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Budi Prayitno
Institut Teknologi PLN
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science

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Prototipe Sistem Monitoring Penggunaan Daya Listrik Peralatan Elektronik Rumah Tangga Berbasis Internet Of Things Budi Prayitno; Pritasari Palupiningsih
PETIR Vol 12 No 1 (2019): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (712.033 KB) | DOI: 10.33322/petir.v12i1.333

Abstract

Power consumption of PLN customers from household sector is quite large. It comes from the use of household appliances, such as refrigerators, televisions, dispensers, lights and air conditioners. The customers assume that their electricity usage is wasteful, but unfortunately it couldn’t be known in detail which household electrical appliances spend the most electricity. So that, difficult for the customers to monitor the power usage of each household electrical equipments. Regarding to this issue, this research focused on making a prototype of a household electrical power usage monitoring system. This system can be used by PLN customers of households sector to find out which household appliances use large power, so that the customers can manage the use of household appliances. In implementing that monitoring, it needs a capable wattmeter devices to measure the power usage of household electronic equipments The results of this measurement electric current, voltage, and power data measured through sensors. Those measurement data is sent to the database server system monitoring through the internet of things (IoT) device so that monitoring can be done through the system in a real time. This study produced a prototype of household electronic equipments power usage monitoring system based on IoT.
Rancang Bangun Sistem Monitoring dan Controlling Penggunaan Daya Peralatan Listrik Rumah Tangga Menggunakan IoT Alwi Muhammad; Budi Prayitno; Rakhmadi Irfansyah Putra; Eka Putra; Pritasari Palupiningsih
PETIR Vol 15 No 1 (2022): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v15i1.1383

Abstract

Sektor rumah tangga menyumbang persentase terbesar dari total konsumsi listrik Indonesia pada tahun 2019. Pengguna di sektor ini tidak mengetahui secara detail peralatan rumah tangga mana yang paling banyak mengkonsumsi listrik. Penelitian ini bertujuan untuk menghasilkan sistem kontrol dan monitoring peralatan listrik rumah tangga dengan paradigma internet of things. Proses pemantauan dan pengendalian dilakukan untuk mendukung beberapa perangkat rumah tangga. Pada penelitian ini metode yang digunakan untuk membuat prototype alat dengan beberapa tahapan yaitu pembuatan prototype alat, pengumpulan data, dan pembuatan aplikasi mobile. Bagian utama dari perangkat prototipe terdiri dari mikrokontroler NodeMCU, sensor PZEM004T, dan relay. Hasil pembacaan sensor berupa data arus, tegangan, dan daya listrik yang kemudian dikirim dan disimpan dalam database. Setelah itu, data listrik dapat ditampilkan pada aplikasi mobile. Selain itu, juga dapat mengontrol on/off perangkat. Hasil dari penelitian ini adalah prototype perangkat dan aplikasi mobile yang dapat mengontrol dan memonitoring listrik untuk peralatan kelistrikan di rumah. Konsumsi listrik setiap alat listrik rumah tangga dapat dipantau dalam kilowatt-hour (KWH), sehingga pengguna dapat menggunakannya untuk menilai konsumsi listrik rutin peralatan listrik rumah tangga mereka.
Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga Nurul Ramadhanti Hikmiyah; Riki Ruli A. Siregar; Budi Prayitno; Dine Tiara Kusuma; Novi Gusti Pahiyanti
PETIR Vol 14 No 2 (2021): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33322/petir.v14i2.1448

