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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Informatika dan Rekayasa Komputer PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Yovi Pratama
Universitas Dinamika Bangsa
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Perancangan Sistem E-Voting Pemilu Raya Mahasiswa Di Universitas Dinamika Bangsa Sika, xaverius; Pratama, Yovi; Riyadi, Willy; Kisbianty, Desi; Zulia, Restutik
Jurnal Ilmiah Media Sisfo Vol 18 No 2 (2024): Jurnal Ilmiah Media Sisfo
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/mediasisfo.2024.18.2.1990

Abstract

Pemilihan umum Badan Eksekutif Mahasiswa (BEM) di Universitas Dinamika Bangsa (UNAMA) saat ini masih menggunakan sistem manual yang rentan terhadap kesalahan penghitungan, memakan waktu, dan membatasi partisipasi mahasiswa. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut dengan mengimplementasikan sistem e-voting berbasis website menggunakan framework Laravel. Sistem e-voting yang dikembangkan akan terintegrasi dengan Sistem Informasi Akademik (SIAKAD) untuk memverifikasi identitas pemilih dan memastikan setiap mahasiswa hanya memiliki satu hak suara. Laravel dipilih karena kemudahan pengembangan dan fitur-fiturnya yang mendukung pembuatan aplikasi web yang dinamis dan aman. Fitur-fitur utama sistem ini meliputi pendaftaran pemilih secara otomatis dari SIAKAD, proses voting yang sederhana dan cepat, serta penghitungan suara secara real-time. Dengan menggunakan sistem e-voting berbasis Laravel, diharapkan dapat meningkatkan akurasi hasil pemilihan, mempercepat proses rekapitulasi suara, serta meningkatkan partisipasi mahasiswa. Selain itu, sistem ini juga dapat mengurangi biaya penyelenggaraan pemilihan dan meningkatkan transparansi proses pemungutan suara.
PENGGUNAAN YOLO UNTUK DETEKSI ROBOT DAN GAWANG PADA ROBOT SEPAK BOLA BERODA Surya, Muhammad; Toscany, Afrizal; Saputra, Chindra; Pratama, Yovi; Bustami, M Irwan
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4575

Abstract

The ability to detect objects in real-time is a crucial factor in enhancing a robot's performance in understanding and adapting to dynamic environments. This research aims to develop and implement an object detection system on a wheeled soccer robot using the YOLOv11 algorithm, applied to images generated by omnidirectional and front-facing cameras. The system leverages deep learning technology for data labeling, model training, and performance evaluation. Testing was conducted by comparing the object detection results from both types of cameras, as well as analyzing performance metrics such as precision, recall, F1-score, and accuracy. The results show that the YOLOv11 model is effective in detecting objects in real-time, with a detection accuracy of 95.91% for the front camera and 96.7% for the omnidirectional camera. The highest precision and recall were recorded in the robot class, with precision of 99.12% and recall of 97.40% for the front camera, and precision of 96.5% and recall of 97.8% for the omnidirectional camera. The use of a combination of cameras proved to expand the robot's field of vision, enhancing object detection accuracy in dynamic environments. This research contributes to the implementation of object detection systems in robotics, particularly in the context of robot soccer competitions.
Design of WEB-Based Transportation Information System and Invoice Recapitulation at Chandra Lie Expedition Angelica, Felicia; Pratama, Yovi; Amroni, Amroni
International Conference on Business Management and Accounting Vol 3 No 1 (2024): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v3i1.4670

Abstract

Chandra Lie Expedition is a truck rental company started by Chandra Lie in 2018 and used for shipping kernels, coal, palm oil, and shells. The address of this expedition is 36135, East Jambi District, Jalan Sentot Ali Basa Payo Agile. When admins want to report invoices, recapitulation of invoices previously recorded using the book technique, this often occurs errors in summing the total bill and entering the transaction date. The admin needs to use Microsoft Word to draft a road letter for the driver after creating an invoice recapitulation report. It will take some time. Owners and admins should meet daily to request invoice reports for the previous day. Data that is not connected to each other. The goal of the project is to develop a web-based design tool that will make it easier for actors to perform. The steps to be taken to solve this challenge include identification, information retrieval based on theoretical foundations, observation techniques, and analysis to identify solutions to the problems faced by the expedition. Devices and software serve as research tools and materials. The purpose of the conclusion of this study is to facilitate reporting and summary of invoice reports.
Perancangan Aplikasi Simpan Pinjam Pada KUD Harapan Makmur Tebing Tinggi Indana Arum , Refi; Rohaini , Eni; Pratama, Yovi
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 1 (2025): JAKAKOM Vol 5 No 1 APRIL 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.1.2176

