https://journal.untar.ac.id/index.php/tesla/issue/feedTESLA: Jurnal Teknik Elektro2024-01-10T04:15:09+00:00Endah Setyaningsihendahs@ft.untar.ac.idOpen Journal Systems<h2>TESLA: Jurnal Teknik Elektro</h2> <p><img 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" width="283" height="195" /></p> <div id="content"> <div id="journalDescription"> <table cellpadding="2"> <tbody align="top"> <tr> <td width="100px">Journal Title</td> <td><strong><a href="https://journal.untar.ac.id/index.php/tesla/index"> TESLA: Jurnal Teknik Elektro</a></strong></td> </tr> <tr> <td>ISSN</td> <td><strong><a href="https://issn.brin.go.id/terbit/detail/1547087042" target="_blank" rel="noopener"> 2655-7967 (online)</a> | <a href="https://issn.brin.go.id/terbit/detail/1180430735" target="_blank" rel="noopener">1410-9735 (print)</a></strong></td> </tr> <tr> <td>DOI Prefix</td> <td><strong> Prefix 10.24912 </strong>by <a href="https://search.crossref.org/?q=Tesla%3A+Jurnal+Teknik+Elektro&from_ui=yes" target="_blank" rel="noopener"><strong>Crossref</strong></a></td> </tr> <tr> <td>Editor in Chief</td> <td><strong><a href="https://journal.untar.ac.id/index.php/tesla/about/editorialTeam" target="_blank" rel="noopener"> Dr. Ir. Endah Setyaningsih, MT.</a></strong></td> </tr> <tr> <td>Publisher</td> <td><strong> Program Studi Teknik Elektro, Fakultas Teknik, Universitas Tarumanagara</strong></td> </tr> <tr> <td>Frequency</td> <td><strong><a href="https://journal.untar.ac.id/index.php/tesla/issue/archive" target="_blank" rel="noopener"> 2 issues per year (May & October)</a></strong></td> </tr> <tr> <td valign="top">Citation Analysis</td> <td><strong><a href="https://fatcat.wiki/container/o3pmhgwwwfdstae2su5mwvnhti" target="_blank" rel="noopener"> WIKIDATA</a></strong> <strong>|</strong> <a href="https://scholar.google.com/citations?hl=id&user=-8hOUtEAAAAJ&view_op=list_works&sortby=pubdate" target="_blank" rel="noopener"><strong>Google Scholar</strong></a><strong> | <a href="https://garuda.kemdikbud.go.id/journal/view/13984?issue=Vol%2025%20No%201%20(2023):%20TESLA:%20Jurnal%20Teknik%20Elektro" target="_blank" rel="noopener">Garuda</a> | <a href="https://sinta.kemdikbud.go.id/journals/profile/5766" target="_blank" rel="noopener">SINTA</a></strong></td> </tr> </tbody> </table> <br /> <p><strong>TESLA: Jurnal Teknik Elektro</strong> is an open-access scientific journal published by the Electrical Engineering Study Program - Faculty of Engineering - Tarumanagara University. TESLA: Jurnal Teknik Elektro is intended as a journal that serves as a forum to publish scientific articles on research results in the fields of Science, namely <a href="https://journal.untar.ac.id/index.php/tesla/Focusandscope"><strong>Internet of Things (IoT), Robotics, Telecommunications, Electronics, Computers, Regulatory Systems, Electric Power Engineering</strong></a>. TESLA: Jurnal Teknik Elektro collaborates with Forum Pendidikan Tinggi Teknik Elektro Indonesia (<a title="FORTEI" href="http://fortei.org/v2/" target="_blank" rel="noopener">FORTEI</a>).</p> <p>All submitted articles should report original, previously unpublished research results, experimental or theoretical that are not published and under consideration for publication elsewhere. The publication of submitted manuscripts is subject to peer review. Both general and technical aspects of the submitted paper are reviewed before publication. Manuscripts should follow the <a href="https://journal.untar.ac.id/index.php/tesla/about/submissions#authorGuidelines" target="_blank" rel="noopener">style of the journal</a> and are subject to both review and editing. Online submission and publishing system using <span class="st">Open Journal Systems</span> (OJS). Submissions should be made online via TESLA: Jurnal Teknik Elektro <a href="https://journal.untar.ac.id/index.php/tesla/about/submissions" target="_blank" rel="noopener">submission</a> site. Accepted papers will be available on line and will not be charged <a href="https://journal.untar.ac.id/index.php/tesla/about#custom-2" target="_blank" rel="noopener">a publication fee</a>.</p> <p><strong>OAI Address</strong></p> <p><strong>Jurnal Edukasi Elektro</strong> has OAI address:<a href="https://journal.untar.ac.id/index.php/tesla/oai">https://journal.untar.ac.id/index.php/tesla/oai</a></p> </div> </div>https://journal.untar.ac.id/index.php/tesla/article/view/27723Cover 25 No. 2 OKTOBER 20232023-12-18T01:23:07+00:00Cover 25 No. 2 OKTOBER 2023tesla@ft.untar.ac.id<p>Cover 25 No. 2 OKTOBER 2023</p>2023-10-31T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/27724Daftar Redaksi 25 No. 2 OKTOBER 20232023-12-18T01:26:25+00:00Daftar Redaksi 25 No. 2 OKTOBER 2023tesla@ft.untar.ac.id<p>Daftar Redaksi 25 No. 2 OKTOBER 2023</p>2023-10-31T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/27725DAFTAR ISI Vol.25 No.2 OKTOBER 20232023-12-18T01:29:49+00:00DAFTAR ISI Vol.25 No.2 OKTOBER 2023tesla@ft.untar.ac.id<p>DAFTAR ISI Vol.25 No.2 OKTOBER 