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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Indonesian Journal of Electrical Engineering and Computer Science Journal of Information Technology and Its Utilization Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Jurnal INFOTEL Journal of Information Technology and its Utilization
Aji Gautama Putrada
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Aplikasi Enterprise Document Digital Signature Menggunakan Rsa Dan Sha256 Untuk Wfh Di Era Pandemi Covid-19 Rafie Afif Andika; Aji Gautama Putradana; Rizka Reza Pahlevi
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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Abstract

Dalam situasi WFH ditengah Pandemi COVID-19 dimana beberapa hal dikerjakan dengan pemanfaatan internet, aktivitas seperti mengirim dokumen secara online dengan scan tanda tangan sudah menjadi hal yang standar. Namun, tanda tangan yang dipindai dapat dengan mudah dipalsukan, dicuri, dan disalahgunakan. Penelitian ini bertujuan untuk membuat dan mengimplementasikan aplikasi tanda tangan digital dokumen perusahaan menggunakan RSA dan SHA256 sehingga sistem WFH di era pandemi COVID-19 dapat terselenggara dengan efektif dan aman. Sebagai bukti konsep, aplikasi dummy untuk perusahaan dibuat. Aplikasi ini merupakan sebuah sistem terdistribusi yang dapat berbagi dokumen untuk pemegang dokumen (pemohon), pemegang tanda tangan (signer), dan pemverifikasi tanda tangan dan dokumen (verifier). Dua skenario tanda tangan digital dibuat untuk perbandingan, satu menggunakan enkripsi RSA 2048 bit dan yang lainnya menggunakan enkripsi RSA 4098 bit. Dari hasil pengujian overhead RSA 4096 bit membutuhkan waktu kurang lebih 4 kali dari waktu overhead RSA 2048 bit untuk proses signature dan proses verifikasi. Namun, melalui perhitungan simulasi brute force, RSA 4096 bit membutuhkan sekitar 10616 kali lebih lama untuk diretas dibandingkan dengan RSA 2048 bit. Selain itu, melalui uji integritas, verifier dapat mendeteksi jika dokumen atau kunci tanda tangan apabila dipalsukan. Kata kunci : digital signature, SHA256, RSA, enterprise, COVID-19, brute force attack
Marketplace Pemesanan Katering Terstandarisasi “ketringan” Berbasis Website Muhammad Dafa Prima Aji; Dody Qori Utama; Aji Gautama Putrada
eProceedings of Engineering Vol 8, No 5 (2021): Oktober 2021
Publisher : eProceedings of Engineering

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Abstract

Pengguna internet di Indonesia pada tahun 2020 tercatat mengalami peningkatan sebanyak 17% dari tahun sebelumnya. Peningkatan tersebut membuka peluang baru di bidang perdagangan barang dan jasa di Indonesia. Penyediaan Akomodasi dan Makan Minum (Kategori I) memiliki peringkat kedua terbanyak dengan jumlah 4.431.154 usaha atau sekitar 17% dari total UMK di Indonesia. Data tersebut menunjukkan bahwa banyaknya UMK di bidang Penyedia makan minum yang bisa dimaksimalkan potensinya untuk mendongkrak perekonomian Indonesia. Permasalahan pemesanan katering juga ditemukan pada sejumlah mahasiswa Telkom. Mereka melakukan survei langsung ke toko fisik pada saat akan memesan katering sehingga 28 dari 30 orang (93%) merasa jasa katering perlu dibuat dalam bentuk platform digital. Survey lanjutan terhadap 10 orang responden juga menunjukkan bahwa 8 orang diantara 10 orang membeli katering dengan cara mendatangi toko fisiknya. Berdasarkan permasalahan dan fenomena tersebut, kami memiliki sebuah solusi yaitu Ketringan, sebuah marketplace katering yang memungkinkan orang bertransaksi dan mencari menu yang sesuai secara instan. Ketringan memiliki Unique Value Proposition (UVP) yaitu standarisasi, sistem pembayaran yang aman, dan harga yang sangat terjangkau. Kata kunci : Marketplace, Katering, UMK, Online, Aplikasi.
NS-SVM: Bolstering Chicken Egg Harvesting Prediction with Normalization and Standardization Aji Gautama Putrada; Nur Alamsyah; Muhamad Nurkamal Fauzan; Syafrial Fachri Pane
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.15140

