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All Journal Majalah Ilmiah Teknologi Elektro Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Transformatika JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING DoubleClick : Journal of Computer and Information Technology Journal of Information Technology and Computer Engineering JURIKOM (Jurnal Riset Komputer) Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat KOMPUTIKA - Jurnal Sistem Komputer Jurnal Riset Informatika Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Building of Informatics, Technology and Science JTIM : Jurnal Teknologi Informasi dan Multimedia RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal E-Komtek Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of Informatics and Vocational Education Teknika ICTEE (Engineering Journals of Information, control, telecommunication and electrical) Insyst : Journal of Intelligent System and Computation Journal of Dinda : Data Science, Information Technology, and Data Analytics IJCOSIN : Indonesian Journal of Community Service and Innovation Journal of Embedded Systems, Security and Intelligent Systems El-Mujtama: Jurnal Pengabdian Masyarakat Majalah Ilmiah Teknologi Elektro JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Faisal Dharma Adhinata
Institut Teknologi Telkom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Peningkatan Kualitas Citra pada Citra Digital Gelap Adhinata, Faisal Dharma; Wardhana, Ariq Cahya; Rakhmadani, Diovianto Putra; Jayadi, Akhmad
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 4 No 2 (2020)
Publisher : Politeknik Dharma Patria Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v4i2.373

Abstract

Salah satu tahap utama dalam pemrosesan citra digital adalah peningkatan kualitas citra. Citra yang berwarna gelap tidak terlihat detail informasi yang terkandung pada citra. Bahkan objek yang tampak pada citra bisa tidak terlihat karena pengambilan citra dilakukan pada pencahayaan kurang. Citra gelap perlu dilakukan peningkatan kualitas citra supaya detail informasi citra dapat terlihat secara visual. Beberapa algoritma peningkatan kualitas citra digital diantaranya negative transformation, log transformation, contrast stretching, bit plane slice, dan histogram equalization. Pada penelitian ini akan dikaji beberapa algoritma peningkatan kualitas citra untuk melihat hasil terbaik dari kasus citra gelap. Berdasarkan hasil percobaan, diperoleh hasil terbaik menggunakan algoritma histogram equalization. Algoritma histogram equalization menghasilkan histogram citra yang tersebar rata sehingga detail informasi citra dapat dilihat secara visual.
Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method Faisal Dharma Adhinata; Diovianto Putra Rakhmadani
Teknika Vol 10 No 1 (2021): Maret 2021
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i1.328

Abstract

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.
Implementasi Continous Integration/Continous Delivery Menggunakan Process Manager 2 (Studi Kasus: SIAKAD Akademi Keperawatan Bina Insan) Danur Wijayanto; Arizona Firdonsyah; Faisal Dharma Adhinata
Teknika Vol 10 No 3 (2021): November 2021
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v10i3.400

Abstract

Pada perkembangan perangkat lunak yang semakin beragam dan kompleks, diperlukan fleksibilitas dan adaptasi terhadap proses pengembangan perangkat lunak. Konsep DevOps muncul dari permasalahan yang muncul antara developer dan operation. CI/CD dapat mendukung DevOps dikarenakan dapat mempercepat proses integrasi dan delivery perangkat lunak kepada pengguna. Dalam menerapkan CI/CD diperlukan tools pendukung seperti git sebagai source code control dan jenkins untuk membantu proses deployment. Penelitian yang dilakukan penulis menggunakan Process Manager 2 (PM2) untuk implementasi CI/CD pada sistem Sistem Informasi Akademik (SIAKAD) Akademi Keperawatan Bina Insan. Diharapkan penelitian ini berkontribusi untuk memperluas wawasan mengenai tools dalam mengimplementasikan CI/CD. Hasil menunjukkan implementasi CI/CD menggunakan GitHub Repository, Jenkins, dan PM2 berhasil dilakukan dan berjalan dengan baik. PM2 menunjukkan performa yang lebih baik daripada Docker jika dilihat dari segi waktu build dan penggunaan RAM. PM2 memerlukan waktu deployment 185 detik, 46% lebih cepat daripada Docker. Sedangkan penggunaam RAM PM2 sebesar 1,9 GB, 45% lebih sedikit daripada Docker.
PERANCANGAN SISTEM INVENTORY RUANG KELAS DENGAN PENDEKATAN METODE QUALITY CONTROL STATISTICAL SAMPLING BERBASIS WEB STUDI KASUS : INSITUT TEKNOLOGI TELKOM PURWOKERTO Diovianto Putra Rakhmadani; Faisal Dharma Adhinata; Ariq Cahya Wardhana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 6 No 1 (2021): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v6i1.1620