2023</p>2023-10-31T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/23804POTENSI BIOMASSA GASIFIKASI: ALTERNATIF BERKELANJUTAN DALAM MENGHASILKAN ENERGI LISTRIK UNTUK MASA DEPAN2023-05-25T05:30:47+00:00Fatimatuz Zahrofatimatuz.20078@mhs.unesa.ac.idMohammad Budiyantomohammadbudiyanto@unesa.ac.idFasih Bintang Ilhamifasihilhami@unesa.ac.id<p><em>The use of sustainable energy sources is becoming increasingly important in responding to future energy challenges. One promising alternative is biomass gasification, which is the process of converting biomass into synthesis gas or methane gas. The potential of gasified biomass as a source of electrical energy has attracted the attention of energy researchers and practitioners. The study aims to illustrate the potential of gasified biomass as a sustainable alternative in generating electrical energy for the future</em><em>. The research method used is the systematic review method. Systematic review research is carried out by reviewing certain criteria in a structured manner to determine the evidence base. The results of the analysis show that gasified biomass has significant potential as a sustainable source of electrical energy. Biomass that can be used in gasification includes agricultural waste, forest waste, industrial waste, and special energy crops grown specifically for energy purposes. The main advantages of gasified biomass include abundant availability, renewable properties, and the ability to reduce greenhouse gas emissions. In addition, gasified biomass can also be integrated with carbon capture and storage technologies to further reduce CO2 emissions. Biomass gasification has great potential as a sustainable alternative in generating electrical energy for the future. The thermal efficiency of biomass gasification can reach a higher level compared to the direct combustion of biomass or the use of conventional steam power plants. Supporting factors for the utilization of gasified biomass include abundant resource potential, waste management, reduction of house gas emissions.</em></p> <p><strong>A</strong><strong>B</strong><strong>S</strong><strong>TRA</strong><strong>K</strong><strong>:</strong><strong> </strong>Penggunaan sumber energi yang berkelanjutan menjadi semakin penting dalam menjawab tantangan energi di masa depan. Salah satu alternatif yang menjanjikan adalah biomassa gasifikasi, yang merupakan proses konversi biomassa menjadi gas sintesis atau gas metana. Potensi biomassa gasifikasi sebagai sumber energi listrik telah menarik perhatian para peneliti dan praktisi energi.Studi ini bertujuan untuk menggambarkan potensi biomassa gasifikasi sebagai alternatif berkelanjutan dalam menghasilkan energi listrik untuk masa depan. Metode penelitian yang digunakan adalah metode systematic review. Penelitian <em>systematic review</em> dilakukan dengan penelaahan dengan kriteria tertentu secara terstruktur untuk mengetahui <em>evidence base</em>. Hasil analisis menunjukkan bahwa biomassa gasifikasi memiliki potensi yang signifikan sebagai sumber energi listrik yang berkelanjutan. Biomassa yang dapat digunakan dalam gasifikasi meliputi limbah pertanian, limbah hutan, limbah industri, dan tanaman energi khusus yang ditanam secara khusus untuk tujuan energi. Keuntungan utama biomassa gasifikasi termasuk ketersediaan yang melimpah, sifat terbarukan, dan kemampuan untuk mengurangi emisi gas rumah kaca. Selain itu, biomassa gasifikasi juga dapat diintegrasikan dengan teknologi penangkapan dan penyimpanan karbon untuk mengurangi emisi CO2 lebih lanjut. Biomassa gasifikasi memiliki potensi besar sebagai alternatif berkelanjutan dalam menghasilkan energi listrik untuk masa depan. Efisiensi termal gasifikasi biomassa dapat mencapai tingkat yang lebih tinggi dibandingkan dengan pembakaran langsung biomassa atau penggunaan pembangkit listrik tenaga uap konvensional. Faktor pendukung pemanfaatan biomassa gasifikasi meliputi potensi sumber daya yang melimpah, pengelolaan limbah, reduksi emisis gas rumah kaca.</p>2023-10-30T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/25074SISTEM PRESENSI DAN PEMBUKA PINTU BERBASIS IoT DENGAN SENSOR FINGERPRINT, SUHU TUBUH DAN RFID 2023-07-14T07:41:25+00:00Heri Andriantoheri.andrianto@eng.maranatha.eduJovan Zhuojovan777888999@gmail.com<p><em>The current attendance device cannot be monitored in real-time through a web application and is not integrated with body temperature sensors and door access systems. In this paper, an Internet of Things (IoT)-based attendance system has been developed, utilizing fingerprint sensors, body temperature sensors, and Radio Frequency Identification (RFID). The fingerprint sensor is used for recording attendance based on fingerprint patterns, the body temperature sensor is used to measure body temperature and ensure that attendees are in good health, while RFID tags on the door access system are used to ensure that only authorized personnel can access the room. The purpose of