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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Pilar Nusa Mandiri Syntax Literate: Jurnal Ilmiah Indonesia Technomedia Journal EXPLORE EDUMATIC: Jurnal Pendidikan Informatika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Journal of Information Systems and Informatics Jurnal Tecnoscienza Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika (JUTIF) Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) Intechno Journal : Information Technology Journal Scientific Journal of Informatics
Anggit Dwi Hartanto, Anggit Dwi
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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PENGEMBANGAN FITUR REKOGNISI KEGIATAN DENGAN METODE SCRUM Nurmasani, Atik; Setiawan, Annas; Hartanto, Anggit Dwi
Information System Journal Vol. 7 No. 02 (2024): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2024v7i02.1965

Abstract

Activity recognition from independent activities can be done according to learning outcomes. The recognition process uses Google form with submission stages, checking stages, and decision-making stages. The problems at checking stage are the conversion calculation process takes a long time and difficulty in finding data. The solution to solve is develop an activity recognition features. Development is carried out using the scrum method containing stages of user story, product backlog, sprint planning, daily scrum, sprint review, and sprint retrospective. The features produced are according to needs.
Literatur Review Bat Algorithm Terhadap Analisis Sentimen Pada Lini Masa Twitter Adipradana, Candra; Utami, Ema; Hartanto, Anggit Dwi
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/v4mwd237

Abstract

Algoritma metaheuristik seperti particle swarm optimization, firefly algorithm and harmony sekarang menjadi metode yang kuat untuk menyelesaikan banyak masalah optimasi yang sulit. Dalam literature review ini, kami mengusulkan suatu metode metaheuristik baru yaitu Binary Bat Algorithm atau Algoritma Kelelawar dengan Biner, hal ini didasarkan pada perilaku ekolokasi kelelawar. Kami juga berniat untuk menggabungkan keunggulan dari algoritma yang ada ke dalam algoritma kelelawar baru. Setelah perumusan terperinci dan penjelasan implementasinya, kami akan melakukannya perbandingan algoritma yang diusulkan dengan algoritma lain yang ada, termasuk genetic algorithms and particle swarm optimization. Simulasi menunjukkan bahwa algoritma yang diusulkan tampaknya jauh lebih unggul daripada algoritma lainnya, dan kedepannya studi lebih lanjut juga akan dibahas. Kata kunci: Biner, Ekolokasi, Metaheuristik, Algoritma Kelelawar
Klasifikasi Kepribadian Dengan Metode DISC Pada Twitter Menggunakan Algoritma Artificial Neural Network Idris, Idris; Utami, Ema; Hartanto, Anggit Dwi
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/3s308g79

Abstract

Maju mundurnya suatu perusahaan biasanya didukung oleh adanya sumber daya yang handal, terutama sumber daya manusia. Perekrutan dan penempatan pegawai pada posisi yang tepat akan membawa dampak yang signifikan bagi suatu perusahaan. Di dunia ini sifat dan karakter manusia sangat beraneka ragam bentuknya. Teori DISC mengklasifikasikan kepribadian menjadi empat tipe yaitu dominance, influence, steadiness dan compliance. Perbedaan karakter setiap tipe tentu saja akan berpengaruh pada gaya perilaku, cara menghadapi tekanan hidup dan juga cara berkomunikasi baik secara langsung maupun dengan media sosial. Melalui sosial media, seseorang dapat meluapkan perasaanya melalui postingan yang diunggahnya. Dari postingan tersebut dapat dilakukan analisis mengenai karakter kepribadian yang ia dimiliki. Penelitian ini bertujuan untuk mengetahui seberapa besar akurasi analisis profiling pada Twitter sehingga bisa menjadi acuan untuk proses perekrutan pegawai. Penelitian ini menggunakan algoritma Artificial Neural Network untuk mengklasifikasikan 275 akun Twitter kedalam teori DISC dan mendapatkan akurasi sebesar 42,91% dari 72 skenario yang dijalankan. Kata kunci: Kepribadian DISC, Media Sosial, Analisis Profiling, Sumber Daya Manusia
Object Detection with YOLOv8 and Enhanced Distance Estimation Using OpenCV for Visually Impaired Accessibility Syahrudin, Erwin; Utami, Ema; Hartanto, Anggit Dwi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2826

