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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIN (Jurnal Online Informatika) MIND (Multimedia Artificial Intelligent Networking Database) Journal Journal of Computer Networks, Architecture and High Performance Computing REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Abdimas Ilmiah Citra Bakti (JAICB) J-Icon : Jurnal Komputer dan Informatika Journal Of Artificial Intelligence And Software Engineering
DEWI, IRMA AMELIA
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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Deep Learning RetinaNet based Car Detection for Smart Transportation Network DEWI, IRMA AMELIA; KRISTIANA, LISA; DARLIS, ARSYAD RAMADHAN; DWIPUTRA, REZA FADILAH
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 3 (2019): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i3.570

Abstract

ABSTRAKDeteksi objek yang merupakan salah satu bagian utama dari sistem Smart Transportasion Network (STN) diajukan pada penelitian ini. Penelitian ini menggunakan salah satu model STN yaitu Infrastructure-to-Vehicle (I2V), dimana sistem ini bekerja dengan mendeteksi kendaraan mobil menggunakan model arsitektur RetinaNet dengan backbone Resnet101 dan FPN (Feature Pyramid Network), kemudian hasil deteksi mentrigger VLC transmitter yang terpasang di lampu penerangan jalan mengirimkan sinyal informasi menuju VLC receiver yang dipasang di mobil. Pada tahap proses training, jumlah dataset mobil yang digunakan adalah sekitar 1600 image dan 400 validation image serta pengulangan proses sebanyak 100 epoch. Berdasarkan 50 kali pengujian pada image test, diperoleh nilai precision mencapai 86%, nilai recall mencapai 85% dan f1-score mencapai 84%.Kata kunci: Object detection, RetinaNet, Resnet101, STN, VLC, I2V ABSTRACTObject detection is one of the main part in Smart Transportation Network (STN) system proposed in this research. This research used one of the STN models, namely Infrastructure-to-Vehicle (I2V), a system works by detecting car using RetinaNet architecture model with ResNet 101 and FPN (Feature Pyramid Network) as backbone, then the detection result triggers VLC transmitter set up on the street lighting to transmit information signal to the VLC receiver which set up in the car. At the training process stage, the number of car datasets is approximately 1600 images, 400 validation images and repetition of processes about 100 epochs. Based on the 50 times testing process on a image test, it is obtained 86% of a precision value, by reaching 85% of recall value, and 84% of f1-score. Keywords: Object detection, RetinaNet, Resnet101, STN, VLC, I2V
Identifikasi Suara Tangisan Bayi menggunakan Metode LPC dan Euclidean Distance DEWI, IRMA AMELIA; ZULKARNAIN, ADRIANA; LESTARI, AYU APRILIA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 6, No 1 (2018): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v6i1.153

Abstract

ABSTRAKKebanyakan orang tua masih jarang memiliki kemampuan mengartikan tangisan bayi. Bagi beberapa orang tua hal tersebut menjadi kendala ketika mengenali kebutuhan dari tangisan bayi. Oleh karena itu, pada penelitian ini telah diirancang sistem mengidentifikasi suara tangisan bayi dengan metode ektstraksi sinyal yaitu metode LPC (Linear Predictive Coding) dan pencocokan pola menggunakan algoritma Euclidean Distance. Data latih tangisan bayi menggunakan database suara Baby language-DBL, sementara data uji suara tangisan bayi diperoleh dari hasil observasi di poliklinik anak suatu rumah sakit. Proses diawali dengan mengektraksi file suara tangisan bayi dan disimpan ke dalam database sebagai data latih. Suara data uji diekstraksi kemudian dicocokkan dengan data latih menggunakan Euclidean Distance. Aplikasi dapat mengidentifikasi suara tangisan bayi dengan hasil pencocokan sebesar 76%.Kata kunci: Tangisan Bayi, Linear Predictive Coding, Euclidean Distance, Dunstan Baby LanguageABSTRACTMost parents still rarely have the ability to interpret the infant cries. Some parents become an obstacle when recognizing the needs of crying babies. Therefore, this research has designed the system to identify the sound of crying baby with method of signal extraction that is LPC (Linear Predictive Coding) method and pattern matching using Euclidean Distance algorithm. Training dataset of infant cries using the Dunstan Baby language database-DBL, while testing dataset of infant cries were obtained from observations in the child polyclinic of a hospital. The process begins by extracting training dataset from the sound of infant cries files and stored in the database. The extraction feature of testing dataset is matched with the training data using the Euclidean Distance. The system can identify the sound of crying babies with matching results of 76%.Keywords: Infant Cries, Newborn Cries, Linear Predictive Coding, Euclidean Distance, Dunstan Baby Language
Ekstraksi Ciri Pelafalan Huruf Hijaiyyah Dengan Metode Mel-Frequency Cepstral Coefficients INDRAWATY, YOULLIA; DEWI, IRMA AMELIA; LUKMAN, RIZKI
MIND Journal Vol 4, No 1 (2019): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (754.334 KB) | DOI: 10.26760/mindjournal.v4i1.49-64

