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All Journal Bulletin of Electrical Engineering and Informatics JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Sistem Cerdas TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika eProceedings of Engineering Journal of Computer Engineering: Progress, Application and Technology
Ratna Astuti Nugrahaeni
Telkom University
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Coordinated COVID-19 vaccination scheduling model by using nearest distance-single course timetabling method Purba Daru Kusuma; Ratna Astuti Nugrahaeni
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3384

Abstract

This work proposes a new coordinated vaccine scheduling model suitable for the city size COVID-19 vaccination program. It is different from the existing COVID-19 vaccination scheduling mechanism where there is no coordination among endpoint providers. On the other side, the vaccine stock in every provider is limited, so that this mismatch creates many unserved participants. Moreover, studies on the COVID-19 vaccination scheduling problem are hard to find. This work aims to solve this mismatch problem. It is developed by combining the nearest distance and the single course timetabling. It is then optimized by using a cloud theory based-simulated annealing algorithm. The simulation result shows that the proposed model outperforms both the uncoordinated and basic course timetabling models. It can minimize the number of unserved participants, total travel distance, and the number of participants with missed timeslot. It produces zero unserved participants if the total vaccine quantity is at least equal to the total number of participants. The proposed model creates lower total travel distance than the uncoordinated or basic course timetabling adopted model. It is also better than the basic course timetabling model in creating a low number of participants with missed timeslot.
Model Design of The Image Recognition of Lung CT Scan for COVID-19 Detection Using Artificial Neural Network Maulana Akbar Dwijaya; Umar Ali Ahmad; Rudi Purwo Wijayanto; Ratna Astuti Nugrahaeni
JURNAL NASIONAL TEKNIK ELEKTRO Vol 11, No 1: March 2022
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (727.224 KB) | DOI: 10.25077/jnte.v11n1.984.2022

Abstract

COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases.
Detection of Skin Cancer Melanoma Using Expert System Forward Chaining Method And Image Processing Of K-nearest Neighbor (knn) Method Based on Android Andri Dwi Saputra; Budhi Irawan; Ratna Astuti Nugrahaeni
Jurnal Sistem Cerdas Vol. 1 No. 2 (2018): Internet of Things for Smart Society
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1000.288 KB) | DOI: 10.37396/jsc.v1i2.11

Abstract

Melanoma is the most dangerous and deadly type of skin cancer. Melanoma can be cured if detected early, but the form of melanoma that resembles a mole makes it difficult to distinguish. Early treatment performed by a dermatologist on melanoma through a biopsy process. However, a lack of biopsy is a long preparation and laboratory results that take a little longer. This fear will make cancer cells spread more widely. With this problem, this final project will design an Android-based mobile application that can detect melanoma early. Applications designed using expert systems are forward chaining methods and image processing methods K-Nearest Neighbor (KNN). From the results of image processing testing that has been done this application has an accuracy of 72% by using training data totaling 70 images. While the expert system with the forward chaining method is suitable to be used in this application because the decisions taken are in accordance with the knowledge representation that has been entered into the system.
IMPLEMENTATION OF CRYPTOGRAPHY AND STEGANOGRAPHY FOR TEXT ON COVER IMAGE USING AES AND F5 ALGORITHM Ratna Astuti Nugrahaeni; R. Rumani M. R. Rumani M.; Surya Michrandi Nasution
TEKTRIKA Vol 1 No 1 (2016)
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v1i1.248

