidjaja Rasjid Saputra]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.8214
<![CDATA[Aviary is a large cage that is designed to resemble the natural habitat of the flora and fauna contained therein. The purpose of making an aviary is as a breeding place for several flora and fauna within the scope of the community. Apart from that, some people build an aviary in their yard as a place to channel their hobbies. The success of aviary development has many supporting factors, one of which is weather conditions. Many aviaries out there do not have a protective roof so that the aviary experiences bad conditions when it rains, considering that Indonesia is a country with a tropical climate where rain can fall at any time. Based on these problems, this research was carried out by designing an automatic roof prototype that was placed on the aviary with a working system to detect the conditions that existed in the aviary and to protect the aviary from exposure to heat and excess rain. The prototype will detect the light intensity value and the value from the rain sensor reading, when it exceeds the specified value limit the tool will work and the roof will be closed. Contribution of this research The application of this prototype directly has a success rate of up to 90% as long as the components used can work optimally. The contribution to this research is the use of the Research and Development method as a research method and the use of telegram bots as a monitoring tool in this study.]]>
<![CDATA[OMRON CP1E-NA20DR-A PLC Based Fish Scales and Manure Cleaner Prototype ]]>
<![CDATA[Teguh Priyono]]>;
<![CDATA[Wahyu Sapto Aji]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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<![CDATA[In fish removal, most of them still use manual cleaning tools whose main crust is human labor so that it can take a not fast time, especially if it is large-scale in cleaning fish and in terms of safety, it is very risky for injured hands due to manual cleaning tools. Aka it is necessary to have a fish cleaning device and clean the dirt automatically, Design and build a prototype of a fish scales and dirt cleaner based on PLC OMRON CP1E-NA20DR-A is a solution in cleaning fish scales and large-scale fish excrement. The way the tool works is that the operator will put the fish into the machine, after the fish is inserted, the sensors at the entrance of the fish will respond to the fish by sending a signal to the PLC so that the PLC will turn on the induction motor as the main crust of the machine, after the engine starts the fish will start to clean the scales and dirt using a knife that has been designed to be used on the machine. Researchers used three fish samples in machine performance experiments to find out how efficient the process of combing and cleaning the dirt was compared to using manual cleaning. The results show that the tool has worked as a whole in scouring and cleaning the dirt as desired by the researcher.]]>
<![CDATA[Naive Bayes Method Monitoring Macro Nutrition and Soil Moisture Using Naive Bayes Method based on Internet of Things ]]>
<![CDATA[Herwindo Rahadian]]>;
<![CDATA[Anton Yudhana]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.6305
<![CDATA[The different land quality causes farmers not to know the exact quality of their agricultural land. Improper processing of agricultural land can result in a decrease in the quality of a land. The content of soil macronutrients consists of nitrogen (N), phospor (P), and potassium (K), these contents that usually affect plant growth. The development of an optical transducer for which is used to measure the wavelength of an object in everyday life can use led sensors as a light source and photo diodes as light detection using the TCRT5000 sensor as an infrared wave transmitter module. The use of the internet of things at this time is very useful to facilitate monitoring. The use of naïve bayes to determine the resulting probability. the soil moisture content obtained averaged 20.63% and nitrogen content values with an average of 590, Phospor 513 and Potassium 670. The sending of data to ThingSpeak can be determined as desired. It is hoped that this research can be developed by refining the use of sensors and methods used to make it easier to apply in life.]]>
<![CDATA[Sentiment Analysis of Electronic System Provider (PSE) Method Using Support Vector Machine ]]>
<![CDATA[Turiyan Turiyan]]>;
<![CDATA[Nurirwan Saputra]]>;
<![CDATA[Ahmad Riyadi]]>;
<![CDATA[Meilany Nonsi Tentua]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.7416
<![CDATA[The study discusses the policy issued by the Indonesian Ministry of Communication and Information on Electronic System Providers (PSE), with a special emphasis on the impact of the policy that has triggered public reactions on social media platforms. Based on the regulations that have been made, it has an impact on the public who believe that the regulation is made to take away access freedom and data privacy on digital systems used by the public. Sentiment analysis can see public opinion on the policy issued by the Ministry of Communication and Information. The data used is taken from social media which is labeled "positive" and "negative". The research method used in sentiment analysis is the Support Vector Machine method. The contribution of this research is to determine the boundary line between the two classes in this research data. The result of the F1 score can be measured in the model, with a achieved value of 75%.]]>
<![CDATA[Industrial Relations Dispute Simulation System Prototype with Artificial Intelligence Approach ]]>
<![CDATA[Naik Henokh Parmenas]]>;
<![CDATA[Ridha Sefina Samosir]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.7607
<![CDATA[There are many cases of industrial relations disputes/IRD every year. IRD cases can be resolved by kinship or mediation to reduce the material burden of all parties, such as case costs other than time and energy. With this background, the researcher proposed developing a system for simulating the IRD judicial process with an AI approach. AI encourages faster and high-accuracy prediction results because AI works through the learning process against a set of training data to produce learning models. The research method used is an experimental laboratory to select AI algorithms with the highest accuracy. Meanwhile, for system development methods, research proposes prototyping methods with designing systems using UML. Prototyping is an option because it takes the intensity of communication between the developer and the end user to determine the prototype of the system being built. System development with platform website. UML provides a variety of diagrams that facilitate communication with developers to illustrate the system being built. System testing uses the black box methodology approach because at this stage, testing is carried out to ensure that the functional system has met the needs. From the experimental results, the decision tree algorithm provides the highest accuracy of 80% in training and testing a set of datasets in the form of cases and IRD court rulings from 2022. The accuracy score means that the learning model by the decision tree algorithm can correctly predict (TP / True positive) 75 % of all cases (test data). The accuracy score is obtained through a confusion matrix that shows the performance of the decision tree algorithm for classification. The results of this research help the process of simulating IRD cases before being taken to judicial line to minimize costs and other efforts that could potentially be incurred during judicial process.]]>