implementing IoT in the attendance and door access system is to integrate all devices with a web application, enabling real-time monitoring of attendance and room usage. The hardware of the attendance and door access system is developed using two ESP8266 devices. The first ESP8266 is used for processing attendance, reading fingerprint sensors, and measuring body temperature, while the second ESP8266 is used to control door access using an RFID card. The research methodology in this paper includes a literature review of attendance systems, system design, implementation, and testing. The testing results indicate an 80% success rate in fingerprint recognition when the fingerprint sensor is moist and a 100% success rate under normal conditions. RFID tag recognition and door access control achieved a 100% success rate. The web application has successfully integrated the attendance and door access devices, can receive data from the devices, store data in a database, and display data on the web page.</em></p> <p><strong>A</strong><strong>B</strong><strong>S</strong><strong>TRA</strong><strong>K</strong><strong>:</strong><strong> </strong>Perangkat presensi yang ada saat ini belum dapat dipantau secara <em>real time</em> melalui aplikasi web serta belum terintegrasi dengan perangkat pembaca suhu tubuh dan pembuka pintu.<strong> </strong>Pada makalah ini, telah dikembangkan sistem presensi berbasis <em>Internet of Things </em>(IoT) dengan sensor <em>fingerprint</em>, sensor suhu tubuh dan<em> Radio Frequency Identification</em> (RFID). Sensor <em>fingerprint</em> digunakan untuk keperluan pencatatan daftar hadir berdasarkan pola sidik jari, sensor suhu tubuh digunakan untuk mengukur suhu tubuh dan memastikan penghadir dalam keadaan sehat, sedangkan penggunaan RFID tag pada pembuka pintu bertujuan untuk memastikan ruangan hanya dapat diakses oleh pihak yang berhak. Tujuan penggunaan IoT pada sistem presensi dan pembuka pintu yaitu untuk mengintegrasikan semua perangkat dengan aplikasi web sehingga pemantauan presensi dan penggunaan ruangan dapat dilakukan secara <em>real time</em>. Perangkat keras sistem presensi dan pembuka pintu dikembangkan menggunakan dua buah ESP8266. ESP8266 pertama digunakan proses presensi, pembacaan sensor <em>fingerprint</em> dan sensor suhu tubuh sedangkan ESP8266 kedua digunakan untuk pengendalian pintu dalam mengakses ruangan menggunakan sebuah kartu RFID. Metodologi penelitian dalam makalah ini terdiri dari tinjauan pustaka mengenai sistem presensi, perancangan, realisasi dan pengujian sistem presensi. Dari hasil pengujian didapatkan tingkat keberhasilan pembacaan sidik jari sebesar 80% pada saat sensor sidik jari dalam kondisi lembap dan 100% pada saat sensor sidik jari dalam kondisi normal. Tingkat keberhasilan pembacaan RFID tag dan pengendalian pembuka pintu didapatkan sebesar 100%. Aplikasi web telah berhasil mengintegrasikan perangkat presensi dan perangkat pembuka pintu, serta dapat menerima data dari perangkat, menyimpan data ke database serta menampilkan data di halaman web.</p>2023-11-07T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/26748SISTEM MONITORING PADA RUANG PERPUSTAKAAN BERBASIS INTERNET OF THINGS2023-10-16T09:08:40+00:00Andre Williamwilliamandre059@gmail.comAgus Wagyanaagus.wagyana@elektro.pnj.ac.id<p><em>An Internet of Things (IoT) based library room monitoring system is an innovative solution that utilizes the latest technology to help monitor temperature, humidity, lighting levels and noise levels in library rooms. This system is designed to provide accurate and fast information to library managers. This system aims to make it easier for library managers to monitor library space. This system can be accessed via a mobile application, allowing library managers to monitor the library situation remotely. This system uses sensors connected to the internet to collect real-time data about the condition of the library room. These sensors are placed in the library reading room to monitor these parameters and send data to the database. The design of this monitoring system uses the ESP32 device as the main microcontroller which controls each sensor and internet connection and the ESP32CAM as the camera. The measurement of the relative error value is calculated based on the difference between the value from the actual tool and the value read by the sensor used. The value of the actual measuring instrument obtained will be used to calibrate each sensor so that the sensor results will be close to the value of the measurement results of the measuring instrument. Testing for each sensor is carried out 10 – 20 times to get more accurate test results. The relative error value for the DHT22 sensor temperature is 1.15% and the humidity is 5.12%. The relative error values</em><em> </em><em>for the BH1750 sensor when the lights are off and on are 2.84% and 2.10% respectively. The relative error values</em><em> </em><em>for the MAX 9814 sound sensor at a distance of 4 meters and 3 meters are 6.02% and 6.24% respectively. When noise occurs, the tool will respond in the form of a buzzer sound and take an image from the ESP32 CAM.