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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Multimedia Jurnal Abdi Insani Bookchapter Pendidikan Universitas Negeri Semarang
Wafi, Ahmad Zein Al
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics

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IMPLEMENTASI PERTANIAN CERDAS BERBASIS IOT PADA KELOMPOK TANI TEGER 02 DESA MANGUNSARI: Implementation of IoT-Based Smart Farming in the TEGER 02 Farmer Group in Mangunsari Village Anan Nugroho; Feddy Setio Pribadi; Mona Subagja; Syahroni Hidayat; Ahmad Zein Al Wafi; Muhammad Fathurrahman; Zidan Vieri Wijaya; Agus Ardiyanto; Haikal Abror
Jurnal Abdi Insani Vol 10 No 4 (2023): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v10i4.1267

Abstract

Plantation is a very important sector to meet food needs in Indonesia. However, plantations in Indonesia do not always increase over time. There are still many plantation sectors whose productivity is still low, especially in rural areas. Factors such as lack of capital and technology, lack of market access, and social problems such as land conflicts often become obstacles to the development of a productive and sustainable plantation sector. One example of a farmer group experiencing problems in water distribution is the TEGER 02 farmer group in Mangunsari Village, Semarang City, Central Java. This group has 7 hectares of land and 20 workers. However, the biggest obstacle faced is ineffective water distribution, especially during the dry season. Currently, to water 3000 𝑚2 of cultivated land, the group needs a full day involving 3 workers. Of course, the ratio between a few workers and a large area of land is inversely proportional, so it takes workers a long time to water the plantation land. To overcome this problem, a tool has been developed that can help farmers distribute water that utilizes strong internet access, the Internet of Things (IoT). IoT is very suitable to be applied to Mangunsari Village plantations. An IoT-based sprinkler that can help farmers in watering automatically which is connected to a website application so that it can be accessed directly using the farmer's smartphone. As an electricity supply, photovoltaics are used as an environmentally friendly energy source with a conservation perspective. The results of the service show that this activity makes it very easy for farmers to overcome watering problems which are difficult to control because the number of workers is not proportional to the size of the plantation land. However, adjustments to the website need to be made to produce an application that is more responsive when accessed via farmers' smartphones.
SOFTWARE BIOFOR MOTION ANALYSIS UNTUK EVALUASI GERAK BIOMEKANIKA TENIS DENGAN TEKNIK PUKULAN FOREHAND Irawan, Ricko; Azam, Mahalul; Rahayu, Setya; Setyawati, Heny; Soedjatmiko, Soedjatmiko; Nurharsono, Tri; Priyono, Bambang; Nugroho, Anan; Afifi, Sesaria Nisa; Salsabila, Alif Mazida; Lestari, Sri; Zein, Ahmad
Bookchapter Pendidikan Universitas Negeri Semarang No. 7 (2024)
Publisher : Bookchapter Pendidikan Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.1529/kp.v1i7.189

Abstract

Modernisasi ilmu pengetahuan dan teknologi khususnya olahraga tenis lapangan sudah sangat pesat di negara lain. Indonesia masih sangat jauh ketinggalan dalam hal Ipteks. Banyak pelatih yang masih menganalisis dan evaluasi program latihan secara konvensional tanpa melibatkan ipteks, sehingga pelatih tidak dapat mengidentifikasi kebutuhan atlet. Belum adanya software di dunia pertenisan Indonesia yang dapat menganalisis gerak biomekanika dalam meningkatkan kualitas teknik secara otomatis serta lemahnya kecepatan pukulan forehand menjadi perhatian. Pelatih masih menganalisis gerak biomekanika dalam menghasilkan kecepatan pukulan tanpa di dukung ipteks, sehingga menyebabkan analisis tidak tepat dan akurat. Permasalahan gerak biomekanika tentunya tidak dapat diamati tanpa adanya alat bantu yang dapat merekam gerakan atlet secara menyeluruh. Alat bantu digunakan untuk mengevaluasi kesalahan gerak atlet dengan media yang lebih modern. Penelitian ini bertujuan mengembangkan aplikasi inovatif berbasis software. Software berupa aplikasi android yang terkoneksi otomatis pada sistem. Software diharapkan mampu menganalisis kebutuhan biomekanika dalam menghasilkan kecepatan pukulan forehand sehingga diharapkan dapat menghasilkan pukulan yang konsisten dan berdaya ledak tinggi. Dikembangkan alat ini dapat membantu pelatih meningkatkan potensi dan performa atletnya secara berjenjang dan berkesinambungan.
COMPARATIVE STUDY OF YOLO VERSIONS FOR DETECTING VACANT CAR PARKING SPACES Muhammad Fathurrahman; Anan Nugroho; Ahmad Zein Al Wafi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6236

Abstract

The increasing vehicle density in urban areas has made parking space availability a significant challenge. With technological advancements, efficient smart parking systems based on object detection have become essential. This study evaluates the performance of YOLO versions 3 to 11 in detecting vacant parking spaces in urban environments, focusing on real-time processing, high accuracy with limited datasets, and adaptability to varying conditions. Using 4,215 annotated images and two test videos, YOLOv7 achieved the highest overall accuracy of 99.57% with an average FPS of 30.79, making it the most effective model for smart parking applications. YOLOv8 and YOLOv11 followed closely, with accuracies of  98.51% and 98.72%, respectively, and average FPS rates of 32.31 and 31.99, balancing precision and speed, which are ideal for real-time applications. Meanwhile, YOLOv5 stood out for its exceptional processing speed of 33.92 FPS. These results highlight YOLO's potential to revolutionize smart parking systems by significantly enhancing both detection precision and operational efficiency.   
Deep Learning Approach for Pneumonia Prediction from X-Rays using A Pretrained Densenet Model Wafi, Ahmad Zein Al; Rochim, Febry Putra; Fathimah, Aisya
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1457

