Article Per Year (5 Year)
p-Index From 2021 - 2026
4.374
P-Index
This Author published in this journals
All Journal Jurnal Informatika Jurnal Inspiration Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Paradigma Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL ILMIAH INFORMATIKA Jurnal Telematika Jurnal Informasi dan Komputer Jurnal Riset Informatika JISICOM (Journal of Information System, Infomatics and Computing) Journal of Electronics, Electromedical Engineering, and Medical Informatics INTI Nusa Mandiri Journal of Robotics and Control (JRC) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Sains Komputer dan Teknologi Informasi Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Jurnal Pengabdian Kreatif Cemerlang Indonesia Jurnal Aruna Mengabdi (ARMI)
Frieyadie
Universitas Nusa Mandiri
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

Published : 67 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 67 Documents
Search

 3   4   5   6   7 
Memanfaatkan Figma Dalam Pelatihan Desain UI/UX Untuk GP Anshor PAC Ciledug Haryanti, Tuti; Handayani, Rani Irma; Kristiana, Titin; Frieyadie, Frieyadie
Jurnal Aruna Mengabdi Vol. 1 No. 2 (2023): Periode November 2023
Publisher : Lotus Aruna Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61398/armi.v1i2.33

Abstract

Kemajuan teknologi informasi yang cepat mengharuskan terciptanya inovasi dalam aplikasi perangkat lunak sebagai kebutuhan primer bagi masyarakat Indonesia. Dalam lingkup ini, persaingan di antara penyedia aplikasi mendorong terus-menerusnya penelusuran inovasi. User Interface (UI) dan User Experience (UX) menjadi sorotan utama dalam meningkatkan reputasi bisnis atau perusahaan dengan bantuan elemen visual dalam aplikasi atau situs web. Pendidikan tentang aplikasi FIGMA bagi Anggota Gerakan Pemuda (GP) Ansor PAC Ciledug menjadi esensial karena GP Ansor berperan signifikan sebagai entitas otonom di bawah Nahdlatul Ulama (NU) yang memiliki dampak yang substansial dalam dinamika sosial, politik, dan budaya di Indonesia. Namun, Anggota GP Ansor Ciledug menghadapi hambatan dalam pemahaman terkait desain UI/UX menggunakan FIGMA disebabkan oleh variasi latar belakang pendidikan mereka. Sementara itu, karena karakter yang beraneka ragam dan besarnya jumlah anggota organisasi ini, dibutuhkan pendekatan penyuluhan untuk menjadikan desain UI/UX situs web lebih ramah pengguna. Dalam konteks ini, pelaksanaan pelatihan UI/UX menggunakan FIGMA bertujuan untuk meningkatkan pemahaman anggota GP Ansor Ciledug mengenai desain antarmuka pengguna yang dapat meningkatkan pengalaman navigasi pengguna dalam situs web. Harapannya, pelatihan ini akan mengatasi kendala-kendala yang dihadapi oleh anggota dan memperkuat reputasi serta kemudahan akses situs web GP Ansor dalam mendukung visi organisasi terhadap perkembangan masyarakat Indonesia.
Cybersentinel: The Cyberbullying Detection Application Based on Machine Learning and VADER Lexicon with GridSearchCV Optimization Ernawati, Siti; Frieyadie, Frieyadie; Yulia, Eka Rini
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.580

Abstract

Cyberbullying is becoming an increasingly troubling issue in today's digital age, with serious impacts on the well-being of individuals and society as a whole. With the number of social media users continuously rising, there is an urgent need to develop effective solutions for detecting cyberbullying. This urgency negatively affects the well-being of individuals, especially children and adolescents. The Big Data era also brings many new challenges, including the ability of organizations to manage, process, and extract value from available data to generate useful information. The aim of this research is to develop Cybersentinel, a cyberbullying detection application that combines Machine Learning and VADER Lexicon approaches to improve classification accuracy. It involves comparing several Machine Learning algorithms optimized using the GridSearchCV technique to find the best combination of parameters. The dataset used consists of social media comments labeled as bullying and non-bullying. The successfully developed model uses the Support Vector Machnine algorithm, achieving a best accuracy of 98.83%. The system is developed using Python with the Streamlit framework. This application development follows the Design Science Research (DSR) approach, which integrates principles, practices, and procedures to facilitate problem-solving and support the design and creation of applications. Testing is conducted using blackbox testing. The results show that parameter optimization using GridSearchCV can significantly enhance model performance, and applying the DSR method allows for the development of Cybersentinel tailored to specific needs. Thus, Cybersentinel provides an effective solution for detecting cyberbullying and contributes to improving the safety of social media users.
HyVADSVM: Hybrid VADER-SVM and GridSearchCV Optimization for Enhancing Cyberbullying Detection Ernawati, Siti; Frieyadie, Frieyadie; Yulia, Eka Rini
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.24385