Abstract

Breeding chickens and chicken eggs are poignant, and recent studies have applied computer science to optimize this field, including chicken egg harvesting prediction. However, existing research does not emphasize the importance of data transformation to obtain optimum chicken egg harvesting prediction. This paper proposes the normalization and standardization-bolstered support vector machine (NS-SVM) method, namely normalization, and standardization, to improve the prediction of chicken egg harvest using SVM. First, we obtain the chicken egg dataset from Africa using Kaggle. The problem and solution become urgent, whereas chicken egg production can ease businesspeople to invest in chicken eggs. We adopt the normalization and standardization method from previous research. However, the notation is to differentiate the method from legacy SVM. The dataset has up to 13 features. Then we apply standard pre- processing such as label encoding and random oversampling. We also review the dataset feature using the Pearson correlation coefficient (PCC). We use two SVM kernels: radial basis function (RBF) and the 2nd-degree polynomial. Then we again apply the same model but by applying normalization and standardization. We use cross- validation with ???? = ???????? to measure the Accuracy of the compared models. The results show that normalization and standardization positively affect the prediction model of the two SVM kernels. The model with the highest performance is NS-SVM with a 2nd-degree kernel, namely ???????????????????????????????? = ????. ????????????. At the same time, the model with the lowest performance is SVM with RBF, namely???????????????????????????????? = ????. ????????????. In addition, the results of ROC AUC analysis show that the performance of our model on the imbalanced dataset with a moderate degree is ???????????? = ????.???????????? to ????.????????????.
Peningkatan Kinerja AMG8833 sebagai Thermocam dengan Metode Regresi AdaBoost untuk Pelaksanaan Protokol COVID-19 Aziz Nurul Iman; Aji Gautama Putrada; Sidik Prabowo; Doan Perdana
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 8 No 1 (2021): JETT Juli 2021
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jett.v8i1.3894

Abstract

Salah satu cara untuk mencegah penyebaran virus COVID-19 adalah dengan melakukan pengecekan suhu tubuh secara rutin. Namun pengecekan suhu tubuh secara manual yaitu dengan mengarahkan thermogun ke wajah seseorang masih sering ditemukan. Penelitian ini mengimplementasikan penggunaan kamera thermal AMG8833 untuk mendeteksi suhu tubuh seseorang tanpa melakukan kontak apapun. AMG8833 adalah kamera pendeteksi suhu tujuan umum sehingga untuk digunakan sebagai pengukur suhu, akurasinya perlu ditingkatkan dengan regresi. Tujuan dari penelitian ini adalah untuk meningkatkan kinerja AMG833 sebagai kamera thermal dengan regresi AdaBoost. AdaBoost adalah jenis pembelajaran ensemble yang menggunakan beberapa model pohon keputusan. Untuk pendeteksian wajah, sistem menggunakan metode Haar Cascade. Hasil pengujian menunjukkan bahwa model pohon keputusan menghasilkan nilai R-Squared sebesar 0,93 dan RMSE sebesar 0,21. Sedangkan AdaBoost berhasil meningkatkan kinerja model regresi dengan nilai R-Squared yang lebih tinggi dan nilai RMSE yang lebih rendah masing-masing dengan nilai 0,95 dan 0,18.
An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control Maya Ameliasari; Aji Gautama Putrada; Rizka Reza Pahlevi
JURNAL INFOTEL Vol 13 No 2 (2021): May 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i2.656

Abstract

Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance.
Imputasi KNN terhadap Nilai yang Hilang dari Prediksi Durasi Hujan Berbasis Regresi pada Data BMKG Ikke Dian Oktaviani; Aji Gautama Putrada
JURNAL INFOTEL Vol 14 No 4 (2022): November 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i4.840

