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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Jurnal INFOTEL
Ema Rachmawati
Universitas Telkom, Bandung
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Public Health Social Sciences Other

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Review of Local Descriptor in RGB-D Object Recognition Ema Rachmawati; Iping Supriana Suwardi; Masayu Leylia Khodra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 4: December 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i4.388

Abstract

The emergence of an RGB-D (Red-Green-Blue-Depth) sensor which is capable of providing depth and RGB images gives hope to the computer vision community. Moreover, the use of local features began to increase over the last few years and has shown impressive results, especially in the field of object recognition. This article attempts to provide a survey of the recent technical achievements in this area of research. We review the use of local descriptors as the feature representation which is extracted from RGB-D images, in instances and category-level object recognition. We also highlight the involvement of depth images and how they can be combined with RGB images in constructing a local descriptor. Three different approaches are used in involving depth images into compact feature representation, that is classical approach using distribution based, kernel-trick, and feature learning. In this article, we show that the involvement of depth data successfully improves the accuracy of object recognition.
Automatic whole-body bone scan image segmentation based on constrained local model Ema Rachmawati; Jondri Jondri; Kurniawan Nur Ramadhani; Achmad Hussein Sundawa Kartamihardja; Arifudin Achmad; Rini Shintawati
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2631

Abstract

In Indonesia, cancer is very burdensome financially for sufferers as well as for the country. Increasing the access to early detection of cancer can be a solution to prevent the situation from worsening. Regarding the problem of cancer lesion detection, a whole-body bone scan image is the primary modality of nuclear medicine for the detection of cancer lesions on a bone. Therefore, high segmentation accuracy of the whole-body bone scan image is a crucial step in building the shape model of some predefined regions in the bone scan image where metastasis was predicted to appear frequently. In this article, we proposed an automatic whole-body bone scan image segmentation based on constrained local model (CLM). We determine 111 landmark points on the bone scan image as the input for the model building step. The resulting shape and texture model are further used in the fitting step to estimate the landmark points of predefined regions. We use the CLM-based approach using regularized landmark mean-shift (RLMS) to lessen the effect of ambiguity, which was struggled by the CLM-based approach. From the experimental result, we successfully show that our proposed image segmentation system achieves higher performance than the general CLM-based approach.
Corn Seeds Identification Based on Shape and Colour Features Haddad Alwi Yafie; Ema Rachmawati; Esa Prakasa; Amin Nur
Khazanah Informatika Vol. 6 No. 2 October 2020
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i2.10840

Abstract

Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and colour features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%.
Toward a New Approach in Fruit Recognition using Hybrid RGBD Features and Fruit Hierarchy Property Ema Rachmawati; Iping Supriana; Masayu Leylia Khodra
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1220.177 KB) | DOI: 10.11591/eecsi.v4.1029

Abstract

We present hierarchical multi-feature classification (HMC) system for multiclass fruit recognition problem. Our approach to HMC exploits the advantages of combining multimodal features  and  the  fruit  hierarchy  property.  In  the construction of hybrid features, we take the advantage of using color feature in the fruit recognition problem and combine it with 3D shape feature of depth channel of RGBD (Red, Green, Blue, Depth) images. Meanwhile, given a set of fruit species and variety, with a preexisting hierarchy among them, we consider the problem of assigning images to one of these fruit variety from the point of view of a hierarchy. We report on computational experiment using this approach. We show that the use of hierarchy structure along with hybrid RGBD features can improve the classification performance.
Pengenalan Batik Indonesia Menggunakan Ciri Warna dan Tekstur Ema Rachmawati; Maula Ilma Ahgnia Dwi Anjani; Febryanti Sthevanie
IJAI (Indonesian Journal of Applied Informatics) Vol 4, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v4i2.41591

