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All Journal Jurnal Simantec REKAYASA e-Jurnal Arus Elektro Indonesia Jurnal Ilmiah Pangabdhi CYCLOTRON TESLA: Jurnal Teknik Elektro Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) International Journal of Science, Engineering and Information Technology Journal of Applied Data Sciences SinarFe7 PELS (Procedia of Engineering and Life Science) Jurnal Ilmiah Edutic : Pendidikan dan Informatika JEECS (Journal of Electrical Engineering and Computer Sciences) UPMI Proceeding Series Journal Nucleus Journal Signal and Image Processing Letters
Sukri, Hanifudin
Universitas Truonojoyo Madura
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Physics Social Sciences Other

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Enhanced Precision Control of a 4-DOF Robotic Arm Using Numerical Code Recognition for Automated Object Handling Sukri, Hanifudin; Ibadillah, Achmad Fiqhi; Thinakaran, Rajermani; Umam, Faikul; Dafid, Ach.; Kurniawan, Adi; Morshed, Md. Monzur; Kurniawan, Denni
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

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

Abstract

This research develops a 4-DOF robotic arm system that utilizes numerical codes for accurate, automated object handling, supporting advancements in sustainable industrial automation aligned with the UN Sustainable Development Goals (SDGs), particularly Industry, Innovation, and Infrastructure (SDG 9). Key contributions include the integration of EasyOCR for reliable code recognition and a control mechanism that enables precise positioning. The robotic system combines a webcam for visual sensing, servo motors for movement, and a gripper for object manipulation. EasyOCR effectively recognizes numerical codes on randomly positioned objects against a uniform background while the microcontroller calculates servo angles to guide the arm accurately to target positions. Testing results show a success rate exceeding 94% for detecting codes 1 to 4, with minor servo angle errors requiring adjustments in arm extension by 30 mm to 50 mm. Positional error analysis reveals an average error of less than 1.5 degrees. Although environmental factors like lighting can influence code visibility, this approach outperforms traditional methods in adaptability and precision. Future research will focus on enhancing code recognition under variable lighting and expanding the system's adaptability for diverse object types, broadening its applications in industries demanding high efficiency.
KLASIFIKASI KUALITAS BUAH APEL MENGGUNAKAN METODE RANDOM FOREST Putra Argadinata, Andicho; Abdul Fatah, Doni; Sukri, Hanifudin
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.12854

Abstract

Di Indonesia perkebunan merupakan salah satu sektor perekonomian yang penting terutama untuk kebun apel, selain rasanya yang cukup enak serta penyebarannya cukup baik apel juga cukup bermanfaat untuk tubuh, namun tidak semua apel layak di konsumsi, walaupun terlihat baik banyak apel yang kuang baik dalam penggolongan nya, Penelitian ini bertujuan mengklasifikasikan kualitas apel berdasarkan fitur fisik seperti Size, Weight, Sweetness, Crunchiness, Juiciness, Ripeness, dan Acidity, dengan Quality sebagai target klasifikasi. Data yang digunakan berasal dari Kaggle, terdiri dari 4000 data. Model klasifikasi yang digunakan adalah Random Forest karena kemampuannya yang baik dalam menangani data dengan banyak fitur. Hasil evaluasi menunjukkan bahwa model mencapai akurasi 88,5%, precision 88,1%, recall 89,0%, dan F1-Score 88,5%. Berdasarkan confusion matrix, terdapat 48 data Bad yang salah diprediksi sebagai Good (False Positive) dan 44 data Good yang salah diprediksi sebagai Bad (False Negative). Penelitian ini membuktikan bahwa Random Forest adalah metode yang andal untuk klasifikasi kualitas apel berdasarkan fitur fisiknya.
Optimization Day Old Chick Incubator Design to Reduce Mortality Rate Using Fuzzy Logic Saputro, Adi Kurniawan; Ramadhan, Muhammad Fajar; Ibaidilah, Achmad Fiqhi; Haryanto, Haryanto; Sukri, Hanifudin; Hardiwansyah, Muttaqin
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.121

