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Metode Vikor dalam Meningkatkan Kualitas Pembelajaran Terhadap Pemilihan Studi Club Junaidi
Jurnal Informasi dan Teknologi 2022, Vol. 4, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v4i1.182

Club studies are more non-formal learning methods and emphasize the participation of members/participants. In club studies, students get more roles than in class lectures, so club studies can be used as a companion method to increase student insight. Club studies are also one of the alternative learning methods designed to improve student competence by emphasizing the active participation of students. By participating in the study club, students have the ability to increase student scores compared to those who do not participate in the study club, so there is a difference between students who take part in the study club and those who do not take part in the study club. Student competence is very necessary, especially in the era of global competition. To find student talent in determining the selection of club studies as an alternative method of learning level in order to improve student competence and improve the quality of learning with the best graduates. Based on the analysis of improving the quality of learning in students against the selection of club studies with several criteria that can be taken from the students themselves, the assessment consists of the value of supporting courses, attendance values of supporting courses, practical test scores and interests. From the various criteria that students have, from this it can provide a determination in the selection of club studies according to the results of the criteria values owned by students..

THE USE OF IOT IN WATER UTILIZATION STRATEGIES FOR SMART IRRIGATION SYSTEMS BASED ON MACHINE LEARNING Junaidi, Junaidi
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 1 (2024): Desember 2024
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i1.3642

Abstract: Water irrigation is a crucial aspect of agriculture that often becomes the primary concern for farmers, especially because suboptimal management can lead to decreased crop yields and reduced income. So far, farmers have been practicing irrigation manually, where plants are watered twice a day, in the morning and evening, based on weather conditions without considering soil temperature or moisture levels. Based on the observations conducted, it was found that excessive water application increases water accumulation, resulting in nutrient loss from the soil and even root diseases. The objective of this study is to develop a system utilizing an ESP32 microcontroller and sensors to detect soil moisture, with a machine learning-based K-Nearest Neighbor (KNN) model, enabling farmers to remotely monitor and control their crops using an Android device. The testing results showed that with input data of 32°C temperature, 40% soil moisture, and 60% air humidity, the system produced a nearest distance of 0.000 and 0.541 from the closest k-nearest neighbors, with a status label of "needs water." As a result, the relay activates the water pump to irrigate the field. Meanwhile, for data with a nearest distance of 0.897, the system identified the status as "does not need water," indicating that the soil remains wet or moist. This study is expected to help reduce farmers' workloads by optimizing water usage according to plant needs and improving crop quality and yield. Keywords: k-nearest neighbor (KNN); mikrokontroller ESP32; machine learning; water irrigation Abstrak: Irigasi air merupakan aspek penting dalam pertanian yang menjadi perhatian utama petani, terutama karena pengelolaan yang kurang optimal berdampak pada penurunan hasil panen dan pendapatan. Selama ini, praktik irigasi oleh petani dilakukan secara manual, di mana penyiraman tanaman dilakukan dua kali sehari pada pagi dan sore berdasarkan kondisi cuaca tanpa memperhatikan suhu atau kelembaban tanah. Berdasarkan hasil observasi yang dilakukan, ditemukan masalah yaitu pemberian air secara berlebih menyebabkan akumulasi air meningkat mengakibatkan kehilangan nutrisi tanah dan bahkan penyakit akar. Tujuan penelitian ini menciptakan sistem yang dirancang menggunakan mikrokontroler ESP32 dan sensor untuk mendeteksi kelembaban tanah, dengan model K-Nearest Neighbor (KNN) berbasis machine learning sehingga memudahkan petani untuk mengontrol tanaman mereka dari jarak jauh menggunakan android. Hasil pengujian yang dilakukan dengan data inputan berupa suhu 32°C, kelembaban tanah 40% dan kelembaban udara 60%, sistem menghasilkan jarak terdekat sebesar 0.000 dan 0.541 dari k-nearest terdekat dengan label status "butuh air". Maka relay akan mengaktifkan pompa air untuk mengairi lahan. Kemudian, pada data dengan jarak terdekat 0.897, sistem mengidentifikasi status "tidak butuh air", menunjukkan bahwa kondisi tanah masih basah atau lembab. Penelitian ini diharapkan dapat membantu meringankan beban kerja petani mengoptimalkan penggunaan air sesuai dengan kebutuhan tanaman dan meningkatkan kualitas hasil panen.Kata kunci: irigasi air; k-nearest neighbor (KNN); mikrokontroller ESP32; machine learning