Abstract

Upaya pelestarian budaya bangsa melalui pengenalan batik merupakan hal yang harus selalu ditingkatkan. Terlebih dengan diakuinya budaya batik Indonesia oleh UNESCO sebagai bagian dari warisan budaya tak berwujud (intangible). Hal inilah yang mendasari dilakukannya sejumlah penelitian terkait pengenalan batik. Hasil kinerja yang sangat baik telah dicapai oleh berbagai sistem pengenalan batik. Namun, berbagai penelitian yang dilakukan tersebut masih terbatas pada jumlah motif batik yang sedikit. Oleh karena itu, pada penelitian ini dibangun sistem pengenalan batik dengan menggunakan 114 motif batik dari 14 daerah di propinsi Jawa Barat. Ciri gabungan dibangun dengan mengkombinasikan ciri tekstur dan warna. Ciri tekstur didapatkan dari Gray Level Co-occurrence Matrix (GLCM) sedangkan ciri warna didapatkan dari Color Difference Histogram (CDH). Penulis juga menambahkan variasi dalam dataset yang berupa rotate dan flip untuk memperbesar variasi intra-class. Hasil utama dari kinerja sistem yang dibuat adalah akurasi sebesar 99,128 % dan F1-Score sebesar 98,9999% pada pengenalan batik berdasarkan daerah, sedangkan pada pengenalan batik berdasarkan motif didapatkan akurasi sebesar 98,2456% dan F1-Score  sebesar 98,3208%.  
CLASSIFICATION OF DOG BREEDS FROM SPORTING GROUPS USING CONVOLUTIONAL NEURAL NETWORK Naufal Harsa Pratama; Ema Rachmawati; Gamma Kosala
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3208

Abstract

The use of convolutional neural networks has been applied to various applications. Such as image clas-sification, object detection and recognition, and others. One of the most popular uses for neural networks is image classification. Image classification mainly identifies and categorizes images according to the specified group. One application is to distinguish between one type of dog to another. Classification of dog breeds has its challenges because several kinds of dogs have similar physical characteristics, espe-cially those that belong to the same group. This study explains how to develop a dog breed classification system from a sporting group using a residual neural network (ResNet). The system's goal is to make it simpler for people to identify the dog breed. Five types of dog breeds were used, which were obtained from the Tsinghua Dogs dataset. In its implementation, two variants of CNN are used to be compared, ResNet 50 and ResNet 101, using the same configuration. Based on the research results, ResNet 101 shows better macro-average f1-score results while maintaining high accuracy. The ResNet 50 produces an f1-score of 84%, while ResNet 101 makes an f1-score of 86%.
Korelasi antara Nilai Latihan Soal Berpikir Komputasional dan Hasil Tantangan Bebras pada Siswa sebagai Bagian dari Peningkatan Kesiapan Guru dalam Gerakan PANDAI Muhammad Arzaki; Ema Rachmawati; Ade Romadhony; Bambang Pudjoatmodjo; Dodi Wisaksono Sudiharto; Putu Harry Gunawan; Agung Toto Wibowo; Selly Meliana; Rimba Whidiana Whidiana Ciptasari; Fazmah Arif Yulianto; Fat’hah Noor Prawira; Bedy Purnama
Charity : Jurnal Pengabdian Masyarakat Vol 5 No 2a (2022): Special Issue
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v5i2a.5071

Abstract

Salah satu konsep dasar dalam pembelajaran Informatika adalah Berpikir Komputasional (BK) yang merupakan cara berpikir dan menyelesaikan masalah secara sistematis dan logis. Pada September-Desember 2021, Biro Bebras Universitas Telkom mengadakan rangkaian kegiatan pengabdian kepada masyarakat melalui gerakan PANDAI (Pengajar untuk Era Digital Indonesia) dengan tujuan untuk meningkatkan kompetensi guru dalam mengintegrasikan BK pada mata pelajaran yang diampunya. Untuk mengukur keberhasilan rangkaian lokakarya yang telah dilaksanakan, dilakukan analisis secara kuantitatif terhadap korelasi antara nilai yang diperoleh siswa ketika latihan terbimbing dengan guru dan nilai yang diperoleh siswa pada tantangan Bebras. Dari hasil analisis yang dilakukan terhadap 242 siswa, diperoleh nilai rata-rata latihan terbimbing adalah 73,28 dan nilai rata-rata tantangan Bebras adalah 38,65. Perbedaan nilai yang cukup signifikan ini juga didukung oleh fakta bahwa korelasi antara nilai latihan terbimbing dan nilai tantangan Bebras adalah sebesar 0,189.
Klasifikasi Genus Tanaman Sukulen Menggunakan Convolutional Neural Network Hamad Fauzi Jessar; Agung Toto Wibowo; Ema Rachmawati
eProceedings of Engineering Vol 8, No 2 (2021): April 2021
Publisher : eProceedings of Engineering