Abstract

The prediction of rain duration based on data from the Meteorology, Climatology, and Geophysics Agency (BMKG) is an important issue but remains an open problem. At the same time, several studies have shown that missing values can cause a decrease in the performance of the model in making predictions. This study proposes k-nearest neighbors (KNN) imputation to overcome the problem of missing values in predicting rain duration. The source of the rain duration prediction dataset is the BMKG data. We compared gradient boosting regression (GBR), adaptive boosting regression (ABR), and linear regression (LR) for the regression model for predicting rain duration. We compared the KNN imputation method with several benchmark methods, including zero imputation, mean imputation, and iterative imputation. Parameters r2, mean squared error (MSE) and mean bias error (MBE) measure the performance of these imputation methods. The test results show that for rain duration prediction using the regression method, GBR shows the best performance, both for train data and test data with r2 = 0.915 and 0.776, respectively. Then our proposed KNN imputation has the best performance for missing value imputation compared to the benchmark imputation method. The prediction values of r2 and MSE when using KNN imputation at Missing Percentage = 90% are 0.71 and 0.36, respectively.
Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.4.01498

Abstract

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.
TICKER SYMBOL IDENTIFICATION WITH CIMA ON NON-STATIONARY STOCK PRICE DATASET Aji Gautama Putrada; Maman Abdurohman; Doan Perdana; Hilal Hudan Nuha
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5349

Abstract

Ticker symbol identification based on stock price data in investor decisions has been proven to be pivotal. Though research exists on stock price forecasting, ticker symbol identification is still a research opportunity. Meanwhile, some temporal-sequential classification methods are available, such as classification-integrated moving average (CIMA) and recurrent neural network (RNN)-based deep learning such as long short-term memory (LSTM), and gated recurrent unit (GRU). Our research aim is to prove that CIMA can perform ticker symbol identification on non-stationary stock price datasets. This research collects ten most well-known stock price dataset from Kaggle and performs pre-processing. Then it designs CIMA with non-stationary data and the benchmark deep learning methods. Both methods are optimized with hyperparameter tuning and model selection between adaptive boosting (AdaBoost) and legacy k-nearest neighbors (KNN). The test results show five non-stationary features in the stock price dataset must go through a differentiation process. Then, AdaBoost has an accuracy of 0.9967 ± 0.001, while KNN has an accuracy of 0.9971 ± 0.001, with no significant difference based on t-test. Meanwhile, AdaBoost has a significantly smaller model size and testing and prediction time than KNN. In benchmarking, CIMA+AdaBoost is superior to the three other methods for accuracy, precision, recall, and f1-score, all of which have a value of 0.996. Our research contribution is ticker symbol identification based on stock price using CIMA on multiple-class sequential classification with non-stationary data. For future research, we advice to perform this method on other stock price data.
QUIDS: A Novel Edge-Based Botnet Detection with Quantization for IoT Device Pairing Aji Gautama Putrada; Nur Alamsyah; Mohamad Nurkamal Fauzan; Sidik Prabowo; Ikke Dian Oktaviani
Indonesia Journal on Computing (Indo-JC) Vol. 8 No. 3 (2023): December 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2023.8.3.878

Abstract

Advanced machine learning has managed to detect IoT botnets. However, conflicts arise due to complex models and limited device resources. Our research aim is on a quantized intrusion detection system (QUIDS), an edge-based botnet detection for IoT device pairing. Using knearest neighbor (KNN) within QUIDS, we incorporate quantization, random sampling (RS), and feature selection (FS). Initially, we simulated a botnet attack, devised countermeasures via a sequence diagram, and then utilized a Kaggle botnet attack dataset. Our novel approach includes RS, FS, and 16-bit quantization, optimizing each step empirically. The test results show that employing a mean decrease in impurity (MDI) by FS reduces features from 115 to 30. Despite a slight accuracy drop in KNN due to RS, FS, and quantization sustain performance. Testing our model revealed 1200 RS samples as optimal, maintaining performance while reducing features. Quantization to 16-bit doesn’t alter feature value distribution. Implementing QUIDS increased the compression ratio (CR) to 175×, surpassing RS+FS threefold and RS by 13 times. This novel method emerges as the most efficient in CR.
XGBoost for Predicting Airline Customer Satisfaction Based on Computational Efficient Questionnaire Nur Ghaniaviyanto Ramadhan; Aji Gautama Putrada
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.864