PERANCANGAN MONITORING DAYA BEBAN LISTRIK UNTUK APLIKASI SISTEM TENAGA SURYA PLTS PADA KANDANG AYAM BERBASIS ESP32 Pasha, Novita; Junaidi, Junaidi
JUTSI: Jurnal Teknologi dan Sistem Informasi Vol 3, No 3 (2023): OCTOBER 2023
Publisher : LPPM STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jutsi.v3i3.2874

Abstract: Solar power systems are an increasingly popular solution to meet electricity needs in various fields including livestock. One thing that is promising is the application of a solar power system in a chicken coop, where solar energy can be used to produce the electricity needed to meet the operational needs of the coop. This research aims to design and implement an electrical load power monitoring system for solar power system applications in chicken coops. This system is based on an ESP32 microcontroller which has WiFi capabilities, enabling the collection and sending of data to the server wirelessly. The main components of this system include current and voltage sensors, ESP32 as the controller brain, and a user interface in the form of a web-based application. Current and voltage sensors are used to measure the power used by equipment in the chicken coop. The resulting data is then processed by the ESP32 and sent to the server for remote monitoring. This allows stable owners to optimize energy use, monitor solar PV system performance, and make smarter decisions regarding energy management. With this system, it is hoped that it can help increase the efficiency of energy use in chicken coops based on solar power systems, as well as make a positive contribution to the environmental and economic sustainability of chicken farming.Keywords: ESP32: Electric; Solar Power Abstrak: Sistem tenaga surya merupakan solusi yang semakin populer dalam memenuhi kebutuhan listrik di berbagai bidang termasuk peternakan. Salah satu yang menjanjikan yaitu penerapan sistem tenaga surya pada kandang ayam, di mana energi matahari dapat digunakan untuk menghasilkan listrik yang diperlukan untuk memenuhi kebutuhan operasional kandang. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem monitoring daya beban listrik pada aplikasi sistem tenaga surya di kandang ayam. Sistem ini berbasis mikrokontroler ESP32 yang memiliki kemampuan WiFi, memungkinkan pengumpulan dan pengiriman data ke server secara nirkabel. Komponen utama dari sistem ini meliputi sensor arus dan tegangan, ESP32 sebagai otak pengendali, dan antarmuka pengguna berupa aplikasi berbasis web. Sensor arus dan tegangan digunakan untuk mengukur daya yang digunakan oleh peralatan di kandang ayam. Data yang dihasilkan kemudian diproses oleh ESP32 dan dikirimkan ke server untuk pemantauan jarak jauh. Hal ini memungkinkan pemilik kandang untuk mengoptimalkan penggunaan energi, memantau kinerja sistem PLTS, dan membuat keputusan yang lebih cerdas terkait pengelolaan energi. Dengan adanya sistem ini, diharapkan dapat membantu dalam meningkatkan efisiensi penggunaan energi di kandang ayam berbasis sistem tenaga surya, serta memberikan kontribusi positif terhadap keberlanjutan lingkungan dan ekonomi peternakan ayam. Kata Kunci: ESP32; Listrik; Tenaga Surya

Penerapan Media Pembelajaran Yolov4 Otomatis Pengenalan Jenis Komponen Elektronika di SMK Negeri 1 Tanjungbalai Junaidi, Junaidi; Amin, Muhammad; Zulkhairani, Zulkhairani; Fitriayu, Suci
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 8, No 1 (2025): Januari 2025
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v8i1.3684