</em> <em>The error values</em><em> </em><em>that appear on the BH 1750 and DHT 22 sensors are because the measuring instrument has the accuracy value of the measuring instrument, as well as the sensor accuracy, whereas in the MAX 9814 Sensor test it is due to deficiencies in the conversion of bit values</em><em> </em><em>to dB form and imperfect sensor calibration.</em><strong> </strong></p> <p><strong>A</strong><strong>B</strong><strong>S</strong><strong>TRA</strong><strong>K</strong><strong>:</strong><strong> </strong>Sistem monitoring ruangan perpustakaan berbasis Internet of Things (IoT) merupakan solusi inovatif yang memanfaatkan teknologi terkini untuk membantu memonitoring suhu, kelembapan, tingkat pencahayaan dan tingkat kebisingan pada ruang perpustakaan. Sistem ini dirancang untuk memberikan informasi yang akurat dan cepat kepada pengelola perpustakaan. Sistem ini bertujuan untuk mempermudah pengelola perpustakaan dalam memantau ruang perpustakaan. Sistem ini dapat diakses melalui aplikasi mobile, memungkinkan pengelola perpustakaan untuk memantau situasi perpustakaan dari jarak jauh. Sistem ini menggunakan sensor yang terhubung dengan internet untuk mengumpulkan data secara real-time tentang kondisi ruangan perpustakaan. Sensor-sensor ini ditempatkan di bagian ruangan baca perpustakaan untuk memantau parameter-parameter tersebut dan mengirimkan data ke <em>database</em>. Perancangan sistem monitoring ini menggunakan perangkat ESP32 sebagai mikrokontroler utama yang mengendalikan setiap sensor dan koneksi internet dan ESP32CAM sebagai kameranya. Pengukuran nilai eror relatif dihitung berdasarkan perbedaan antara nilai dari alat aktual yang ada dengan nilai yang dibaca oleh sensor yang digunakan. Nilai dari alat ukur aktual yang didapatkan akan digunakan untuk kalibrasi dari setiap sensor agar hasil sensor nantinya mendekati nilai dari hasil pengukuran alat ukur. Pengujian tiap sensor dilakukan 10 – 20 kali untuk mendapatkan hasil pengujian yang lebih akurat. Nilai eror relatif suhu sensor DHT22 adalah 1,15% dan kelembapannya 5,12%. Nilai eror relatif pada sensor BH1750 pada saat lampu mati dan menyala masing masing 2,84% dan 2,10%. Nilai eror relatif pada sensor suara MAX 9814 pada jarak 4 meter dan 3 meter masing masing 6,02% dan 6,24%. Saat terjadi kebisingan, alat akan memberikan respon berupa bunyi buzzer dan pengambilan gambar dari ESP32 CAM. Nilai eror yang muncul pada sensor BH 1750 dan DHT 22 karena alat ukur memiliki nilai akurasi dari alat ukur, serta akurasi sensor sedangkan pada pengujian Sensor MAX 9814 adalah karena kekurangan dalam konversi nilai bit ke bentuk dB serta kalibrasi sensor yang kurang sempurna.</p>2023-11-13T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/26714RANCANG BANGUN KENDALI ROBOT HEXAPOD MENGGUNAKAN SMARTPHONE2023-10-12T08:23:23+00:00Syahban Rangkutisyahban3477@gmail.com<p><em>Hexapod robot technology has developed rapidly, starting from robots that can work independently (autonomously) to robots that are controlled via smartphones. In this research, we will design a 6-legged robot (hexapod) that can move stably and follow the commands given. The ESP32 DevKit microcontroller module is used as the center of the hexapod robot control system because it has many digital inputs and outputs, has I2C serial communication, and also has wireless communication. One method used to obtain stable robot movement is to use an inertial measurement unit (IMU) sensor, which is capable of producing angular movement data in a 3-dimensional plane on the pitch (ϕ), roll (θ) and yaw (ψ) axes. The shape of the hexapod robot body is made so that it can be easily integrated with the robot legs that will be used. To drive the robot's legs, a servo motor is used for each joint. Each robot leg consists of 3 joints, so the 6-legged robot drive system requires 18 units of servo motors. Inverse kinematics with a PID control system can control robot movement by applying a tripod gait step pattern. The smartphone is used to control the direction of movement of the hexapod robot in forward, backward, left turn, right turn, and stop directions using a Bluetooth or WiFi connection. Based on the test results, the hexapod robot can be appropriately controlled according to the commands given via smartphone. The test results of 5 meters in a straight direction for forward and backward movement produced a robot speed of around 20cm/S with a deviation error ranging between 7% - 9% from the target point.