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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Jurnal Ilmiah FIFO TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika IJAIT (International Journal of Applied Information Technology) JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Almufi Jurnal Pengabdian kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknik Elektro eProceedings of Engineering Jurnal Ilmiah Agroust Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika SWAGATI: Journal of Community Service Jurnal Nasional Sains dan Teknik
Suryo Adhi Wibowo
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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ANALISIS KINERJA DETEKSI GERAKAN DAN PENGENALAN OBJEK PRODUK RITEL BERBASIS YOLOV8 wibowo, suryo adhi
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 11 No 1 (2024): JETT Juli 2024
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jett.v11i1.7482

Abstract

Teknologi yang saat ini paling umum digunakan oleh industri ritel untuk mengidentifikasi produk adalah barcode. Karena keterbatasan barcode, QR (quick response) code lalu diusulkan. Namun, tantangan dari QR code adalah tidak semua produk memiliki QR code untuk dipindai pada mesin transaksi. Banyaknya variasi kode kemudian memicu penelitian untuk penggunaan teknologi visi komputer untuk mengenali sebuah produk. Berbagai teknologi deep learning telah diterapkan untuk mengenali produk, diantaranya adalah Faster R-CNN, Mask R-CNN, FCIS, RetinaNet, dan YOLO. Teknologi YOLO pada penelitian sebelumnya menggunakan versi YOLOv2 dan mampu mengenali produk pada datasetVOC 2012 dengan nilai mAP sebesar 78,2%. Penelitian ini bertujuan untuk mengimplementasikan dan menganalisis YOLO versi terbaru yaitu YOLOv8 untuk mengenali dan mendeteksi arah gerak produk ritel. Data yang digunakan terdiri dari 987 gambar dari 10 produk. Hasil pengujian pada proses pengenalan produk secara umum diperoleh nilai mAP50 sebesar 98% dan mampu mendeteksi arah gerak produk dengan baik. Berdasarkan hasil pengujian tersebut dapat disimpulkan bahwa penggunaan YOLOv8 secara signifikan dapat mendeteksi arah dan mengenali produk retail dengan baik.
Improved bidirectional long short term memory-based QRS complex detection using autoencoder Insani, Asep; Wibowo, Suryo Adhi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7033

Abstract

In this paper, we propose a new technique to improve QRS complex detection. This technique consists of incorporating an autoencoder and bidirectional long short term memory (BiLSTM). The autoencoder used is a stacked autoencoder and functions as signal filtering. Meanwhile, BiLSTM is used as a detector. Exploration of the effect of hyperparameter in the autoencoder was also carried out to determine the effect on QRS complex detection. Furthermore, the dataset used in this study is the MIT-BIH arrhythmia database. Based on the experimental results, the hyperparameter in the autoencoder that gives a better effect on QRS complex detection is 16-8. Finally, the proposed method out-of-perform state of the art algorithm with accuracy 99.94%.
MEDIA PEMBELAJARAN MATEMATIKA UNTUK SISWA SEKOLAH DASAR KELAS 4 MENGGUNAKAN ADOBE FLASH Rosadi, Choiron; Dedy Irawan, Joseph; Adhi Wibowo, Suryo
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 4 No. 1 (2020): JATI Vol. 4 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v4i1.2633

