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All Journal Jurnal Simantec e-Jurnal Arus Elektro Indonesia JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) CYCLOTRON Multitek Indonesia : Jurnal Ilmiah JE-Unisla Jurnal Teknik Elektro dan Komputasi (ELKOM) INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) Journal of Electrical Engineering and Computer (JEECOM) J-Eltrik JASEE Journal of Application and Science on Electrical Engineering JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Fortech SinarFe7 PELS (Procedia of Engineering and Life Science) Jurnal INFOTEL
Ulum, Miftachul
Program Studi S1 Teknik Elektro, Universitas Trunojoyo Madura
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Journal : JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA)

 1 
Design of Automatic Pesticide Sprayers on Internet-Based Chilli Plants M. Azka Mujaddidin; Miftachul Ulum; Diana Rahmawati; Koko Joni
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 4 No 2 (2020): October
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v4i2.312

Abstract

Chili (Capsicum annum L.) is one of the priority needs for consumption by the Indonesian people in general. With these factors, the soaring price of chilies can not be avoided anymore, one of the factors is the attack of pests and diseases of chili plants. Therefore it is necessary to take appropriate and quick action so that pests and diseases attack on chili plants do not spread widely. However, manual spraying has a weakness that is the time needed by farmers for longer, physical fatigue and exposure to pesticides can endanger the health of farmers in the short and long term. Therefore spraying pesticides electronically can be a solution to this problem. The testing process can be seen from the top of the leaves affected by the disease, then the results of this study can design a system for automatic spraying of pesticides based on the type of disease that attacks by using the Internet of Things and the wifi module ESP8266. The overall results of the trial can be concluded that in testing 10 trials determine the automatic spraying of pesticides 100% success indicator. And Quality of Service for sending value during the trial with index value 3 (satisfactory).
Automatic Pesticide Spray Based on Digital Image Processing in Chili Plants by Classification Backpropagation Neural Network Method Ian Faizal Idenugraha; Diana Rahmawati; Kunto Aji Wibisono; Miftachul Ulum
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 4 No 1 (2020): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v4i1.317

Abstract

In Indonesia demand for chili still quite high and as if it has become a basic necessity for the community. Along with the world in the food processing industry, there has been an increase in the need for chillies, in addition to the high demand and the selling price of chilli peppers, it has encouraged the interest of the community to cultivate chili plants. However, biotic disorders that cause obstacles in efforts to increase chili production. On the leaves and fruit of the chili plant is a part of body the plant that allows the identification process of disease in the chili plant, because there will be changes in color and texture. The process of disease detection in chili plants through digital image processing using the feature extraction method, which has previously been done pre-processing. Then at the segmen-tation stage a thresholding operation is carried out to separate the healthy / diseased leaves / chili. For the classifi-cation of diseases using BPNN (Backpropagation Neural Network) method. The identification process will results five types of diseases, namely fusarium wilt, bacterial wilt, leaf foliage, curly leaves, and anthracnose. From this data will be sent by smartphone via IoT to the automatic sprayer to spray the type of pesticide in accordance with the dose and type of disease identified. Based on the results of testing using 150 samples of leaf and fruit images on chili plants obtained a success percentage of 43% in the leaves and 83.33% in the chilli fruit.
Image Processing Based Aquaponics Monitoring System Haryanto; Desi Anis Anggraini; Miftachul Ulum; Achmad Fiqhi Ibadillah
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 5 No 1 (2021): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v5i1.1220