Abstract

The classroom is the principal place in the implementation of teaching and learning activities. Classroom comfort indirectly affects the learning atmosphere and the comfort level of students in implementing teaching and learning activities. In its operational activities, classrooms have several supporting tools such as blackboards, projectors, air conditioners, chairs, and desks. These objects often experience problems such as minor damage, decreased function, to heavy damage. The existence of these obstacles can make teaching and learning activities less qualified in an academic classroom. Facilities and infrastructure to support quality teaching and learning activities require an inventory information system with quality control methods to ensure the classroom's quality of facilities is maintained. This research uses the Waterfall software development method and produces a web-based computerized system that can be used to monitor the quality of classroom facilities and follow-up. With this system, the quality of facilities and infrastructure in the classroom is maintained.
Similarity Identification of Large-scale Biomedical Documents using Cosine Similarity and Parallel Computing Merlinda Wibowo; Christoph Quix; Nur Syahela Hussien; Herman Yuliansyah; Faisal Dharma Adhinata
Knowledge Engineering and Data Science Vol 4, No 2 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i22021p105-116

Abstract

Document similarity computation is an important research topic in information retrieval, and it is a crucial issue for automatic document categorization. The similarity value is between 0 and 1, then the closest value to 1 is represented both documents is considered more relevant, vice versa. However, the large scale of textual information has created the problem of finding the relevance level between documents. Therefore, the relevance between mesh heading text in the PubMed documents is higher than the relevance of the abstract text in the PubMed documents. Furthermore, parallel computing is implemented to speed up the large-scale documents similarity identification process that automatically calculates in the PubMed application. The execution time of mesh heading is 15.447 seconds, and the timely execution of abstract is 74.191 seconds. The execution time of mesh heading is higher than abstract because abstract contains more words than mesh heading. This study has successfully identified the similarity between large-scale biomedical documents of the PubMed documents that implemented a cosine similarity algorithm. The result has shown that the cosine similarity of the mesh heading texts is higher than the abstract text in the form of a graph and table shown in the PubMed application. The cosine similarity is useful to measure the similarity between documents based on the TF*IDF calculation result.
Corn Disease Classification Using Transfer Learning and Convolutional Neural Network Faisal Dharma Adhinata; Gita Fadila Fitriana; Aditya Wijayanto; Muhammad Pajar Kharisma Putra
JUITA : Jurnal Informatika JUITA Vol. 9 No. 2, November 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1076.489 KB) | DOI: 10.30595/juita.v9i2.11686

Abstract

Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.
A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Merlinda Wibowo; Akhmad Jayadi
JUITA : Jurnal Informatika JUITA Vol. 9 No. 1, May 2021
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1047.417 KB) | DOI: 10.30595/juita.v9i1.9624

Abstract

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.
Sistem Kendali Proporsional pada Robot Penghindar Halangan (Avoider) Pioneer P3-DX Akhmad Jayadi; Try Susanto; Faisal Dharma Adhinata
Jurnal Teknologi Elektro Vol 20 No 1 (2021): (Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2021.v20i01.P05

Abstract

The basic ability that a mobile robot must have is to avoid obstacles, by being able to avoid obstacles, the robot will be able to do its job well without having to hit any obstacles, because by hitting an obstacle it will make the robot take longer to complete the mission even the robot can experience disorientation With the implementation of a control system on obstacle avoidance robots, the robot can overcome existing obstacles. Proportional control is a simple and easy to use control on a mobile robot, with eight sensors on the robot making the robot more sensitive to obstacles in front of it, so the pioneer type mobile robot P3-DX was used in this study. The robot has been able to pass through the existing obstacles with a Kp value of 2 and a constant speed of 4 without hitting it.
Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition Faisal Dharma Adhinata; Nia Annisa Ferani Tanjung; Widi Widayat; Gracia Rizka Pasfica; Fadlan Raka Satura
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 2 (2021): August
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i2.20758