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

Abstract

Abstrak Tanaman sukulen merupakan jenis tanaman hias yang banyak ditemukan jenisnya di indonesia. Tanaman sukulen mempunyai banyak jenis genus yang dimana setiap genus mempunyai ciri dan karakteristik yang beragam sehingga sulit untuk mengidentifikasi jenis genus pada tanaman sukulen.Oleh karena itu, penulis membuat sebuah sistem yang dapat mengenali jenis genus tanaman sukulen melalui gambar menggunakan metode Convolutional Neural Network (CNN). CNN merupakan salah satu teknik deep learning yang dapat digunakan untuk mengenali objek dua dimensi seperti gambar dan video. CNN memiliki banyak jenis arsitektur jaringan, arsitektur jaringan CNN yang digunakan penulis untuk membangun sistem ini adalah custom arsitektur dan penulis juga menggunakan k fold cross validation yang bertujuan untuk memastikan keakuratan akurasi yang dihasilkan oleh model sistem. Penelitian dilakukan penulis dengan membandingkan antara model yang dilatih menggunakan dataset berwarna (RGB) dan model yang dilatih menggunakan dataset grayscale. Dari hasil penelitian didapatkan bahwa model yang dilatih menggunakan dataset berwarna mempunyai akurasi testing yang lebih tinggi dibandingkan dengan model yang dilatih menggunakan dataset grayscale. Akurasi testing yang dihasilkan model yang dilatih dengan dataset berwarna sebesar 93% sedangkan model yang dilatih menggunakan dataset grayscale sebesar 64%. Kata kunci : convolutional neural network, deep learning, k fold, grayscale, RGB Abstract Succulent plants are a type of ornamental plant that are found in many species in Indonesia. Succulent plants have many types of genera, where each genus has various characteristics and characteristics making it difficult to identify the type of genus in succulent plants. Therefore, the authors created a system that can recognize the types of succulent plant genera through images using the Convolutional Neural Network (CNN) method. ). CNN is a deep learning technique that can be used to recognize two-dimensional objects such as images and videos. CNN has many types of network architectures, the CNN network architecture used by the author to build this system is a custom architecture and the author also uses k fold cross validation which aims to ensure the accuracy of the accuracy generated by the system model. The research was conducted by the author by comparing the model trained using the color dataset (RGB) and the model trained using the grayscale dataset. From the results of the study, it was found that models trained using color datasets have higher accuracy than models trained using grayscale datasets, namely 93% for models with color datasets while models with grayscale datasets have 64% accuracy. Keywords: convolutional neural network, deep learning, k fold cross validation, RGB, grayscale
ANALISIS KORELASI NILAI MICROTEACHING GURU DENGAN KEMAMPUAN PEMBUATAN SOAL YANG MENGINTEGRASIKAN BERPIKIR KOMPUTASIONAL PADA MATA PELAJARAN MELALUI GERAKAN PANDAI Muhammad Arzaki; Ade Romadhony; Putu Harry Gunawan; Rimba Whidiana Ciptasari; Fazmah Arif Yulianto; Selly Meliana; Agung Toto Wibowo; Bambang Pudjoatmodjo; Dodi Wisaksono Sudiharto; Fat’hah Noor Prawita; Ema Rachmawati
Prosiding COSECANT : Community Service and Engagement Seminar Vol 1, No 2 (2021)
Publisher : Universitas telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.678 KB) | DOI: 10.25124/cosecant.v1i2.17506