Abstract

The use of electricity in household sector has increased, especially during the Covid-19 pandemic. The large number of activities carried out in home such as Work from Home, online schools, and online businesses caused difficulty to monitor the electricity consumption. The absence of electricity usage provisions affects the electricity monitoring process. Hence it takes a real time monitoring application of electricity consumption. Fuzzy subtractive clustering is an unsupervised method to form the number and center of clusters according to data conditions. This method serves to classify the household electricity users with the parameters used, is the amount of usage in rupiah and electric power. The grouping results from this method help users to monitoring electricity consumption in real time. The output describes the level of high, medium and low user electricity consumption. Based on the test results, the best Silhouette Coefficient value is 0.8322535 and three clusters are formed, with an accept ratio is 0.5, a reject ratio of 0.15, a radius of 1.7 and a squash factor of 0.5 hence a high level of use is obtained with an average value of the number of uses in IDR 655,993, power 2757 VA, medium level 240,553, 1071 VA and low level 46,479, 675 VA
Pendampingan Masyarakat Dalam Pengaplikasian Sistem Informasi Pelayanan Dan Managemen Desa Berbasis Android Aplikasi Kelor Di Desa Citimun Pritasari Palupiningsih; Andi Dahroni; Rakhmadi Irfansyah Putra; Muhammad Fadli Pratama; Budi Prayitno; Eka Putra
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 4, No 1 (2023): Volume 4, Nomor 1, March 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v4i1.2587

Abstract

Digitalisasi desa bertujuan menyetarakan pola kehidupan berbasis digital masyarakat desa dan masyarakat kota, merupakan upaya menghapus dikotomi orang desa dan orang kota, menghapus senjang gaya hidup tradisional dan modern, ditambah perkembangan ekonomi desa, diharapkan dapat membangun warga desa untuk turut serta dalam peningkatan wawasan terhadap dunia digital.Salah satu masalah yang terjadi pada desa adalah lambatnya pelayanan publik yang terjadi di kantor desa, dikarekanan tidak tersimpanya data dengan baik oleh karena itu aparatur desa dengan terpaksa harus menginput ulang setiap permintaan warga. Dengan adanya digitalisasi pada pelayanan desa dapat mempercepat proses pelayanan karena data setiap warga dapat tersimpan secara cloud. Data kependudukan sangatlah penting untuk menghadirkan sejumlah program pemerintah demi kemajuan kesejahteraan masyarakat. Oleh karena itulah data yang akan diberikan oleh desa harus valid dan sesuai dengan fakta lapangan yang ada.Alat untuk membantu proses pelayanan desa sudah ada, salah satunya aplikasi Kelor. Namun aparatur desa masih ada yang belum memaksimalkan aplikasi yang ada dikarenakan belum adanya pelatihan yang intens dalam pemanfaatan aplikasi yang ada. Oleh sebab itu tim PKM IT PLN akan melakukan bimbingan intens penggunaan sistem informasi pelayanan dan manajemen desa berbasis android dalam program iptek bagi masyarakat.
IMPLEMENTASI ALGORITMA FP-GROWTH UNTUK PENENTUAN REKOMENDASI PRODUK UMKM BERDASARKAN FREKUENSI PEMBELIAN Rd.M.Dimas Burhanudin Akbar; Pritasari Palupiningsih; Budi Prayitno
Jurnal Teknoinfo Vol 17, No 2 (2023): Vol 17, No 2 (2023) : JULI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v17i2.2585