Abstract

Huruf hijaiyyah merupakan huruf penyusun ayat dalam Al Qur?an. Setiap hurufhijaiyyah memiliki karakteristik pelafalan yang berbeda. Tetapi dalam praktiknya,ketika membaca huruf hijaiyyah terkadang tidak memperhatikan kaidah bacaanmakhorijul huruf. Makhrorijul huruf adalah cara melafalkan atau tempatkeluarnya huruf hijaiyyah. Dengan adanya teknologi pengenalan suara, dalammelafalkan huruf hijaiyyah dapat dilihat perbedaannya secara kuantitatif melaluisistem. Terdapat dua tahapan agar suara dapat dikenali, dengan terlebih dahulumelakukan ekstraksi sinyal suara selanjutnya melakukan identifikasi suara ataubacaan. MFCC (Mel Frequency Cepstral Coefficients) merupakan sebuah metodeuntuk melakukan ektraksi ciri yang menghasilkan nilai cepstral dari sinyal suara.Penelitian ini bertujuan untuk mengetahui nilai cepstral pada setiap hurufhijaiyyah. Hasil pengujian yang telah dilakukan, setiap huruf hijaiyyah memilikinilai cepstral yang berbeda.
AN ANALYSIS OF DISTANCE EXTENSION METHOD IN VISIBLE LIGHT COMMUNICATION (VLC) PERFORMANCE KRISTIANA, LISA; DARLIS, ARSYAD RAMADHAN; DEWI, IRMA AMELIA; LIDYAWATI, LITA; ARCHANDHIKA, HEGAR REFALDY
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 1 (2020): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i1.218

Abstract

ABSTRAK A Visible Light Communication (VLC) adalah teknologi yang menawarkan konsep inovatif karena VLC menerapkan cahaya tampak untuk mentransmisikan informasi dari satu titik ke titik lain. Tantangan utama dalam penerapan VLC adalah pelemahan sinyal cahaya tampak karena faktor jarak dari titik sumber ke titik tujuan. Penelitian ini berfokus pada metode untuk merancang dan menerapkan pemancar dan penerima VLC pada media udara. Dengan membandingkan berbagai macam tipe LED, pengukuran yang didapatkan menunjukkan bahwa pemancar dan penerima VLC dapat ditingkatkan kemampuannya sehingga mencapat jarak maksimum 8.5 meter dengan menggunakan LED HPL. Kata kunci: Visible Light Communication, VLC Transceiver, Distance Extension Method, Light Emitting Diodes (LEDs). ABSTRACT A Visible Light Communication (VLC) offers the innovative concept in telecommunication since it implements visible lights to transmit information from one point to other points. The main challenge in VLC is the attenuation due to the distance from source to destination. This research focuses on extension method to design and implement the VLC transceiver in an air medium. By comparing the real measurement of several types of LEDs, the distance of VLC transceiver can be extended up to 8.5 meters by applying HPL LED. Keywords: Visible Light Communication, VLC Transceiver, Distance Extension Method, Light Emitting Diodes (LEDs).
The Development of Company Profile Website for CV. Rian using Waterfall Model SDLC Dewi, Irma Amelia; Fitria, Lisye; Desrianty, Arie; Afifah, Alif Ulfa; Dzaky, M. Faishal
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 1, No 2 (2020): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v1i2.75-85

Abstract

Nowadays, the benefits of using the internet can be felt in society or even in the industrial world, either for business or just for entertainment purposes. Until 2017, Indonesia is one of the countries with the highest internet users globally, with around 112,6 million internet users. On the other hand, many small businesses have not used the internet to advertise their products. This project's main objective is to design and develop a company profile website of CV Rian as one of the brick micro-industry in Nagrek, West Java, enabling people to search and access information easily and quickly. This project uses Waterfall Model as Sofware Development Life Cycle (SDLC), MySQL as the database, and PHP for server-side scripting. The database is used for storing posts, admin data, and images. 
Development of Attendance Information System For Teacher Attendance at Pertiwi Elementary School Bandung Dewi, Irma Amelia; Miftahuddin, Yusup; Triseptiyadi, Fahmi; Vito, Nicola; Naufal, Muhammad Thoriq; Fahreza, Algi
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 5, No 2 (2024): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v5i2.135-144