Abstract

In poultry farming, particularly for Day-Old Chicks (DOC), maintaining an ideal environmental condition is a significant challenge due to the limited ability of mother hens to provide adequate warmth and care. This often leads to a high mortality rate among DOC, especially in broiler chickens. The research contribution is the development of an intelligent incubator system based on fuzzy logic to automate environmental control and reduce DOC mortality rates. The system employs a DHT22 sensor to measure temperature and humidity, and an MQ-135 sensor to detect ammonia levels. An ESP32 microcontroller is used for data processing, chosen for its built-in Wi-Fi capability and high processing power. The DHT22 sensor controls a fan and UVA+UVB lamp via an AC dimmer, while the MQ-135 sensor controls a DC motor through the L298N driver. The fuzzy logic method is applied to make more accurate control decisions, and the entire system is connected to an IoT-based monitoring platform that provides a real-time dashboard for farmers. Preliminary results show that the system successfully maintains temperature within the optimal range (30–34?) and humidity (40–70%), and responds efficiently to changes in ammonia concentration. Compared to conventional systems, this intelligent incubator offers better automation, lower energy consumption, and cost efficiency. In conclusion, the proposed system provides a scalable and efficient solution for DOC management. Future work includes AI-based prediction integration, mobile application development, and historical data analysis for smarter poultry farm management.
Optimizing The XGBoost Model with Grid Search Hyperparameter Tuning for Maximum Temperature Forecasting Sugiarto, Sugiarto; Mas Diyasa, I Gede Susrama; Alhamda, Denisa Septalian; Aryananda, Rangga Laksana; Fatmah Sari, Allan Ruhui; Sukri, Hanifudin; Dewi, Deshinta Arrowa
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.885

Abstract

This study presents a novel comparative approach to maximum temperature forecasting in Surabaya, Indonesia, by integrating Extreme Gradient Boosting (XGBoost) with Grid Search Hyperparameter Tuning and benchmarking it against Autoregressive Integrated Moving Average (ARIMA) and Neural Prophet models. The main idea is to evaluate the capability of XGBoost in capturing nonlinear patterns in environmental time series data, which traditional models often fail to address. Using 15,388 historical daily maximum temperature records from the BMKG Juanda weather station spanning 1981–2022, the objective is to identify the most accurate predictive model for short- and medium-term forecasts. The modeling process involved four stages: data acquisition, preprocessing, training, and evaluation, with performance assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings show that, after hyperparameter tuning, XGBoost achieved the best performance with MAE = 0.32 and RMSE = 0.65, outperforming ARIMA (MAE = 0.85, RMSE = 1.20) and Neural Prophet (MAE = 0.70, RMSE = 0.98). Prediction results for 2025 indicate peak maximum temperatures in January, October, and November, aligning with recent climate patterns. The contribution of this research lies in demonstrating the superiority of a tuned XGBoost model for complex environmental datasets, offering a practical tool for urban climate planning, agricultural scheduling, and heatwave risk mitigation. The novelty of this work is the systematic integration of Grid Search-based optimization with XGBoost for meteorological forecasting in a tropical urban context, producing higher accuracy than both classical statistical and modern hybrid time series methods. These results highlight the model’s adaptability and potential for broader climate-related applications, with future research recommended to incorporate additional meteorological variables such as humidity and wind speed for even greater predictive capability.
Penggunaan Cairan Anolyte Disinfectant Pada Automatic Humidifier Dengan Metode Logika Fuzzy Pada Ruangan Putra, Rahendra; Rahmawati, Diana; Sukri, Hanifudin
Nucleus Journal Vol. 2 No. 2 (2023): November
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/nucleus.v2i2.2201

Abstract

Virus SARS-COV2 atau COVID-19 adalah virus yang menyerang saluran pernafasan manusia. Sistem sterilisasi yang sering digunakan adalah sistem bilik, sistem bilik ini dirasa sudah tertinggal dikarenakan sekarang sudah saatnya untuk vaksinasi. Masalah lainnya adalah sistem bilik yang ada saat ini menggunakan disinfektan kimiawi cair, yang dapat membuat lingkungan yang telah dibersihkan menjadi basah kembali. Sistem ini kurang efektif jika dilakukan di dalam ruangan yang dipenuhi peralatan kantor yang banyak berisi kertas dan perangkat elektronik lainnya yang seharusnya selalu dalam keadaan kering agar terhindar dari kerusakan. Dari permasalahan di atas, maka pada penelitian ini akan dirancang sebuah alat untuk mensterilkan ruangan dengan humidifier atau pengkabutan desinfektan dengan metode logika fuzzy. Input logika fuzzy adalah nilai kelembapan yang diperoleh oleh 3 sensor SHT20 dan output logika fuzzy merupakan timer yang digunakan untuk mengatur aktifnya humidifier dalam kurun waktu yang sudah ditentukan berdasarkan logika fuzzy. Berdasarkan hasil pengujian dapat dianalisa bahwa sistem dapat menentukan mana ruangan ber AC (Air Conditioning) maupun tidak ber AC, Apabila ruangan ber AC akan memiliki nilai kelembapan yang tinggi sedangkan ruangan non – AC akan memiliki nilai kelembaban yang lebih rendah. Pada sistem wireless system network didapatkan kualitas Quality of Service (QoS) berupa latency yang cukup besar dan troughput yang kecil hanya berkisar 192 Bps. Pada program logika fuzzy pada mikrokontroler juga ditemukan persentase error 1%, dikarenakan dalam percobaan ini dibandingkan dengan MATLAB. Selisih yang paling besar didapatkan nilai 19 ms dan selisih paling kecil bernilai 0 ms.
Rancang Bangun Sensor Deteksi Gizi Berdasarkan Standar Atropometri Anak Maknunah, Lu'lu'ul; Ulum, Miftachul; Sukri, Hanifudin
Rekayasa Vol 16, No 3: Desember 2023
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v16i3.19116

