Search

Meningkatnya limbah masker sekali pakai dari masyarakat pada masa pandemi COVID-19 menjadi perhatian khusus pemerintah dikarenakan dampak yang ditimbulkannya berpotensi tinggi terhadap penularan COVID-19, pencemaran lingkungan dan penyalahgunaan masker bekas. Tempat sampah pintar berbasis RAMPS 1.4 pada penelitian ini dirancang untuk mampu mengelola limbah masker sekali pakai sehingga aman ditangani lebih lanjut. Tahap-tahap pengelolaan limbah masker sekali pakai dilakukan sesuai aturan dari Kementerian Kesehatan Republik Indonesia yaitu, identifikasi, sterilisasi, pengubahan bentuk dan pengemasan. Seluruh tahapan dilakukan secara otonom dan non-kontak untuk mnghindari kontaminasi COVID-19. Objek penelitian ini berupa limbah masker sekali pakai berbahan dasar kain non-woven yang umum digunakan. Dari hasil pengujian terhadap 10 sampel limbah masker sekali pakai, terdapat 1 sampel yang tidak mampu melewati seluruh tahap sehingga keberhasilan kinerja tempat sampah pintar adalah 90 %. Waktu yang dibutuhkan untuk sekali proses pada seluruh tahap adalah 45 detik.

Informasi mengenai data persebaran ikan merupakan informasi yang sangat berguna untuk nelayan dalam menemukan lokasi persebaran ikan. Informasi tersebut dikeluarkan oleh Balai Riset dan Observasi Laut dari kementrian Kelautan yang berupa data lokasi latitude dan longitude. Layanan ini berupa peta digital, peta tersebut kita kenal dengan Peta Perkiraan Daerah Penangkapan Ikan (PPDPI). Pada kenyataannya masih banyak nelayan yang kurang memanfaatkan informasi tersebut yang menyebabkan hasil tangkap ikan oleh nelayan kurang maksimal. Penelitian ini membuat prototipe perangkat portabel navigasi untuk menemukan lokasi sebaran ikan. Prototipe bekerja berdasarkan data informasi yang berupa data latitude dan longitude dari Balai Riset dan Observasi Laut. Dengan memasukan data persebaran ikan maka jarak lokasi dan arah lokasi target dapat diketahui. Data informasi dapat dimasukkan kedalam prototipe navigasi dengan menggunakan komunikasi bluetooth yang kemudian data tersebut diolah oleh mikrokontroller. Sensor yang digunakan dalam perangkat adalah sensor GPS dan sensor kompas. Hasil penelitian didapatkan prototipe navigasi dapat menghitung jarak lokasi tujuan terhadap lokasi awal dengan tingkat keberhasilan sebesar 99.404106% dan pengukuran sudut target lokasi tujuan dengan tingkat keberhasilan sebesar 99.0271%.

The face is one of the easiest way to identify individual and to distinguish. Therefore, the face recognition system is usually needed in the security system in the restricted rooms of the company. This research is to minimize all fraudulent actions such as theft of the company data. In this final project, the method used is histogram equalization and fisherface. The main step in this security system is that the user's face will be taken using a webcam. Then the process of face recognition uses the histogram equalization and the fisherface method using a PC (Personal Computer). Fisherface is a combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods. When an RFID sensor matches the employee data, the camera will capture and the process of face recognition will be run. After that the face data will be matched with the existing face data. When the both is match, the PC sends a command to the Arduino microcontroller to open the solenoid door lock. So that the security systems through face recognition can be more effective than conventional security systems. The test result of the face recognition system which has been done in this Final Project, has a success rate of 88.33% which was obtained from 120 times of experiments consisted of 12 poses. The test success level of the security system with 3 correspondents was 88.33%.

One indicator of road’s feasibility can be seen from the conditions on the road surface. Road damage that often occurs is the number of potholes or holes [1]. In this study, we made an inspection device that can detect hollow roads automatically. Where the results of the detection will be sent to the server along with the coordinates of the location of the hole that will be processed and used as data on information on roadworthiness in an area. So when conducting road inspections we do not calculate the number of manually perforated roads so that the inspection process will run faster and more efficiently. This tool works with the camera as the main sensor, the camera works with the eye to provide visual data in the form of real-time video [2]. The visual data is processed using the Convolutional Neural Network algorithm which has 480 x 320 pixels with convoluted 2 times and produces smaller pixels, so we obtain weights to be classified with datasets [3]. These results will result in a decision whether there is a hole or not on the road that has been inspected by this tool. In addition to knowing where the location of the damage is this tool is equipped with GPS. If a hole is detected, the tool will take a picture, then send the image and coordinates to the server, so that the server can find information on the hole in an area [4]. This study obtained a success percentage of 92.8% in detecting holes using the CNN (Convolutional Neural Network) method.

The development of installed capacity in power plants in 2018 was 41.696 MW or increased from the previous year in 2017 amounted to 39.652 MW (PLN Statistics, 2018). This has an impact on the reduced availability of fuel due to overexploitation. The highest energy sold per customer group in 2018 was the household customer group that was 41.7% higher than the industrial sector customer group by 32.8% (PLN Statistics, 2018). At present the use of electronic equipment for household needs is increasingly diverse. Many equipment that is often used in daily life is electronic equipment that is inductive load. Inductive load causes the value of the power factor to fall so that the power usage (Watt) becomes less than optimal. To overcome the problem caused by the large number of inductive loads a reactive power compensator is needed which is to use a capacitor. In this final project, a system designed to be able to measure and correct power factors automatically uses the Neural Network method and can monitor power online based on IoT. The results of testing the power factor improvement system were 97.8% successful in the trained electric load and 94.8% in the untrained electrical load.

