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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Journal of ICT Research and Applications Teknika: Jurnal Sains dan Teknologi Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Manajemen Informatika Jurnal Kridatama Sains dan Teknologi Infotekmesin Jurnal Mnemonic Abdimasku : Jurnal Pengabdian Masyarakat Variabel Journal of Intelligent Computing and Health Informatics (JICHI) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Journal of Soft Computing Exploration Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Advance Sustainable Science, Engineering and Technology (ASSET)
Eko Hari Rachmawanto
Teknik Informatika S1, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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The AirNav Semarang Employee Presence System Using Face Recognition Based on Haar Cascade Azzahra, Fidela; Sari, Christy Atika; Rachmawanto, Eko Hari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.672

Abstract

The presence of employees is a key factor in supporting the needs of the workplace. At present, the employee presence system at PT. AirNav Indonesia Semarang Branch still uses fingerprint and RFID-based employee ID cards for authentication. This RFID-based system can increase employee fraud by allowing employees to misuse each other's ID cards. To avoid such fraud, a system needs to be built and it will be using face recognition technology as the primary authentication method, with the Haar Cascade Algorithm. This algorithm has the advantage of being computationally fast, as it only relies on the number of pixels within a rectangle, not every pixel of an image. In addition to fast computation, this algorithm also has the advantage of identifying objects that are relatively far away. With the implementation of the Haar Cascade algorithm, the results indicate the capability of face recognition in detecting the faces of registered employees within the system based on facial angles with an accuracy rate of 60%, expressions with an accuracy rate of 100%, as well as obstructive parameters such as glasses and masks with an accuracy rate of 33.33%. The ability to detect objects from various camera angles, recognize faces with different expressions, and identify objects obstructed by parameters can serve as reasons why this algorithm needs to be implemented
Implementation of DenseNet121 Architecture for Waste Type Classification Zulhusni, Munis; Sari, Christy Atika; Rachmawanto, Eko Hari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.673

Abstract

The growing waste management problem in many parts of the world requires innovative solutions to ensure efficiency in sorting and recycling. One of the main challenges is accurate waste classification, which is often hampered by the variability in visual characteristics between waste types. As a solution, this research develops an image-based litter classification model using Deep Learning DenseNet architecture. The model is designed to address the need for automated waste sorting by classifying waste into ten different categories, using diverse training datasets. The results of this study showed that the model achieved an overall accuracy rate of 93%, with an excellent ability to identify and classify specific materials such as batteries, biological materials, and brown glass. Despite some challenges in metal and plastic classification, these results confirm the great potential of using Deep Learning technology in waste management systems to improve sorting processes and increase recycling efficiency
Classification of Movie Recommendation on Netflix Using Random Forest Algorithm Salsabila, Alifia Salwa; Sari, Christy Atika; Rachmawanto, Eko Hari
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.676

Abstract

Netflix is one of the most popular streaming platforms in this world. So many movies and shows with various genres and production countries are available on this platform. Netflix has their own recommendation systems for the subscribers according to their data and algorithm. This research aims to compare two methods of data classifications using Decision Tree and Random Forest algorithm and make a recommendation system based on Netflix dataset. This paper use feature importance to selecting relevant feature and how n_estimators affect the classification. In this research, Random Forest with 50 trees estimator with 96.84% accuracy before feature selection and 96.92% accuracy after feature selection has the best accuracy compared to the Decision Tree classification. Besides, Decision Tree has only 95.64% accuracy before feature selection and increases to 96.07% accuracy after feature selection. Trees estimator also affect the accuracy of Random Forest classification. After comparing the results, Random Forest with 50 trees estimators using feature selection provides best accuracy and it will be used to predict some similar movies and shows recommendation
High-Quality Evaluation for Invisible Watermarking Based on Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) Sofyan, Ega Adiasa; Sari, Christy Atika; Rachmawanto, Eko Hari; Cahyo, Nur Ryan Dwi
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17186

Abstract

In this research, we propose an innovative approach that integrates Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) to enhance the quality and security of digital images. The purpose of this technique is to embed imperceptible watermarks into images, preserving their integrity and authenticity. The integration of DCT allows for an efficient transformation of image data into frequency components, forming the basis for embedding watermarks that are nearly invisible to the human eye. In this context, SVD offers an advantage by separating singular values and corresponding vectors, facilitating a more sophisticated watermarking process. The quality evaluation using metrics such as MSE, PSNR, UQI, and MSSIM demonstrates the effectiveness of this approach. Low average MSE values, ranging from 0.0058 to 0.0064, indicate minimal distortion in the watermarked images. Additionally, high PSNR values, ranging from 67.20 dB to 67.22 dB, affirm the high image quality achieved after watermarking. These results validate that the integration of DCT and SVD provides a high level of security while maintaining optimal visual quality in digital images. This approach is highly relevant and effective in addressing the challenges of image protection in this digital era.
A Good Evaluation Based on Confusion Matrix for Lung Diseases Classification using Convolutional Neural Networks Kamila, Izza Putri; Sari, Christy Atika; Rachmawanto, Eko Hari; Cahyo, Nur Ryan Dwi
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17330