Abstract

Pneumonia remains a major global health concern, particularly affecting young children and older adults, contributing to significant morbidity and mortality. Traditional diagnostic methods using chest CT scans are time-consuming and prone to errors due to the reliance on manual interpretation. This study investigates the application of DenseNet architectures DenseNet121, DenseNet169, and DenseNet201—for automated pneumonia detection from chest X-ray images. The dataset, obtained from the Guangzhou Women and Children’s Medical Center, consists of 5,216 training images and 624 testing images categorized into normal and pneumonia cases. Data augmentation techniques, including rotation, normalization, and shear, were applied to improve training efficiency. The DenseNet models were pre-trained on ImageNet and fine-tuned by adding fully connected layers with 256 neurons and sigmoid activation. The models were trained for 20 epochs using the Adam optimizer and binary cross-entropy loss function. Performance evaluation revealed that DenseNet201 outperformed the other models, achieving a precision of 0.99 and a recall of 0.61 for normal cases (F1-score of 0.75) and a precision of 0.81 with a recall of 0.99 for pneumonia cases (F1-score of 0.89). These findings demonstrate that DenseNet201 provides a reliable and effective solution for automated pneumonia detection, offering improved diagnostic efficiency and accuracy compared to traditional methods.
Investigating Liver Disease Machine Learning Prediction Performancethrough Various Feature Selection Methods Wafi, Ahmad Zein Al; Rochim, Febry Putra; Bezaleel, Veda
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4531

Abstract

Given the increasing prevalence and significant health burden of liver diseases globally, improving the accuracy of predictive models is essential for early diagnosis and effective treatment. The purpose of the study is to systematically analyze how different feature selection methods impact the performance of various machine learning classifiers for liver disease prediction. The research method involved evaluating four distinct feature selection techniques—regular, analysis of variance (ANOVA), univariate, and model-based on a suite of classifiers, including decision forest, decision tree, support vector classifier, multi-layer perceptron, and linear discriminant analysis. The result revealed a significant and variable impact of feature selection on model accuracy. Notably, the ANOVA method paired with the multi-layer perceptron achieved the highest accuracy of 0.801724, while the univariate method was optimal for the decision forest classifier (0.741379). In contrast, model-based selection often degraded performance, particularly for the decision tree classifier, likely due to the introduction of noise and overfitting. The support vector classifier, however, demonstrated robust and consistent accuracy across all selection techniques. These findings underscore that there is no universally superior feature selection method; instead, optimal predictive performance hinges on tailoring the selection technique to the specific machine learning model. This study contributes practical, evidence-based insights into the critical interplay between feature selection and model choice in medical data analysis, offering a guide for improving classification accuracy in liver disease prediction. Future work should explore more sophisticated and hybrid feature selection methods to enhance model performance further.
Pengembangan Aplikasi Pose Detection untuk Asesmen Kemajuan Fisioterapi Pasien Pasca Stroke dari Jarak Jauh Febry Putra Rochim; Anan Nugroho; Sri Sukamta; Ahmad Zein Al Wafi; Muhammad Fathurrahman; Amelia Damayanti; Hildatul Wardah
Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 4 (2024): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v5i4.415

Abstract

Assessment has an important role in determining the diagnosis and subsequent treatment plan. In an effort to increase access and effectiveness of rehabilitation, this research aims to develop a mobile application that is able to report the results of post-stroke patient pose assessment remotely. Telemedicine approaches in post-stroke rehabilitation have become increasingly popular, allowing patients to access rehabilitation services remotely. This is especially beneficial for patients who live in remote areas or have limited mobility. Telemedicine also allows for real-time patient monitoring, allowing adjustments to rehabilitation plans as needed. The mobile app is designed to provide easy access to rehabilitation programs that can be tailored to individual patient needs. In addition to making access easier, this application is equipped with a monitoring feature that allows health professionals to follow patient progress in detail. Data collected from patients' daily exercise and activities provides valuable insight into their progress, which can be used in tailoring rehabilitation plans in real-time. The development of this mobile application technology has great potential to improve rehabilitation outcomes for post-stroke patients. Testing by three experts with two experts as healthy patients and stroke patients, as well as one patient who acts as a medical personel to monitor, shows that from the graph, healthy patients tend to be consistent. On the other hand, post-stroke patients tend to be inconsistent. These results indicate that this application is effective for identifying patient movements during the rehabilitation process. Although there are several obstacles, such as delays in predictions on some devices, this application has great potential to improve the quality of life of post-stroke patients. Thus, the development of a pose detection application for remotely assessing the progress of physiotherapy in post-stroke patients has great potential in improving rehabilitation outcomes. The app facilitates patient access to appropriate, personalized and effective care, while providing medical personnel with objective and accurate data for monitoring and adjusting rehabilitation plans. This is a significant step in advancing the care of post-stroke patients.
Co-Authors Afifi, Sesaria Nisa Agus Ardiyanto Amelia Damayanti Anan Nugroho Anan Nugroho Anan Nugroho Bambang Priyono Bezaleel, Veda Fathimah, Aisya Febry Putra Rochim Feddy Setio Pribadi Haikal Abror Heny Setyawati Hildatul Wardah Mahalul Azam Mona Subagja Muhammad Fathurrahman Muhammad Fathurrahman Muhammad Fathurrahman Nugroho, Anan Nurharsono, Tri Ricko Irawan Rochim, Febry Putra Salsabila, Alif Mazida Setya Rahayu Soedjatmiko Soedjatmiko Sri Lestari Sri Sukamta Syahroni Hidayat Zidan Vieri Wijaya