</em></p> <p><strong>A</strong><strong>B</strong><strong>S</strong><strong>TRA</strong><strong>K</strong><strong>:</strong><strong> </strong>Teknologi robot <em>hexapod</em> telah berkembang pesat, mulai dari robot yang dapat bekerja secara mandiri (<em>autonomous</em>) maupun robot yang dikendalikan melalui <em>smartphone</em>. pada penelitian ini akan merancang bangun robot berkaki 6 (<em>hexapod</em>) yang mampu bergerak dengan stabil dan sesuai dengan perintah yang diberikan. Modul mikrokontroler ESP32 DevKit digunakan sebagai pusat sistem kendali robot <em>hexapod</em> karena memiliki banyak input dan output digital serta memiliki komunikasi serial <em>I2C</em> dan juga memiliki komunikasi <em>wireless</em>. Salah satu cara yang digunakan untuk mendapatkan gerakan robot stabil dapat menggunakan sensor <em>inertial measurement unit (IMU</em>) yang mampu menghasilkan data pergerakan sudut pada bidang 3 dimensi pada sumbu<em> pitch </em><em>(</em><em>ϕ)</em><em>, roll </em>(θ)<em>,</em> dan<em> yaw </em><em>(</em>ψ). Bentuk badan robot <em>hexapod</em> dibuat agar dapat dengan mudah diintegrasikan dengan kaki robot yang akan digunakan. Sebagai penggerak kaki robot digunakan motor <em>servo</em> untuk setiap sendinya, setiap kaki robot terdiri dari 3 sendi, sehingga untuk sistem penggerak robot berkaki 6 membutuhkan 18unit motor <em>servo</em>. <em>Invers</em> kinematik dengan sistem kendali <em>PID </em>dapat digunakan untuk mengatur gerak robot dengan menerapkan pola langkah <em>tripod gait</em>. <em>Smartphone</em> digunakan mengatur arah gerakan robot <em>hexapod</em> pada arah maju, mundur, belok kiri, belok kanan, dan berhenti dengan menggunakan koneksi <em>bluetooth</em> atau <em>wifi</em>. Berdasarkan hasil pengujian robot <em>hexapod</em> dapat dikendalikan dengan baik sesuai dengan perintah yang diberikan melalui <em>smartphone</em>. Hasil pengujian sejauh 5meter pada arah lurus untuk gerakan maju dan mundur menghasilkan kecepatan robot sekitar 20cm/S dengan <em>error</em> simpangan berkisar antara 7% - 9% dari titik yang dituju.</p>2023-11-13T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/27065PERANCANGAN SISTEM PENGENDALI ON/OFF STOP KONTAK LISTRIK DENGAN MENGGUNAKAN ANDROID2023-11-13T09:10:04+00:00Suraidisuraidi@ft.untar.ac.id<p><em>This research creates a system to control the On/Off electric socket using Android as a controller. The system uses a bluetooth connection as communication between the controller and the system. System application for 6 electrical sockets that can be controlled on / off. Sockets are used to supply electrical power from electrical equipment which can be in the form of lamps, fans, and so on with a maximum power of 2000 watts for each socket. The system uses the Arduino nano microcontroller as the system controller, the HC05 bluetooth module, the relay module and programming using the Arduino IDE (Integrated Development Environment), as well as applications on Android. Applications on Android use applications that are already available in PlayStore. Module testing consists of testing the Arduino Nano module, relay module, Bluetooth module, installing the Android application, and testing the entire system. Testing the Arduino Nano module using the Blink program to turn on and off the Arduino Nano onboard LED based on the delay given. Testing the relay module by giving high and low data, then seeing the reaction of the relay. Testing applications by installing on android. Testing the Bluetooth module by connecting the Arduino Nano module and using the Android application to connect to the system and turn it on and off based on the function of the application. The test results for each module went well. This test is to determine the characteristics of each module, useful for combining all modules into one system. Testing the system by combining the Bluetooth module, the Arduino Nano module, 6 relay modules and 6 sockets, a connection is made between the system and Android, then pressing the button function in the application to turn on the light on the socket. System testing works well, according to the initial design. This research made a prototype of the system in question to make it easier to understand the form of the overall system overview.</em></p> <p> </p> <p><strong>A</strong><strong>B</strong><strong>S</strong><strong>TRA</strong><strong>K</strong><strong>:</strong>Penelitian ini membuat sebuah sistem untuk mengendalikan On/Off stop kontak listrik dengan menggunakan android sebagai alat pengendali. Sistem menggunakan koneksi bluetooth sebagai komunikasi antara alat pengontrol dengan sistem. Aplikasi sistem untuk 6 buah stop kontak listrik yang dapat dikendalikan On/Off. Stop kontak digunakan untuk memenuhi daya listrik dari peralatan listrik yang dapat berupa lampu, kipas angin, dan sebagainya dengan maksimum daya 2000 watt untuk setiap stop kontak. Sistem menggunakan mikrokontroler Arduino nano sebagai pengendali sistem, modul bluetooth HC05, modul relay dan pemrograman dengan menggunakan arduino IDE (Integrated Development Environment), serta aplikasi pada android. Aplikasi pada android menggunakan aplikasi yang sudah tersedia di playstore. Pengujian modul terdiri dari pengujian modul arduino nano, modul relay, modul