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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Sinergi Jurnal Ilmiah FIFO JIKO (Jurnal Informatika dan Komputer) Indonesian Journal of Applied Informatics Jurnal Abdimas Prakasa Dakara Malcom: Indonesian Journal of Machine Learning and Computer Science Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Lokomotif Abdimas: Jurnal Pengabdian kepada Masyarakat Journal of Engineering and Technological Sciences
Zico Pratama Putra
Universitas Nusa Mandiri
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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DESIGNING TRANSLATION TOOL: BETWEEN SIGN LANGUAGE TO SPOKEN TEXT ON KINECT TIME SERIES DATA USING DYNAMIC TIME WARPING Zico Pratama Putera; Mila Desi Anasanti; Bagus Priambodo
SINERGI Vol 22, No 2 (2018)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (815.326 KB) | DOI: 10.22441/sinergi.2018.2.004

Abstract

The gesture is one of the most natural and expressive methods for the hearing impaired. Most researchers, however, focus on either static gestures, postures or a small group of dynamic gestures due to the complexity of dynamic gestures. We propose the Kinect Translation Tool to recognize the user's gesture. As a result, the Kinect Translation Tool can be used for bilateral communication with the deaf community. Since real-time detection of a large number of dynamic gestures is taken into account, some efficient algorithms and models are required. The dynamic time warping algorithm is used here to detect and translate the gesture. Kinect Sign Language should translate sign language into written and spoken words. Conversely, people can reply directly with their spoken word, which is converted into literal text together with the animated 3D sign language gestures. The user study, which included several prototypes of the user interface, was carried out with the observation of ten participants who had to gesture and spell the phrases in American Sign Language (ASL). The speech recognition tests for simple phrases have therefore shown good results. The system also recognized the participant's gesture very well during the test. The study suggested that a natural user interface with Microsoft Kinect could be interpreted as a sign language translator for the hearing impaired.
Analisis Desain Software Process Improvement Untuk Organisasi Pengembang Perangkat Lunak Skala Usaha Kecil Ade Priyatna; Kukuh Panggalih; Deny Robyanto; Zico Pratama Putra
Pixel :Jurnal Ilmiah Komputer Grafis Vol 15 No 1 (2022): Vol 15 No 1 (2022): Jurnal Ilmiah Komputer Grafis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v15i1.759

Abstract

The development of software today in large and small organizations encourages every organization to develop and control in terms of development. Every organization in software development must grow and be able to improve itself, one of which is by handling advancement, if the company cannot do this. Therefore, the company will never be able to learn to take advantage of previous experience, so it will not be able to improve the quality of the existing process. Computer program Prepare Advancement can be done by referring to the CMMI (Capability Development Demonstrate Integration) made by SEI (Computer Program Designing Founded). SEI is a research center in the field of program designing, especially those related to procurement, engineering item lines, and prepare change. CMMI itself defines several levels of development, in order to go up to a higher level a number of processes must be carried out.
Penyuluhan Literasi Media untuk Bijak di Media Sosial dan Pemanfaatan Media Digital Dwiza Riana; Agus Subekti; Hilman F. Pardede; Zico Pratama Putra; Faruq Aziz
Jurnal Abdimas Prakasa Dakara Vol. 2 No. 2 (2022): Literasi Media dan Promosi Kreatif dalam Kegiatan Kemasyarakatan
Publisher : LPPM STKIP Kusuma Negara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37640/japd.v2i2.1522

Abstract

Understanding the power of media must be promoted at all levels. Efforts to develop media literacy, both in the form of thoughts and in conducting outreach activities, need to be carried out and supported by various stakeholders. Especially in the current era of digital media, people are used to and easily access social media. There is also growing concern about the negative impact of social media use on young people. Therefore, it is necessary to teach the younger generation media skills to use social media. This is the basis for making joint activities aimed at educating the younger generation to be wise in using social media and being able to use digital media well. This activity took place on April 3, 2022 with a total of 15 participants. Based on the results of the activities carried out, the application of positive communication resulted in positive changes in the insights, knowledge, skills, values, and attitudes of adolescents, and this activity has important benefits for community activities. to successfully achieve the goals and benefit the community, especially the partners of the SIGMA Foundation.
Pelatihan Aplikasi E-Learning “MojadiPro” Berbasis Android untuk Siswa SMK IT Raflesia Depok Agus Subekti; Zico Pratama Putra; Dwiza Riana; Hilman Ferdinandus Pardede
Jurnal Pengabdian Masyarakat dan Inovasi Vol 3 No 1 (2023)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang Jl. Rangga Sentap, Dalong Sukaharja, Ketapang 78813. Telp. (0534) 3030686 Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/jurnalpengabdianmasyarakatdaninovasi.v3i1.1194