Abstract

Aquaponics means a culture that is very necessary to be applied, because in this system it is a combination of fish farming techniques as well as plant enlargement techniques by hydroponics. This research develops a smart aquaponics system that can control and increase the acidity level, air temperature, fish feed, and the installation of a camera to monitor fish development. In this system, there are sensors installed to retrieve data. Thus, air quality and circulation is well maintained. The results obtained from this study are to test an automatic feed system that runs well for each experiment, with an accurate proportion of 93.33%, and PH measurements that have been calibrated run well, the comparison of manual measurements using the PH meter measurement sensor gets the proportion 97, 83. for the meter Flow measurement results obtained a proportion of 91.02%, then for plant development every week got pretty good results, in the first week the plants grew 1cm after sowing, 3cm for the 2nd week, 7cm for the second week. -3. The results of measuring the weight of fish using image processing are not much different from manual measurements, the length of the fish is measured manually, it is 7 cm, and in the image it is 5.6 cm, the weight of manual fish is 11g, in the image it is 11.66g. Keywords: Aquaponics; Camera; Android; image processing, flow. Abstrak. Akuaponik merupakan suatu budaya yang sangat disarankan untuk diterapkan, karena pada sistem ini berupa kombinasi dari teknik budidaya ikan sekaligus teknik pembesaran tanaman dengan cara hidroponik. Penelitian ini merancang sistem akuaponik pintar yang bisa mengendalikan dan pantau tingkat keasaman, pakan ikan, dan pemasangan kamera untuk memantau perkembangan ikan. Dalam sistem ini, ada sensor yang dipasang untuk mengambil data,. Dengan demikian, kualitas dan sirkulasi air terjaga dengan baik. Hasil yang didapat dari penelitian ini yaitu untuk pengujian sistem pakan otomatis berjalan dengan cukup baik, dengan persentase keberhasilan 93.33 %, untuk pengukuran PH yang sudah terkalibrasi berjalan dengan baik, perbandingan pengukuran manual dengan pengukuran menggunakan sensor PH meter mendapatkan persentase keberhasilan 97.83% untuk hasil pengukuran sensor Flow meter didapatkan persentase keberhasilan sebesar 91.02%, selanjutnya untuk perkembangan tanaman setiap minggu mendapatkan hasil yang cukup baik, pada minggu pertama tanaman diperkirakan tumbuh 1cm setelah penyemaian, 3 cm untuk minggu ke-2, 7cm untuk minggu ke-3. Pengukuran berat ikan menggunakan Image processing mendapatkan hasil yang tidak jauh berbeda dengan pengukuran secara manual, panjang ikan yang diukur secara manual yaitu 7 cm, dan secara image yaitu 5.6 cm, berat ikan manual 11g, secara image 11.66g
Identification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method Diana Rahmawati; Mutiara Puspa Putri I; Miftachul Ulum; Koko Joni
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 5 No 1 (2021): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v5i1.1221

Abstract

Bacteria are a group of living things or organisms that do not have a core covering. In the grouping, some bacteria are pathogenic. With a microscopic size, many pathogenic bacteria are found around and spread through the food eaten or by touching objects around them, then cause diseases such as diarrhea, vomiting, and others. As a more effective effort to help the government and society prevent disease caused by pathogenic bacteria, a system for the identification and classification of pathogenic bacteria K-Nearest Neighbor was created. This system uses a biological microscope that is attached to a webcam camera above the ocular lens as a tool to see bacterial objects and assist in bacterial capture. Rough player rotates automatically (auto-focus) in image capture. In the process of classification and identifying bacteria, the K-Nearest Neighbor method is used, which is a method with the calculation of the nearest neighbor or calculation based on the level of similarity to the dataset. In this study, the bacteria vibrio chlorae, staphylococcus aereus, and streptococcus m. with the highest accuracy is the K = 9 value of 97.77% using the Chebyshev method.
Planning and Manufacturing of Four Axis Solar Panels With Reflector Angle Adjustments Miftachul Ulum; Adi Kurniawan Saputro; Koko Joni; Riza Alfita; Rosida Vivin Nahari; Siti A’isya; Achmad Ubaidillah
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 6 No 1 (2022): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v6i1.1628