Bluetooth, install aplikasi android, dan pengujian sistem keseluruhan. Pengujian modul arduino nano dengan menggunakan program blink untuk menyalakan dan mematikan lampu led onboard arduino nano berdasarkan delay yang diberikan. Pengujian modul relay dengan diberikan data high dan low, kemudian melihat reaksi dari relay tersebut. Pengujian aplikasi dengan meng-install pada android. Pengujian modul Bluetooth dengan menghubungkan modul arduino nano serta menggunakan aplikasi android untuk melakukan koneksi dengan sistem serta menyalakan dan mematikan berdasarkan fungsi dari aplikasi. Hasil pengujian tiap modul berjalan dengan baik. Pengujian ini untuk mengetahui karakteristik tiap modul, berguna untuk penggabungan seluruh modul menjadi satu sistem. Pengujian sistem dengan menggabungkan modul Bluetooth, modul arduino nano, 6 buah modul relay serta 6 buah stop kontak, dilakukan koneksi antara sistem dan android, lalu ditekan fungsi button pada aplikasi untuk menyalakan lampu pada stop kontak. Sistem bekerja dengan baik, sesuai dengan rancangan awal. Penelitian ini dibuat purwarupa dari sistem yang dimaksud agar lebih mudah untuk dimengerti bentuk gambaran sistem secara keseluruhan</p>2023-11-13T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/26749ANALISIS PENGARUH SUDUT KEMIRINGAN TERHADAP DAYA KELUARAN PANEL SURYA DI PERUM PEMDA SUKAHARJA KARAWANG2023-10-16T11:59:02+00:00Regita Aulia Safitriregitaas227@gmail.comAkbar Rizki Priadiakbar10dc.net@gmail.comTyo Bima Pratamatyobimapratama5@gmail.comYuliarman Saragihyuliarman@staff.unsika.ac.id<p><em>Solar energy is a sustainable and most promising source of energy because the quantity is very large. Energy sources from the sun are expected to be a solution to the problem of energy needs, after many fossil energy sources are decreasing and causing environmental problems. Given Indonesia's location on the equator, it allows sunlight to be received optimally in almost all parts of Indonesia throughout the year. To optimize the intensity of sunlight absorbed by solar panels, the system design requires a panel tilt angle that is most suitable for receiving the greatest solar radiation. The unstable position of the sun caused changes in the output power of the solar panel. Based on these problems, this research was conducted on the effect of tilt angle on the output power of solar panels in Karawang District. The research was conducted through direct data collection on the rooftop of Perum Pemda Sukaharja, Karawang District on October 5, 2023 from 07.00 to 16.00. Solar panels were faced north and given tilt angles of 10°, 20°, and 30°. The highest output power (P) was obtained at 12:00 and the lowest at 07:00. The conclusion of this test is that the solar panel facing north with an angle of 10° has the highest output power of 31.414 Watts with an average measurement result of 18.87 Watts. The 10° tilt angle is the most optimal angle recommendation for solar panels to be applied in Perum Pemda Sukaharja, Karawang District</em></p> <p><strong>ABSTRAK:</strong></p> <p>Energi surya merupakan sumber energi yang berkelanjutan dan paling menjanjikan karena kuantitasnya sangat besar. Sumber energi dari matahari diharapkan mampu menjadi solusi dari permasalahan kebutuhan energi, setelah banyaknya sumber energi fosil yang semakin berkurang dan menimbulkan permasalahan lingkungan. Mengingat letak Indonesia yang berada di garis khatulistiwa, memungkinkan sinar matahari diterima secara optimal hampir di seluruh wilayah Indonesia sepanjang tahun. Untuk mengoptimalkan intensitas sinar matahari yang diserap oleh panel surya, perancangan sistemnya memerlukan sudut kemiringan panel yang paling sesuai untuk menerima radiasi matahari terbesar. Posisi matahari yang tidak stabil menyebabkan perubahan daya keluaran panel surya. Berdasarkan permasalahan tersebut, dilakukan penelitian mengenai pengaruh sudut kemiringan terhadap daya keluaran panel surya di Kabupaten Karawang. Penelitian dilakukan melalui pengambilan data secara langsung di rooftop Perum Pemda Sukaharja Kabupaten Karawang pada tanggal 5 Oktober 2023 dari pukul 07.00 sampai 16.00. Panel surya dihadapkan ke arah utara serta diberikan sudut kemiringan 10°, 20°, dan 30°. Diperoleh hasil daya keluaran (P) paling tinggi pada pukul 12.00 dan paling rendah pada pukul 07.00. Kesimpulan dari pengujian ini adalah panel surya yang dihadapkan ke arah utara dengan sudut 10° memiliki daya keluaran tertinggi sebesar 31,414 Watt dengan rata-rata hasil pengukurannya adalah 18,87 Watt. Pada sudut kemiringan 10° merupakan rekomendasi sudut paling optimal bagi panel surya untuk diterapkan di Perum Pemda Sukaharja Kabupaten Karawang</p>2023-11-22T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/26536RANCANG BANGUN SANDING MACHINE OTOMATIS MENGGUNAKAN CONVEYOR DENGAN PROXIMITY SENSOR2023-11-08T01:41:55+00:00Irfan Daniirfandani12345@gmail.comAmaliah Herlinaamalia@unuja.ac.idSafrudinsafrudin89@gmail.com<p><em>These sanding