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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Communications in Science and Technology JMM (Jurnal Masyarakat Mandiri) Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Mantik Jurnal Teknologi Informasi dan Multimedia Jurnal Pengabdian kepada Masyarakat Conten : Computer and Network Technology Electron: Jurnal Ilmiah Teknik Elektro Bakti Sekawan : Jurnal Pengabdian Masyarakat
Rochim, Febry Putra
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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IMPLEMENTASI DIAGNOSA SISTEM INJEKSI SELF-DIAGNOSE KENDARAAN MENGGUNAKAN APLIKASI BERBASIS ANDROID Abdurrahman Abdurrahman; Andri Setiyawan; Lelu Dina Apristia; M. Hilman Gumelar Syafei; Adhitya Muhammad Firdaus; M Bagus Isnen; Doni Yusuf Firdaus; Febry Putra Rochim
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 3 No. 3: Agustus 2023
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v3i3.6279

Abstract

Pengetahuan dan keterampilan mekanik merupakan aspek utama yang wajib dimiliki untuk menjadi mekanik profesional. Teknologi yang semkain berkembang menuntuk mekanik harus ditingkatkan pengetahuan dan keterampilannya terutama pada diagnose kendaraan. Kegiatan ini merupakan serangkaian pengabdian masyarakat. Tahapan kegiatan antara lain: perencanaan, persiapan, pelaksanaan, dan evaluasi. Pada artikel ini fokus pada tahapan perencanaan dan persiapan kegiatan. Hasil perencanaan dan persiapan ditunjukkan dengan rencana kegiatan dan hasil rata-rata tes awal mekanik terkait pengetahuan dasar diagnose kendaraan
Pengembangan Aplikasi Pose Detection untuk Asesmen Kemajuan Fisioterapi Pasien Pasca Stroke dari Jarak Jauh Febry Putra Rochim; Nugroho, Anan; Sukamta, Sri; Wafi, Ahmad Zein Al; Fathurrahman, Muhammad; Damayanti, Amelia; Wardah, Hildatul
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 5 No 4 (2024): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

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.
Optimasi Deteksi Kebocoran dengan Menggunakan Phase Stretch Transform pada Retina Fluorescein Angiography Images untuk Penyakit Malaria Rochim, Febry Putra; Nugroho, Hanung Adi; Setiawan, Noor Akhmad
Communications in Science and Technology Vol 3 No 2 (2018)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.523 KB) | DOI: 10.21924/cst.3.2.2018.82

Abstract

Malarial Retinopathy (MR) is indicated by retina alteration such as white dots occurrence which is caused by malaria. Leak detection is a key factor of MR’s early diagnosis. Inconsistent size and shape of the leakages with the colour contrast that relatively similar with the background. Leak detection’s algorithm is one of the most complex algorithms on the fundus image analysis field. Therefore, improving performance in the leakage detection is essential. This study focuses on automated leakage detection on fluorescein angiography (FA) images. The methods used in this study are vessel segmentation, saliency detection, phase stretch transform (PST), optic disk removal and leak detection to extract some features which then classified to correctly validate the leak. From 20 patient data large focal leak images with 31 leak points, 28 of them have been correctly detected. So, the experiment produced the accuracy and specificity of 0.98 and 0.9, respectively. With the proposed method of this study, there is a potential to enhance the knowledge on MR field in the future.
PENINGKATAN KEBERDAYAAN MASYARAKAT MELALUI IMPLEMENTASI TEKNOLOGI HOT STIRRER - IOT UNTUK MENGOLAH LIMBAH ORGANIK MENJADI PEMBERSIH SEPATU Vera Noviana Sulistyawan; Maharani Kusumaningrum; Ima Winaningsih; Febry Putra Rochim; Lambang Setyo Utomo; Faizal Ghozali Abas; Very Mareta Rahmawati Sulistyawan; Muhammad Abyan Nizar Muntashir
JMM (Jurnal Masyarakat Mandiri) Vol 8, No 4 (2024): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v8i4.25334