Abstract

SMK IT Raflesia Depok is one of the high schools in the Depok City area which currently has a website that is used as a medium of information and communication, but does not yet provide teaching material services online. Apart from getting subject matter from school, students are usually still looking for additional material by attending private classes. With the development of technology that has developed in the field of education, access to subject matter can be done online, or what is commonly called e-learning. In order to carry out the Tri Dharma of Higher Education, Nusa Mandiri University held an android-based "MojadiPro" e-learning application training for students of SMK IT Raflesia Depok which aims to provide information regarding the existence of the MojadiPro e-learning application, which later can be used by students as a companion application learning that can be accessed via a smartphone. The training was held on January 14 2023 at UNM Margonda Campus with 15 participants. The research results can increase participants' understanding of the development of e-learning from time to time and the technology used in the development of e-learning platforms
Pelatihan Transformasi Digital Organisasi Melalui Implementasi AI Tanpa Coding Pada Remaja Islam Al Hikmah (RISMAH) Pratama Putra, Zico; Parningotan Manik , Lindung; Gata , Windu; Rianto, Yan; Kurniawati, Laela
LOKOMOTIF ABDIMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 2 (2024)
Publisher : LPPM UIN Sulthan Thaha Saifuddin Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30631/lokomotifabdimas.v3i2.2744

Abstract

Pada era modern saat ini teknologi berkembang dengan sangat pesat khususnya dalam bidang komputer yang telah didukung dengan teknologi artificial intelligence. Artificial intelligence sebuah studi tentang bagaimana membuat komputer dapat melakukan hal-hal yang pada saat ini dapat dilakukan lebih baik dari apa yang telah dilakukan oleh manusia. Dalam pembuatan aplikasi AI, saat ini pengguna atau developer tidak perlu lagi menuliskan kode. Dalam rangka melaksanakan kegiatan Tri Dharma Perguruan Tinggi yaitu Pengabdian kepada Masyarakat, Fakultas Teknologi Informasi Universitas Nusa Mandiri akan menyelenggarakan pelatihan dengan tema “Pelatihan Transformasi Digital Organisasi melalui Implementasi AI Tanpa Coding Pada Remaja Islam Al Hikmah (RISMAH)”. Kegiatan ini bertujuan untuk meningkatkan wawasan dan kemahiran peserta untuk membuat aplikasi artificial intelligence dengan mudah tanpa menggunakan coding, pelaksanaan kegiatan ini dilakukan dengan 3 tahapan yaitu persiapan, pelaksanaan yang terakhir evaluasi dan monitorin dengan hasil yang dicapai dari kegiatan ini adalah adalah peserta memiliki keterampilan untuk membuat aplikasi artificial intelligence dengan mudah tanpa menggunakan coding.
Augmentasi Gambar untuk Identifikasi Labu dengan Menggunakan Teachable Machine Prayogi, Kurnia; Putra, Zico Pratama
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1375

Abstract

Penggunaan Google Teachable Machine pada dasarnya melibatkan algoritma Convolutional Neural Network (CNN), yang memungkinkan model untuk belajar mengenali objek, mengekstrak fitur, dan melakukan klasifikasi dengan akurasi tinggi. Implementasi ini diharapkan dapat meningkatkan efisiensi dan akurasi proses identifikasi labu dalam sektor pertanian, mengurangi kesalahan manusia, dan meningkatkan produktivitas. Selain itu, penelitian ini memberikan wawasan praktis tentang bagaimana teknologi pembelajaran mesin yang dapat diterapkan dalam kehidupan sehari – hari, memperluas aksesibilitas teknologi ini kepada lebih banyak individu. Berdasarkan analisis dan pengujian dengan menggunakan berbagai parameter seperti epoch, model pelatihan menunjukkan akurasi mendekati 100%, sementara akurasi pengujian hanya berkisar antara 60% - 80%. Konfigurasi batch size 64, learning rate 0.001, dan epoch 100 menghasilkan hasil terbaik dengan akurasi pengujian stabil di sekitar 80%. Diharapkan di masa mendatang, penelitian ini dapat diperluas lebih lanjut dengan melakukan penyesuaian parameter secara lebih teliti untuk mencapai kinerja yang optimal dalam menangani data yang baru atau belum pernah dilihat sebelumnya.
Analisis Perbandingan Algoritma Pembelajaran Mesin untuk Meningkatkan Akurasi dan Klasifikasi Tumor Otak Joy Lawa Rizky; Zico Pratama Putra
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i1.90101