Abstract

Cyberbullying detection is becoming increasingly crucial in today’s digital era, as many individuals suffer from online harassment. The main challenge lies in accurately identifying patterns of harassment in social media texts, which often use informal languages, slang, and sarcasm. Existing methods struggle to capture emotional context owing to the vast amount of data and rapid digital interactions. This study aims to improve the detection accuracy by combining advanced sentiment analysis using VADER and parameter tuning with GridSearchCV. Data were collected from Instagram, Twitter, and YouTube, with TF-IDF employed for feature extraction. Multiple machine-learning classifiers (SVM, K-NN, NB, LR, DT, and RF) were tested to determine the best-performing model. VADER was selected for its reliability in processing social media texts rich in informal contexts, effectively capturing emotional nuances, such as sarcasm and varying sentiment intensities. This makes it well suited for complex language patterns typical of cyberbullying scenarios, enhancing data labeling and analysis accuracy. Using 10-fold cross-validation for reliable testing, performance metrics (accuracy, precision, recall, and F1-Score) were evaluated using a confusion matrix. The findings highlight SVM as the most effective model when optimized with GridSearchCV, achieving accuracy (98.83%), precision (98.78%), recall (98.83%), and F1-Score (98.62%) with kernel =linear, C=1, and gamma=scale. This optimized model, HyVADSVM model has significant potential in cyberbullying detection, contributing to academic research and serving as an effective tool to prevent online harassment. Future work could integrate this model into real-time systems, improve user safety, and support digital policymaking.
Comparison of the Application of Linear Regression with Sliding Window Validation and K-Fold Cross-Validation for Forecasting Covid-19 Recovered Cases Setiyorini, Tyas; Frieyadie, Frieyadie
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.288

Abstract

The increase in confirmed cases and deaths due to Covid-10 continues to spread and increase day by day throughout the world. This has resulted in a world health crisis that impacts all sectors of life. The government declared a movement to suppress the spread of Covid-19, so it is necessary to understand the pattern of Covid-19 problems. Researchers contribute scientifically to finding patterns of death or recovery due to COVID-19 by applying Machine Learning methods. The Linear Regression and Sliding Window preprocessing methods are appropriate for forecasting time series data. This research obtained RMSE results at 0.320 with linear regression with sliding window validation and RMSE at 0.320 with linear regression with K-Fold cross-validation. This proves that Linear Regression with Sliding Window Validation can improve performance much better than k-fold cross-validation in forecasting COVID-19 recovery cases in China. The sliding window validation method has been proven to increase accuracy for forecasting with time series data compared to other standard preprocessing methods, namely K-Fold cross-validation. In the future, further research is needed to test different types of time series data by comparing the application of sliding window validation and K-Fold cross-validation or developing other validation models.
VILLAGE GROUPING BASED ON THE NUMBER OF HEALTH FACILITIES IN WEST JAVA USING K-MEANS CLUSTERING ALGORITHM Frieyadie, Frieyadie; Andriansyah, Anggie; Setiyorini, Tyas
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.140

Abstract

Health is very important for the welfare and development of the Indonesian nation because as a capital for the implementation of national development, it is essentially the development of all Indonesian people and the development of all Indonesian people. Due to the outbreak of the Covid-19 virus, many health facilities must be provided for patients. Of course, the government must pay attention to the health facilities that can be used in every district/city in West Java in the future. Therefore, to determine the level of availability of sanitation facilities in each district/city in West Java, we need a technology that can classify data correctly. One method of data processing in data mining is clustering. The application of clustering to this problem can use the K-Means algorithm method to group the most frequently used data. The purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities.
COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING COVID-19 RECOVERED CASES Setiyorini, Tyas; Frieyadie, Frieyadie
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.184