Abstract

KUD Harapan Makmur Tebing Tinggi merupakan salah satu koperasi di Provinsi Jambi yang pengolahan data simpan pinjam menggunakan buku agenda. Sehingga terjadi permasalahan yaitu proses pengolahan data simpanan dan pinjaman yang membutuhkan waktu cukup lama dan juga terkadang terjadi salah dalam perhitungan jumlah simpanan dan pinjaman yang ada pada anggota, sulitnya mendapatkan informasi mengenai data simpanan dan pinjaman dikarenakan harus datang ke tempat secara langsung dan pembuatan laporan-laporan yang tidak terselesaikan pada waktunya khususnya untuk laporan simpanan dan laporan pinjaman. Oleh karena itu, penelitian ini bertujuan memberikan solusi untuk permasalahan yang terjadi dengan menawarkan aplikasi simpan pinjaman menggunakan bahasa pemograman PHP dan DMBS MySQL dimana penulis melakukan pengembangan sistem dengan metode waterfall dan menggunakan pendekatan model sistem unified model language menggunakan use case diagram, activity diagram, class diagram dan flowchart. Sistem informasi simpan pinjam pada KUD Harapan Makmur Tebing Tinggi memberikan hasil yang memudahkan pegawai dalam melakukan pengelolaan data simpan pinjam dan mencetak laporan yang diperlukan dan juga memudahkan anggota dalam melihat informasi transaksi dan mengajukan pinjaman
Perancangan Sistem E-Lapor Pada Kantor Desa Lagan Tengah Berbasis Web Rezky Pramudia, Muhammad; Rohaini, Eni; Pratama, Yovi
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 5 No 1 (2025): JAKAKOM Vol 5 No 1 APRIL 2025
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2025.5.1.2177

Abstract

Kantor Desa Lagan Tengah merupakan salah satu instansi pemerintahan yang mengelola administrasi pelayanan masyarakat khusus Desa Lagan tengah, Kecematan Geragai, Kabupaten Tanjung Jabung Timur. Adapun permasalahan yang dihadapi oleh Kantor Desa Lagan Tengah yaitu dalam proses Pengaduan Masyarakat belum dilakukan dengan maksimal karena Pengaduan Masyarakat  tidak terkomputerisasi dengan baik dimana data di simpan pada file-file yang terpisah dan ditempatkan pada folder yang cukup banyak. Selain itu, bagi masyarakat yang membutuhkan informasi berkaitan dengan program Pengaduan Masyarakat, Pelaporan Pencurian, Kekerasan dan sejenisnya harus datang langsung ke Kantor Desa Lagan Tengah untuk mendapatkan informasi yang dibutuhkan hal tersebut dinilai mempersulit masyarakat apabila tidak langsung membawa persyaratan ke kantor, maka masyarakat akan bolak-balik dari kantor ke rumah untuk menyiapkan data yang dibutuhkan. Tujuan penelitian ini adalah untuk menganalisa sistem yang sedang berjalan, agar dapat mengatasi masalah-masalah yang dihadapi pada pada Kantor desa lagan tengah, dengan cara merancang Perancangan Sistem E-Lapor Pada Kantor Desa Lagan Tengah Berbasis Web hingga menghasilkan aplikasi pengolahan data yang di harapkan dapat mempermudah dalam pengolahan data maupun pembuatan laporan.
Increasing the Accuracy of Brain Stroke Classification using Random Forest Algorithm with Mutual Information Feature Selection Fachruddin, Fachruddin; Rasywir , Errissya; Pratama, Yovi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5795