The implementation of YOLOv4 technology in the image-processing-based learning system at SMK Negeri 2 Tanjungbalai aims to enhance students' understanding of various electronic components in the Audio-Video Engineering field. Currently, the learning process is still conducted manually, where teachers bring components such as resistors, capacitors, and other electronic parts. This method presents challenges in teaching the names of electronic components, as manual learning requires more time and students must recognize each component physically, one by one. Furthermore, students need to process resistor color codes and IC markings, which demand significant time and understanding. The YOLOv4 algorithm, known for its efficiency and accuracy, is utilized to detect and identify the type and value of components such as resistors, capacitors, transistors, and ICs automatically. This community service activity includes training, workshops, and the implementation of an image-processing-based application designed to assist students in a more interactive and efficient learning process. The results of this initiative indicate that the use of this technology positively impacts students' comprehension and improves the effectiveness of learning media in the school laboratory. Keywords: YOLOv4; image processing; electronic components; learning media Abstrak: Penerapan teknologi YOLOv4 dalam sistem pembelajaran berbasis image processing di SMK Negeri 2 Tanjungbalai bertujuan untuk meningkatkan pemahaman siswa dalam mengenali berbagai komponen elektronika di bidang Teknik Audio Video. Media pembelajaran yang dilakukan saat ini masih menggunakan secara manual dengan cara guru membeawa komponen seperti resistor, kapasitor dan komponen lainnya. Dengan cara ini terdapat masalah dalam media pembelajaran untuk pengenalan nama nama komponen elektronika. Masalah yang terjadi pembelajaran manual membutuhkan waktu yang lebih lama dan siswa harus mengenali satu per satu nama nama komponen secara fisik. Selanjutnya siswa membutuhkan proses membaca kode warna resistor marking IC membutuhkan waktu dan pemahaman yang sangat lama. Algoritma YOLOv4 yang dikenal efisien dan akurat digunakan untuk mendeteksi serta mengidentifikasi jenis dan nilai komponen seperti resistor, kapasitor, transistor, dan IC secara otomatis. Kegiatan pengabdian ini mencakup pelatihan, workshop, serta implementasi aplikasi berbasis image processing yang dirancang untuk membantu siswa dalam proses pembelajaran yang lebih interaktif dan efisien. Hasil kegiatan menunjukkan bahwa penggunaan teknologi ini memberikan dampak positif terhadap pemahaman siswa serta meningkatkan efektivitas media pembelajaran di laboratorium sekolah. Kata kunci: YOLOv4; image processing; komponen elektronika; media pembelajaran

PENGEMBANGAN SISTEM KENDALI PENGERINGAN PADI OTOMATIS BERBASIS MULTIMODAL DEEP LEARNING MENGGUNAKAN DATA SENSOR DAN CITRA VISUAL Ramadhani, Andrew; Junaidi, Junaidi; Fitriayu, Suci
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4052

Abstract: Rice drying is a crucial post-harvest stage that affects the quality, shelf life, and economic value of rice. Conventional methods, such as sun drying and timer-based systems, are still predominantly used but are less adaptive to weather changes, often resulting in reduced product quality. This study developed an automated rice drying control system based on multimodal deep learning by integrating visual images and weather sensor data. The YOLOv5 model was used to detect grain conditions with 95% accuracy, while sensor analysis using LSTM and Transformer achieved accuracies of 90% and 93%, respectively. Multimodal integration improved control accuracy to 96% through an automatic roof opening/closing mechanism responsive to weather conditions and grain moisture status. Test results show that this system is more efficient than the baseline method, with an average drying time of 12 hours, moisture content accuracy of ±96%, and 30% lower yield loss. These findings highlight the potential of multimodal deep learning in supporting precision agriculture and modernizing post-harvest processes in Indonesia, while also opening opportunities for developing similar systems for other food commodities to support sustainable food security. Keywords: Rice Drying, Intelligent Control System, Multimodal Deep Learning, Sensor Data, Visual Imagery Abstrak: Pengeringan padi merupakan tahap krusial pascapanen yang memengaruhi mutu, daya simpan, dan nilai ekonomis gabah. Metode konvensional, seperti penjemuran matahari dan sistem berbasis timer, masih dominan digunakan namun kurang adaptif terhadap perubahan cuaca, sehingga sering menurunkan kualitas hasil. Penelitian ini mengembangkan sistem kendali pengeringan padi otomatis berbasis multimodal deep learning dengan mengintegrasikan citra visual dan data sensor cuaca. Model YOLOv5 digunakan untuk mendeteksi kondisi gabah dengan akurasi 95%, sedangkan analisis sensor menggunakan LSTM dan Transformer menghasilkan akurasi masing-masing 90% dan 93%. Integrasi multimodal meningkatkan akurasi kendali menjadi 96% melalui mekanisme buka–tutup atap otomatis yang responsif terhadap kondisi cuaca dan status kekeringan gabah. Hasil uji menunjukkan sistem ini lebih efisien dibandingkan metode baseline, dengan waktu pengeringan rata-rata 12 jam, akurasi kadar air ±96%, serta kehilangan hasil 30% lebih rendah. Temuan ini menegaskan potensi penerapan multimodal deep learning dalam mendukung pertanian presisi dan modernisasi proses pascapanen di Indonesia, sekaligus membuka peluang pengembangan sistem serupa pada komoditas pangan lain untuk mendukung ketahanan pangan berkelanjutan. Kata Kunci: Pengeringan Padi, Sistem Kendali Cerdas, Multimodal Deep Learning, Data Sensor, Citra Visual