Abstract

In general, children's growth is monitored through Posyandu activities. This Posyandu seeks to monitor the growth and development of children so they do not experience malnutrition or malnutrition. One of the main factors in the process of physical growth and development is nutrition. However, the problem facing Posyandu today is the infrastructure used, namely measurement tools that still use conventional tools. This certainly affects the efficiency of parents in monitoring the growth and development of children, therefore this study designed a nutritional detection system for infants. The working principle of this tool utilizes an ultrasonic sensor to determine the baby's length and a load cell sensor to determine the baby's weight. The data detected by the two sensors will be processed using the stm32 microcontroller. By using the nutritional z-score formula, it can be classified into several statuses based on child anthropometric standards. Processed data will be displayed on the LCD and then stored in the MySQL database to make it easier to read the measurement results. In this study the test was carried out 10 times with different respondents. The data obtained from testing the design tool is compared with the measurement results on conventional tools so that an error is obtained on the weight sensor (load cell) of 3.3% and a success rate of 96.7%, the average percentage error on the height sensor (ultrasonic ) of 0.3% and a success rate of 99.7%.
Pengembangan Trainer Internet of Things (IoT) Sebagai Media Pembelajaran Dengan Menggunakan NodeMCU ESP32CAM Umam, Khotibul; Ibadillah, Achmad Fiqhi; Ubaidillah, Achmad; Sukri, Hanifudin; Rahmawati, Diana; Alfita, Riza
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 14 No 1 (2024): Jurnal Energy Vol. 14 No. 1 Mei 2024
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v14i1.1937

Abstract

Penelitian ini membahas potensi dan implementasi pengembangan fitur pada ESP32CAM sebagai media pembelajaran berbasis Internet of Things (IoT) dalam bentuk trainer. Penelitian ini mengatasi kesenjangan dalam penggunaan NodeMCU ESP32CAM sebagai teknologi IoT dalam media pembelajaran yang masih belum banyak digunakan. Tujuan utama penelitian ini adalah menganalisis dan mengeksplorasi penggunaan ESP32CAM sebagai trainer untuk meningkatkan pembelajaran berbasis IoT. Penelitian ini menggunakan pendekatan eksploratif untuk menyelidiki penggunaan ESP32CAM, termasuk analisis penerapan teknologi dalam lingkungan pembelajaran. Data diperoleh melalui kuesioner yang disebarkan kepada para ahli dan pengguna/mahasiswa, melibatkan 45 mahasiswa program studi Teknik Elektro di Universitas Trunojoyo Madura. Hasil penelitian menunjukkan bahwa trainer IoT menggunakan NodeMCU ESP32CAM sangat layak, dengan persentase tingkat kelayakan sebesar 91,08% dari penilaian para ahli dan 87,25% dari penilaian pengguna. Diharapkan hasil penelitian ini dapat memberikan wawasan baru terkait inovasi dalam penggunaan IoT serta menjadi landasan dasar untuk pengembangan lebih lanjut dalam bidang terkait.
Sistem Otomasi Untuk Menyortir Barang Pada Ruang Produksi Menggunakan Scada dan PLC Saputro, Adi Kurniawan; Sukri, Hanifudin; Baihaqi, M. Rifqi Al
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 14 No 1 (2024): Jurnal Energy Vol. 14 No. 1 Mei 2024
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v14i1.1942