Abstract

Brain stroke stands out as a leading cause of death, distinguishing it from common illnesses and highlighting the critical need to utilize machine learning techniques to identify symptoms. Among these techniques, the Random Forest (RF) algorithm emerged as the main candidate because of its optimal accuracy values. RF was chosen for its ensemble learning properties that optimize accuracy while simultaneously, bagging all outputs (DT), thus increasing its efficacy. Feature Selection, an important data analysis step, which is mainly achieved through pre-processing, aims to identify influential features and ignore less impactful features. Mutual Information serves as an important feature selection method. Specifically, the highest level of accuracy was achieved by cross-validating the test data - 10, resulting in 0.7760 without feature selection and 0.7790 with mutual information. Most of the attributes in the brain stroke dataset show relevance to the stroke disease class, but the resulting decision tree shows age as a particularly important node. So, the research results show that the selection feature (Mutual Information) can increase the accuracy of brain stroke classification, although it is not significant, namely an increase of 0.0030%. With an increase, where there is no significant difference, it can be said that almost all the attributes contained in the brain stroke dataset used have an influence on their relevance to the stroke disease class.
Enhancing Areca Nut Detection and Classification Using Faster R-CNN: Addressing Dataset Limitations with Haar-like Features, Integral Image, and Anchor Box Optimization Pratama, Yovi; Rasywir, Errissya; Suyanti; Siswanto, Agus; Fachruddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6496

Abstract

The classification and detection of areca nuts are essential for agriculture and food processing to ensure product quality and efficiency. The manual classification of areca nuts is time-consuming and prone to human error. For a more accurate and efficient automated approach, a deep learning-based framework was proposed to address these challenges. This study optimizes the Faster R-CNN by integrating Haar-like features and integral images to enhance object detection. However, dataset limitations, including low image quality, inconsistent lighting, cluttered backgrounds, and annotation inaccuracies, affect the model performance. In addition, the small dataset size and class imbalance hindered generalization. The Faster R-CNN model was trained with and without Haar-like Features and Integral Image enhancement. Performance was evaluated based on training loss, accuracy, precision, recall, F1-score, and mean average precision (mAP). The effects of the dataset limitations on detection performance were also analyzed. The optimized model achieved better stability, with a final training loss of 0.2201, compared to 0.1101 in the baseline model. Accuracy improved from 62.60% to 73.60%, precision from 0.6161 to 0.7261, recall from 0.3094 to 0.4194, F1-score from 0.2307 to 0.3407, and mAP from 0.1168 to 0.2268. Despite these improvements, dataset constraints remain a limiting factor. While the integration of Haar-like features and integral images into faster R-CNN contributes to detection accuracy, the study also reveals that high-resolution images, precise annotations, and dataset scale significantly amplify model performance.
Optimized Non-Overlapping Multi-Object Segmentation for Palm Oil Images Using FCN with Squeeze-and-Excitation and Attention Mechanisms Pratama, Yovi; Rasywir, Errissya; Siswanto, Agus
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.22212

Abstract

Purpose: Palm oil plantation monitoring using UAV imagery presents significant challenges in multi-object segmentation due to homogeneous texture, low resolution, and difficulty in distinguishing disease symptoms. Traditional segmentation methods struggle to accurately separate overlapping and visually similar objects, reducing the effectiveness of automated analysis. This study aims to address these issues by proposing an optimized Fully Convolutional Network (FCN) incorporating Squeeze-and-Excitation (SE-Block) and Attention Mechanisms to enhance segmentation accuracy for multi-object, non-overlapping palm oil images. Methods: The proposed model utilizes ResNet50 as a backbone, integrating SE-Block to enhance the feature representation of important regions while suppressing less relevant features. Additionally, Attention Mechanisms are incorporated to improve the model's spatial understanding and feature discrimination, which is crucial for segmenting visually similar objects in UAV imagery. A dataset of UAV-captured palm oil images was used to train and evaluate the model, applying deep learning techniques for feature extraction and classification. Result: Experimental results demonstrate that the proposed method achieves an average Intersection over Union (IoU) of 0.7928, accuracy of 0.9424, precision of 0.9126, recall of 0.8622, F1-score of 0.8693, and mAP of 0.7673. The highest-performing model attained a maximum IoU of 0.8499 and an accuracy of 0.9490, significantly outperforming conventional FCN models. These findings confirm that incorporating SE-Block and Attention Mechanisms enhances segmentation accuracy, making the model more robust in handling UAV imagery complexities. Novelty: The novelty of this research lies in the integration of SE-Block and Attention Mechanisms within FCN for palm oil segmentation, specifically targeting multi-object, non-overlapping segmentation in challenging UAV imagery conditions. By improving feature extraction and spatial attention, this approach advances deep learning-based agricultural monitoring and can be extended to other remote sensing applications requiring high-precision segmentation.
Experimental of vectorizer and classifier for scrapped social media data Setiawan Assegaff; Errissya Rasywir; Yovi Pratama
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 4: August 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i4.24180