PELATIHAN PEMASANGAN PANEL SURYA DAN IOT UNTUK MONITORING LISTRIK BUDIDAYA LELE Junaidi, Junaidi; Ramadhani, Andrew; Larasati, Mustika Fitri; Abimanyu, Yogi
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol 5, No 1 (2025): April 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v5i1.3981

Abstract: The POKDAKAN Tani Makmur group, a freshwater fish farming group in Gerak Tani Village, has problems related to high electricity costs due to dependence on PLN which is used 24 hours a day for operational uses such as aerators, water pumps, lighting, and automatic feeding. To overcome this, this group plans to install a Solar Power Plant (PLTS) and develop an Internet of Things (IoT) application to monitor electricity consumption. PLTS will provide 24-hour electricity from solar energy, expected to reduce operational costs and save electricity costs. The IoT monitoring system will monitor voltage, current, power, energy (kWh), frequency, and power factor, and provide an estimate of electricity costs. This program involves lecturers and students in community service activities, including field surveys, observations, workshop training on PLTS technology and the creation of IoT applications. The goal is to increase harvest yields, provide technological understanding, and broaden the horizons of the POKDAKAN Tani Makmur group in the use of electricity and monitoring electrical loads through IoT. This solution aims to reduce operational costs and increase energy efficiency in catfish farming. Keywords: POKDAKAN prosperous farmers; PLTS; internet of things; pzem sensor; solar charge controller. Abstrak: Kelompok POKDAKAN Tani Makmur, kelompok budidaya ikan air tawar di Desa gerak tani, masalah yang terjadi terkait dengan biaya listrik tinggi karena ketergantungan pada PLN yang digunakan selama 24 jam dalam sehari untuk penggunaaan operasional seperti aerator, pompa air, penerangan, dan pemberian pakan otomatis. Untuk mengatasi hal ini, kelompok ini berencana memasang Pembangkit Listrik Tenaga Surya (PLTS) dan mengembangkan aplikasi Internet of Things (IoT) untuk memantau konsumsi listrik. PLTS akan menyediakan listrik 24 jam dari energi surya, diharapkan dapat mengurangi biaya operasional dan menghemat pengeluaran listrik. Sistem monitoring IoT akan memantau tegangan, arus, daya, energi (kWh), frekuensi, dan faktor daya, serta memberikan estimasi biaya listrik. Program ini melibatkan dosen dan mahasiswa dalam kegiatan pengabdian kepada masyarakat, mencakup survei lapangan, observasi, pelatihan workshop tentang teknologi PLTS dan pembuatan aplikasi IoT. Tujuannya adalah meningkatkan hasil panen, memberikan pemahaman teknologi, dan memperluas wawasan kelompok POKDAKAN Tani Makmur dalam pemanfaatan listrik dan pemantauan beban listrik melalui IoT. Solusi ini bertujuan untuk mengurangi biaya operasional dan meningkatkan efisiensi energi dalam budidaya ikan lele. Kata kunci: internet of things; POKDAKAN tani makmur; PLTS; sensor pzem; solar charge controller.

PELATIHAN DASAR JARINGAN KOMPUTER UNTUK SISWA SMK TEKNIK KOMPUTER JARINGAN PADA LKP PELITA MEDIA Maha Putra, Guntur; Junaidi; Bela, Bela Astuti
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 8 No. 3 (2025): Juli 2025
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v8i3.4046