Abstract

CNN has been widely used to detect a pattern with image classification. This study used CNN to perform a classification analysis of lung abnormality detection on chest X-ray images. The dataset consists of 5,732 2D images with dimensions of 200 x 200 x 1 divided into training data (85%) and testing data (15%). The preprocessing process includes image resizing, enhancement to increase contrast and reduce image complexity, and filtering to improve visibility and reduce noise. CNN is used to classify imagery into three categories, Normal (no abnormalities), Pneumonia, and Tuberculosis. The results showed a good level of accuracy, with an average accuracy of 97.24% in 3 trainings, and a 100% success rate in 6 classification experiments. This research provides insights into the detection of lung disorders and encourages further exploration in medical diagnosis.
Klasifikasi Citra Mengkudu Berdasarkan Perhitungan Jarak Piksel pada Algoritma K-Nearest Neighbour Irawan, Candra; Rachmawanto, Eko Hari; Atika Sari, Christy; Umah Nur, Raisul
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1827

Abstract

Noni fruit is included in exported food commodities in Indonesia. The size of noni fruit, based on human vision, generally has varied shapes with distinctive textures and various patterns, so that the process of filtering fruit based on color and shape can be done in large quantities. In this study, K-Nearest Neighbor (KNN) has been implemented as a classification algorithm because it has advantages in classifying images and is resistant to noise. Noni imagery is a personal image taken from a noni garden in the morning and undergoes a background subtraction process. The imagery quality improvement technique uses the Hue Saturation Value (HSV) color feature and the Gray Level Co-Occurrence Matrix (GLCM) characteristic feature. KNN accuracy without features is lower than using HSV and GLCM features. From the experimental results, the highest accuracy was obtained using HSV-GLCM at K is 1 and d is 1, namely 95%, while the lowest accuracy was 55% using KNN only at K is 5 and d is 8.
PNEUMONIA PREDICTION USING CONVOLUTIONAL NEURAL NETWORK Praskatama, Vincentius; Sari, Christy Atika; Rachmawanto, Eko Hari; Mohd Yaacob, Noorayisahbe
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.1353

Abstract

Pneumonia is condition which our lungs become inflamed due to infection from viruses, bacteria, or fungi. Pneumonia can affect anyone, both adults and children. Because of this, prevention of pneumonia is important. Prevention can be done by the process of maintain our immunity and lungs. In this study, had been done classify pneumonia based on X-ray images. This study using X-ray images dataset with total data is 5840 images in .jpg extensions. With a total number of images from training data is 5216 images and number of images from the test data is 624 images. The dataset that used in this research has 2 main classes, namely class normal and pneumonia. Normal class indicates that the X-Ray results are not detected with pneumonia. While the pneumonia class indicates that the processed X-Ray results are diagnose affected by pneumonia. The purpose of this research is building model that can be used to classify pneumonia based on X-Ray images. The classification process carried out in this study uses the Convolutional Neural Network method. The purpose of using the CNN method in the classification process of this research is because, in the process, CNN can extract features automatically and independently, so that the data provided does not need to be preprocessing first, but the data still produces good extraction features and can provide accurate classification results. The results from the testing process is carried out to run or perform in the pneumonia classification process, the CNN model built obtained a classification test accuracy of 87.82051205635071%.
A ROBUST AND IMPERCEPTIBLE FOR DIGITAL IMAGE ENCRYPTION USING CHACHA20 Nugroho, Widhi Bagus; Susanto, Ajib; Sari, Christy Atika; Rachmawanto, Eko Hari; Doheir, Mohamed
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1470