Abstract

In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided learning. Our tests use social media data. Based on the tests that have been carried out on classifier variations and vectorizer variations, it was found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering. Meanwhile, from the test results on the count vectorizer, term frequency-inverse document frequency (TFIDF), and hashing, the best accuracy is achieved on the TFIDF vectorizer. In this case, it means that the TFIDF vectorizer has a better value in presenting word feature dimensions.
Network and layer experiment using convolutional neural network for content based image retrieval work Fachruddin Fachruddin; Saparudin Saparudin; Errissya Rasywir; Yovi Pratama
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 1: February 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i1.19759

Abstract

In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.
Co-Authors Abdul Haris Abdul Harris Achpal Haddid Afrizal Nehemia Toscany Agung Islamy Aryanto Agus Siswanto Akbar Ramadhan Akwan Sunoto Alvito Widianto Amroni, Amroni Angelica, Felicia Anggraini, Dila Riski Annisa putri Anton Prayitno Arahmad Taupiq asih asmarani Bayu saputra Beni Irawan Borroek, Maria Rosario Cahyana Putra Pratama Candra Adi Rahmat Carenina, Babel Tio Chindra Saputra Defrin Azrian Desi Kisbianty, Desi Despita Meisak Dimas Pratama Dimas Yudha Prawira elvi yanti Emelia, Shinta Errissya Rasywir Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fingki Lamhot Pasaribu fiqri ansyah Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hilda Permatasari Hussaein, Ahmad Ilham Adriansyah ilham permana Imelda Yose Indana Arum , Refi Irawan Irawan Irawan, Beni Istoningtyas, Marrylinteri Janu Hadi Susilo Jopi Mariyanto Julia Triani khalil gibran ahmad Kholil Ikhsan Luthfi Rifky M Fikrul Hakimi M Irwan Bustami M Reihan Al Fajri M.Rizky Wijaya Manyu, Dimas Abi Maria Rosario Borroek Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marshal` Koko Anand masgo Maulana Qaedi Aufar Mayang Ruza Moh. Ismail Muhammad Afif Dzaky Khairullah Muhammad Diemas Mahendra Muhammad Riza Pahlevi MUHAMMAD SURYA Muhammad Wahyu Prayogi Muhammad Zulfi Tisna Tama Mumtaz Ilham S Mumtaz Ilham Syafatullah NAIBAHO, RONALD Najmul Laila Naldi Irfan Nanda Ghina Nur Aini Nurhadi Nurhadi Pahlevi, M. Riza Pahlevi, M.Riza Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Ramadhan Saputra, Tri Reza Pahlevi Rezky Pramudia, Muhammad Riki Bayu Andhika Rio Ferdinand ROBY SETIAWAN Rohaini, Eni Rosario B, Maria Rosario, Maria Rudolf Sinaga Sandi Pramadi Santoso Saparudin, Saparudin Setiawan Assegaff SIKA, XAVERIUS Steven Ie Sudewo, Raden Tio Putra Sutoyo, Mochammad Arief Hermawan Suyanti taupiq, Arahmad Verwin Juniansyah virginia casanova andiko andiko Warcita Warcita WILLY RIYADI Xaverius Sika Yaasin, Muhammad Yanti, Elvi Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yuga Pramudya Zahlan Nugraha Zulia, Restutik