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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Jurnal Ilmiah FIFO MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Abdimasa TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JMECS (Journal of Measurements, Electronics, Communications, and Systems) Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Prosiding SNAST Jurnal Ilmu Komputer dan Informatika eProceedings of Engineering
Rita Magdalena
Universitas Telkom
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
INDIVIDUAL IDENTIFICATION SYSTEM DESIGN THROUGH VOICE USING LINEAR PREDICTIVE CODING METHOD AND K-NEAREST NEIGHBOR Davita Nadia Fadhilah; Rita Magdalena; Sofia Sa’idah
Jurnal Teknik Informatika (Jutif) Vol. 2 No. 2 (2021): JUTIF Volume 2, Number 2, December 2021
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2021.2.2.71

Abstract

Humans have a variety of characteristics that are different from one another. Characteristics possessed by humans are genuine which can be used as a differentiator between one individual and another, one of which is sound. Voice recognition is called speech recognition. In this study, it was developed as an individual voice recognition system using a combination of the Linear Predictive Coding (LPC) method of feature extraction and K-Nearest Neighbor (K-NN) classification in the speech recognition process. Testing is done by testing changes in several parameters, namely the LPC order value, the number of frames, the K value, and different distance methods. The results of the parameter combination test showed a fairly good presentation of 73.56321839% with the combination parameter or LPC 8, the number of frames 480, the value of K 5, with the distance method used by Chebychev.
CONVOLUTIONAL NEURAL NETWORK FOR ANEMIA DETECTION BASED ON CONJUNCTIVA PALPEBRAL IMAGES Rita Magdalena; Sofia Saidah; Ibnu Da’wan Salim Ubaidah; Yunendah Nur Fuadah; Nabila Herman; Nur Ibrahim
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.2.197

Abstract

Anemia is a condition in which the level of hemoglobin in a person's blood is below normal. Hemoglobin concentration is one of the parameters commonly used to determine a person's physical condition. Anemia can attack anyone, especially pregnant women. Currently, many non-invasive anemia detection methods have been developed. One of non-invasive methods in detecting anemia can be seen through its physiological characteristics, namely palpebral conjunctiva images. In this study, conjunctival image-based anemia detection was carried out using one of the deep learning methods, namely Convolutional Neural Netwok (CNN). This CNN method is used with the aim of obtaining more specific characteristics in distinguishing normal and anemic conditions based on the image of the palpebral conjunctiva. The Convolutional Neural Network proposed model in this study consists of five hidden layers, each of which uses a filter size of 3x3, 5x5, 7x7, 9x9, and 11x11 and output channels 16, 32, 64, 128 respectively. Fully connected layer and sigmoid activation function are used to classify normal and anemic conditions. The study was conducted using 2000 images of the palpebral conjunctiva which contained anemia and normal conditions. Furthermore, the dataset is divided into 1,440 images for training, 160 images for validation and 400 images for model testing. The study obtained the best accuracy of 94%, with the average value of precision, recall and f-1 score respectively 0.935; 0.94; 0.935. The results of the study indicate that the system is able to classify normal and anemic conditions with minimal errors. Furthermore, the system that has been designed can be implemented in an Android-based application so that the detection of anemia based on this palpebral conjunctival image can be carried out in real-tim.
GLAUCOMA CLASSIFICATION BASED ON FUNDUS IMAGES PROCESSING WITH CONVOLUTIONAL NEURAL NETWORK Yunendah Nur Fu'adah; Sofia Saidah; Nidaan Khofiya; Rita Magdalena; Ibnu Da'wan Salim
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.276

