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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi International Journal of Advances in Intelligent Informatics Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Building of Informatics, Technology and Science Journal of Applied Engineering and Technological Science (JAETS) Journal of Applied Data Sciences Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Proceeding of Community Service and Engagement
Mahmud Dwi Sulistiyo
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Sistem Identifikasi Individu Berbasis Palm Vein Dengan Metode Local Binary Pattern Rifqi Luthfil Hadi; Tjokorda Agung Budi Wirayudha; Mahmud Dwi Sulistiyo
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract

Abstrak - Biometrik kini berkembang sebagai suatu langkah alternative dalam sistem autentikasi user dengan menerapakan representasi ciri individu sebagai suatu alat identifikasi seseorang dalam mengakses sistem yang merupakan hak akses mereka. Palm vein merupakan salah satu biometrik-feature yang dapat diadaptasi sebagai suatu alat identifikasi pengenalan individu, karena memiliki pola maupun struktur vein yang khas yg membedakan suatu individu dengan individu lainnya serta memiliki kehandalan tersendiri jika dibandingkan dengan ciri biometrik lainnya, karena palm vein terletak pada bagian dibawah kulit, sulit untuk dilihat dengan mata telanjang ataupun kamera biasa. Dengan menerapkan metode LBP sebagai algoritma ekstraksi ciri pada citra palm vein dan histogram intersection pada proses pencocokan dengan rasio data model dan data uji adalah 3:3, performansi sistem mampu menghasilkan nilai FAR dan FRR sebesar 0.006666667 dan 0.003333, dengan recognition rate mencapai 98%.
Analisis Dan Implementasi Collaborative Filtering Menggunakan Strategi Smoothing Dan Fusing Pada Recommender System Wahyu Rismawan; Agung Toto Wibowo; Mahmud Dwi Sulistiyo
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract

Abstrak Collaborative Filtering (CF) adalah salah satu pendekatan yang populer untuk membangun Recommender System dengan memanfaatkan informasi dan preferensi dari user lain untuk memberikan rekomendasi item. Salah satu permasalahan mendasar dalam CF adalah data rating yang sangat sedikit (data sparsity) yang mampu mempengaruhi hasil rekomendasi. Secara umum terdapat dua tipe algoritma pada CF, yaitu memory-based dan model-based yang memiliki kelebihan dan kekurangan masing-masing. Dalam tugas akhir ini, digunakan strategi smoothing dan fusing yang merupakan pendekatan hybrid dari memory-based dan model-based untuk menangani permasalahan data sparsity. Berdasarkan hasil pengujian, strategi smoothing dan fusing mampu menurunkan error sistem yang diukur menggunakan MAE dari 2,277 menjadi 0,746 atau menurun sebesar 50.62% dibandingkan tanpa menggunakan strategi smoothing dan fusing. Selain itu, akurasi sistem juga dipengaruhi oleh level sparsity dari data rating. Semakin sparse data rating yang dimiliki, maka akurasi yang dihasilkan semakin buruk. Kata kunci : Collaborative Filtering, Recommender System, Smoothing and Fusing, Data Spasity
Analisis Dan Implementasi Pengenalan Wajah Berbasis Video Dengan Menggunakan Algoritma Viola Jones Dan Svm (support Vector Machine) Marliani Harahap; Bedy Purnama; Mahmud Dwi Sulistiyo
eProceedings of Engineering Vol 2, No 3 (2015): Desember, 2015
Publisher : eProceedings of Engineering

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Abstract

Saat ini telah banyak aplikasi yang memanfaatkan pengolahan citra yang digunakan dalam bidang biometrik. Biasanya teknik pengolahan citra ini digunakan untuk proses deteksi dan pengenalan, melalui karateristikkarakteristik alami yang terdapat pada tubuh manusia, contohnya pengenalan wajah. Berbagai penelitian pengenalan wajah telah dilakukan sebelumnya salah satunya dengan menggunakan Algoritma Viola Jones dan SVM berbasis gambar dengan akurasi 84,28%.Pada tugas akhir ini dibangun suatu sistem yang mampu mengenal wajah dan menghitung jumlah wajah yang dikenali, dengan inputan berbasis video. Data yang diperoleh dari hasil perekaman diproses sehingga mampu mengklasifikasi nama setiap wajah dengan memanfaatkan metode Viola Jones sebagai deteksi dan SVM (Support Vector Machine) sebagai pengenalan. Dengan implementasi metode tersebut, sistem dapat mengenali nama wajah yang dikelompokkan kedalam kelas-kelas. Dari hasil pengujian sistem tersebut didapat akurasi 21.79% pada kernel linier. Kata Kunci : video, wajah,Viola Jones, SVM
Peningkatan Wawasan Kecerdasan Artifisial di SMK Telkom Bandung Melalui Kegiatan Workshop Mahmud Dwi Sulistiyo; Febryanti Sthevanie; Gia Septiana Wulandari
Charity : Jurnal Pengabdian Masyarakat Vol 6 No 1a (2023): Special Issue
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v6i1a.5918