machines generally operate on a limited scale or on a modern scale. In order for the newly made wood sanding machine to be more effective in the entire production process, it is necessary to have a mode of transportation used to transport raw materials, especially wood. Literature studies, field research, and the design process are the methods used. The results of the research that has been done, transporting a wood sanding machine with a 24Volt DC engine drive system shows that the difference in speed/Rpm can be changed by using a dc dimer. From a 12 Volt DC voltage, you get a speed of 38 Rpm, a 17 Volt DC voltage gets a speed of 43 Rpm, while a 21 Volt DC voltage gets a speed of 80 Rpm. DC motors as drives, belt regulators, and conveyor belts. And also use a tachometer to calculate the speed/Rpm on the conveyor and sanding drum. The sensor in this system functions as an automatic assistance system that will operate the sanding machine and as a proximity sensor to identify objects that pass through the sensor. The detection range of the proximity sensor is 30 cm, which makes it possible to detect objects passing through the sensor.</em></p> <p><strong>ABSTRAK:</strong></p> <p><strong>A</strong><strong>B</strong><strong>S</strong><strong>TRA</strong><strong>K</strong><strong>:</strong><strong> </strong>Mesin amplas ini umumnya bergerak dalam skala terbatas maupun dalam skala modern. Agar mesin pengamplas kayu baru yang dibuat dapat lebih efektif dalam seluruh proses produksinya, diperlukan adanya moda transportasi yang digunakan untuk mengangkut bahan baku khususnya kayu. Studi literatur, penelitian lapangan, dan proses desain adalah metode yang digunakan.Hasil dari penelitian yang telah dilakukan, pengangkutan pada mesin amplas kayu dengan sistem penggerak mesin 24Volt DC menunjukkan bahwa perbedaan kecepatan/Rpm dapat diubah dengan menggunakan dimer dc. Dari tegangan DC 12 Volt mendapatkan hasil kecepatan 38 Rpm, Tegangan DC 17 Volt mendapatkan hasil kecepatan 43 Rpm, Sedangkan tegangan DC 21 Volt mendapatkan hasil kecepatan 80 Rpm. Motor DC sebagai penggerak, pengatur belt, dan belt konveyor. Dan juga menggunakan tachometer untuk menghitung kecepatan/Rpm pada koveyor dan drum amplas. Sensor pada sistem ini berfungsi sebagai sistem bantuan otomatis yang akan mengoperasikan mesin pengamplasan dan sebagai sensor proximity untuk mengidentifikasi benda yang melewati sensor. Jangkauan deteksi sensor jarak adalah 30 cm, yang memungkinkan untuk mendeteksi objek yang melewati sensor</p>2023-10-30T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/25831ANALISIS POTENSI ENERGI MATAHARI MENJADI ENERGI LISTRIK DI INDONESIA: PROYEKSI DAN PERAMALAN KAPASITAS TERPASANG PLTS DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING2023-11-13T07:26:34+00:00Hizkia Timotiushizkia.timotius@student.president.ac.idJoni Welman Simatupangjoniw@ieee.orgMutiara Andrianimutiara.andriani@student.president.ac.idPraja Situmeangpraja.situmeang@student.president.ac.idIndra Ramos SMindra.sm@student.president.ac.idMuamar Fauzimuamar.fauzi@student.president.ac.id<p><em>This study endeavors to assess the progression in the installed capacity of Solar Power Plants (PLTS) in Indonesia, given that the obtained data indicates a solar energy potential of 207,898 MW in the country. However, the installed capacity of Solar Photovoltaic (PV) Power Plants (PLTS) is still low compared to other types of power plants. In the projection for the National Electricity Supply Business Plan (RUPTL) 2021-2030, it is expected that the installed capacity of PLTS will increase to 4.7 GWp by 2030, but this is still far from the target projection of 800-840 GWp by 2050 according to IRENA. Through forecasting using three methods, namely moving average, single exponential smoothing, and double exponential smoothing, the projected installed capacity for the year 2050 significantly deviates from IRENA's projection. Historical data of installed capacity of PLTS from 2010 to 2022 was used as the basis for the forecasting. The results of the three forecasting methods were compared to assess their accuracy and performance in predicting the installed capacity of PLTS in the future years. This study suggests using the double exponential smoothing method as a feasible forecasting technique and emphasizes the need for further research on inhibiting factors and support from various stakeholders to successfully utilize solar energy as a source of electricity in Indonesia. The findings of this research provide guidance for the planning and development of PLTS in the future, as well as important implications for achieving the national renewable energy projection targets.