Abstract

Solar thermal energy is one type of renewable energy, so this type of energy can be converted into other energy. This study uses a four-axis solar tracker with angle settings on the reflector to get optimal sunlight, scanning to determine the optimal lighting angle, measurement results are stored in real-time in the data logger. This study uses an LDR (Light Dependent Resistor) as a sunlight detector, equipped with several sensors, namely: current, voltage and power sensor (INA219), light sensor (MAX4409), and temperature sensor (DS18B20), and reflector angle as a parameter of solar efficiency panels. . The results showed that a four-axis solar tracker equipped with a reflector was able to increase the output power. The maximum power production produced by solar panels is: At a reflector angle of 300, the maximum power generated by a static panel is 143.43 W while a solar tracker is 175.15 W. At a reflector angle of 450 the maximum power generated by a static panel is 170.01 W and solar tracker 236.36 W. At an angled reflector of 600 the full power generated by a static panel is 87.77 W, and a solar tracker is 123.36 W. This study concludes that a solar tracker panel with an angle setting of 300 is more capable of maximizing power output than a static solar panel.
Implementation of a Digital Microscope as an Identification System for Parasitic Worms in Cattle Based on Image Processing Kunto Aji; Miftachul Ulum
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 6 No 1 (2022): April
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v6i1.1640

Abstract

Masalah yang sering terjadi pada peternak sapi biasanya karena masalah penyakit yang secara langsung mempengaruhi kesehatan hewan ternak. Gangguan penyakit pada ternak merupakan salah satu faktor yang mempengaruhi pemeliharaan ternak. Hal ini menyebabkan peternak sapi merugi secara ekonomi karena kurangnya pemahaman tentang gejala penyakit sapi yang sulit dikenali. Salah satu gejala penyakit sapi yang sering terjadi disebabkan oleh infeksi cacing parasit. Untuk memastikan hewan tersebut sehat atau tidak, ditentukan dari hasil uji laboratorium melalui sampel fiskal segar sehingga penyakit sapi dapat diketahui secara akurat. Namun, keterbatasan fasilitas laboratorium, terutama di daerah pedesaan, membuat para peternak sulit untuk mengidentifikasi penyakit cacing parasit ini. Tujuan dari penelitian ini adalah menerapkan rancangan mikroskop digital sebagai alat untuk mengidentifikasi jenis telur cacing parasit pada sapi berdasarkan pengolahan citra menggunakan algoritma Yolo V3, sehingga diharapkan dapat membantu peternak dalam mendeteksi cacing parasit di ternak. Pada penelitian ini dirancang mikroskop digital sebagai alat untuk mengidentifikasi jenis telur cacing parasit pada sapi berdasarkan pengolahan citra. Sistem deteksi ini menggunakan mikroskop digital sebagai instrumen untuk mendeteksi keberadaan cacing parasit pada sampel uji. Mikroskop digital ini dilengkapi dengan kamera sehingga data deteksi dari proses pembacaan terhubung langsung ke alat pengolah data. Pada penelitian ini digunakan mekanisme pengolahan citra digital dengan metode Yolov3 yang berfungsi sebagai pengenal cacing parasit pada sapi. Algoritma ini bekerja dengan prinsip ekstraksi ciri yang digunakan untuk membedakan bentuk dan tekstur telur cacing parasit dengan menghitung sekumpulan piksel dengan nilai tertentu. Percobaan telah dilakukan terhadap 100 sampel kotoran sapi. Rata-rata akurasi sistem dalam mengidentifikasi adalah 78,42% untuk parasit dan 80,84% untuk non-parasitProblems that often occur in cattle farmers are usually due to disease problems that directly affect the health of livestock. One of the symptoms of cow disease that often occurs is caused by parasitic worm infection. To make sure the animal is healthy or not, it is determined from the results of laboratory tests through fiscal samples so that cow disease can be known accurately. However, limited laboratory facilities, especially in rural areas, make it difficult for farmers to identify this parasitic disease. The purpose of this study is to apply a digital microscope design as a tool to identify the type of parasitic worm eggs in cattle based on image processing using the Yolo V3 algorithm, so that it is hoped that it can assist farmers in detecting parasitic worms in livestock. In this study, a digital microscope was designed as a tool to identify the type of parasitic worm eggs in image processing cows. This detection system uses a digital microscope as an instrument to detect the presence of parasitic worms in the test sample. This digital microscope is equipped with a camera so that the detection of data from the reading process is directly connected to the data processing tool. In this study, a digital image processing mechanism with the Yolov3 method was used which functions as an identification of parasitic worms in cattle. This algorithm works with the principle of feature extraction which is used to distinguish the shape and texture of parasitic eggs by counting pixels with a certain value. Experiments have been carried out on 100 cow dung. The average accuracy of the system in identifying is 78.42% for parasites and 80.84% for non-parasites
Design and Build a Vaname Shrimp Sorting System Based on Image Processing Miftachul Ulum; Kunto Aji Wibisono; Haryanto Haryanto; Riza Alfita; Adi Kurniawan Saputra
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 6 No 2 (2022): October
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v6i2.1639