Abstract

The emergence of the Covid-19 outbreak for the first time in China killed thousands to millions of people. Since the beginning of its emergence, the number of cases of Covid-19 has continued to increase until now. The increase in Covid-19 cases has a very bad impact on health and social and economic life. The need for future forecasting to predict the number of deaths and recoveries from cases that occur so that the government and the public can understand the spread, prevent and plan actions as early as possible. Several previous studies have forecast the future impact of Covid-19 using the Machine Learning method. Time series forecasting uses traditional methods with Linear Regression or Artificial Intelligence methods with neural networks. The research proves a linear relationship in the time series data of Covid-19 recovered cases in China, so it is proven that Linear Regression performance is better than the Neural Network.
SALES LEVEL ANALYSIS USING THE ASSOCIATION METHOD WITH THE APRIORI ALGORITHM Samuel, Samuel; Sani, Asrul; Budiyantara, Agus; Ivone S, Merliani; Frieyadie, Frieyadie
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (822.013 KB) | DOI: 10.34288/jri.v4i4.194

Abstract

The company does not yet know the pattern of consumer purchases because, so far, the sales transaction data has not been used correctly and does not have a unique method to determine consumer buying patterns. The problems on the company, this research was done to reprocess sales transaction data for 2018-2019 using data mining techniques with association methods and apriori algorithms. RapidMiner is a supporting application to find association rules derived from transaction data. Processed transaction data using the Knowledge Discovery in Database approach. Thus, the company can determine consumer habits in buying goods from sales transaction data for 2018-2019. The results of this study are that in 2018, nine association rules were obtained, of which the best were CT G-246 ⇒ CT G-250 and CT G-250 ⇒ CT G-246. In 2019, nineteen association rules were received, of which the best were PN 0441, SK 0175 ⇒ SK 0530, and SK 0175, SK 0283, ⇒ SK 0530. From the best association rules, the goods in the Coat (imported), Pants, and Skirt categories are often bought together.
Co-Authors Achmad Bayhaqy Achmad Bayhaqy Ade Fitria Lestari Ade Priyatna Aditiya Yoga Pratama Agung Sudrajat Ahmad Baihaqi Andriansyah, Anggie Angga Ardiansyah Anggie Andriansyah Anton Hindardjo Ari Puspita Asrul Sani Asrul Sani Budiyantara, Agus Dedi Dwi Saputra Dedik Erwanto Deny Robyanto Dewi Alramuri Dian Ambar Wasesha Doharma, Rouly Dwiza Riana Eka Rini Yulia Eko Supriyanto Eni Heni Hermalani Eni Heni Hermaliani Ernawati, Siti Fachri Amsury Fajar Permadi Faldanu, Chaidir Rahman Fariati Fariati Febri Ainun Jariyah Frisma Handayanna Frisma Handayanna Gata, Windu Geby Oktaviani Hafifah Bella Novitasari Herlawati Herlawati Herlina Aryanti, Herlina Hilda Amalia Islamy, Faqih Thoriq Ivone S, Merliani Izni Nur Karimah Jordy Lasmana Putra Kaman Nainggolan, Kaman Khairunisa Hilyati Kristiana, Titin Laela Kurniawati Laela Kurniawati Lili Dwi Yulianto M. Daryono, Dadang Maryanah Safitri Mashyur, Riduan Syaiful Muhamad Hasan Muhamad Ryansyah Muhammad Ifan Rifani Ihsan Muhammad Romadhona Kusuma Nita Merlina, Nita Nunung Hidayatun Nurajijah Nurajijah Nurmalasari Nurmalasari Rafly Pratama Rani Irma Handayani Rani Irma Handayani Rani Irma Handayani, Rani Irma Rezki, Muhammad Rizka Dahlia Rosadi Rosadi Samuel Samuel Sandra Jamu Kuryanti Setiyawan, Riki Sfenrianto, Sfenrianto Siti Aisyah Siti Fauziah Siti Fauziah Siti Fauziah Siti Nurdiani Sri Sri Hadianti SRI RAHAYU Sri Rahayu Suharyanto Suharyanto Sulistyowati, Daning Nur Surya Mahendra Ramadhan Syahriani Syahriani Titin Kristiana Titin Kristiana Titin Kristiana Tuti Haryanti Tuti Haryanti Tuti Haryanti Tuti Haryanti, Tuti Tyas Setiyorini Ummi Fatayat Virda Mega Ayu Warosatul Ilmiyah Windu Gata Windu Gata Windu Gata Yessica Fara Desvia