Abstract

The rapid growth of the MSMEs business is caused by the development of digital technology that makes it easy to open a business. Business competition is a challenge for MSMEs, to be able to survive in business competition, it is necessary to make the right business decisions in order to maximize the business potential of MSME. One way that MSMEs can do in order to maximize business potential is by providing the right product recommendations to consumers. The right product is obtained by analyzing consumer purchasing patterns. This research uses the association rule technique and the FP-Growth algorithm to get the right rule as a product recommendation for MSME. The analysis uses transaction data on MSME from January 1, 2021 to April 30, 2021, obtained 1483 transactions with 3 trials at a minimum support of 1%, 2%, and 3% and a minimum confidence of 30%. To determine the correlation found in the rules formed, a lift ratio was used. In the first experiment, 13 rules were found that had a lift ratio value > 1 of the 24 rules formed. The rule that has the highest lift ratio value is if you buy Strawberry Tea, you buy Kopi Susu Pagi, and Mineral Water of 6.9. In the second experiment, there were 3 rules that had a lift ratio value > 1 of the 6 rules formed, the rule that had the highest lift ratio value, namely, if you buy strawberry tea, you buy mineral water of 3.69. In the third experiment, there was no rule that had a lift ratio value > 1. The established rules could be the basis for MSME in providing the right product recommendations to consumers.
IMPLEMENTASI DEEP LEARNING UNTUK IDENTIFIKASI UMUR TANAMAN BERDASARKAN CITRA DAUN PADA SMART FARMING Budi Prayitno; Pritasari Palupiningsih; Farhan Muhamad Ikhsan
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Technology plays an important role in optimizing agricultural production, one of which is through the application of smart farming. Smart Farming is a paradigm in agriculture that utilizes information and communication technology (ICT). The case study raised in this study is the use of smart farming in determining plant age. Plant age is an important factor in determining the harvest. Plants that are harvested at the right time can produce quality products in optimal quantities. Traditional farmers determine plant age manually. This has challenges, namely the process takes a long time and a lot of energy, especially for large agricultural areas. Plant age must be identified quickly and easily, the results of plant age identification are accurate and consistent and can be applied to large agricultural areas. The urgency of this research is the creation of a deep learning model that is used to detect the optimum plant age with a high accuracy value. The importance of this research lies not only in the development of technology but also in its contribution to the farmer's economy and the progress of the agricultural sector. This study aims to implement deep learning to form a classification model for identifying plant age based on leaf images and to evaluate the classification model to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preparation, modeling, and evaluation. The deep learning method used is classification with the application of the Convolutional Neural Network (CNN) VGG architecture algorithm, which has been proven effective in image analysis. The results of this study are Research on age classification models on plant leaf images using the classification method with the CNN algorithm is carried out with the stages of data collection and class division, image resizing, data augmentation, adding keras models, convolution, max pooling, flatten, relu, and with the training of 20 epochs. The results of model formation with the CNN algorithm using VGG16 get higher accuracy than VGG19. The best accuracy value is 78% from the confusion matrix results using VGG19 with a data ratio of 60% training data, 20% validation data, and 20% testing data.
Development of a Plant Weed Detection Model Using the Mask R-CNN Algorithm for Smart Farming Budi Prayitno; Pritasari Palupiningsih; Atam Rifai Sujiwanto
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.873

Abstract

A more efficient and sustainable agricultural system is urgently needed during world population growth and global climate change. One of the main challenges is that ineffective weed management can significantly reduce crop yields. Conventional farming methods, such as large-scale herbicide application, also negatively impact the environment. Therefore, the development of smart farming technology based on artificial intelligence (AI) is a crucial innovative solution. This research is urgent in the context of developing AI-based systems that significantly contribute to agricultural technology. The urgency of this study is the creation of a plant weed detection model using deep learning to determine the readiness of planting land with high accuracy values. The importance of this research lies not only in the development of technology, but also in its contribution to the farmer economy and the progress of the agricultural sector in Indonesia. This research aims to build and develop a plant weed detection model using deep learning to determine the readiness of planting land, as well as evaluate the detection model built to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preprocessing, modelling, and evaluation. The deep learning method used is object detection by applying the Mask R-CNN algorithm with the ResNet-50 architecture as the backbone. The evaluation of model performance was carried out using Mean Average Precision (MAP). The results of this study demonstrated the development of a deep learning-based weed detection model using the Mask R-CNN algorithm, which achieved a MAP of 37.32 and was able to overcome the challenges of varying weed types, lighting conditions, and complex field conditions.
Co-Authors Alwi Muhammad Andi Dahroni Atam Rifai Sujiwanto Eka Putra Farhan Muhamad Ikhsan Muhammad Fadli Pratama Novi Gusti Pahiyanti Nurul Ramadhanti Hikmiyah Palupiningsih, Pritasari Rakhmadi Irfansyah Putra Rd.M.Dimas Burhanudin Akbar Riki Ruli A. Siregar, Riki Ruli A. Tiara Kusuma, Dine