Abstract

Indonesia is one of the countries affected by the coronavirus pandemic, which has taken too many lives. The coronavirus pandemic forces us to continue to wear masks daily, especially when working to break the chain of the spread of the coronavirus. Before the pandemic, face recognition for attendance used the entire face as input data, so the results were accurate. However, during this pandemic, all employees use masks, including attendance, which can reduce the level of accuracy when using masks. In this research, we use a deep learning technique to recognize masked faces. We propose using transfer learning pre-trained models to perform feature extraction and classification of masked face image data. The use of transfer learning techniques is due to the small amount of data used. We analyzed two transfer learning models, namely VGG16 and MobileNetV2. The parameters of batch size and number of epochs were used to evaluate each model. The best model is obtained with a batch size value of 32 and the number of epochs 50 in each model. The results showed that using the MobileNetV2 model was more accurate than VGG16, with an accuracy value of 95.42%. The results of this study can provide an overview of the use of transfer learning techniques for masked face recognition.
Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Danur Wijayanto
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.22-30

Abstract

Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of  resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes. 
Co-Authors Abdul Majid Abdurrahman Ibnul Rasidi Adam Nur Kridabayu Adil El-Faruqi Aditya Wijayanto Afzal Ziqri Ahmad Muslih Syafi’i Ajeng Fitria Rahmawati Akhmad Jayadi Aldhan Tri Maulana Alfan Adi Chandra Alissyah Putri Alon Jala Tirta Segara Alya Aulia Hanafi Ananda Aulia Rizky Ananda Aulia Rizky Andra Aulia Rizaldy Anshari Rusmeniar R.A Apri Junaidi, Apri Arief Rais Bahtiar Arif Amrulloh Ariq Cahya Wardhana Bagus Bayu Sasongko Christoph Quix Christyan, Timothy Condro Kartiko Dani Azka Faz Darmawan, Bagus Tri Yulianto Dayal Gustopo Setiadjit Dian Nugraha Diovianto Putra Rakhmadani Emmanuel Genesius Evan Devara Fadlan Raka Satura Fajar Malik Falah Arfani Fauzi, Muhammad Dzulfikar Fawwaz Muhammad Zulfikar Febry Ardiansyah Firdonsyah, Arizona Fitran Dwi Pramakrisna Fitran Dwi Pramakrisna Gilang Aditia GITA FADILA FITRIANA Gracia Rizka Pasfica Herman Yuliansyah Ibnul Rasidi, Abdurrahman Ikadhanny Yudyan Pratama Irsyad Zulfikar Jahfal Rizqi Putra Pradhana Kridabayu, Adam Nur M Alfian Maulana Al Azhar Merlinda Wibowo Metha Khafifah Isty Rikhanah Mohammad Rifqi Zein Muhammad Arif Saputra Muhammad Fajar Ahadi Muhammad Ikhsan Muhammad Iqbal Rasyid Muhammad Pajar Kharisma Putra Narantyo Maulana Adhi Nugraha Naseh Hibban Nasution, Annio Indah Lestari Nia Annisa Ferani Tanjung Nike Prasetyo Nisrina Eka Salsabila Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nur Ghaniaviyanto Ramadhan Nur Syahela Hussien Nursatio Nugroho Pasaribu, Yolanda Al Hidayah Purnama Dileon Yamora Nainggolan Putra, Muhammad Daffa Arviano Rachma Wukir Purwitasari Rahardian, Reva Rahmanda Trinova Putra Renna Nur Injiyani Retno Hendrowati Reva Rahardian Rifki Adhitama, Rifki Rifqi Akmal Saputra Rifqi Akmal Saputra Rifqi Alfinnur Charisma Rival Fahmi Hidayat Rizki Rafiif Amaanullah Rohman Beny Riyanto Saputro, Satria Nur Satria Adi Nugraha Sayyid Yakan Khomsi Pane Sofiyudin Pamungkas Teguh Rijanandi Teguh Rijanandi Teguh Rijanandi Tri Dimas Cipto Satrio Wibowo Try Susanto Ummi Athiyah Utama, Safitri Yuliana Utami, Annisaa Vincent Nathaniel Wahyono Wahyono Widi Widayat Wijayanto, Danur Winanto, Tawang Sahro Yaqutina Marjani Santosa Yohani Setiya Rafika Nur Yolanda Al Hidayah Pasaribu Yuni nur fari'ah Zanuar Rahmat Saputra Ziqri, Afzal