Abstract

Abstrak: Pertumbuhan populasi dan perubahan gaya hidup menyebabkan peningkatan volume limbah, menciptakan dampak negatif terhadap lingkungan dan kesejahteraan masyarakat. Dalam pengabdian ini, tim pengabdi mengimplementasikan teknologi Hot Stirrer berbasis IoT untuk limbah organik menjadi pembersih sepatu. Tujuan utama kegiatan ini adalah meningkatkan keberdayaan masyarakat. Metode pelaksanaan kegiatan pengabdian berfokus pada pendekatan kolaboratif dan partisipatif, yang mencakup kegiatan pelatihan dan penyuluhan kepada masyarakat. Kegiatan ini memberikan peningkatan terhadap pengetahuan, kesadaran, dan motivasi masyarakat Desa Sabranglor untuk memanfaatkan limbah menjadi sesuatu yang memiliki nilai ekonomi sebanyak 46,7%. Hasil angket menunjukkan bahwa kegiatan pengabdian ini bermanfaat bagi masyarakat dengan tingkat kebermanfaatan sebesar 89,4%. Selain itu, kegiatan ini berhasil meningkatkan kebersihan lingkungan sebesar 87,2%. Tingkat pemahaman peserta terhadap cara mengelola limbah sampah organik juga meningkat sebesar 90,6%, dan kemampuan dalam mengoperasikan teknologi Hot Stirrer berbasis IoT untuk pengelolaan limbah organik mencapai 80,6%.Abstract: Population growth and lifestyle changes have led to an increase in waste volume, creating negative impact on the environment and community welfare. In this community service, the community service team implemented IoT-based Hot Stirrer technology for organic waste into shoe cleaners. The main objective of this activity is to increase community empowerment. The method of implementing community service activities focuses on a collaborative and participatory approach, which includes training and outreach activities to the community. This activity provides an increase in the knowledge, awareness, and motivation of the Sabranglor Village community to use waste into something that has economic value by 46.7%. The results of the questionnaire showed that this community service activity was beneficial to the community with a level of usefulness of 89.4%. In addition, this activity succeeded in increasing environmental cleanliness by 87.2%. The level of understanding of participants on how to manage organic waste also increased by 90.6%, and the ability to operate IoT-based Hot Stirrer technology for organic waste management reached 80.6%.
Tinjauan Komprehensif: Simulasi Sistem Disk Scheduling dengan Berbagai Algoritma Menggunakan OS Sim Putri, Fidel Lusiana; Djuniadi; Rochim, Febry Putra
ELECTRON Jurnal Ilmiah Teknik Elektro Vol 6 No 1: Jurnal Electron, Mei 2025
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/electron.v6i1.302

Abstract

Disk scheduling is one of the important components in operating system resource management to optimize data access on storage devices. Operating systems use various algorithms to manage input/output (I/O) requests more efficiently, including Shortest Seek Time First (SSTF), LOOK, and Circular LOOK (C-LOOK). This research aims to evaluate and compare the performance of these algorithms in managing disk requests through simulations using OS-SIM. The applied methodology includes simulating a queue of random I/O requests on disks with specific configurations. The data analyzed includes Total Head Movement (THM) and Average Seek Time (AST) for each algorithm. The simulation is performed with a predefined initial position of the disk head, and each algorithm is tested based on how each of them manages the disk access sequence. The results show that the SSTF algorithm is the most efficient with the lowest THM and AST values of 208 tracks and 29.71 ms AST, compared to the LOOK and C-LOOK algorithms. The LOOK algorithm provides a balance between efficiency and fairness, as it is able to minimize head movement without ignoring distant requests. Meanwhile, C-LOOK is more effective in reducing the possibility of starvation on requests at the end of the disk, but at the cost of increasing the number of head movements. This research provides a clearer picture of the advantages and disadvantages of each algorithm, which can be used as a reference in the selection of disk scheduling algorithms in systems that require high efficiency and reliability in I/O management.
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.
Prisma-based systematic review of video based AI applications and challenges in multiple domains Li Yaoqi, Li Yaoqi; Rochim, Febry Putra
Jurnal Mantik Vol. 9 No. 2 (2025): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i6.6452

Abstract

Video-based artificial intelligence has emerged as a rapidly growing field, driven by advancements in deep learning and the increasing demand for automation across sectors. This study aims to summarize the trends, applications, and major challenges in the implementation of video-based AI using a PRISMA-based systematic literature review approach. The data synthesized from 17 selected articles indicates that deep learning models such as CNNs, LSTMs, and hybrid architectures have been successfully employed for various tasks including anomaly detection, deepfake classification, long-range surveillance, video compression, and video-based educational assessment. Applications span across security, healthcare, education, and entertainment, with notable improvements in efficiency and accuracy. However, challenges remain concerning privacy, algorithmic bias, and the gap between technological progress and regulatory readiness. Hardware demands and variability in model performance also pose limitations. These findings underscore the importance of interdisciplinary approaches to foster responsible and sustainable innovation in video-based AI. The review offers a comprehensive overview that may serve as a foundation for future research directions and technological development.
PENGEMBANGAN WEBSITE POINT OF SALES UNTUK GALERI MUTIARA BATIK SOLO Fadila Ainun Zaqi Irmadhani; Naufal Hilmi Fathul Ihsan; Feddy Setio Pribadi; Mukhlas Fajar Putra; Mahen, Mahendra Adiastoro; Febry Putra Rochim; Muhammad Hanif Al Ghifaari; Moh Nafi Adhi Rajasa; Ning Imas Ati Zuhrotal Afifah; Tedwin Arif Muhammad
CONTEN : Computer and Network Technology Vol. 5 No. 1 (2025): Juni 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bcjhzy47