Abstract

The development of the industrial revolution is progressing. Especially on production performance or machine parts. Industrial revolution 4.0 uses the internet and digitalization. So there are lots of new innovations. In a large-scale company, of course there are many tools and machines that need to be regulated whether they are on or not with regular supervision. With regular supervision, it is impossible for human workers to check one by one, thereby wasting time and reducing production levels. Therefore, it is necessary to develop a control system using PLC and HMI (Human Machine Interface) as the brain or control center for the company. In the industrial sector, PLC (Programmable Logic Controller) is an important factor in the operation of automatic machines in factories replacing relay control systems. So that PLC as a control system in the industrial sector can move machines according to needs. One part of the industry is the production room. The production room is the part where there are manufacturing materials or goods up to packaging. This research aims to design and implement a PLC (Programmable Logic Controller) and SCADA by adding sensors and actuators as output. The method used is a rule base system, the system runs according to a predetermined sequence and flow. The results of this research are the PLC, the SHT 20 temperature and humidity sensor can produce temperature and humidity values, the capacitive photosensor can detect items with 35 trials accurately but the distance is limited to only 27cm, the proximity sensor can only detect metal objects by attaching to the sensor and The actuator in the form of a stepper motor can run according to the commands given and is well integrated into the HMI (Human Machine Interface).
Evaluation of the Effectiveness of Hand Gesture Recognition Using Transfer Learning on a Convolutional Neural Network Model for Integrated Service of Smart Robot Umam, Faikul; Dafid, Ach.; Sukri, Hanifudin; Asmara, Yuli Panca; Morshed, Md Monzur; Maolana, Firman; Yusuf, Ahcmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14507

Abstract

This study aims to develop and evaluate the effectiveness of a transfer learning model on CNN with the proposed YOLOv12 architecture for recognizing hand gestures in real time on an integrated service robot. In addition, this study compares the performance of MobileNetV3, ResNet50, and EfficientNetB0, as well as a previously funded model (YOLOv8) and the proposed YOLOv12 development model. This research contributes to SDG 4 (Quality Education), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities) by enhancing intelligent human–robot interaction for educational and service environments. The study applies an experimental method by comparing the performance of various transfer learning models in hand gesture recognition. The custom dataset consists of annotated hand gesture images, fine-tuned to improve model robustness under different lighting conditions, camera angles, and gesture variations. Evaluation metrics include mean Average Precision (mAP), inference latency, and computational efficiency, which determine the most suitable model for deployment in integrated service robots. The test results show that the YOLOv12 model achieved an mAP@0.5 of 99.5% with an average inference speed of 1–2 ms per image, while maintaining stable detection performance under varying conditions. Compared with other CNN-based architectures (MobileNetV3, ResNet50, and EfficientNetB0), which achieved accuracies between 97% and 99%, YOLOv12 demonstrated superior performance. Furthermore, it outperformed previous research using YOLOv8 (91.6% accuracy.
Co-Authors Achmad Fiqhi Ibadillah Achmad Ubaidillah Adi Kurniawan Adi Kurniawan Saputro Adi Kurniawan Saputro Adi Kurniawan Saputro Alfian, Muhammad Afif Alhamda, Denisa Septalian Anam, Nafi'ul Andre Putra Pratama Aryananda, Rangga Laksana Baihaqi, M. Rifqi Al Candra Arif Kurniawan Dafid, Ach Dewi, Deshinta Arrowa Dian Neipa Purnamasari Diana Rahmawati Diana Rahmawati Doni Abdul Fatah Dwiky Ariyanto, Kemas Faikul Umam Fajar Sotya Pratama Fatmah Sari, Allan Ruhui Fifin Ayu Mufarroha Firmansyah Adi Putra Hairil Budiarto Handika Nadyawan, Ahmad Hardiwansyah, Muttaqin Haryanto Haryanto Haryanto Haryanto Heri setiawan Ibaidilah, Achmad Fiqhi Ika Oktavia Suzanti Khotibul Umam Khusnul Fatimah Koko Joni Kunto Aji Wibisono Kurniawan, Denni Maknunah, Lu'lu'ul Maolana, Firman Mas Diyasa, I Gede Susrama Miftachul Ulum Miftachul Ulum, Miftachul Miftahur Rahman Mochamad Faris Kurniawan Monika Faswia Fahmi Morshed, Md Monzur Morshed, Md. Monzur Putra Argadinata, Andicho Putra, Rahendra R Daksena Abdi Putera Rachmat Setiawibawa Rahmawat, Diana Ramadhan, Muhammad Fajar Riza Alfita Riza Alfita Rohmat Khaironi Rullie Annisa Sholeh, Mahrus Sugiarto S Syahid Abdillah Thinakaran, Rajermani Tri Laksono, Deni Ubaidillah, Achmad Ulum, Miftachul Yuli Panca Asmara Yusuf, Ahcmad Zainul Arifin Zainul Arifin