Abstract

Customer satisfaction can be created through a well-crafted service quality strategy, which forms the cornerstone of a successful business-customer relationship. Establishing and nurturing these relationships with customers is vital for long-term success. Within the airline industry, a persistent challenge lies in enhancing the passenger experience during flights, necessitating a comprehensive understanding of customer demands. Addressing this challenge is crucial for airlines aspiring to thrive in a competitive landscape, thus underlining the significance of providing top-notch services. This study addresses this issue by leveraging predictive airline customer satisfaction data analysis. We forecast customer satisfaction levels using a powerful Extreme Gradient Boosting (XGBoost) ensemble-based model. An integral aspect of our methodology involves handling missing values in the dataset, for which we utilize mean-value imputation. Furthermore, we introduce a novel logistic Pearson Gini (Log-PG) score to identify the factors that significantly influence airline customer satisfaction. In our predictive model, we achieved notable results, showing an accuracy and precision of 0.96. To ascertain the efficiency of our model, we conducted a comparative analysis with other boosting-type ensemble prediction models, such as gradient boosting and adaptive boosting (AdaBoost). The comparative assessment established the superiority of the XGBoost model in predicting airline customer satisfaction.
Co-Authors Abdillah, Hilal Nabil Abiyan Bagus Baskoro Adrian Gusti Nurcahyo Agita Rachmad Muzakhir Algi Fajardi Alieja Muhammad Putrada Andrian Rakhmatsyah Angga Anjaini Sundawa Anita Auliani Argo Surya Adi Dewantoro Aziz Nurul Iman Baginda Achmad Fadillah Bambang Setia Nugroho Bayu Kusuma Belva Rabbani Driantama Bramantio Agung Prabowo Calvin M.T Manurung Catur Wirawan W Catur Wirawan Wijiutomo Daniel Arga Amallo Dawani, Febri Dicky Prasetiyo Dita Oktaria Doan Perdana Dodi W. Sudiharto Dodi Wisaksono Sudiharto Dody Qori Utama Endro Ariyanto Erwid Musthofa Jadied Fachrial Akbar Fadhlillah Fadhlillah Fadhlurahman Irwan Fairus Zuhair Azizy Atoir Fakhri Akbar Pratama Farisah Adilia Fauzan Ramadhan Sudarmawan Fauzan, Mohamad Nurkamal Fauzan, Mohamad Nurkamal Fazmah Arif Yulianto Febrina Puspita Utari Fitra Ilham Gabe Dimas Wicaksana Gentur Cipto Tri Atmaja Hamman Aryo Bimmo Hanifa Zahra Dhiah Hilal Hudan Nuha Hirianinda Malsegianty S Ikbar Mahesa Ikke Dian Oktaviani Ikke Dian Oktaviani Ikrimah Muiz Ilham Fadli Surbakti Imas Nur Tiarani Irfan Dwi Wijaya Irfan Nugraha Januar Triandy Nur Elsan Krisna Kristiandi Hartono Kurnia Wisuda Aji Mahmud Imroba Maman Abdurohman Maman Abdurrahman Mar Ayu Fotina Mas'ud Adhi Saputra Maya Ameliasari Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Nurkamal Fauzan Muhammad Al Makky Muhammad Alkahfi Khuzaimy Abdullah Muhammad Dafa Prima Aji Muhammad Fahmi Nur Fajri Muhammad Ihsan Muhammad Kukuh Alif Lyano Muhammad Shibgah Aulia Muhhamad Affan Hasby Muhhamad Affan Hasby Muhtadu Syukur A Mulia Hanif Nando, Parlin Nando, Parlin Niken Cahyani Novian Anggis Suwastika Nuha, Hilal H Nur Alamsyah NUR ALAMSYAH Nur Alamsyah, Nur Nur Ghaniaviyanto Ramadhan Nurkamal Fauzan, Mohamad Pahlevi , Rizka Reza Pamungkas, Rizaldi Ramdlani Parman Sukarno Putrada, Alieja Muhammad Putri Azanny Raden Muhamad Yuda Pradana Kusumah Rafie Afif Andika Rahmat Suryoputro Rahmat Yasirandi Randy Agustyo Raharjo Reynaldo Lino Haposan Pakpahan Rizki Jamilah Guci Seli Suhesti Sena Amarta Sidik Prabowo Siti Amatullah Karimah Subkhan Ibnu Aji Sulthan Kharisma Akmal Syafrial Fachri Pane Syafwan Almadani Azra Syiarul Amrullah, Muhammad Taufik Suyanto Vera Suryani Wanda Firdaus Yahya Ermaya Yuda Prasetia Zidni Fahmi Suryandaru