Abstract

Accessibility challenges for the visually impaired are getting more serious yearly. To address this issue, this study presents an advanced object detection system that utilizes YOLOv8, enhanced with OpenCV for distance estimation. The methodology involves data preparation with diverse scenarios to test system accuracy, including environments like busy streets and indoor settings. Precision, recall, and F1-score metrics evaluate performance under varying lighting conditions. Results show a decrease in performance during low-light conditions, emphasizing the need for adequate lighting for effective detection. The system also includes a real-time implementation with a panic button feature, allowing immediate activation of object detection and distance estimation processes. The results are translated into Indonesian using a translation service and converted to speech, making the information accessible to users. By integrating YOLOv8 and OpenCV, the research achieves an average object detection accuracy of 91% with a low error rate of about 3.6%. Rigorous testing and evaluation under various conditions ensure reliability and effectiveness. The implications of this research extend to real-time applications like navigation assistance for the visually impaired, highlighting the potential for improved quality of life and independence. Future work will focus on optimizing detection in low-light conditions, incorporating additional sensors like infrared cameras, and enhancing real-time text translation services for accurate information delivery to visually impaired users. Additionally, continuous training with diverse datasets will be conducted further to improve the robustness and accuracy of the detection system.
Augmentation for Accuracy Improvement of YOLOv8 in Blind Navigation System Syahrudin, Erwin; Utami, Ema; Hartanto, Anggit Dwi
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.v8i4.5931

Abstract

This study addresses the critical need for enhanced accuracy in YOLOv8 models designed for visually impaired navigation systems. Existing models often struggle with consistency in object detection and distance estimation under varying environmental conditions, leading to potential safety risks. To overcome these challenges, this research implements a rigorous approach combining data augmentation and meticulous model optimization techniques. The process begins with the meticulous collection of a diverse dataset, essential for training a robust model. Subsequent preprocessing of images in the HSV color space ensures standardized input features, crucial for consistency in model training. Augmentation techniques are then applied to enrich the dataset, enhancing model generalization and robustness. The YOLOv8 model is trained using this augmented dataset, leading to significant enhancements in key performance metrics. Specifically, mean average precision (mAP) improved by 13.3%, from 0.75 to 0.85, precision increased by 10%, from 0.80 to 0.88, and recall rose by 10.3%, from 0.78 to 0.86. Further optimization efforts, including parameter tuning and the strategic integration of a Kalman Filter, notably improved object tracking and distance estimation capabilities. Final validation in real-world scenarios confirms the efficacy of the optimized model, demonstrating its readiness for practical deployment. This comprehensive approach showcases tangible advances in navigational assistance technology, significantly improving safety and reliability for visually impaired users.
Comparative analysis of YOLOv8 techniques: OpenCV and coordinate attention weighting for distance perception in blind navigation systems Utami, Ema; Syahrudin, Erwin; Hartanto, Anggit Dwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3267-3278

Abstract

Blindness is a very important issue to consider in research aimed at assisting vision. This condition requires further study to provide solutions for the blind. This study evaluates and compares the effectiveness of the you only look once v8 (YOLOv8) model integrated with OpenCV and the coordinate attention weighting (CAW) technique for distance estimation in a blind navigation system. Initially, YOLOv8 integrated with OpenCV produced less than optimal results, prompting further improvement efforts to surpass the performance of CAW. The goal is to enhance the accuracy and efficiency of distance perception without the need for additional sensors. The materials used include a variety of datasets annotated with distance information to train and evaluate the model. The methods employed include integrating YOLOv8 with OpenCV for baseline comparison and applying CAW to improve distance perception through enhanced feature attention. The results show that YOLOv8+OpenCV Improved achieves the lowest mean squared error (MSE) across the entire distance range: 0-1 m (0.44), 1-2 m (0.50), 2-3 m (0.58), 3-4 m (0.64), and 4-5 m (0.71). YOLOv8+CAW also outperforms YOLOv8+OpenCV original, demonstrating a notable enhancement in accuracy. The model achieves a detection accuracy of 95.7%, showcasing the effectiveness of computer vision techniques in supporting blind navigation systems, offering precise distance estimation capabilities and reducing the reliance on external sensors. The implications include improved real-time performance and accessibility for the blind, paving the way for more efficient and reliable navigation assistance technologies.
Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor dalam Menentukan Kriteria Masyarakat Miskin Santoso, Arif; Utami, Ema; Hartanto, Anggit Dwi
Jurnal Informa : Jurnal Penelitian dan Pengabdian Masyarakat Vol 8 No 1 (2022): Juni
Publisher : Politeknik Indonusa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46808/informa.v8i1.211

