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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Informatika dan Teknik Elektro Terapan InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JIKO (Jurnal Informatika dan Komputer) Information System for Educators and Professionals : Journal of Information System KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Teknik Informatika UNIKA Santo Thomas JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal ICT : Information Communication & Technology Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak JATI (Jurnal Mahasiswa Teknik Informatika) MEANS (Media Informasi Analisa dan Sistem) Instal : Jurnal Komputer Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Informasi interaktif : jurnal informatika dan teknologi informasi
Ade Rizki Rinaldi
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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E-Learning Satisfaction Menggunakan Metode Auto Model Arif Rinaldi Dikananda; Fidya Arie Pratama; Ade Rizki Rinaldi
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i2-2.1864

Abstract

E-Learning just like learning media in general need to be evaluated to find out and measure how much effectiveness, efficiency and user satisfaction with the quality of the overall learning process. One effort that can be done to find out and evaluate the quality of a learning is to use satisfaction evaluation. Measurement of satisfaction requires data derived from questionnaires that are presented using a Likert scale. The data illustrates the perception of users who have uncertainty because it is very subjective so that it has the potential to cause misinterpretation. The auto model method can be used to evaluate e-Learning satisfaction because the auto model method has the advantage of solving a problem with the various models produced, which in this case are in accordance with the context of the satisfaction problem that is often presented in natural language that has uncertainty, such as "how satisfied? "," How efficient? "And" how much is user satisfaction. Based on the auto model method, the results of the satisfaction scores of each respondent, shown in the table above, are summed and the average is calculated. With the auto model, the results show that SVM is the best performance method with an acceleration rate of 90% and best gains with a value of 38.
ANALISIS MINAT BELI PRODUK FASHION MENGGUNAKAN ALGORITMA FP-GROWTH (FREQUENT PATTEN GROWTH) Tineka Handayani; Muhamad Aditya Sunaryo; Ade Rizki Rinaldi; Cep Lukman Rohmat
Jurnal Ilmiah Informatika Komputer Vol 29, No 1 (2024)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2024.v29i1.10808

Abstract

Toko Sahabat Collection adalah toko pakaian yang menjual berbagai produk fashion dan memiliki data penjualan yang sangat besar. Penjualan produk fashion di toko ini sangat diminati oleh pelanggan. Penelitian mencakup informasi penjualan produk dari tahun 2021 hingga tahun 2022, termasuk tanggal transaksi, code produk, barcode, dan item produk. Langkah awal dalam penelitian ini melibatkan pemilihan data dengan memilih atribut yang relevan dan menghapus data yang tidak lengkap. Selanjutnya, data yang sudah divalidasi diubah menjadi format tabular. Proses selanjutnya melibatkan penerapan teknik data mining, seperti algoritma asosiasi rule, untuk meningkatkan target penjualan. Penelitian ini bertujuan untuk mengetahui pola minat beli produk yang diminati oleh pelanggan di toko sahabat collection yang dapat dipengaruhi oleh beberapa faktor seperti harga kualitas produk dan kebutuhan customer. Minat beli konsumen dianggap sebagai faktor kunci yang sangat penting dalam ruang lingkup bisnis, penelitian ini menerapkan metode data mining asosiasi, dengan fokus khusus pada algoritma FP-Growth, untuk menganalisis pola pembelian produk secara bersamaan. Hasil dari penelitian ini menunjukkan 32 aturan dengan minimum support 0,1 dan confidence 0,8. Salah satu aturan terbaik yang dihasilkan adalah: jika pelanggan membeli Hijab Ar Rafi, Hijab Bergo Pita, dan Kaos Kaki Hitam Smp, maka kemungkinan besar mereka juga akan membeli Sepatu Nb K, support 0,105 (10%) dan Confidence 0,949 (94%).
Prediksi Harga Mobil Bekas Menggunakan Algoritma Regresi Linear Berganda Dea Miftahul Huda; Gifthera Dwilestari; Ade Rizki Rinaldi; Iin Solihin
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10266

Abstract

The lack of information regarding used car prices creates obstacles for people in buying and selling vehicles because they don't understand the market prices that are used as a reference. This information is very important to know price predictions with the range of variables that can be considered. The aim is to process an algorithm model that is capable of carrying out statistics using appropriate techniques to make predictions. Prediction is a very important technique in decision making. The linear regression algorithm is a model building technique used to predict the value of a given dataset. In this study, a multiple linear regression algorithm was used to predict used car prices. The dataset used to create a prediction model with a linear regression algorithm was sourced from the Kaggle repository for used car prices and then the results were visualized in Rapminer. The prediction process uses a comparison of testing data and training data with a ratio of 90 training data and 10 testing data in the process of building the model and evaluating the model that has been produced. The result of the prediction process using the linear regression algorithm is a prediction model of Price 1637.49. The prediction model will be evaluated with 2 assessment indicators, namely RMSE and Relative Error. The results obtained from this model, in the Price category, the RMSE value is 1637.49 and the Relative Error value is 11.89%. And the application of the regression model to new data from the independent variables used is the attribute Age (Age) 24 X1, Kilometers (KM), 783764 X2, Horse power (HP) 100 X3, Transmission (Automaitc) 0 X4, Engine capacity (CC) 1500 regression equation Y = b1 + b2X1 + b3X2 + b4X3 + b5X4 +b6X5 +b7X6.
Analisis Keadaan Stunting pada Kelompok Balita di Kecamatan Tukdana dengan Pendekatan Decision Trees Asep Budiyanto; Dodi Solihudin; Ryan Hamonangan; Cep Lukman Rohmat; Ade Rizki Rinaldi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10230