Abstract

Wawasan kecerdasan artifisial (AI) merupakan pengetahuan tentang teknologi dan cara kerja kecerdasan artifisial, termasuk bagaimana mesin dan sistem dapat diprogram untuk melakukan tugas-tugas yang biasanya dilakukan oleh manusia. Dengan meningkatnya penggunaan AI di berbagai bidang, termasuk industri, teknologi, dan bisnis, penting bagi siswa di SMK Telkom Bandung untuk memahami dan memiliki wawasan tentang AI. Sayangnya, sampai saat ini, SMK Telkom Bandung masih belum menerapkan materi terkait AI di dalam kurikulumnya. Salah satu cara untuk meningkatkan wawasan tentang AI di kalangan siswa SMK adalah melalui kegiatan workshop. Workshop merupakan forum yang memungkinkan siswa untuk belajar secara langsung dari para ahli, akademisi, atau praktisi di bidang terkait, dan memiliki kesempatan untuk bertanya dan berdiskusi tentang topik yang dibahas. Memahami permasalahan dan kebutuhan SMK Telkom Bandung tersebut, tim Pengabdian Masyarakat dari kelompok keahlian Intelligent System, Fakultas Informatika, Universitas Telkom mengadakan kegiatan workshop tentang wawasan AI. Kegiatan ini bertujuan untuk membantu siswa SMK Telkom Bandung dalam mempersiapkan diri menghadapi tantangan di masa depan dan berkarir di bidang yang terkait dengan AI. Serangkaian workshop diselenggarakan selama tiga hari dengan materi meliputi pengenalan dunia AI, penerapan metode AI, dan aplikasi AI yang kekinian. Materi disampaikan secara interaktif dengan selalu melibatkan peserta melalui quiz online dan penugasan di tempat. Kegiatan workshop ini mendapatkan respon yang positif, baik dari siswa-siswi maupun para guru, serta antusiasme yang tinggi untuk diadakannya workshop lanjutan tentang wawasan AI ini.
Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach Wahyu Hauzan Rafi; Mahmud Dwi Sulistiyo; Sugondo Hadiyoso; Untari Novia Wisesty
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4083

Abstract

Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.
Javanese Script Letter Detection Using Faster R-CNN Muhammad Helmy Faishal; Mahmud Dwi Sulistiyo; Aditya Firman Ihsan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24641

Abstract

The Javanese script is now rarely used, and some people no longer recognize it. The construction of a Javanese script recognition system based on digital image processing is one of its preservation efforts. This study proposes a model capable of detecting and recognizing Javanese characters using Faster R-CNN to help people who are not familiar with the Javanese script. Faster R-CNN was chosen because it does not require additional processing compared to the previous method and Faster R-CNN has better accuracy and the ability to detect small objects. Faster R-CNN shows good results in text detection, but the use of Faster R-CNN in detecting Javanese script has not been found which makes its performance unknown, so this study will show how Faster R-CNN performs in detecting Javanese script. In this study, Faster R-CNN was able to show good performance by obtaining mean average precision (mAP) values up to 0.8381, accuracy up to 96.31%, precision up to 96.53%, recall up to 96.38 %, and F1-Score up to 96.41%. These results indicate that Faster R-CNN has better results than the previous method and can detect Javanese characters well.
Klasifikasi Gender Berdasarkan Citra Wajah Menggunakan Vision Transformer Ganjar Gingin Tahyudin; Ema Rachmawati; Mahmud Dwi Sulistiyo
eProceedings of Engineering Vol 10, No 2 (2023): April 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak-Gender seseorang dapat dilihat salah satunya secara visual berdasarkan citra wajah manusia. Selain itu, dengan kemajuan teknologi saat ini, komputer juga dapat melakukan klasifikasi gender berdasarkan data yang dilatih. Proses klasifikasi gender menggunakan komputer dapat diaplikasikan terhadap berbagai sektor seperti industri atau pemerintahan. Pada penelitian sebelumnya, terdapat berbagai metode konvensional yang digunakan untuk melakukan klasifikasi citra, khusus klasifikasi gender berdasarkan citra wajah, namun sebagian besar tidak melakukan Cross-Dataset Evaluation untuk melakukan uji performa terhadap model yang dihasilkan. Tugas akhir ini akan membahas bagaimana melakukan klasifikasi gender berdasarkan citra wajah menggunakan metode Vision Transformer menggunakan dataset AFAD sebagai dataset training dan melakukan Cross-Dataset Evaluation terhadap model yang dihasilkan menggunakan dataset UTKFace. Model yang dibangun berhasilkan mendapatkan akurasi validasi sebesar 0,9676 dan akurasi testing sebesar 0,9661 pada pengujian training atau Same-Dataset serta mendapatkan akurasi 0,8174, Precision 0,8188, Recall 0,8189, dan F1 Score sebesar 0,8189 pada pengujian Cross-Dataset Evaluation.Kata kunci- transformer, vision transformer, gender classification, image processing, computer vision.
Pengembangan Website untuk Pembaruan Media Informasi dan Promosi FJ Studio Bandung dengan memanfaatkan Google Sites Gia Septiana Wulandari; Mahmud Dwi Sulistiyo; Febryanti Sthevanie
I-Com: Indonesian Community Journal Vol 4 No 1 (2024): I-Com: Indonesian Community Journal (Maret 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i1.4028