Abstract

Galeri Mutiara Batik Solo merupakan salah satu pelaku usaha mikro yang bergerak di bidang penjualan batik dan kerajinan khas Solo, namun masih menggunakan proses manual dalam pencatatan transaksi, pengelolaan stok, dan pelaporan keuangan. Kondisi ini menyebabkan ketidakefisienan operasional dan keterbatasan dalam pengambilan keputusan berbasis data. Penelitian ini mengusulkan pengembangan sistem Point of Sales (POS) berbasis web dengan pendekatan user-centered design yang didukung framework Laravel. Metode pengembangan menggunakan model waterfall dengan pendekatan pengembangan paralel antar pengembang. Penelitian ini mencakup analisis kode lama, modifikasi dan penambahan fitur-fitur baru seperti manajemen hak akses berbasis peran, sistem pelaporan PDF, keamanan akun, barcode generator, dan penyederhanaan antarmuka. Hasil pengujian menunjukkan sistem POS yang dikembangkan mampu meningkatkan efisiensi kerja, keamanan data, serta kualitas pelayanan pelanggan. Sistem ini juga memiliki potensi untuk dikembangkan lebih lanjut dalam mendukung penjualan omnichannel dan integrasi teknologi mobile maupun cloud untuk UMKM.
Chili Ripeness Level Detection with YOLOv8 and Fuzzy Logic for Harvest Decision Making Rahmawati, Ninda Yulia Dwi; Rochim, Febry Putra
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 5 (2025): October 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i5.1807-1818

Abstract

Conventional chili harvesting relies on subjective human judgment, resulting in inconsistencies that necessitate a computer vision-based automation system. This study develops a decision support system integrating YOLOv8 for object detection and Mamdani fuzzy logic to assess chili ripeness levels. The YOLOv8 model was trained on 5,598 annotated chili images divided into three ripeness categories: ripe, unripe, and defective (rotten, diseased, or physically damaged), using an 80:20 training-testing split. YOLOv8 classification results serve as inputs to a fuzzy inference system that outputs three linguistic harvest decisions: delay, partial, or full harvest. Experimental evaluation indicates that YOLOv8 achieved 91.2% accuracy, 89.6% precision, and 87.3% recall on the test set. The fuzzy logic system obtained 88% accuracy in harvest decision-making on unseen data, demonstrating output consistency across repeated inferences. Overlapping triangular membership functions enable the fuzzy system to manage intra-class variations and image noise, thereby improving adaptability. These results confirm the feasibility of integrating YOLOv8 and fuzzy logic to support reliable and adaptive automated harvest decisions in chili farming, with potential application in precision agriculture.
Co-Authors Abdurrahman Abdurrahman Adhitya Muhammad Firdaus Andri Setiyawan Andriano, Niko Arlinto Arlinto, Arlinto Bezaleel, Veda Damayanti, Amelia Djuniadi Djuniadi Doni Yusuf Firdaus Fadila Ainun Zaqi Irmadhani Faizal Ghozali Abas Fathimah, Aisya Feddy Setio Pribadi Hanung Adi Nugroho Ima Winaningsih Kladius, Rafik Lambang Setyo Utomo Lelu Dina Apristia Li Yaoqi, Li Yaoqi M Bagus Isnen M. Hilman Gumelar Syafei Maghribi, Mohammad Maulana Maharani Kusumaningrum Mahen, Mahendra Adiastoro Moh Nafi Adhi Rajasa Muhammad Abyan Nizar Muntashir Muhammad Fathurrahman Muhammad Hanif Al Ghifaari Mukhlas Fajar Putra Naufal Hilmi Fathul Ihsan Ning Imas Ati Zuhrotal Afifah Noor Akhmad Setiawan Nugroho, Anan putri, fidel lusiana Rahmawati, Ninda Yulia Dwi Saputro, Bagaskoro Sri Sukamta Sulistyawan, Vera Noviana Tedwin Arif Muhammad Vera Noviana Sulistyawan Very Mareta Rahmawati Sulistyawan Wafi, Ahmad Zein Al Wardah, Hildatul