Abstract: This activity aims to enhance students’ competence in understanding the basic concepts of computer networks as well as mastering the skills of network installation and configuration. The main problems faced by the students include limited learning resources, lack of practice-based training, and minimal access to adequate networking equipment, resulting in low readiness to enter the industrial workforce. This program provides a solution through a learning approach that combines theoretical instruction, hands-on practice, and case studies aligned with industry demands. The activities include preparation of training materials, implementation of training across four main sessions, and a final evaluation through both theoretical and practical examinations. The expected outcomes of this program include improved student competencies, the development of training modules, certificates of participation, and comprehensive documentation for evaluation and publication purposes. Through this program, it is expected that TKJ vocational students will be better prepared to enter the workforce and possess skills that are relevant to current advancements in computer networking technology. Keywords: evaluation; industry; network; competence; training; Abstrak: Kegiatan ini bertujuan untuk meningkatkan kompetensi siswa dalam memahami konsep dasar jaringan komputer serta menguasai keterampilan instalasi dan konfigurasi jaringan. Permasalahan utama yang dihadapi siswa adalah keterbatasan sumber belajar, kurangnya pelatihan berbasis praktik, dan minimnya akses terhadap perangkat jaringan yang memadai, yang mengakibatkan rendahnya kesiapan mereka dalam memasuki dunia industri. Kegiatan ini menawarkan solusi melalui pendekatan pembelajaran berbasis teori, praktik langsung, dan studi kasus yang relevan dengan kebutuhan industri. Kegiatan ini mencakup persiapan materi, pelaksanaan pelatihan dalam empat sesi utama, serta evaluasi akhir melalui ujian teori dan praktik. Luaran yang diharapkan dari program ini meliputi peningkatan kompetensi siswa, pembuatan modul pelatihan, sertifikat serta dokumentasi kegiatan sebagai bahan evaluasi dan publikasi. Dengan terlaksananya program ini, diharapkan siswa SMK TKJ lebih siap menghadapi dunia kerja serta memiliki keterampilan yang relevan dengan perkembangan teknologi jaringan komputer. Kata kunci: evaluasi; industri; jaringan; kompetensi; pelatihan

PEMBELAJARAN MENDALAM DETEKSI KELELAHAN WAJAH MENGEMUDI BERDASARKAN ALGORITMA YOLOV5 UNTUK MENGHINDARI KECELAKAAN DALAM SISTEM TRANSPORTASI CERDAS Junaidi, Junaidi; Ramadhani, Andrew; Abimanyu, Yogi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4093

Abstract: Traffic accidents due to driver fatigue are a serious problem in transportation systems, especially in Indonesia. This research aims to develop a computer vision-based early warning system capable of detecting driver fatigue in real-time through facial expressions. This system integrates the YOLOv5 algorithm for face detection, EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) for eye blink and mouth movement analysis, CNN (Convolutional Neural Network) for fatigue expression classification, and LSTM (Long Short-Term Memory) for analyzing the time-varying patterns of facial expressions. Data were obtained from public Kaggle datasets and facial data taken directly from cameras, which were then trained with augmentation techniques to improve model generalization. Test results show that the system is able to achieve validation accuracy of up to 90.5% and a confidence score of 97.9% for sleepy face detection. This system successfully recognizes sleepiness through EAR and MAR patterns and expression classification with real-time performance, and can be implemented efficiently on minicomputer devices. This research contributes to improving driving safety through early detection of driver fatigue in intelligent transportation systems.Keyword: drowsiness detection; YOLOv5; CNN; LSTM; EAR & MAR; facial expression; intelligent transportationAbstrak: Kecelakaan lalu lintas akibat kelelahan pengemudi menjadi permasalahan serius dalam sistem transportasi, khususnya di Indonesia. Penelitian ini bertujuan untuk mengembangkan sistem peringatan dini berbasis visi komputer yang mampu mendeteksi kondisi kelelahan pengemudi secara real-time melalui ekspresi wajah. Sistem ini mengintegrasikan algoritma YOLOv5 untuk deteksi wajah, EAR (Eye Aspect Ratio) dan MAR (Mouth Aspect Ratio) untuk analisis kedipan mata dan gerakan mulut, CNN (Convolutional Neural Network) untuk klasifikasi ekspresi lelah, serta LSTM (Long Short-Term Memory) untuk menganalisis pola perubahan waktu dari ekspresi wajah. Data diperoleh dari dataset public kaggle dan data wajah yang di ambil langsung dari kamera, yang kemudian dilatih dengan teknik augmentasi untuk meningkatkan generalisasi model. Hasil pengujian menunjukkan bahwa sistem mampu mencapai akurasi validasi hingga 90,5% dan confidence score deteksi wajah mengantuk sebesar 97,9%. Sistem ini berhasil mengenali kondisi kantuk melalui pola EAR dan MAR serta klasifikasi ekspresi dengan performa real-time, dan dapat diimplementasikan secara efisien di perangkat mini-komputer. Penelitian ini berkontribusi dalam meningkatkan keselamatan berkendara melalui deteksi dini kelelahan pengemudi dalam sistem transportasi cerdas.Kata kunci: deteksi kantuk; YOLOv5; CNN; LSTM; EAR & MAR; ekspresi wajah; transportasi cerdas

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