Abstract

Vannamei shrimp is a species of shrimp that has high economic value. In the process of trading vaname shrimp, there are different price classes. Determination of the price class of vaname shrimp is based on the size of the shrimp. But in the post-harvest process, the sorting of vaname shrimp is still done manually, namely by placing the white shrimp on a flat table and then separating it by size so that it takes a long time and the level of accuracy of the sorting process also becomes imprecise, as is done by cultivating in the coastal area of ​​Madura, this is due to the limitations of available shrimp post-harvest processing equipment. In addition, the limited supply of electrical energy for the coastal area of ​​Madura is also another factor that hinders the post-harvest process of vanname shrimp. The purpose of this study is to design and create a vannamei shrimp sorting system based on image processing. In processing this shrimp image using the Background Subtraction method. The Background Subtraction method is used as a separator between the object and the background. The sorting process is based on the size detection of shrimp by using the blob detection algorithm. BLOB (Binary Large Object) detection is an image segmentation method based on region growing. The goal is to analyze textures specifically and accurately. Because blob detection distinguishes colors that have thin gradations. Based on the tests that have been carried out, the average accuracy of the system in sorting vannamei shrimp is in the range of 90%.
Co-Authors Abidin, Zainal Achmad Fiqhi Ibadillah Achmad Ubaidillah Achmad Ubaidillah Achmad Ubaidillah Adi Kurniawan Saputro Ahmad Azem Ainur Risal Akhmad Efendi Apriliyanto, Muhammad Aris Jujur Prasetyo Bagus Wahyudi Bana, Muhammad Sulton Clarisna Evita Deni Tri Laksono Desi Anis Anggraini Desy Fatjriyatun Dian Neipa Purnamasari Diana Rahmawati Diana Rahmawati Farid Amir Marzelly Faridatul Husniyah Febrian Andi Pratama Gian Putra Hoetomo Hafid Wihangga Haryanto Haryanto Haryanto Haryanto - Haryanto Haryanto Haryanto Haryanto Haryanto Haryanto, Hujjatur Rofiq I Nyoman Widya Marta Wijaya Ian Faizal Idenugraha Indana Nihayatul Husna Irfanuddin Mudzaki Koko Jon Koko Joni Komang Adi Kurniawan Saputra Kunto Aji Kunto Aji Kunto Aji W Kunto Aji Wibisono Kunto Aji Wibisono Kunto Aji Wibisono luqman hardianto M. Azka Mujaddidin M. Dikhyak Falakhul Akmal M.Zainul Arifin Machrus Ali Maulina Safitri Mirza Pramudia Mochamad Faris Kurniawan Monika Faswia Fahmi Muhammad Apriliyanto Muhammad Farisal Muhammad Ilham Rasyidi Muhammad Sulton Bana Mutiara Puspa Putri I Nur Farida Arini Nurul Isnainin Nurus Sholeh Rahmawati, Diana Riza Alfita Riza Alfita Riza Alfita Riza Alfita Riza Alfita ROSIDA VIVIN NAHARI Sigit Rolis Siti A’isya Sukri, Hanifudin Wahyudi Wahyudi Yois Balian Ilyasa Yuandika Nur Pratama