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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Informatika dan Rekayasa Komputer PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Unama Jurnal Lentera Ilmiah Pengabdian Masyarakat
Yovi Pratama
Universitas Dinamika Bangsa
Aerospace Engineering Arts Humanities Automotive Engineering 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Building of Informatics, Technology and Science

 1 
Eksperimen Pengujian Optimizer dan Fungsi Aktivasi Pada Code Clone Detection dengan Pemanfaatan Deep Neural Network (DNN) Errissya Rasywir; Yovi Pratama; Fachruddin Fachruddin
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1776

Abstract

The problem of similarity (similarity) of program code can be one solution to the plagiarism detection approach. Plagiarism raises a form of action and consequences of plagiarism itself if the source used is not open source. Plagiarism is an act of deception of the work of others without the knowledge of the original author, which violates a Copyright and Moral Rights. With the increasing amount of data and data complexity, deep learning provides solutions for predictive analytics, with increased processing capabilities and optimal processor utilization. Deep learning shows success and improves the classification model in this field. On the other hand, clone detection code with massive, varied and high-speed data volumes requires feature extraction. With the potential of deep learning to extract better features, deep learning techniques are suitable for code clone detection. For this reason, it is necessary to develop a clone detection code that can process data from a programming language by utilizing deep learning. Based on the results of experiments conducted on 100 PHP program code data files, experimented with several types of activation function and optimizer methods. The average value of the resulting accuracy is good. With a variety of activation functions that we use such as Relu, Linear, Sigmoid, Softmax, Tanh, Elu, Selu, Softplus, Softsign, hard, and sigmoid, as well as variations of the optimizer used are Adagrad, RMSProp, SGD, Adadelta, Adam, Adamax and Nadam , the best attribute selection is in the Selu function and the RMSProp optimizer. The number of epochs used is 1000, the number of neurons per layer is 500 and the best number of hidden layers is 10, the average accuracy is 0.900
Penerapan Algoritma K-Means clustering Untuk Mengelompokkan Provinsi Berdasarkan Banyaknya Desa/Kelurahan Dengan Upaya Antisipasi/Mitigasi Bencana Alam Pratama, Yovi; Hendrawan, Hendrawan; Rasywir, Errissya; Carenina, Babel Tio; Anggraini, Dila Riski
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2549

Abstract

Natural disasters are one of the natural phenomena that threaten human survival. The negative impacts can be in the form of material or non-material losses. However, with the ability to recognize the early symptoms of a disaster, humans can prepare themselves for disaster. Application of the K-Means clustering Algorithm in Grouping Provinces Based on the Number of Villages / Sub-districts with Anticipation / Mitigation Efforts for Natural Disasters Using the WEKA Application. The data sources for this research were collected based on documents describing the Number of Villages/ Urban According to Natural Disaster Anticipation/Mitigation Efforts produced by the National Statistics Agency. The data used in this study is provincial data which consists of 34 provinces. There are 4 variables used, namely Natural Disaster Early Warning System, Tsunami Early Warning System, Safety Equipment, Evacuation Path. The data will be processed by clustering in 2 clusters, namely clusters with high anticipation/mitigation levels and low anticipation/mitigation levels. The results obtained from the assessment process are that there are 5 (14.71 %) provinces with a high level of anticipation/mitigation and 29 (85.29%) other provinces including a low level of anticipation/mitigation. This can be an input for the government to pay more attention to the Village/Kelurahan based on the clusters that have been carried out
Eksperimen Layer Pooling menggunakan Standar Deviasi untuk Klasifikasi Dataset Citra Wajah dengan Metode CNN Pratama, Yovi; Rasywir, Errissya; Fachruddin, Fachruddin; Kisbianty, Desi; Irawan, Beni
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3604