Abstract

Seiring dengan perkembangan zaman dan teknologi saat ini, generasi muda di tuntut untuk terus berinovasi termasuk pendidikan. Selain pendidikan yang terus berinovasi dalam metode belajar mengajar, siswa juga di tuntut untuk mandiri dalam memahami materi pelajaran. Namun, banyak siswa yang merasa kesulitan untuk memahami materi pembelajaran khususnya materi matematika. Banyak metode yang telah dikembangkan untuk proses belajar mengajar sambil bermain. Salah satunya adalah memanfaatkan media interaktif sebagai media edukasi pembelajaran. Dengan menggabungkan materi dan visulaisasi yang menarik, sehingga dapat membantu proses pemahaman siswa dalam memahami materi pembelajaran khususnya matematika. Metode seperti ini juga bisa meningkatkan motivasi dan keinginan siswa untuk belajar. Pada pembuatan media interaktif di sini penulis menerapkan metode observasi dan wawancara. Metode observasi adalah metode yang digunakan pengamatan secara langsung di lapangan.sedangkan metode wawancara adalah metode yang di gunakan untuk tanya jawab ke siswa berupa soal. Oleh sebab itu penulis membuat media pembelajaran interaktif matematika menggunakan Adobe Flash dengan tujuan untuk memudahkan proses belajar mengajar, dan untuk menarik minat belajar siswa khususnya siswa kelas 4 SD. Hasil dari penelitian ini berupa media pembelajaran matematika kelas 4 sekolah dasar menggunakan adobe flash cs6 dan media pembelajaran tersebut berbasis desktop.
Integrasi Digital Marketing dan Community-Based Tourism dalam Pengembangan Agrowisata di Desa Warjabakti Kabupaten Bandung Susanti, Hesty; Rahmawati, Dien; Mukhtar, Husneni; Wibowo, Suryo Adhi; Priharti, Wahmisari; Cahyadi, Willy Anugrah; Nugroho, Bambang Setia; Aziz, Burhanuddin; Susanto, Kusnahadi
SWAGATI : Journal of Community Service Vol. 2 No. 2 (2024): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2024v2i2.1095

Abstract

Warjabakti Village has various agro-tourism potentials that have not been managed properly, namely in the form of a charming natural scenery, natural conditions that are still maintained, and cool air. The problems faced are the need for more exposure to technology, the unavailability of supporting facilities and infrastructure, and the absence of groups capable of coordinating these potentials. This community service aims to develop a community-based tourism system by integrating digital marketing technology to empower target communities. The results achieved from this community service activity are the formation of Pokdarwis, which have been legally approved by the village government and will oversee the management of the tourism village in Warjabakti Village with governance that the team and partners have made. In addition, the e-digital map and the Haruman Tourism Village website have been produced. This website contains village content and branding that displays attractive village information, videos, and animations that have been structured and integrated, as well as a complete and attractive e-digital map of tourist areas.
Multi-Head Voting based on Kernel Filtering for Fine-grained Visual Classification Khairunnisa, Mutiarahmi; Wibowo, Suryo Adhi
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.9.2.2920

Abstract

Research on Fine-Grained Visual Classification (FGVC) faces a significant challenge in distinguishing objects with subtle differences within intra-class variations and inter-class similarities, which are critical for accurate classification. To address this complexity, many advanced methods have been proposed using feature coding, part-based components for modification, and attention-based efforts to facilitate different classification phases. Vision Transformers (ViT) has recently emerged as a promising competitor compared to other complex methods in FGVC applications for image recognition, which are mainly capable of capturing more fine-grained details and subtle inter-class differences with higher accuracy. While these advances have shown improvements in various tasks, existing methods still suffer from inconsistent learning performance across heads and layers in the multi-head self-attention (MHSA) mechanisms that result in suboptimal classification task performance. To enhance the performance of ViT, we propose an innovative approach that modifies the convolutional kernel.  Our method considerably improves the method's capacity to identify and highlight specific crucial characteristics required for classification by using an array of kernels. Experimental results show kernel sharpening outperforms other state-of-the-art approaches in improving accuracy across numerous datasets, including Oxford-IIIT Pet, CUB-200-2011, and Stanford Dogs. Our findings show that the suggested approach improves the method's overall performance in classification tasks by achieving more concentration and precision in recognizing discriminative areas inside pictures. Using kernel adjustments to improve Vision Transformers' ability to differentiate somewhat complicated visual features, our strategy offers a strong response to the problem of fine-grained categorization.
Implementasi Mesin Roasting Kopi Untuk Peningkatan Kualitas Produksi Kopi UMKM Darma Coffee Mukhtar, Husneni; Priharti, Wahmisari; Rahmawati, Dien; Susanti, Hesty; Cahyadi, Willy Anugrah; Nugroho, Bambang Setia; Wibowo, Suryo Adhi; Muttaqien, Teuku Zulkarnain; Rizal, Achmad; Susanto, Kusnahadi
SWAGATI : Journal of Community Service Vol. 1 No. 3 (2023): November
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2023v1i3.1082