Abstract

This journal explains about implementation that combine both cryptography and steganography method for texton cover image to increase the security level. Text will be encrypted with AES algorithm, and then it will be embedded to the cover image using F5 algorithm. The implemented AES algorithm has a good performance, with Avalanche Effect value ranges from 0.43 – 0.59. The resulting image, or stego image, has a very similar histogram with the original image, so there is no significant difference between the two of them. However, the file size change about 1.25 – 3.25 times larger than theoriginal image. If noise or disruption is given to stego image, the information can not be extracted.Keywords: cryptography, steganography, AES, F5
REKOMENDASI SISTEM PEMILIHAN MOBIL MENGGUNAKAN K-NEAREST NEIGHBOR (KNN) COLLABORATIVE FILTERING Ilham Gumantung Gusti; Muhammad Nasrun; Ratna Astuti Nugrahaeni
TEKTRIKA Vol 4 No 1 (2019): TEKTRIKA Vol.4 No.1 2019
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v4i1.1846

Abstract

Mobil merupakan kendaraan yang sangat dibutuhkan pada masa ini. Banyak dari pengguna ketika ingin memilih mobil hanya mengetahui sebagian dari informasi mobil yang disukainya tanpa mengetahui informasi mobil lain yang sejenis. Rekomendasi sistem pemilihan mobil merupakan sistem yang dapat digunakan oleh pengguna dalam memilih mobil. Dengan diterapkannya rekomendasi sistem pemilihan mobil, pengguna akan mendapatkan informasi lebih mengenai mobil yang ingin dipilih, dan mobil lain yang mungkin mobil tersebut sama sekali belum diketahui oleh pengguna. Dalam rekomendasi sistem pemilihan mobil, penulis menerapkan metode K-Nearest Neighbor (KNN) Collaborative Filtering yang dilakukan berdasarkan jarak kedekatan Data Testing dengan Data Training. Kedekatan data (kemiripan data) tersebut digunakan untuk merekomendasikan mobil ke pengguna. Hasil yang diperoleh dalam penelitian ini adalah jika ingin mendapatkan 10 mobil terbaik maka jarak maksimal yang digunakan adalah 5%, dan akurasi terbaik didapatkan ketika K = 10 yaitu sebesar 95,15%.
Pengembangan Motif Karang Anacropora Forbesi Pada Aplikasi Batik Berbasis Web Astrid Melati; Muhammad Ken; Purba Daru Kusuma; Ratna Astuti Nugrahaeni
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Batik merupakan warisan asli milik Indonesia, bukan hanya kesenian biasa tetapi batik sudah mendarah daging bagi masyarakat Indonesia, bahkan di berbagai daerah terdapat motif khas milik masing-masing daerah, motif yang dibuat tidak semata-mata tanpa arti, bahkan dari motif-motif tersebut mengandung makna, seperti motif batik sekar jagad yang bermaknakan keragaman dunia. Motif telah banyak berkembang dengan seiring waktu. Motif bisa saja dikembangkan dari berbagai objek, contohnya karang . Keanekaragaman biota laut seperti karang merupakan kekayaan yang tak ada habisnya , karena Indonesia memiliki 1/8 karang Dunia. Oleh karena itu penulis ingin memanfaatkan keanekaragaman biota laut yaitu karang menjadi motif batik yang baru , dan jenis karang yang digunakan adalah jenis Anacropora Fobresi, yang mudah ditemukan di daerah Indonesia bagian timur.Pengembangan motif batik akan menggunakan aplikasi dengan memanfaatkan algoritma L-System yang sering digunakan untuk mendapatkan topologi dari suatu tumbuhan. Aplikasi yang akan dibuat yaitu berbasis web, sehingga orang lain khususnya perajin batik dapat menggunakanya kapan saja. Kata kunci: Batik, Karang, Anacropora Fobresi , L-System, Sistem L Abstract Batik is Indonesia's original heritage, not only ordinary arts but batik is ingrained for the people of Indonesia, even in various regions there are distinctive motives belonging to each region, the motive is made not solely without meaning, even from these motifs contain meaning, such as batik motif sekarn jagad that berkaknakan world diversity. Motives have evolved over time. Motifs can be developed from various objects, for