</em></p> <p><strong>ABSTRAK:</strong></p> <p>Studi ini berupaya untuk menilai perkembangan kapasitas terpasang Pembangkit Listrik Tenaga Surya (PLTS) di Indonesia mengingat data yang diperoleh menunjukkan potensi energi surya sebesar 207.898 MW di Indonesia<strong>. </strong>Namun, kapasitas terpasang Pembangkit Listrik Tenaga Surya (PLTS) masih rendah dibandingkan jenis pembangkit lainnya. Dalam proyeksi RUPTL 2021-2030, diharapkan kapasitas terpasang PLTS meningkat menjadi 4,7 GWp pada tahun 2030, tetapi tetap jauh dari target proyeksi IRENA yang mencapai 800-840 GWp pada tahun 2050. Melalui peramalan menggunakan tiga metode, yaitu <em>moving average</em>, <em>single exponential smoothing</em>, dan <em>double exponential smoothing</em>, diperoleh hasil kapasitas terpasang pada tahun 2050 dengan selisih yang signifikan dari proyeksi IRENA. Data historis kapasitas terpasang PLTS dari tahun 2010 hingga 2022 diambil sebagai dasar peramalan. Hasil peramalan dari ketiga metode tersebut dibandingkan untuk menilai akurasi dan kinerjanya dalam memprediksi kapasitas terpasang PLTS pada tahun-tahun mendatang. Penelitian ini menyarankan penggunaan metode <em>double exponential smoothing</em> sebagai metode peramalan yang layak, dan menekankan perlunya penelitian lebih lanjut mengenai faktor penghambat dan dukungan dari berbagai pihak untuk berhasilnya pemanfaatan energi surya sebagai sumber tenaga listrik di Indonesia. Hasil penelitian ini memberikan panduan untuk perencanaan dan pengembangan PLTS di masa depan, serta implikasi penting dalam mencapai target proyeksi energi terbarukan nasional.</p>2023-11-29T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektrohttps://journal.untar.ac.id/index.php/tesla/article/view/27371PEMODELAN PEMANTAUAN TEMPERATUR UNTUK INKUBATOR EUBLEPHARIS MACULARIUS (LEOPARD GECKO) JANTAN BERBASIS ZIGBEE2024-01-10T04:15:09+00:00Meirista Wulandarimeiristaw@ft.untar.ac.idChristopher Calvin Adriantochristopher.525160025@stu.untar.ac.idHugenghugeng@ft.untar.ac.idSuraidisuraidi@ft.untar.ac.id<p><em>One type of reptile that is currently developing is the Eublepharis Macularius or better known as the Leopard Gecko. Leopard Gecko is a tame reptile class animal that has a unique pattern on the outer skin of its body. The size of this animal is also not too big but also not too small, around 24 - 27 cm. The breeding of this animal is one of the interesting things to study because the sex of the Leopard Gecko can be influenced through the temperature setting of the egg incubator. Engineering the Leopard Gecko incubator for temperature is one of the interesting implementations of electrical engineering to enliven the Leopard Gecko industry. A simple plastic container can be engineered as a means of monitoring and controlling the temperature of the Leopard Gecko incubator by wireless Xbee. By controlling the temperature of the incubator, the sex of the Leopard Gecko eggs can be monitored. A certain temperature can determine the sex tendency of the Leopard Gecko. A temperature of 26°C can hatch female Leopard Gecko eggs, while a temperature of 32°C can hatch male Leopard Gecko eggs. Some electronic equipment that can be used are such as temperature sensors, microcontrollers, ZigBee, relays, incandescent lamps, and monitors. With this set of electronic equipment, the temperature in the incubator can be monitored and controlled wirelessly.</em> <em>Translated with DeepL.com (free version)</em></p> <p><strong>ABSTRAK</strong></p> <p>Salah satu hewan jenis reptil yang sedang berkembang saat ini adalah Eublepharis Macularius atau yang lebih dikenal dengan Leopard Gecko. Leopard Gecko merupakan hewan berkelas reptil yang jinak dan mempunyai corak yang unik pada kulit luar tubuhnya. Ukuran hewan ini juga tidak terlalu besar namun juga tidak terlalu kecil, sekitar 24 – 27 cm. Perkembangbiakan hewan ini merupakan salah satu hal yang menarik untuk diteliti karena jenis kelamin dari Leopard Gecko dapat dipengaruhi melalui pengaturan temperatur dari inkubator telurnya. Rekayasa inkubator Leopard Gecko terhadap temperatur merupakan salah satu implementasi bidang teknik elektro yang menarik untuk menyemarakan perindustrian Leopard Gecko. Kontainer plastik sederhana pun dapat direkayasa sebagai alat pemantauan dan pengendalian temperatur inkubator Leopard Gecko secara nirkabel Xbee. Dengan dikendalikannya temperatur pada inkubator, jenis kelamin hasil penetasan telur Leopard Gecko dapat dipantau. Temperatur tertentu dapat menentukan kecenderungan jenis kelamin dari Leopard Gecko tersebut. Temperatur 26<sup>o</sup>C dapat menetaskan telur Leopard Gecko berjenis kelamin betina, sedangkan pada temperatur 32<sup>o</sup>C dapat menetaskan telur Leopard Gecko berjenis kelamin jantan. Beberapa peralatan elektronika yang dapat digunakan adalah seperti sensor suhu, mikrokontroler, ZigBee, relay, lampu pijar, dan monitor. Dengan rangkaian peralatan elektronika tersebut, temperatur pada inkubator dapat dipantau dan dikendalikan secara nirkabel pada rentang suhu 32,00<sup>o</sup>C - 32,50<sup>o</sup>C. Hal ini dilakukan untuk mengupayakan didapatkannya hasil penetasan telur Leopard Gecko berjenis kelamin jantan.</p>2023-12-14T00:00:00+00:00Copyright (c) 2023 TESLA: Jurnal Teknik Elektro