Abstract

Abstrak: Klasifikasi tumor otak bertujuan untuk mengevaluasi dan membandingkan kinerja beberapa algoritma pembelajaran mesin dalam klasifikasi tumor otak menggunakan Citra MRI. Dalam penelitian ini, metodologi yang digunakan melibatkan pengujian algoritma tradisional seperti K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machines (SVM), dan beberapa arsitektur Deep Learning seperti Neural Network Data yang digunakan dalam penelitian ini terdiri dari citra MRI otak yang telah dilabeli secara manual oleh ahli radiologi. Kami membandingkan kinerja algoritma berdasarkan beberapa metrik evaluasi,  termasuk akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma berbasis Neural Network (0.99) secara signifikan mengungguli algoritma tradisional seperti KNN (0.98), Naive Bayes (0.97), dan SVM (0.98) dalam hal akurasi dan ketahanan terhadap variasi data. Namun, algoritma Neural Network dan metode ensemble menunjukkan kinerja yang kompetitif dengan keuntungan dalam hal interpretabilitas dan kecepatan pelatihan. Studi ini menyoroti keunggulan dan keterbatasan masing-masing algoritma dalam konteks klasifikasi tumor otak dan memberikan panduan praktis untuk memilih algoritma yang paling sesuai berdasarkan kebutuhan klinis dan karakteristik dataset. Penelitian lebih lanjut diperlukan untuk mengoptimalkan integrasi metode-metode ini dalam sistem pendukung keputusan klinis guna meningkatkan hasil diagnosis dan perawatan pasien===============================================Abstract:Brain tumor classification aims to evaluate and compare the performance of various machine learning algorithms in classifying brain tumors using MRI images. In this study, the methodology involves testing traditional algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machines (SVM), and several deep learning architectures, including Neural Networks. The dataset used consists of brain MRI images manually labeled by radiology experts. We compared the performance of these algorithms based on several evaluation metrics, including accuracy, precision, recall, and F1-score. The results show that Neural Network-based algorithms (0.99) significantly outperform traditional algorithms such as KNN (0.98), Naïve Bayes (0.97), and SVM (0.98) in terms of accuracy and robustness to data variation. However, Neural Networks and ensemble methods demonstrated competitive performance with advantages in interpretability and training speed. This study highlights the strengths and limitations of each algorithm in the context of brain tumor classification and provides practical guidance for selecting the most suitable algorithm based on clinical needs and dataset characteristics. Further research is needed to optimize the integration of these methods into clinical decision support systems to enhance diagnosis and treatment outcomes for patients
Evaluasi Kinerja Algoritma Klasifikasi Deep Learning dalam Prediksi Diabetes Pinem, Tuahta Hasiholan; Putra, Zico Pratama
Jurnal Ilmiah FIFO Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i1.003

Abstract

Penelitian yang bertujuan untuk mengembangkan dan mengevaluasi algoritma model prediksi diabetes telah dilakukan dengan menggunakan algoritma model K-Nearest Neighbor Classifier, Naive Bayes, Regresi Logistik, SVM, dan Neural Network. Dataset yang digunakan didapatkan dari Kaggle yang terdiri dari 768 data pasien yang dibagi menjadi data training 60%, data validation 20%, dan data test 20%. Hasil penelitian menunjukkan bahwa akurasi tertinggi diperoleh oleh model Regresi Logistik dan Neural Network, masing-masing sebesar 73% dan 72%. Model Regresi Logistik unggul dalam presisi untuk kelas non-diabetes dan recall untuk kelas diabetes, sedangkan model Neural Network menunjukkan keseimbangan performa yang baik antara presisi dan recall untuk kedua kelas. Model Naive Bayes juga menunjukkan performa yang kompetitif dengan akurasi 72% dan recall tinggi untuk kelas diabetes, model ini dapat menjadi pilihan yang baik dalam situasi yang memprioritaskan deteksi positifKinerja yang lebih rendah ditunjukkan oleh model KNN dan SVM jika dibandingkan dengan model lainnya. Masalah utama yang diangkat dalam penelitian ini adalah pentingnya meningkatkan akurasi prediksi diabetes untuk mendukung deteksi dini dan pengobatan. Secara keseluruhan, model Regresi Logistik dan Neural Network diidentifikasi sebagai model yang paling potensial untuk prediksi diabetes, dengan Regresi Logistik menunjukkan efektivitas yang tinggi dalam mengidentifikasi kasus non-diabetes, sementara Neural Network memberikan keseimbangan performa yang baik di kedua kelas.
Validasi Efektivitas Logistic Regression untuk Diagnosa Penyakit Jantung melalui Pendekatan Machine Learning Fabiyanto, Dedik; Pratama Putra, Zico
Jurnal Ilmiah FIFO Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i2.006