Abstract

UMKM Darma Coffee adalah salah satu UMKM bersifat industri rumah tangga yang ada di Desa Warjabakti, Kecamatan Cimaung, Kabupaten Bandung. Saat ini, masalah yang dihadapi oleh UMKM Darma Coffee ini adalah masih manualnya proses roasting kopi. Pengabdian masyarakat ini berfokus pada penyediaan solusi roasting kopi karena saat ini proses roasting kopi tersebut masih manual. Petani juga diharuskan fokus dengan pekerjaan memutar alat agar kopi tidak gosong, dan temperatur yang presisi juga tidak diketahui sehingga kualitas roasting kopi di bawah standar. Tujuan pengabdian masyarakat ini adalah pengembangan sebuah mesin roasting kopi otomatis dengan timer digital serta dilengkapi pemantauan suhu, kecuali pengaturan sumber pemanasnya. Hal ini sesuai dengan hasil wawancara dengan ketua UMKM Darma Coffee. Hasil yang dicapai adalah tersedianya mesin roasting kopi otomatis serta desain packaging kopi yang lebih menarik. Terjadi peningkatan kecepatan proses roasting menjadi 10-15 menit. Terdapat pula peningkatan soft skill pada para petani kopi di UMKM Darma Coffee.
Design and Implementation of Smart Shopping Cart Based On Web Application Technology Setiawan, Jonathan Vito; Dawwam, Muhammad; Lazuardi, Aldira Fadillah; Wibowo, Suryo Adhi; Rahmania, Rissa; Hidayat, Siddiq Wahyu
IJAIT (International Journal of Applied Information Technology) Vol 08 No 02 (November 2024)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v8i2.7483

Abstract

Due to waiting time in cashier queues, resulting in a decline in consumer satisfaction and loss of precious time. Because of this problem, we proposed the solution for reducing waiting time in the cashier queue is a tool that can identify the type of retail product and the quantity that customers will be purchasing. In creating the design, Blender is used for the 3D, and Figma is used for creating User Inteface (UI). Hardware implementation using Jetson NANO microcomputer with camera modules to capture products and Wi-Fi capabilities to enable seamless cloud integration with its web application. Implemented in parallel with hardware implementation, the software implementation focused on creating a web application that displays every product that enters the Smart Cart. A combination of sensors, wifi modules, and a microcomputer ensures optimal performance for data handling with the database. The approximate round trip connection to the database has a minimum time as small as 17ms while the maximum is 57ms and the average is 23ms.
Frontend Website Implementation for Breast Cancer Classification System Using Machine Learning Wardani , Shania; Wibowo, Suryo Adhi; Usman, Koredianto
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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

Abstract

Early detection of breast cancer is essential to improve patient survival rates. One way to be used for such detection is to develop a classification system based on genomic data, which can provide more accurate and efficient results. This study aims to design and implement a Streamlit-based website frontend, which functions as a breast cancer classification system interface using Machine Learning technology. This user interface is designed with ease of use and optimal user experience, allowing medical personnel to quickly access and understand the analysis results. The main features of this website include an educational dashboard about breast cancer, a simple and structured patient data input form, and predictive analysis results displayed in an interactive format and can be downloaded for further documentation purposes. Tests conducted on the front of this website show that the system response time to display the analysis results is no more than 5 minutes, making it an efficient solution in supporting medical decision-making. With an intuitive and easily accessible interface, this website makes it easy for medical personnel to perform breast cancer analysis faster and more accurately, supporting more effective early detection efforts. Keywords: Streamlit, User Interface, Breast Cancer, Website
Implementation of Machine Learning for Breast Cancer Classification Based on Genomic Data: Backend Solution with Supabase and Streamlit Humayra, Tia Hasna; Wibowo, Suryo Adhi; Usman, Koredianto
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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