example corals. The diversity of marine biota like corals is an endless wealth, because Indonesia has 1/8 of the World's reefs. Therefore the authors want to take advantage of the diversity of marine biota that is a new batik motifs, and the type of coral that is used is Anacropora Fobresi, which is easily found in the eastern part of Indonesia. Development of batik motif will use the application by utilizing L-System algorithm which is often used to get topology from a plant. Applications to be made that is web-based, so that others, especially batik crafters can use it anytime. Keywords: Batik, Reef, Anacropora Fobresi, L-System, System L
Deep Neural Network Untuk Pengenalan Ucapan Pada Bahasa Sunda Dialek Utara Ghiffari Arwandani; Andrew Brian Osmond; Ratna Astuti Nugrahaeni
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Indonesia merupakan Negara dengan banyak ragam suku. Dari berbagai macam suku tadi, Indonesia mempunyai banyak Bahasa daerahnya masing-masing sebagai pembeda atau identitas dari daerah tersebut. Dalam hal ini pengenalan ucapan sangat penting untuk mempermudah pengenalan Bahasa yang digunakan. Pengenalan ucapan memiliki banyak metode sebagai pembelajaran, salah satunya menggunakan Deep Learning. Deep learning sebuah model jaringan syaraf tiruan yang akhir-akhir ini mulai ramai dikembangkan. Pendekatan yang sering digunakan untuk mengimplementasikan Deep Learning adalah graphical methods atau Multilayer Representation, atau Multilayer Graphical model seperti Belief Network, Neural Network, Hidden Markov, dan lain-lain. Deep Learning telah menunjukkan hasil yang baik dalam meningkatkan akurasi pengenalan suara atau kasus-kasus lainnya yang serupa. Oleh karena itu pada penelitian ini penulis akan mencoba untuk mengimplementasikan Deep Neural Network pada Speech Recognition untuk mengklasifikasian Bahasa Sunda dialek Utara. Dari hasil penelitian yang dilakukan, dari nilai parameter tertentu didapatkan akurasi sebesar 100%. Setelah mendapatkan parameter ideal dilakukan klasifikasi dengan rasio dari data latih : data data uji sebesar 50% : 50%, 60% : 40%, 70% : 30%, 80% : 20% dan 90 : 10%. Dari pengujian dengan rasio tesebut didapatkan kesimpulan bahwa, semakin banyak data latih semakin baik akurasi yang didapatkan. Kata kunci : Deep learning, Speech Recognition, Deep Neural Network Abstract Indonesia is a country with many tribes. From various tribes earlier, Indonesia has many languages of their respective regions as a differentiator or identity of the region. In this case speech recognition is very important to facilitate the introduction of the language used. Speech recognition has many methods as learning, one of them using Deep Learning. Deep learning of a model of artificial neural network which recently began to be developed. A common approach used to implement Deep Learning is graphical methods or Multilayer Representation, or Multilayer Graphical models such as Belief Network, Neural Network, Hidden Markov, and others. Deep Learning has shown good results in improving the accuracy of speech recognition or other similar cases. Therefore in this study the authors will try to implement Deep Neural Network on Speech Recognition to classify the Sundanese language of the Northern dialect. From the results of research conducted, obtained accuracy by changing each parameter of 100%. After obtaining the ideal parameters are classified with the ratio of the training data: the test data data is 50%: 50%, 60%: 40%, 70%: 30%, 80%: 20% and 90: 10%. From the test with the ratio, it is concluded that, the more train data the better the accuracy obtained. Keywords : Deep learning, Speech Recognition, Deep Neural Network
Pengembangan Motif Karang Jenis Montipora Aequituberculata Pada Aplikasi Batik Berbasis Web Muhammad Ken; Astrid Melati; Purba Daru Kusuma; Ratna Astuti Nugrahaeni