Abstract

Berpikir Komputasional (BK) merupakan metode berpikir secara sistematis dan logis dalam menyelesaikan suatu masalah. Biro Bebras Universitas Telkom menyelenggarakan lokakarya dengan tujuan melatih guru untuk mengintegrasikan BK ke dalam mata pelajaran pada rumpun STEM maupun non-STEM untuk tingkat SD, SMP dan SMA. Pada lokakarya ini, terdapat 146 peserta guru yang setengahnya merupakan guru SMA atau sederajat dan lebih dari seperempatnya adalah guru SD atau sederajat. Dalam kegiatan lokakarya, guru-guru diberikan pelatihan BK, membuat rencana pembelajaran (RP), membuat deskripsi soal (DS) dan melaksanakan microteaching (MT) sebagai penerapan dari kegiatan lokakarya. Dari hasil analisis data yang dilakukan, nilai korelasi antara nilai pembuatan DS dan aktivitas MT secara keseluruhan adalah 0,08151 dari total 33 peserta yang mengikuti serangkaian tugas DS dan MT. Sehingga, secara statistik dapat disimpulkan bahwa tidak ada pengaruh signifikan antara aktivitas pembuatan DS dengan aktivitas MT pada kegiatan lokakarya ini.
Pelatihan Berpikir Komputasional untuk Peningkatan Kompetensi Guru Telkom Schools sebagai Bagian dari Gerakan PANDAI Muhammad Arzaki; Selly Meliana; Ema Rachmawati; Ade Romadhony; Agung Toto Wibowo; Bambang Pudjoatmodjo; Bedy Purnama; Dodi Wisaksono Sudiharto; Fat'hah Noor Prawira; Fazmah Arif Yulianto; Putu Harry Gunawan; Rimba Whidiana Ciptasari
I-Com: Indonesian Community Journal Vol 3 No 3 (2023): I-Com: Indonesian Community Journal (September 2023)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v3i3.2988

Abstract

Berpikir komputasional (BK) atau computational thinking (CT) merupakan salah satu keahlian esensial yang diperlukan sumber daya manusia Indonesia dalam rangka menghadapi revolusi industri 4.0 dan masyarakat 5.0. Gerakan PANDAI (Pengajar Era Digital Indonesia) merupakan suatu gerakan nasional yang merupakan kolaborasi nirlaba antara komunitas Bebras Indonesia, Kementerian Pendidikan dan Kebudayaan Indonesia, dan Google Indonesia dalam rangka meningkatkan kompetensi BK yang dimiliki oleh guru sekolah dasar dan menengah. Pada tahun 2022, Biro Bebras Universitas Telkom mengadakan pelatihan BK kepada lebih dari 60 guru Telkom Schools sebagai bagian dari gerakan ini. Pelatihan ini terdiri dari lima tahapan besar yang meliputi lokakarya luring, pembelajaran mandiri, lokakarya daring, dan dua kegiatan microteaching. Hasil analisis kuantitatif menunjukkan peningkatan kemampuan konseptual peserta terkait BK, meskipun masih banyak hal yang perlu dibenahi dari sisi kemampuan teknis dalam pengerjaan soal-soal BK.
Co-Authors Abdul Khaliq Al Bari Achmad Hussein Sundawa Kartamihardja Ade Romadhony Agung Toto Wibowo Agus Cahya Ananda Yoga Putra Amin Nur Arfianto Fahmi Arifudin Achmad Bambang Pudjoatmodjo Bedy Purnama Dodi Wisaksono Sudiharto Edwar Esa Prakasa Faishal Daffa Fat'hah Noor Prawira Fat’hah Noor Prawira Fazmah Arif Yulianto Febryanti Sthevanie Gamma Kosala Ganjar Gingin Tahyudin Grandhys Setyo Utomo Haddad Alwi Yafie Hamad Fauzi Jessar Indra Lukmana Sardi Iping Supriana Iping Supriana Suwardi Jondri Jondri Kurniawan Nur Ramadhani Lidya Ningsih Mahmud Dwi Sulistiyo Masayu Leylia Khodra Maula Ilma Ahgnia Dwi Anjani Muhammad Arzaki Naufal Harsa Pratama Prawita, Fat’hah Noor Putu Harry Gunawan Rimba Whidiana Ciptasari Rina Pudji Astuti Rini Shintawati Selly Meliana Simon Siregar Supriana, Iping Yayan Agustian