Roads are land transportation infrastructure that covers all parts of the road. Roads with bad conditions will interfere with the achievement of activities to a destination. The situation also includes damage to the road surface in the form of holes. To overcome this, in this Final Project a hole detector was detected in the road using the Gray Level Co-occurrence Matrix (GLCM) and Neural Network (NN). The tool detects holes in the surface of the road using a camera by walking along the road being examined. The camera is used instead of the eye to detect road surface damage. The method used to detect holes is the GLCM. The GLCM method produces several features, namely entropy, contrast, energy, homogeneity, and correlation which will then be processed using a NN to produce a decision whether there is a hole or not. In addition to knowing where the location of the damage is equipped with GPS (Global Positioning System). The results of image feature extraction using the GLCM and road classification using NN can be used in the hole detection process. Testing is done using a car prototype that is monitored through the computer. The percentage of successful hole detection is 86.6% using 10 hidden. When a hole is detected the device manages to take a picture, then sends the hole coordinates to the server.

Improving the English skills of polytechnic students is a great challenge in the era of AEC and Revitalisation. Problems found in Shipbuilding Institute of Polytechnic Surabaya (PPNS) are students’ lack of motivation and insufficient time to study outside the classroom due to their hectic schedule. Therefore, the researchers develop an Android-based 'Technical English' application to support English teaching and learning process in PPNS. This application is also equipped with attractive visual images, dictionary and menu designed specifically for each study program in PPNS. The software used in this research is ReactNative and its development method is System Development Life Cycle (SDLC) prototyping model. By developing and applying this application, students are more motivated and really into learning English, thus improve their skills. They can learn technical English anytime anywhere as ‘easy as pie’.

On this paper, the predicted results of the two types of methods will compare. This is very important because, the error in the determination method will cause the prediction result is not optimal. Two types of methods to be coordinated are the Extreme Learning Machine and Back propagation Neural Network. Testing will be done by entering the calculation from each method to Hand Typist Robot to perform the same task. This robot is used to help Quadriplegic Person in operating a computer keyboard and has 4 Degrees of Freedom (DOF) in both arms. The actuator for all DOFs is Dynamixel AX-12A. This robot processed CMPS10 sensor data which sense the changes provided by Quadriplegic Person. The change of motion will be processed using a method to predict the movement of each Dynamixel AX-12A motor. The most optimal method will produce the lowest Mean Square Error (MSE).

This paper presents Automated Guided Vehicle (AGV) to move cardboard from one place to another using modul communication method as communication medium between AGV. In previous agv usage agv operation is done automatically with human monitoring to allow agv to operate within its working area and no accident occurs between agv. In this study, the input signal value of the received signal strength indicator (RSSI) method is used to control and regulate the alternate workspace. The test results show that the use of the received signal strength indicator (RSSI) method can optimize AGV work with work orders that are run interchangeably when some agv go to the same point. Furthermore, Line Follower system can accelerate the operation of Automated Guided Vehicle (AGV) on the determination of the operation path.

Tiga perempat wilayah Negara Kesatuan Republik Indonesia (NKRI) merupakan wilayah perairan.NKRI adalah negara kepulauan dengan jumlah pulau terbanyak di dunia yaitu 17.504 pulau serta mempunyai panjang garis pantai terpanjang kedua di dunia setelah Kanada. Kebiasaan nelayan Indonesia memasuki wilayah perairan negara lain membuat nelayan Indonesia ditangkap oleh penegak hukum negara lain. Kebiasaan nelayan Indonesia memasuki wilayah perikanan Australia kerap menimbulkan pasang surut hubungan kedua negara. Oleh karena itu, dalam penelitian ini dibuat prototipe perangkat yang dapat memberikan informasi kepada nelayan ketika berlayar bahwa posisi kapal melanggar batas perairan negara lain atau tidak. Sebagian besar kapal-kapal nelayan tradisional tersebut tidak dilengkapi dengan alat navigasi yang memadai. Sehingga perlu perangkat yang dapat memberikan informasi dini kepada jika telah mendekati batas zona perairan negara lain. Prototipe dibuat dengan menggunakan Arduino Mega 2560 / Arduino Uno dan GPS Neo-6M. Modul GPS Neo 6M digunakan sebagai penentuan lokasi posisi kapal kapal, posisi kapal latitude (x) dan longitude (y). Kemudian titik-titik pada garis perbatasan garis, data latitude (xi) dan longitude (yi) diinputkan terlebih dahulu dalam mikrokontroller. Mikrokontroller menghitung jarak posisi kapal dengan titik-titik pada garis perbatasan. Dari pengujian dan pengambilan data yang telah dilakukan, diketahui bahwa rata-rata error dengan Arduino Mega adalah 3,19 % dan dengan menggunakan Arduino Uno nilai error (rata-rata) adalah 5,32 %.

The Internet of Things (IoT) provides ease to monitor and to gain sensor data through the Internet [1]. The need of high quality data is increasing to the extent that data monitoring and acquisition system in real time is required, such as smart city or telediagnostic in medical areas [2]. Therefore, an appropriate communication protocol is required to resolve these problems. Lately, researchers have developed a lot of communication protocols for IoT, of which each has advantages and disadvantages. This study proposes the utilization of MQTT as a communication protocol, which is one of data communication protocols for IoT. This study used temperature and humidity sensors because the physical parameters are often needed as parameters of environment condition [3]. Data acquisition was done in real-time and stored in MySQL database. This study is also completed by interface web-based and mobile for online monitoring. This result of this study is the enhancement of data quality and reliability using MQTT protocol.