Abstract

FJ Studio is a women's fitness studio located in Bandung. The problem is, since its establishment in 2018, FJ Studio has relied solely on word-of-mouth promotion and through the Instagram account of a local influencer. The promotional methods used are deemed ineffective; furthermore, the cost of the influencer is considered high. Therefore, to make FJ Studio's promotions more effective and cost-efficient, we, as a community service team from the Faculty of Informatics, Telkom University, developed a website to serve as an informational and promotional platform for FJ Studio. Due to the limited personnel at FJ Studio, ease of website management was prioritized. We utilized the Google Sites framework for the development of the FJ Studio website, which can be easily managed online and offers various practical and user-friendly content management features. We conducted training sessions for the owners and staff of FJ Studio to introduce them to the website management environment so that they can use and update it regularly.
Mask Detection on Motorcyclists Using YOLOv4 Firdauz, Salma Salsabila; Rachmawati, Ema; Sulistiyo, Mahmud Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2980

Abstract

The use of mask is a mandatory for everyone in the pandemic regulation to prevent the spread of COVID-19 infection. This becomes a pandemic regulation for everyone, especially in public places like in traffic situation, such as pedestrian and motorcyclists. However, many motorcyclists ignore this rule or do not use the mask properly, let alone they have high risk in being infected by the virus; Thus, a computer vision-based solution is required to help monitoring it. This study aims to built a system to automatically detect the use of mask on motorcyclists. Here, we propose a YOLOv4 model, one of YOLO variants, which is popular in the object detection task and featured with a considerably high speed in real-time situation. This study also implements domain adaptation to discuss the object detection performances. Based on the experimental results in various scenarios, our model obtained average accuracy of 78.3% and IoU of 64.8% for class with_mask, average accuracy of 78.4% and IoU of 56.3% for class without_mask, and average accuracy of 87% and IoU of 55.5% for class incorrect_mask
Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic Fachri Pane, Syafrial; Adiwijaya, Adiwijaya; Dwi Sulistiyo, Mahmud; Akbar Gozali, Alfian
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2415

Abstract

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.
Co-Authors Ade Romadhony Aditya Firman Ihsan Adiwijaya Agung Toto Wibowo Aji Gautama Putrada Akbar, M Raehan Alfian Akbar Gozali Andre Sitompul Azzam, Muhammad Abdullah Bayu Prabawa Bedy Purnama Bimmo Satryo Wicaksono Dawami, Hasbi Deny Haryadi Dewi Nasien Didit Adytia Ema Rachmawati Ema Rachmawati Fachri Pane, Syafrial Faturahman, Farhan Febryanti Sthevanie Firdauz, Salma Salsabila Gamma Kosala Ganjar Gingin Tahyudin Gia Septiana Wulandari Hamdy Nur Saidy Hifzi Priabdi Jondri Jondri Marliani Harahap Muhammad Arzaki Muhammad Helmy Faishal Muhammad Rakha Muhammad Ridha Nabila Wardah Zamani Nurfadhlina Mohd Sharef Prasti Eko Yunanto Pratama, Alif Adwitiya Raihani Mohamed Rasyid Kurniawan Rasyid Kurniawan Richasdy, Donny Rifqi Luthfil Hadi Rita Rismala Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Sugondo Hadiyoso Syafrial Fachri Pane, Syafrial Fachri Tahyudin, Ganjar Gingin Tjokorda Agung Budi Wirayudha Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin, Tri Kusnandi Ubaidillah, Rifky Fahrizal Untari Novia Wisesty Wahyu Hauzan Rafi Wahyu Rismawan ZK Abdurahman Baizal