Abstract

Penelitian yang bertujuan untuk mengevaluasi efektivitas Logistic Regression dalam diagnosa penyakit jantung telah dilakukan dengan menggunakan dataset UCI Heart Disease. Dataset ini terdiri dari 303 data pasien dengan 14 fitur, termasuk usia, jenis kelamin, tekanan darah, dan kolesterol, yang dibagi menjadi 60% data pelatihan, 20% data validasi dan 20% data pengujian. Penelitian dimulai dengan perumusan masalah dan studi literatur, diikuti oleh proses preprocessing data yang mencakup imputasi nilai hilang, normalisasi fitur, dan pembagian dataset. Beberapa model machine learning, yaitu k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, dan Neural Network, diterapkan dan dibandingkan. Model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1 score. Hasil penelitian menunjukkan bahwa Logistic Regression memperoleh akurasi tertinggi sebesar 0.89, dengan keunggulan dalam presisi dan recall. Naïve Bayes dan kNN menunjukkan akurasi masing-masing 0.87 dan 0.85, sedangkan Neural Network memperoleh akurasi terendah 0.77. Tuning hyperparameter pada Neural Network tidak menunjukkan pola yang jelas dalam meningkatkan akurasi. Pada penelitian ini Logistic Regression adalah model yang paling efektif untuk prediksi penyakit jantung dibandingkan dengan model lainnya, terutama dalam konteks aplikasi medis yang membutuhkan interpretabilitas dan efisiensi.
Evaluating the Performance of Classification Algorithms on the UNSW-NB15 Dataset for Network Intrusion Detection Putra, Zico Pratama
Jurnal Ilmiah FIFO Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i1.009

Abstract

 Network intrusion detection is a critical aspect of cybersecurity, aiming to distinguish between normal and malicious network activities. This study evaluates the performance of various machine learning algorithms on the UNSW-NB15 dataset for binary classification of network traffic into normal and attack categories. We employed several preprocessing steps, including handling missing values, encoding categorical features, and addressing class imbalance using a mix of Synthetic Minority Over-sampling Technique (SMOTE) and undersampling. The models evaluated include k-Nearest Neighbors (k-NN), Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and Neural Networks. Our experimental results show that complex models like Neural Networks and SVMs significantly outperform simpler models. The Neural Network model achieved the highest accuracy of 92%, with a precision of 91%, recall of 93%, and an F1-score of 92%. SVM also performed robustly with an accuracy of 90%. Simpler models, while less effective, still achieved respectable performance, with Logistic Regression and k-NN reaching accuracies of 88% and 85%, respectively. The study highlights the importance of comprehensive preprocessing and the implementation of advanced machine learning techniques for effective network intrusion detection. The results suggest that while complex models offer superior detection capabilities, simpler models can still be valuable in resource-constrained environments. Future research should focus on applying these models to real-world data, exploring more advanced neural network architectures, and implementing cost-sensitive learning techniques to further enhance detection performance and efficiency.
Co-Authors Abdurahman, Asep Duduh Ade Priyatna Agus Subekti Anasanti, Mila Desi Ari Gunawan Baddu, Nasrul Bagus Priambodo Bagus Priambodo Budiarti, Kusumawati Dewi Cahyono Nugroho Damayanti, Iren Darmawan Darmawan Deny Robyanto Dwiza Riana Fabiyanto, Dedik Faisal Faisal Faruq Aziz Gata , Windu Hilman F. Pardede Hilman Ferdinandus Pardede Joy Lawa Rizky Kukuh Panggalih Laela Kurniawati, Laela Lindung Parningotan Manik Mila Desi Anasanti Ngudiwaluyo, Suharto Parningotan Manik , Lindung Pinem, Tuahta Hasiholan Pramono, Adhi Prayogi, Kurnia Purnomo, Niko Rianto, Yan Satmintareja, Satmintareja Suyatmin Suyatmin, Suyatmin Wibowo, R. Susalit Setya