Abstract

Deep Learning, especially the Convolutional Neural Network (CNN) has proven to be reliable in processing data from various programming language platforms by utilizing deep learning. In this study, we modified it by calculating the statistical variance. The modifications made are replacing calculations on the Pooling Layer which generally use two formulas, namely max pooling and average pooling. We use the standard deviation to change the reduced image intensity value. With the research experiments built, it is expected to be able to perform facial recognition as an indicator for testing modifications. The Layer Pooling experiment uses the Standard Deviation for Classifying Face Image Datasets with the CNN Method, including the type of dataset used is the Aberdeen dataset https://pics.stir.ac.uk/2D_face_sets.htm. From the results of the experiments conducted, it was found that the highest value was using the Elu activation function and the Adagrad optimizer worth 77.844% for max pooling and 79.844% for pooling with a standard deviation. The Cellu activation function and the RMSprop optimizer are 77.986% for max pooling and 75.986% for pooling with a standard deviation. The highest score with the Softplus activation function and the Sgd optimizer is 77.844% for max pooling usage and 76.344% for pooling with standard deviation. The Tanh activation function and the Adadelta optimizer are 87.844% for max pooling and 85.844% for pooling with a standard deviation. The Elu activation function and the Adam optimizer are 87.853% for the use of max pooling and 85.285% for pooling with a standard deviation. By using the Elu activation function and the Adamax optimizer, the value is 87.842% for max pooling and 86.242% for pooling with a standard deviation. The highest score is using the Elu activation function and the Nadam optimizer with a value of 87.845% for max pooling usage and 86.345% for using standard deviation calculations as pixel pooling. From all experiments it was stated that the use of pooling with the highest value technique or max pooling still gave a better value than using the standard deviation calculation with the best tuning results using the Elu activation function and Adam's Optimiser, which was 87.853%.
Reduksi False Positive Pada Klasifikasi Job Placement dengan Hybrid Random Forest dan Auto Encoder Pahlevi, M. Riza; Rasywir, Errissya; Pratama, Yovi; Istoningtyas, Marrylinteri; Fachruddin, Fachruddin; Yaasin, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.4864

Abstract

The False Positive (FP) interpretation shows a negative prediction result and is a type 1 error answer with an incorrect positive prediction result. Based on this, we try to reduce type 1 errors to increase the accuracy value of the classification results. A low FP rate is critical for the use of Computer Aided Detection (CAD) systems. In this research proposal, to reduce FP, we use a Random Forest (RF) evaluation result design which will be reinterpreted by the Auto Encoder (AE) algorithm. The RF algorithm was chosen because it is a type of ensemble learning that can optimize accuracy in parallel. RF was chosen because it performs bagging on all Decision Tree (DT) outputs used. To suppress TP reduction more strongly, we use the Auto Encoder (AE) algorithm to reprocess the class bagging results from RF into input in the AE layer. AE uses reconstruction errors, which in this case is Job Placement classification. From the test results, it was found that combining the use of a random forest using C4.5 as a decision tree with an Autoencoder can increase accuracy in the Job Placement Classification task by a difference of 0.004652 better than without combining it with an autoencoder. Apart from that, in testing using a combination of RF and AE, fewer False Positive (FP) values ​​were produced, namely 11 items in the Cross Validation-5 (CV-5) Test, then 13 items in the Cross Validation-10 (CV-10) test and in testing split training data of 60%, the FP was only 12. This value is less than the false positives produced by testing without Autoencoder, namely 12 items on CV-5, 15 items on CV-10, and 13 on split training data
Deteksi Objek Boneka Korban pada Kontes Robot SAR Indonesia Menggunakan ESP32-cam Taupiq, Arahmad; Pratama, Yovi; Bustami, M Irwan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5979

Abstract

The 2024 Indonesian SAR Robot Contest demands the ability of robots to differentiate between dummy dolls and victim dolls in emergency situations. This SAR robot has the main goal of rescuing victims and bringing them to a safe zone, so the author explores the implementation of object detection on SAR robots using ESP32-cam to detect victim dolls. The authors used the Edge Impulse platform, a TinyML platform, to train an object detection model using the Faster Objects, More Objects (FOMO) architecture. This model is optimized to run efficiently on resource-limited devices such as the ESP32-cam microcontroller. Training data was obtained by taking pictures of dummy dolls and victim dolls in various angles, lighting conditions and backgrounds using a camera from the ESP32-cam. The confusion matrix results from the model training process showed that the F1 score reached 100% and when testing the model, the object detection model was able to detect the victim doll with adequate accuracy, even though there were challenges such as variations in position and environmental conditions so the researchers used additional algorithms to increase detection accuracy. . The use of FOMO allows faster object detection and is able to detect more objects in one frame. This implementation shows great potential in the development of more efficient and autonomous SAR robots for rescue missions. These findings contribute to improving robotic technology, one of which is in SAR operations and provide a basis for further research in the application of object detection.
Kontrol Navigasi Robot Hexapod berbasis Inverse Kinematic dan Body Kinematic untuk Stabilitas Optimal di Medan Ekstrem Pratama, Yovi; Saputra, Chindra; Toscany, Afrizal Nehemia; Bustami, M Irwan; Taupiq, Arahmad
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6007