Abstract

Breast cancer remains one of the leading causes of cancer-related deaths worldwide, highlighting the need for accurate and efficient diagnostic tools. This study focuses on implementing machine learning models, particularly Artificial Neural Networks (ANN), to classify breast cancer types based on genomic data. Using the METABRIC RNA Mutation dataset, the system combines a cloud-based backend with Supabase and an intuitive frontend built with Streamlit. To ensure data compatibility with the models, preprocessing steps such as standardization, label encoding, and one-hot encoding are applied. TensorFlow is used to load models saved in .h5 format, with two approaches tested: a 30-feature model achieving 99% accuracy and an average prediction time of 80 milliseconds, and a 6-feature model achieving 100% accuracy with a faster prediction time of 42.25 milliseconds. Prediction results are stored securely in Supabase, complete with timestamps for tracking and exported as PDF reports for easy documentation. Data security is prioritized through the use of API keys, JWT tokens, and Streamlit secret management to safeguard sensitive information. The integration of Supabase for backend processing, Streamlit for real-time visualization, and GitHub for CI/CD automation results in a scalable, reliable, and efficient system. This study presents a robust solution for breast cancer classification, providing real-time predictions, secure data handling, and a user-friendly interface suitable for clinical and research applications. Keywords— breast cancer classification, artificial neural network, genomic data, Supabase, Streamlit, real-time prediction, data security.
Analisis Performansi Hate Comments pada Learning Rate 10-1- 10-3 dengan Dataset dari X Budiyanto, Anggara; Maharani, Kartika Dwi; Huljannah, Miftah; Syahanifa, Nancy Olivia; Wibowo, Suryo Adhi; Usman, Koredianto
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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