Abstract

The advancement of technology particularly in telecommunications, has led to the creation of various software applications that are beneficial to human life. One such application is modern attendance management systems. The objective of this research is to construct and implement a QR-code-based attendance information system for teachers and staff at SD Pertiwi Kota Bandung. The research methodology comprises socialization, counseling, implementation, and monitoring and evaluation. The responses indicate that the application has met or exceeded user expectations in terms of functionality and accessibility. Of the respondents, 30.74% gave "Normal" responses, 41.85% gave "Baik" responses, and 27.41% gave "Sangat Baik" responses. Additionally, the responses from program partners, SD Pertiwi, Bandung City, indicate positive evaluation and feedback.. The results of this development are intended to improve the accuracy of the attendance process, facilitate teachers and staff in reporting attendance, and minimize fraud and human error.
RESIDUAL NETWORK LAYER COMPARISON FOR SEAT BELT DETECTION Dewi, Irma Amelia; Nasrulloh, Nur Zam Zam
J-Icon : Jurnal Komputer dan Informatika Vol 11 No 2 (2023): Oktober 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i2.9903

Abstract

Most of the monitoring of traffic violations on Indonesian roads is currently done manually by monitoring through CCTV cameras, so drivers still have the possibility of violating the use of seat belts. Residual Network (ResNet) as one of the architectures with an accuracy rate of up to 96.4% in 2015, which is intended to overcome the vanishing gradient problem that commonly occurs in networks with many layers. Therefore, in this study, a system was developed using the RetinaNet architecture to detect drivers who use seat belts and drivers who do not use seat belts with the ResNet backbone. In addition, this study compares the performance of ResNet-101 and ResNet-152. The hyperparameters used include a dataset of 10,623 images in the training process, and the batch size parameter is 1, with a total of 10,623 steps, and the number of epochs is 16. Based on 60 tests conducted in this study, the RetinaNet model with the ResNet-152 architecture performed better than the ResNet-101 architecture. The ResNet-152 architecture resulted in a system performance with an accuracy of 98%, precision value of 99%, recall value of 99%, and an f1 score of 99%.
Segmentation-Based Fractal Texture Analysis (SFTA) to Detect Mass in Mammogram Images DEWI, IRMA AMELIA; FAHRUDIN, NUR FITRIANTI; RAINA, JODI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 1: Published January 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i1.203

Abstract

ABSTRAKDi Indonesia, kasus kanker paling banyak adalah kanker payudara yaitu 58.256 kasus atau 16,7% dari total 348.809 kasus kanker. Dibutuhkan suatu sistem yang dapat membantu pakar untuk mendeteksi kanker payudara pada wanita dengan mengindentifikasi citra mammogram. Keabnormalan dapat dideteksi dari massa pada mammogram yaitu area dengan pola tekstur dan bentuk serta batas tertentu. Berdasarkan hal tersebut maka dibuat sebuah sistem yang dapat mendeteksi massa kanker pada citra mammogram menggunakan Segmentation-Based Fractal Texture Analysis (SFTA). Tahapan pertama akuisisi citra, dilanjut dengan segmentasi menggunakan k-means dan thresholding. Hasil dari segmentasi citra dilakukan tahapan morfologi menggunakan opening dan masking. Setelah itu dilakukan ekstraksi fitur SFTA, dan klasifikasi Support Vector Machine (SVM). Hasil pengujian penelitian ini didapatkan nilai akurasi sebesar 90%, presisi sebesar 87,75%, recall sebesar 93,33%dan f1-score 90,32% dengan nilai number of threshold (nt) SFTA adalah 3Kata kunci: mammogram, SFTA, kanker payudara, klasifikasi ABSTRACTIn Indonesia, the most cancer cases were breast cancer, namely 58,256 cases or 16.7% of the total 348,809 cancer cases. A system is required to assist the expert in detecting breast cancer in women by identifying mammogram images. Abnormalities in a mammogram are determined in part of texture with a particular form and specific limit, usually called a ‘mass.’ Image acquisition is perceived as the first step, followed by segmentation using the k-means and the thresholding. Image segmentation undergoes the morphological analysis steps using opening and masking methods, after feature extraction processing by SFTA, using Support Vector Machine (SVM) for classification processing. The obtained research result revealed an accuracy value of 90%, a precision value of 87.75%, a recall value of 93.33%, and an F1-Score of 90.32%, with the number of thresholds (nt) of SFTA amounting to 3.Keywords: Breast cancer, Mammogram, Classification, SFTA
Identifikasi Suara Tangisan Bayi menggunakan Metode LPC dan Euclidean Distance DEWI, IRMA AMELIA; ZULKARNAIN, ADRIANA; LESTARI, AYU APRILIA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 6, No 1: Published January 2018
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v6i1.153