Abstract

Mewabahnya Virus Covid-19 memberikan dampak yang sangat besar dibidang ekonomi. Melemahnya ekonomi masyarakat menjadi permasalahan serius yang sangat perlu segera diatasi. Dalam upaya pemulihan ekonomi, pemerintah mengeluarkan kebijakan-kebijakan untuk pengentasan ekonomi antara lain penyaluran BLT. Akan tetapi kebijakan tersebut justru menimbulkan masalah baru yaitu penyaluran yang tidak tepat sasaran. Hal ini menibulkan gejolak dimasyarakat. Penelitian ini bertujuan untuk mengklasifikasikan data sesuai dengan kriteria dan memperoleh hasil terbaik dua metode yang akan digunakan. Penlitian ini menggunakan dua algoritma Naïve Bayes dan K-Nearest Neighbor dengan data yang diperoleh dari Data terpadu kesejahteraan Sosial (DTKS) dengan dua variabel yaitu mampu dan miskin. Hasil klasifikasi dengan dua algoritma Naïve Bayes dan K-Nearset Neighbor diperoleh hasil masing-masing 72.64% dan 95.40% dengan nilai AUC 0.836 dan 0.877. Berdasarkan nilai AUC yang diperoleh kedua algoritma tingkat akurasi termasuk good classification. Algoritma K-Nearset Neighbir lebih baik dalam klasifikasi masyarakat miskin dibandingkan algoritma Naïve Bayes dengan akurasi 95.40% dan nilai AUC 0.877.
Analisis Sentimen Terhadap Tokoh Publik Menggunakan Support Vector Machine Fitriyani, Nurul Khasanah; Hartanto, Anggit Dwi
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (683.29 KB) | DOI: 10.54367/means.v5i1.615

Abstract

Anies Baswedan adalah seorang gubernur DKI Jakarta yang menjabat pada masa bakti 2017-2022. Pada bulan Desember 2019 ini nama Anies Baswedan hangat diperbincangkan di berbagai media karena pemberian penghargaan Adikarya Wisata 2019 sebuah diskotek yaitu ke Diskotek Colosseum meskipun pada akhirnya penghargaan tersebut dicabut kembali. Namanya juga hangat diperbincangkan karena dianggap cuci tangan setelah mencopot dua pejabat karena dua masalah yang berbeda. Kemudian juga mengenai masalah banjir yang terjadi di Jakarta, namanya juga disebut belum bisa menangani dengan baik banjir yang selalu terjadi di Jakarta. Media twitter memiliki tampilan simpel, topik terupdate, terbuka dalam mengakses tweet dan cepat dalam menyampaikan opini. Dari berbagai komentar dan tanggapan di Twitter diperlukan teknik untuk membagi ke dalam kelas opini negatif atau positif. Penelitian ini, menggunakan preprocessing dan melabeli opini kedalam kelas positif dan negatif. Sedangkan untuk klasifikasinya menggunakan metode Support Vector Machine. Data yang digunakan berupa opini tentang seseorang Anies Baswedan dari media sosial Twitter yang berjumlah 1000 tweet yang diambil pada tanggal 17 Desember 2019. Dari hasil pelabelan didapatkan banyaknya komentar positif berjumlah 429 dan yang berkomentar negatif berjumlah 530. Sedangkan klasifikasi metode Support Vector Machine mendapatkkan nilai akurasi sebesar 95,9%, nilai presisis sebesar 94,49%, dan nilai recall sebesar 96,4%.
Evaluating YOLOv8-Based Distance Estimation: A Comparison of OpenCV and Coordinate Attention Weighting in Blind Navigation Systems Syahrudin, Erwin; Utami, Ema; Hartanto, Anggit Dwi; Raharjo, Suwanto
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24395