Abstract

Cyberbullying merupakan fenomena sosial yang se- makin meningkat seiring dengan meningkatnya penggunaan media sosial, dan seringkali menyebabkan dampak psikologis serta emosional yang merugikan, terutama melalui hate com- ments. Penelitian ini bertujuan untuk mengevaluasi kinerja model IndoBERT dan Cendol dalam mendeteksi komentar kebencian yang berhubungan dengan cyberbullying. Survei terhadap 328 partisipan menghasilkan 64 kata kunci terkait cyberbullying. Proses penelitian mencakup pengumpulan dataset yang berisi kata kunci tersebut, serta pengujian kedua model menggunakan metrik evaluasi seperti akurasi, presisi, recall, dan F1-Score. Hasil eksperimen menunjukkan bahwa model Cendol unggul dengan akurasi sebesar 90,5% pada konfigurasi batch size 15, epoch ke-4, dan learning rate 10-3, sementara IndoBERT hanya mencapai akurasi 36% pada konfigurasi batch size 5, epoch ke- 4, dan learning rate 10-3. Meskipun kedua model menunjukkan potensi dalam mendeteksi ujaran kebencian, model IndoBERT menunjukkan performa yang lebih rendah pada dataset yang digunakan, kemungkinan disebabkan oleh keterbatasan dalam menangani konteks lokal. Penelitian ini memberikan kontribusi signifikan dalam pengembangan teknologi deteksi ujaran keben- cian berbasis bahasa Indonesia, yang dapat diimplementasikan pada berbagai platform media sosial seperti X, Facebook, Insta- gram, dan TikTok untuk mengurangi dampak negatif dari hate comments. Kata Kunci: Cyberbullying, Hate Comments, IndoBERT, Cen- dol, NLP.
Co-Authors Achmad Rizal Adam Wisnu Pradana Agnes Gabriela Putri Winata Agus Pratondo Al Rasyid, Sadam Aldo Tripolyta Aldra Kasyfil Aziz Amara, Dhiva Byantika Angga Rusdinar ANGGUNMEKA LUHUR PRASASTI Anky Aditya P Annisa Istiqomah Asep Insani Atina Nur Azizah Aulia Aushaf Abidah Aulia, Agniya Tazkiya Aziz, Burhanuddin Bambang Hidayat Bambang Hidayat Bambang Setia Nugroho Budiyanto, Anggara Casi Setianingsih Dany Eka Saputra David Chandra Dawwam, Muhammad Devita Rahma Apriliani Dien Rahmawati Dini Himmah Al Aliyyah Al Aliyyah Djoko Heru Pamungkas Djoko Heru Pamungkas Dwiki Kurniawan Dyah Avita Sari Fahmi Oscandar Fahmi Oscandar Fajri, Farhan Ulil Farah Hana Kusumaputri Fathiyya, Dhiya Felix Corputty Fiky Y. Suratman Firdaus, Rifqi Fadhilah Fityanul Akhyar Gelar Budiman Gusdi, Angelita Hanaluthfina Nurhadiati Hashfi Fadhillah Hesty Susanti Hoka Cristian Son Hudzaifa, Muhammad Altaharik Huljannah, Miftah Humayra, Tia Hasna Husneni Mukhtar Indra Aulia Iwan Iwut Tritoasmoro Jangkung Raharjo Julyano , Muhammad Billy Khairunnisa, Mutiarahmi Koredianto Usman Kris Sujatmoko Kurnia Ramadani Kusnahadi Susanto Kusuma Nindia Rizki Lazuardi, Aldira Fadillah Ledya Novamizanti Liyana Faiza Lulud Annisa Ainun Mahmuddah Lyra Vega Ugi M. Faiz Nashrullah Maharani , Kartika Dwi Maharani, Kartika Dwi Mahfuz, Muhammad Rafi Maulana , Muhammad Dafa Mertu, Aidi Miftadi Sudjai Muhammad Alief Hidayah Baso Muhammad Azwar Zulmi Muhammad Raia Pratama Putra Wibowo Muthia Saada Nadya Sindi Safitri Nasirudin, Akhmad Yusuf Nauw, Alvaro Septra Dominggo Oriza Intani Prasaja Wibawa Utama Prihananto, Jeremia Pandu Putra Putri Utami Hafgianti Qomariyati, Laily Nur Rabby Fitriana Adawiyah Radhibilla, Maulaya Raditiana Patmasari Rahmalisty , Fiona Okki Rahmalisty, Fiona Okki Raihan Putra Darmawan Ramadhan, Ferdian Ilham Rambi, Wesli Yeremi Valentino Ricky Hilmi Sudrajad Rissa Rahmania Rissa Rahmania Rissa Rahmania Rissa Rahmania Rissa Rahmania, Rissa Rita Purnamasari Rizal , Syamsul Rofifi, M. Faiq Rosadi, Choiron Ruslan , Ramah Rinaldi Setiawan, Jonathan Vito Siddiq Wahyu Hidayat Sunaryo, Yacobus Susi Diriyanti Novalina Syahanifa , Nancy Olivia Syahanifa, Nancy Olivia Syamsul Rizal Syifa, Vito Devara Taufan Umbara Tembang Florian Falah Teuku Zulkarnain Muttaqien Unang Sunarya Viky Premeita Mitayani Vivian Alfionita Sutama Wahmisari Priharti Wahyu Maulana, Andi Wardani , Shania Widianto, Kiki Willy Anugrah Cahyadi Wiwit Ratri Wulandari Yacobus Sunaryo Yurika Ambar Lita Yuti Malinda