Abstract

In the current era, data security is mandatory because it protects our personal data from being used by irresponsible people. The objective of this research is to show the robustness of the method we propose to encrypt images using the chacha20 algorithm which is included in the symmetric encryption cryptography technique and uses one key for both encryption and decryption processes. we use the encryption method by reading the bits from a digital image which is processed using the chacha20 algorithm to get the results of the digital image encryption. The results of this study indicate that the Chacha20 algorithm is secure to use when encrypting and decrypting digital images. The average MSE value generated by the chacha20 algorithm is 0.1232. The average PSNR value is 57.4784. The average value of UACI is 49.99%. The average value of NPCR is 99.602%. The test values were acquired by executing encryption and decryption processes on 5 distinct colour digital images with different size. Additionally, this study displays histograms for the original digital image, as well as for the encrypted and decrypted digital images, illustrating the pixel distribution in each. The histogram also serves as material for analysis of the success of the encryption and decryption processes in digital images.
VGG-16 ARCHITECTURE ON CNN FOR AMERICAN SIGN LANGUAGE CLASSIFICATION Meitantya, Mutiara Dolla; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2160

Abstract

Every country has its sign language such as in Indonesia there are 2 types namely Indonesian Sign Language System called SIBI and BISINDO (Indonesian Sign Language). American Sign Language (ASL) is a sign language that is widely used in the world. In this research, the classification of American Sign Language (ASL) using the Convolutional Neural Network (CNN) method using VGG-16 architecture with Adam optimizer. The data used is 14000 ASL image data with 28 classes consisting of letters A to Z plus space and nothing with a division of 90% training data and 10% validation data. From this research, the overall accuracy is obtained with a value of 98% and the accuracy value of validation data evaluation is 89.07%.
CLASSIFICATION OF ORGANIC AND NON-ORGANIC WASTE WITH CNN-MOBILENET-V2 Oktayaessofa, Eqania; Sari, Christy Atika; Rachmawanto, Eko Hari; Yaacob, Noorayisahbe Mohd
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2165