Abstract

Background: Recent developments in assistive technologies for the visually impaired have increasingly utilized computer vision techniques for real-time distance estimation. However, challenges remain in balancing accuracy, latency, and robustness under dynamic environmental conditions. Objective: This study aimed to evaluate and compare the performance of OpenCV and Coordinate Attention Weighting (CAW) models for distance estimation in blind navigation systems, particularly focusing on their effectiveness in real-time scenarios. Methods: A quantitative experimental study was conducted using an image dataset labeled with actual distances. The baseline performances of OpenCV and CAW were measured and compared. Subsequently, targeted optimizations were applied to the OpenCV model, including adaptive image filtering, hyperparameter tuning, and integration of a Kalman filter. Results: Initial evaluation showed that CAW achieved a higher baseline accuracy of 88% compared to OpenCV. However, after optimizations, OpenCV’s accuracy improved by 15%, reaching approximately 85%. Additionally, the optimized OpenCV model demonstrated reduced latency, outperforming CAW in real-time detection speed. Under varying lighting and motion conditions, OpenCV also exhibited superior robustness compared to CAW. Conclusion: The findings suggest that with proper optimization, OpenCV can match or exceed CAW in key performance aspects, making it a viable and efficient alternative for real-time distance estimation in blind navigation systems. Future research should explore further model integration and hardware acceleration for deployment in wearable devices.
Analysis of the Similiarity Level of Source Code in the Kotlin Programming Language using Winnowing Algorithm Astica, Yustikamasy; Utami, Ema; Hartanto, Anggit Dwi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.902

Abstract

Plagiarism is an act of imitating the work of others directly or indirectly. In an academic environment, plagiarism applies not only to textual documents but also to source code documents. Source code plagiarism in academia usually occurs when students copy another student's code and submit it as if it were the student's work. So that an automatic plagiarism check is needed, the winnowing algorithm will be used to help detect similarities in source code as a way to detect an act of plagiarism. The Winnowing algorithm, which is usually used to detect document plagiarism, this research detects the source code. The results produced in this study are that the degree of similarity in the two source codes will produce different similarity values if the dataset used has gone through the text preprocessing stage or without preprocessing. If the dataset has gone through the text preprocessing stage, the similarity value will be pretty low because the number of characters used is significantly reduced. The Winnowing and Jaccard Similarity algorithms quickly detect plagiarism in source code and can be used to minimize plagiarism.
Co-Authors Adiatma, Biva Candra Lutfi Adipradana, Candra Aji Santoso, Bayu Ananda, Fadhilah Dwi arif nur rohman Arif Santoso Astica, Yustikamasy ATIK NURMASANI Budi, Agung Prasetio Christyawan, Fajar Ema Utami Fajri, Ika Nur Febriyanti, Nada Rizki Fiardin, Tsintani Tisaga Fitriyani, Nurul Khasanah Hartatik Hedy Leoni Hidayat, Jati Arif Idris Idris Iedwan, Aisha Shakila Iskandar, Ahmad Fikri Julianto, M. Rayhan Wahyu Kanoena, Melcior Paitin Kencana, Adi Kresna Khairullah, Irfan Khalil Kurniawan, Aas Andri Kurniawan, Febri Dwi Kusnawi Kusnawi Kusrini, Kusrini Linda, Kumara Dewi Mauliza, Nia Pattimura, Yudha Bagas Pratama, Jibran Yoga Purwoko, Joshua Dwi Giovani Ramadhan, Muhammad Jauharul Ramadhani, Zanuarestu Rasyid, Muhammad Khulqi Safira, Livia salisah, Tuhfatus Sari, Bunga Permata Setiadi F., A. Farhan Setiawan, Annas Setiawanda, Yongky Budi Suwanto Raharjo Syahrudin, Erwin Tara, Yudi Tuhpatussania, Siti Wahyu Hidayat wahyuni, wenti ayu Wasistha, Dyah Wicaksono, Anggito Whiku Wisnu Nugroho Aji, Wisnu Nugroho Yoga Pristyanto Yulianto, Yogi Yusa, Aldo