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak : Banyak peninggalan bersejarah Indonesia yang diakui oleh negara tetangga, salah satunya adalah batik. Kurangnya minat warga Indonesia terhadap batik mengakibatkan batik diakui oleh negara tetangga. Berkembangnya teknologi di Indonesia bisa dijadikan titik balik supaya batik bisa lebih diminati lagi oleh warga Indonesia, dengan memanfaatkan teknologi dalam pembuatan desain motif batik yang baru. Pada penelitian ini, telah tercipta sebuah desain motif batik menggunakan jenis karang Montipora Aequituberculata dengan aplikasi berbasis web. Pada pembuatannya terdapat beberapa rumus yang memilikir peranan masing-masing dalam membangun desain motif batik. Hasil dari pengujian melalu survey pun menunjukkan masih perlunya pengembangan lagi terhadap pola batik supaya bisa lebih baik kedepannya. Kata kunci : Batik, Karang, Web, L-System, Montipora Aequituberculata Abstract : Many historic relics of Indonesia are claimed by neighboring countries, one of them is batik. The lack of interest of Indonesian citizens towards batik resulted in batik claimed by neighboring countries. The development of technology in Indonesia can be a turning point so that batik can be more desirable by citizens of Indonesia, by utilizing technology in making new batik motif. In this research, has created a design of batik motifs using Montipora Aequituberculata coral species with web-based applications. In the making there are several formulas that have their respective roles in building design batik motif. Results from testing through the survey also shows the need for more development of batik patterns in order to better the future Keyword : Batik, Coral, Web, L-System, Montipora Aequituberculata
Detektor Kebohongan Dengan Analisa Gerakkan Mata Dan Perubahan Diameter Pupil Berbasis Video Kamera Dan Image Processing Menggunakan Metode Haar Cascade Classifier Dan Neural Network (multilayer Perceptron) Bagus Tryanto; Muhammad Nasrun; Ratna Astuti Nugrahaeni
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Berbohong adalah sifat yang tidak terpuji, semua manusia didunia ini pasti pernah berbohong. Berbohong boleh dilakukan untuk kebaikan, namun banyak sekali orang – orang yang menggunakan kebohongan untuk menguntungkan dirinya sendiri. Sangat dibutuhkan sekali alat untuk mendeteksi kebohongan, namun harganya yang sangat mahal dan memiliki komponen yang banyak membuat masyarakat sulit memilikinya. Untuk menyelesaikan tugas akhir ini penulis membuat sistem untuk mendeteksi kebohongan berbasis video kamera dengan parameter yang berikan yaitu pergerakkan bola mata dan perubahan diameter pupil menggunakan metode haar cascade classifier. Teori psikologi menyimpulkan, seseorang yang berbohong akan cenderung melihat kearah kanan dan akan terjadi pembesaran pupil 4% sampai 7%. Dengan metode haar cascade classifier dan Neural network (multilayer perceptron) didapat hasil akurasi sistem sebesar 87%. Kata kunci : Lie detector, Haar cascade classifier, Neural network, Multilayer perceptron, Video kamera, Pupil mata, Eye tracking. Abstract Lying is a trait that is not commendable, all humans in this world must have lie. Lying can be done for good sake, but there are a lot of people who use lies in the wrong way, for example to slander others or to benefit themselves. The lie detector is urgently needed nowadays, but the price is expensive and there a lot of its components which make it difficult to own for the society and the lie detector only belongs to state security organization. Therefore, the affordable and easy components lie detector is needed, so that the society can understand the tool and use it wisely. To finish this final assignment, the Author make a system to detect someone’s lie based on camera video by anlysing the given parameters, which are eye moving (eye tracking) and the change of pupil diameter. With the method of Haar Cascade Classifier and Neural Network (Multilayer Perceptron). Psychological