Abstract

Data from the Ministry of Environment and Forestry shows that the amount of organic and non-organic waste in 2023 has started to decline compared to the previous year. However, waste management in the central landfill is still not optimal. This is a problem for the community and the environment because it can cause pollution and disrupt public health around the disposal site. The reason for the difficulty of waste management at the landfill is that people still dispose of waste without separating it first. In addition, it is also due to a lack of public awareness and knowledge. One of the things that can be done to help overcome the problem of waste and its management is to develop an application that can help people understand the importance of waste selection and facilitate socialization in the community. For that, a model is needed that can classify waste based on its type with accurate accuracy. In this study, we propose a deep learning model, CNN with mobilenetV2 architecture, to classify organic and non-organic waste. This model uses a dataset consisting of 4380 images of organic and non-organic waste. Then 3 preprocessing stages were carried out, namely resize, normalization, and augmentation. From this process, data training was carried out and researchers obtained model evaluation results with 98.47% accuracy, 97% precision, 97% recall, and 97% F1 Score evaluation results. These results show that the proposed model is categorized as excellent.
Co-Authors Abdussalam Abdussalam Abdussalam Abdussalam, Abdussalam Abu Salam Adhitya Nugraha Adiyah Mahiruna Agustina, Feri Ahmad Salafuddin Ajib Susanto Akbar Aji Nugroho Akbar, Ilham Januar Al-Ghiffary, Maulana Malik Ibrahim Ali, Rabei Raad Alifia Salwa Salsabila Alvin Faiz Kurniawan Anak Agung Gede Sugianthara Andi Danang Krismawan Anidya Nur Latifa Annisa Sulistyaningsih Antonio Ciputra Antonius Erick Handoyo Aqsel, Aryasatya Muhammad Ardika Alaudin Arsa Arfian, Aldi Azmi Ariska, Ratih Aristides Bima Wintaka Aryanta, Muhammad Syifa Aryaputra, Firman Naufal Astuti, Yani Parti Asyari, Fajar Husain Aulia, Lathifatul Auni, Amelia Gizzela Sheehan Azzahra, Fidela Bijanto Bijanto Briliantino Abhista Prabandanu Cahaya Jatmoko Cahyo, Nur Ryan Dwi Candra Irawan Candra Irawan Candra Irawan Castaka Agus Sugianto Chaerul Umam Chaerul Umam Christy Atika Sari Cinantya Paramita Ciputra, Antonio D.R.I.M. Setiadi Danar Bayu Adi Saputra Danu Hartanto Daurat Sinaga De Rosal Ignatius Moses Setiadi Deddy Award Widya Laksana Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Desi Purwanti Kusumaningrum Destriana, Rachmat Didik Hermanto Dila Ananda Oktafiani Doheir, Mohamed Doheir, Mohamed Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto Egia Rosi Subhiyakto Elkaf Rahmawan Pramudya Ellen Proborini Erna Daniati Erna Zuni Astuti Ery Mintorini Faisal, Edi Farrel Athaillah Putra Fazlur Rahman Hafidz Fida Maisa Hana Fidela Azzahra Florentina Esti Nilawati Florentina Esti Nilawati Florentina Esti Nilawati Folasade Olubusola Isinkaye Giovani Ardiansyah Gumelar, Rizky Syah Guruh Fajar Shidik Hadi, Heru Pramono Haryanto, Christanto Antonius Haryanto, Christanto Antonius Hasbi, Hanif Maulana Herman Yuliansyah, Herman Heru Agus Santoso Heru Lestiawan Hidayat, Muhammad Taufiq Hidayati, Ulfa Himawan, Reyshano Adhyarta Hussain Md Mehedul Islam Hyperastuty, Agoes Santika Ibnu Utomo Wahyu Mulyono Ibnu Utomo Wahyu Mulyono Ihya Ulumuddin, Dimas Irawan Imam Prayogo Pujiono Inzaghi, Reza Bayu Ahmad Iqtait, Musab Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul Istiawan, Deden Istiqomah, Annisa Ayu Ivan Stepheng Kamila, Izza Putri Kas Raygaputra Ilaga Krismawan, Andi Danang Kumala, Raffa Adhi Kunio Kondo Kurniawan, The, Obed Danny Kusuma, Edi Jaya L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Lalang Erawan Liya Umaroh Liya Umaroh, Liya Lucky Arif Rahman Hakim Lungido, Joshua Mabina, Ibnu Farid Mahadika Pradipta Himawan Mahiruna, Adiyah Maulana Malik Ibrahim Al-Ghiffary Md Kamruzzaman Sarker Md Kamruzzaman Sarker Md Kamruzzaman Sarker Mehta Pradnyatama Meitantya, Mutiara Dolla Mohammad Rizal, Mohammad Mohd Yaacob, Noorayisahbe Muchamad Akbar Nurul Adzan Muhammad Mahdi Mulyono, Ibnu Utomo Wahyu Munis Zulhusni Musfiqur Rahman Sazal Muslih Muslih Muslih Muslih Nabila, Qotrunnada Nanna Suryana Herman Naufal, Muhammad Khanif NGATIMIN, NGATIMIN Ningrum, Amanda Prawita Nisa, Yuha Aulia Noor Ageng Setiyanto Noor Ageng Setiyanto, Noor Ageng Noorayisahbe Mohd Yacoob Nova Rijati Novi Hendriyanto, Novi Nugroho, Dicky Anggriawan Nugroho, Widhi Bagus Nur Ryan Dwi Cahyo Nuri Nuri Oktaridha, Harwinanda Oktayaessofa, Eqania Oleiwi, Ahmed Kareem Parti Astuti, Yani Parti Astuti, Yani parti astuti, yani Parti Astuti1, Yani Parti Astuti1, Yani Pradana, Luthfiyana Hamidah Sherly Pradana, Rizky Putra Praskatama, Vincentius Pratama, Zudha Pratiwi, Saniya Rahma Proborini, Ellen Pulung Nurtantio Andono Purwanto Purwanto Putra, Ifan Perdana Putri, Ni Kadek Devi Adnyaswari Rabei Raad Ali Rabei Raad Ali Rabei Raad Ali Rabei Raad Ali Raisul Umah Nur Ramadhan Rakhmat Sani Ratih Ariska Reza Arista Pratama Ruri Suko Basuki Safitri, Melina Dwi Saifullah, Zidan Salsabila, Alifia Salwa Sania, Wulida Rizki Santoso, Bagus Raffi Saputra, Danar Bayu Adi Saputro, Fakhri Rasyid Sarker, Md Kamruzzaman Setiarso, Ichwan Setiawan, Fachruddin Ari Setiawan, Tan Valencio Yobert Geraldo Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sinaga, Daurat Sofyan, Ega Adiasa Solichul Huda, Solichul Sudibyo, Usman Sudibyo, Usman Sudibyo, Usman Sumarni Adi, Sumarni Suprayogi Suprayogi Suprayogi Suprayogi Sutrisno, Hendra Syabilla, Mutiara Tan Samuel Permana Tan Samuel Permana Titien Suhartini Sukamto Tri Esti Rahayuningtyas Umah Nur, Raisul Umam, Choerul Umaroh, Liya Umaroh, Liya Utomo, Danang Wahyu Velarati, Khoirizqi Wahyu Dwy Permana Wellia Shinta Sari Wellia Shinta Sari Wellia Shinta Sari Winarsih, Nurul Anisa Sri Winaryanti, Hida Sekar Yaacob, Noorayisahbe Bt Mohd Yaacob, Noorayisahbe Mohd Yani Parti Astuti Zulhusni, Munis