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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

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IMPLEMENTATION OF THE SAW METHOD AS A DECISION SUPPORT FOR GIVING FEASIBILITY OF KUR ON BANK MANDIRI DRAMAGA BOGOR Frieyadie, Frieyadie; Setiyawan, Riki
Jurnal Pilar Nusa Mandiri Vol 16 No 1 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1237.576 KB) | DOI: 10.33480/pilar.v16i1.1302

Abstract

Currently, the public's interest is very high to get KUR, but it makes it difficult for banks to determine who is eligible to receive the KUR and in the process of giving credit using the "LOS" system but this system is still quite a time consuming to analyze customer data and the process requires consideration and good analysis from the leader, due to the high number of problem loans. The SAW method used in this study. The SAW method is able to simplify and accelerate the results of credit lending recommendations. The calculation results obtained by debtors who are very worthy given credit as much as 1 debtor (4%), decent debtors with low risk as many as 16 debtors (70%), and worthy of being given with high risk as much as 6 debtors (26%) The purpose of this study to know the process and requirements for granting business credit at Bank Mandiri Dramaga Bogor.
COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION Setiyorini, Tyas; Frieyadie, Frieyadie
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1459

Abstract

Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions. This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.
COMPARISON OF APPLE IMAGE SEGMENTATION USING BINARY CONVERSION AND K-MEANS CLUSTERING METHODS Nurdiani, Siti; Rezki, Muhammad; Dahlia, Rizka; Ihsan, Muhammad Ifan Rifani; Frieyadie, Frieyadie; Fauziah, Siti
Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i1.2256

Abstract

Apples are quite popular consumption among the community and have different kinds of shapes and colors. Apples themselves have many nutrients and various vitamins including fat, as well as energy, carbohydrates, protein, vitamin C, vitamin A, vitamin B2, vitamin B1, and many more. Because of the variety of types of apples, it is difficult for people to distinguish between these types of apples. However, with the development of technology and sophistication, it is now possible to classify the types of apples using digital images. This study aims to segment the image of apples by comparing 2 methods at once to find out which method is the best. This process is an initial stage that must be done before classifying. From the comparison results of apple image segmentation with binary conversion methods and k-means clustering, it can be concluded that the best method is k-means clustering. Because it can segment the image of apples almost perfectly.
APPLICATION OF DECISION TREE AND NAIVE BAYES ON STUDENT PERFORMANCE DATASET amalia, Hilda; Puspita, Ari; Lestari, Ade Fitria; Frieyadie, Frieyadie
Jurnal Pilar Nusa Mandiri Vol 18 No 1 (2022): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v18i1.2714

Abstract

Student performance is the ability of students to deal with the entire academic series taken during school. Student performance produces two labels, namely successful and unsuccessful students. Successful students can graduate with excellent, excellent, and suitable performance labels. At the same time, students who have a label on average are students who get poor performance. Measurement of student performance is needed for every educational institution to take strategic steps to improve student performance. This study aimed to obtain a data mining method that worked well on student performance datasets. In this study, student performance datasets were processed, which had 11 indicators with one result label. Student performance datasets are processed using data mining methods, namely decision tree and nave Bayes, while the tool used for dataset processing is WEKA. The research results from processing student performance datasets obtained that the accuracy value for the decision tree method was 94.3132%, and the accuracy produced by the naive Bayes method was 84.8052%.
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.
Co-Authors Achmad Bayhaqy Achmad Bayhaqy Ade Fitria Lestari Ade Priyatna Aditiya Yoga Pratama Agung Sudrajat Ahmad Baihaqi Angga Ardiansyah Anggie Andriansyah Anton Hindardjo Ari Puspita 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 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 Merliani Ivone Merliani Ivone S 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