Abstract

This study discusses the application of Inverse Kinematics (IK), Body Kinematics (BK), and Bézier Curves in a hexapod robot to efficiently control leg movements in a three-dimensional space. IK is used to calculate joint angles based on the desired target position, while BK enables adjustments to the robot's body posture to maintain stability during movement. Simulations demonstrate that these two approaches can produce accurate and controlled movements. Additionally, Bézier Curves are applied to the foot trajectory, significantly enhancing the smoothness of movements and the robot's stability during transitions from one step to the next. Testing the hexapod robot over a distance of 2.10 meters showed a 70% success rate with an average error of 4.2 cm. Further testing of the robot's stability on an inclined X-axis revealed that the robot could adapt to inclines up to 35 degrees; however, at inclines exceeding 35 degrees, the robot was unable to maintain balance. Based on the results, it can be concluded that the combination of IK, BK, and Bézier Curves effectively supports the hexapod robot's movement with a step accuracy of 70% and high stability when adapting to inclines up to 35 degrees. Improving stability in more extreme terrains and enhancing performance in more diverse environments are the primary focuses for maximizing the hexapod robot's capabilities.
Co-Authors Abdul Haris Abdul Harris Achpal Haddid Adelia Putri, Ananda Afrizal Nehemia Toscany Agus Siswanto Akbar Ramadhan Akwan Sunoto Alvito Widianto Amroni, Amroni Angelica, Felicia Anggraini, Dila Riski Anggy Utama Putri Annisa putri Anton Prayitno Arahmad Taupiq asih asmarani Bayu saputra Beni Irawan Borroek, Maria Rosario Bustami, M Irwan Cahyana Putra Pratama Candra Adi Rahmat Carenina, Babel Tio Chindra Saputra Defrin Azrian Desi Kisbianty, Desi Despita Meisak Dimas Pratama Dimas Yudha Prawira Dinata, Despan elvi yanti Emelia, Shinta Enjelina, Mia Errissya Rasywir Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fingki Lamhot Pasaribu fiqri ansyah Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hilda Permatasari Hussaein, Ahmad Ilham Adriansyah ilham permana Imelda Yose Indana Arum , Refi Irawan Irawan Irawan Irawan Irawan, Beni Istoningtyas, Marrylinteri Janu Hadi Susilo Jopi Mariyanto Julia Triani khalil gibran ahmad Kholil Ikhsan Luthfi Rifky M Fikrul Hakimi M Reihan Al Fajri M.Rizky Wijaya Manyu, Dimas Abi Maria Rosario Borroek Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marshal` Koko Anand masgo Maulana Qaedi Aufar Mayang Ruza Muhammad Afif Dzaky Khairullah Muhammad Diemas Mahendra Muhammad Irwan Bustami Muhammad Ismail Muhammad Riza Pahlevi MUHAMMAD SURYA Muhammad Wahyu Prayogi Muhammad Zulfi Tisna Tama Mumtaz Ilham S Mumtaz Ilham Syafatullah NAIBAHO, RONALD Najmul Laila Naldi Irfan Nanda Ghina Nia Azzahra Nur Aini Nurhadi Nurhadi Pahlevi, M. Riza Pahlevi, M.Riza Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Ramadhan Saputra, Tri Ramadhani, Utari Reza Pahlevi Rezky Pramudia, Muhammad Riki Bayu Andhika Rio Ferdinand ROBY SETIAWAN Rohaini, Eni Rosario B, Maria Rosario, Maria Rudolf Sinaga Sandi Pramadi Santoso Saparudin, Saparudin Sariyani SIKA, XAVERIUS Steven Ie Sudewo, Raden Tio Putra Sutoyo, Mochammad Arief Hermawan Suyanti taupiq, Arahmad Toscany, Afrizal Verna Anatasya, Rara Verwin Juniansyah virginia casanova andiko andiko Warcita Warcita WILLY RIYADI Xaverius Sika Yaasin, Muhammad Yanti, Elvi Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yuga Pramudya Zahlan Nugraha Zulia, Restutik