theories conclude that, someone who lies will have certain characteristics, especially in the part of eye, such as enlarged pupil diameter of eyes, the eyelid does not blink when it says lies, and the movement of eyeballs which always moving to indicate someone is thinking something. These parameters are to be tested and taken with video cameras that are integrated with the software to be analysed whether someone is lying or not. With the method of haar casecade classifier and neural network (multilayer perceptron) get the accuracy of the research system 87%. keywords:Lie Detector, Haar cascade classifier, Neural network, Multilayer Perceptron, Camera video, Eye pupils, Eye tracking.
Detektor Kebohongan Dengan Analisa Gerakan Mata Dan Jumlah Kedipan Mata Menggunakan Metode Viola-jones Dan Jaringan Saraf Tiruan Backpropagation Hanif Afianto Dwi Nugroho; 2Muhammad Nasrun; Ratna Astuti Nugrahaeni
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Setiap Manusia memiliki kelebihan dan kekurangan dalam kehidupannya. Terkadang untuk dapat diterima oleh masyarakat, sesorang akan berusaha menutupi kekurangannya atau bahkan kelebihannya dengan cara melakukan kebohongan. Pada tugas akhir ini telah membuat sistem untuk mendeteksi kebohongan dengan analisis gerakan bola mata dan jumlah kedipan mata menggunakan metode Viola-Jones. Menurut teori psikologi, jika mata seseorang cenderung menghadap kesebelah kiri maka hal ini dikarenakan mereka sedang memikirkan hal-hal yang sudah terjadi sebelumnya, sesuai juga dengan fungsi otak kiri sebagai memori yang telah lalu. Sedangkan tatapan seseorang yang cenderung menghadap kesebelah kanan berhubingan dengan otak kanan atau daya imajinasi. Sementara rata-rata orang dewasa berkedip adalah 10-15 kali dalam satu menit dan terdapan jeda antara 2-10 detik antara sebuah kedipan dengan kedipan berikutnya.Kedua paramenter dalam sistem yang dibuat akan digabungkan dengan metode Backpropagation untuk dapat melakukan prediksi kebohongan, dimana akurasi yang didapat adalah sebesar 85.33%. Kata Kunci Detektor Kebohongan, Gerakan Bola Mata, Kedipan Mata, Viola-Jones, Facial Landmark, Jaringan Saraf Tiruan Backpropagation Abstract Every human being has abundance and weakness in his life. Sometimes to be accepted by the community, someone will try to cover up the shortcomings or even the excess by lying. In fact, lies will cause profit on one side and loss on the other. In this final project has made a system to detect lies with eyeball movement analysis and the number of blinks using the Viola-Jones method. According to psychological theory, if someone's eyes tend to face the left, this is because they are thinking about things that have happened before, according to the function of the left brain as a past memory. While the gaze of someone who tends to face the right side is related to the right brain or imagination. While the average adult blinks is 10-15 times in one minute and takes a pause between 2-10 seconds between a blink and the next blink. The two parameters in the system that are made will be combined with the Backpropagation method to predict the lies, where the accuracy is 85.33% . Keywords: Lie Detector, Eyeball Movement, Blinking, Viola-Jones, Facial Landmark, Backpropagation Neural Network
Co-Authors 2Muhammad Nasrun Andi Nurfita F Andrew Brian Osmond Andri Dwi Saputra ANGGUNMEKA LUHUR PRASASTI Astrid Melati Azhar Eka Mulia Wiguna Bagus Tryanto Budhi Irawan Burhanuddin Dirgantoro Fawwaz Aboeruslan Muyadi Ghiffari Arwandani Hanif Afianto Dwi Nugroho Hanif Kukuh Raharjo Ilham Gumantung Gusti Jabal Rachmah Maulana Akbar Dwijaya Miftah Meinaldi Nurrochman Muhammad Ken Muhammad Nasrun Muhammad Pascal Aryan Nofrialdi Dwi Putra Nurya Fahru Rosyidin Purba Daru Kusuma R. Rumani M. R. Rumani M. Raka Putra Gustian Rudi Purwo Wijayanto Stephen Lie Juliando Soetopo Surya Michrandi Nasution Tito Waluyo Purboyo Umar Ali Ahmad