Abstract

Glaucoma is an eye disease that causes damage to the optic nerve due to increased pressure in the eyeball. Delay in diagnosis and treatment of optic nerve damage due to glaucoma can lead to permanent blindness. Thus, several studies have developed a glaucoma early detection system based on digital image processing and machine learning. This study carried out glaucoma classification based on fundus image processing using Convolutional Neural Network (CNN). The CNN architecture proposed in this study consists of three convolutional layers with output channels 8, 16, 32 sequentially and a filter size of 5×5 at each layer, followed by a pooling layer and a dropout layer at the feature extraction stage. Furthermore, a fully connected layer and softmax activation function was implemented at the classification stage to classify fundus images into normal conditions, early glaucoma, moderate glaucoma, deep glaucoma, and ocular hypertension (OHT). The total amount of fundus image data used in this study consisted of 2000 fundus images divided into 1280 training data, 320 validation data, and 400 test data. 5-fold cross-validation is implemented in the training phase to select the best model. At the testing stage, the best accuracy generated by 99%, with the precision value, recall, f-1 scores and the AUC score are close to 1. According to the system performance results obtained, the proposed model can be used as a tool for medical personnel in classifying glaucoma conditions to provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
Co-Authors A F Akbar Abel Bima Wiratama Achmad Rizal Adham Nurjati Adinda Maulida Agung Aditama Putra Agustina Trifena Dame.S AGUSTINA, REGITA Ahmad Zendhaf Aldo Setiawan Alva Rischa Qhisthana Pratika Andria Sufy Angga Prihantoro Ardhi Fibrianto Arianto Sirandan Arintyo Archamadi Ayu Putu Wida Vanhita Bagas Farhan Hadyantoro Bagus Robbiyanto Bambang Hidayat Bambang Hidayat Bayuaji Kurniadhani Brian Adam Danding Adhi Priutomo Davita Nadia Fadhilah Dea Sifana Ramadhina Dewa Nyoman Indra Dewi Siskawati Dian Ayu Nurlitasari Dimas Frandisyah Putra Donny Janu Sundoro Dwi Anggreni Novitasari Dyah Ajeng Pramudhita Dyah Ayu Pratiwi Efri Suhartono Eko Susatio Eky Yuliansyah Eriel Mar Estananto Faizhal Rifky Alfaris Fathurrahman, Muhammad Hanif Fatima Azzahra FAUZI FRAHMA TALININGSIH Fauzi, Muhammad Ilham Febriani Ruming Sari Firmanda Robi Firmansyah Patriandhika Fitya Nur Fadhilah Galih Surya Gede Hari Yogiswara Gusty Aditya Arrazaq HARSONO, ALI BUDI Herdian Anantya Risma Hilman Fauzi, Hilman I Dewa Gede Agung Kurniawan I Gusti Agung Dian Wintara I Nyoman Apraz Ramatryana I Nyoman Apraz Ramatryana I NyomanApraz Ramatryana Ibnu Da'wan Salim Ibnu Da’wan Salim Ubaidah Ibnu Da’wan Salim Ubaidah Ignatius Yoslan Kurniawan Ikhwanda, Alfan Ikrar Khaera Arfat Ilma Rahma Dewi Imanuel Boyke Nainggolan Immanuel Rayuzi Pandapotan Sinaga Indrafaqih Eskamara Inung Wijayanto Iqbal Kurniawan Perdana Irham Bani Alfafa Ivan Prayoga Prawiro Ivandy Chaniago Jangkung Raharjo Jonthala Tambunan Koredianto Usman Krisna Prayoga Kurnia Khafidhatur Rafiah Ledya Novamizanti Lugina Perceka Putri M.Aldia Abilisa M.Fajar Zulvan Nugraha Mahendra, Dio Maisaroh Agustina Rahayu Malardy , Muhammad Andriyansyah Masykur, Muhammad Fadhel Affandi Misbakhul Munir Muhamad Rokhmat Isnaini MUHAMMAD ADNAN PRAMUDITO Muhammad Akhyar Ghifari Muhammad Ardhi Prakasa Muhammad Bayu Adinegara Muhammad Fadly Mustakim Muhammad Ihsan Fadhil Muhammad Ilham Muhammad Ilham Fauzi Muhammad Najiburahman Muhammad Tezar Muhammad Yuqdha Faza Nabila Herman Naufal Adi Gifran Nidaan Khofiya Nor Kumalasari Nor Kumalasari Caecar Nor Kumalasari Caecar Pratiwi Nor Kumalasari Caesar Pratiwi Nur Andini Nur Ibrahim NURFAJAR, FEBI Obed Simanungkalit Octavian Putera Kesuma Sugeng Olyvia Fernanda Soedradjat PERDANI, WAHYUNI RIZKY Prayudi, Yoshi Putra, Akbar Trisnamulya Putri Andriani R Ricki Juniansyah R Yunenda Nur Fu'adah R. Rumani R. Rumani R. Yunendah Nur Fu’adah Raditiana Patmasari Rafid Fakhri Rahmad Hidayatullah Salam Raihan Nur Fadhlillah Rama Arjun Setiawan Ramdhan Nugraha Ratri Dwi Atmaja Renny Rahmawati Reyfaldi Wahyu Pradana Reyhan Radifan Jordy Rezki Ariz Rahadian Ricardo Ricardo Richard Bina Jadi Simanjuntak Ridwan Firdaus Rifqi Muhammad Fikri Rissa Rahmania Rizki Muhammad Iqbal Rizqi Surya Utama Rosyita Ayuning Mauludiya Sa’idah, Sofia Saidah, Sofiah Sari, Febriani Ruming Sayidia Rizki Arfina Sean Alexander Suryaman Septian Eko Kuncahyono Shimon Anterio Armando Sinaga Sofia Sa'idah Sofia Sa'idah Sofia Sa’idah SOFIA SAIDAH Sofia Sa’idah Steven Palondongan Suci Aulia Sugondo Hadiyoso Susilo, Mochammad Hilmi Suwandhi, Adhisty Putrina Suwitrisna Putra Syafiq Hilmi Abdullah Syamsul Rizal Tahta Restu Adiguna Tamardi Pranata Tampubolon Tauhid Nur Azhar Teguh Dian Arifandi Tri Siswanto Twinarya Bagus Wibawa Varian Mohammad Sutama Yohana Karina Candra Sari Yunendah Fu’adah