Abstract

ABSTRAKKebanyakan orang tua masih jarang memiliki kemampuan mengartikan tangisan bayi. Bagi beberapa orang tua hal tersebut menjadi kendala ketika mengenali kebutuhan dari tangisan bayi. Oleh karena itu, pada penelitian ini telah diirancang sistem mengidentifikasi suara tangisan bayi dengan metode ektstraksi sinyal yaitu metode LPC (Linear Predictive Coding) dan pencocokan pola menggunakan algoritma Euclidean Distance. Data latih tangisan bayi menggunakan database suara Baby language-DBL, sementara data uji suara tangisan bayi diperoleh dari hasil observasi di poliklinik anak suatu rumah sakit. Proses diawali dengan mengektraksi file suara tangisan bayi dan disimpan ke dalam database sebagai data latih. Suara data uji diekstraksi kemudian dicocokkan dengan data latih menggunakan Euclidean Distance. Aplikasi dapat mengidentifikasi suara tangisan bayi dengan hasil pencocokan sebesar 76%.Kata kunci: Tangisan Bayi, Linear Predictive Coding, Euclidean Distance, Dunstan Baby LanguageABSTRACTMost parents still rarely have the ability to interpret the infant cries. Some parents become an obstacle when recognizing the needs of crying babies. Therefore, this research has designed the system to identify the sound of crying baby with method of signal extraction that is LPC (Linear Predictive Coding) method and pattern matching using Euclidean Distance algorithm. Training dataset of infant cries using the Dunstan Baby language database-DBL, while testing dataset of infant cries were obtained from observations in the child polyclinic of a hospital. The process begins by extracting training dataset from the sound of infant cries files and stored in the database. The extraction feature of testing dataset is matched with the training data using the Euclidean Distance. The system can identify the sound of crying babies with matching results of 76%.Keywords: Infant Cries, Newborn Cries, Linear Predictive Coding, Euclidean Distance, Dunstan Baby Language
Deep Learning RetinaNet based Car Detection for Smart Transportation Network DEWI, IRMA AMELIA; KRISTIANA, LISA; DARLIS, ARSYAD RAMADHAN; DWIPUTRA, REZA FADILAH
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 3: Published September 2019
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i3.570

Abstract

ABSTRAKDeteksi objek yang merupakan salah satu bagian utama dari sistem Smart Transportasion Network (STN) diajukan pada penelitian ini. Penelitian ini menggunakan salah satu model STN yaitu Infrastructure-to-Vehicle (I2V), dimana sistem ini bekerja dengan mendeteksi kendaraan mobil menggunakan model arsitektur RetinaNet dengan backbone Resnet101 dan FPN (Feature Pyramid Network), kemudian hasil deteksi mentrigger VLC transmitter yang terpasang di lampu penerangan jalan mengirimkan sinyal informasi menuju VLC receiver yang dipasang di mobil. Pada tahap proses training, jumlah dataset mobil yang digunakan adalah sekitar 1600 image dan 400 validation image serta pengulangan proses sebanyak 100 epoch. Berdasarkan 50 kali pengujian pada image test, diperoleh nilai precision mencapai 86%, nilai recall mencapai 85% dan f1-score mencapai 84%.Kata kunci: Object detection, RetinaNet, Resnet101, STN, VLC, I2V ABSTRACTObject detection is one of the main part in Smart Transportation Network (STN) system proposed in this research. This research used one of the STN models, namely Infrastructure-to-Vehicle (I2V), a system works by detecting car using RetinaNet architecture model with ResNet 101 and FPN (Feature Pyramid Network) as backbone, then the detection result triggers VLC transmitter set up on the street lighting to transmit information signal to the VLC receiver which set up in the car. At the training process stage, the number of car datasets is approximately 1600 images, 400 validation images and repetition of processes about 100 epochs. Based on the 50 times testing process on a image test, it is obtained 86% of a precision value, by reaching 85% of recall value, and 84% of f1-score. Keywords: Object detection, RetinaNet, Resnet101, STN, VLC, I2V
Co-Authors Afifah, Alif Ulfa Almawahib, Zulfa Sulthany ARCHANDHIKA, HEGAR REFALDY Arie Desrianty ARSYAD RAMADHAN DARLIS Aryanti P, Safira Dwi DWIPUTRA, REZA FADILAH Dzaky, M. Faishal Fadhlurrohman, Naufal Mufid Fahreza, Algi FAHRUDIN, NUR FITRIANTI Haidar, Muhamad Rafi Ibrahim, Farrel Adi KRISTIANA, LISA LESTARI, AYU APRILIA Lisye Fitria LITA LIDYAWATI LUKMAN, RIZKI Maryadi, Nadhiva Adzra Tsania Miftahuddin, Yusup Muhammad Ichwan Nana H, Asep Nasrulloh, Nur Zam Zam Naufal, Muhammad Thoriq Nurhasanah, Youllia Indrawaty Pratama, Yanuarsyah RAINA, JODI riansyah, rizki rahmat Salsabilla, Nadiati Shaneva, Amelia Suryadikarsa, Fawwaz Muhammad Tahttadu, Winter Bee Triseptiyadi, Fahmi Vito, Nicola ZULKARNAIN, ADRIANA