Abstract

The impact of stunting on babies is an important parameter for assessing the health and welfare of children in an area. Stunting, often triggered by demographic and health factors, has serious implications for children's physical and cognitive growth. This research aims to understand the impact of demographic and health factors on stunting in children in Tukdana District, Indramayu Regency. Through data analysis, factors such as maternal age, access to clean water, sanitation facilities, and baby weight and length status were identified as significant contributors to stunting. The Decision Trees method was used to identify factors that play a role in stunting in babies, with an accuracy rate of 95.43%. The implications of this research include planning more effective interventions to deal with stunting, both in Tukdana District and in similar areas in Indonesia. Even though the majority of babies in Tukdana District have good nutritional status, further monitoring and prevention efforts are still needed to ensure optimal nutritional well-being for them. In conclusion, this research highlights the importance of identifying factors that cause stunting in infants in Tukdana District, as a basis for planning more effective interventions.
KOMPARASI ALGORITMA MACHINE LEARNING DALAM KLASIFIKASI LOYALITAS NASABAH BANK BERBASIS PARTICLE SWARM OPTIMIZATION Anam, Khaerul; Rinaldi, Ade Rizki; Fathurrohman, Fathurrohman
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10941

Abstract

Konsumen atau nasabah dalam bisnis adalah aset berharga untuk keberlanjutan bisnis di masa depan. Memperoleh kepercayaan dan meningkatkan loyalitas nasabah memerlukan perhatian dan kepedulian yang tulus. Kemajuan teknologi telah membuat persaingan bisnis lebih ketat, meningkatkan potensi churn atau penghentian penggunaan produk oleh nasabah. Salah satu upaya untuk mengatasi penurunan loyalitas adalah dengan melakukan klasifikasi loyalitas nasabah bank menggunakan teknik machine learning. Penelitian ini bertujuan untuk membandingkan algoritma Random Forest, Decision Tree, dan Artificial Neural Network yang dioptimasi dengan metode boosting Particle Swarm Optimization (PSO) pada dataset nasabah bank XYZ yang diperoleh dari situs www.kaggle.com. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki nilai akurasi tertinggi sebesar 86,34%, nilai AUC sebesar 0,854, rata-rata precision sebesar 83,55%, dan rata-rata recall sebesar 70,42%. Atribut yang paling signifikan dalam Random Forest adalah Geography, Gender, Tenure, dan Estimated Salary. Pada Decision Tree, atribut signifikan adalah Tenure dan Estimated Salary, sementara pada Artificial Neural Network, atribut signifikan adalah Geography, Has Card, dan Estimated Salary.
Bibliometrik Analysis: Signal Preprocessing Techniques for Kualitas Sinyal Electrogram Odi Nurdiawan; Dadang Sudrajat; Fathurrohman; Ade Rizki Rinaldi
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study explores electroencephalogram (EEG) signal preprocessing techniques used in the early detection and diagnosis of epilepsy, aiming to enhance the quality and reliability of data used in clinical applications. Effective signal preprocessing techniques are crucial for minimizing artifacts and noise, which can obscure critical information in EEG signals. More accurate EEG signal processing allows for the identification of abnormal patterns associated with various neurological conditions, such as epilepsy, which heavily relies on this signal analysis for precise diagnosis. This study conducted a bibliometric analysis using a descriptive approach to identify research trends, geographic distribution, institutional contributions, and key authors in this field. Data was collected from the Scopus database using the keywords "electroencephalogram AND signal AND processing AND epilepsy". The analysis results show a significant increase in the number of publications related to EEG signal preprocessing techniques over the past five years, with major contributions from countries like China, India, and the United States, reflecting the high global interest and focus on this topic. Additionally, deep learning and machine learning techniques emerged as the most dominant methods in this research, indicating future trends in the development of increasingly sophisticated EEG signal processing technologies. The findings also suggest that using techniques such as artificial neural networks, convolutional neural networks (CNN), and deep learning can enhance the accuracy of epilepsy diagnosis and prediction, making a significant contribution to modern clinical practice. Moreover, this study emphasizes the importance of developing and integrating more advanced preprocessing techniques to improve the effectiveness of EEG signal detection and classification, which is expected to enhance diagnostic outcomes and patient management with neurological disorders. This study provides valuable contributions to the development of medical diagnostic technologies, particularly for neurological disorders such as epilepsy, and highlights the need for further research to optimize these techniques for broader clinical application.
Mengoptimalkan Kinerja Naïve Bayes Pada Ancaman Modern Dengan Menggunakan PCA Pada Data Intrusion Detection System (IDS) Arlandy, Kevin Salsabil; Faqih, Ahmad; Rinaldi, Ade Rizki
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 8, No 1 (2025): Januari
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v8i1.303

Abstract

Abstrak: Intrusion Detection System (IDS) digunakan untuk mendeteksi serangan atau aktivitas mencurigakan dalam jaringan. Dengan meningkatnya ancaman siber modern, penelitian ini mengusulkan kombinasi metode Naïve Bayes dan Principal Component Analysis (PCA) untuk meningkatkan akurasi dan efisiensi deteksi. Metode tambahan PCA dapet mereduksi dimensi dataset menjadi 30 komponen utama tanpa kehilangan informasi penting, menggunakan dataset UNSW-NB15. Proses melibatkan standarisasi data dengan StandardScaler, reduksi dimensi menggunakan PCA, serta evaluasi model Naïve Bayes pada dataset dengan dan tanpa PCA. Analisis ini menggunakan program Python yang di eksekusi dengan Google Collab, dengan hasil menunjukkan bahwa model dengan PCA mencapai akurasi sebesar 96.65% dengan recall 1.00 untuk kelas ancaman, meskipun presisi masih rendah (0.49). Sebaliknya, tanpa PCA, akurasi hanya mencapai 92.72% dengan presisi 0.31 untuk kelas yang sama. Selain itu, penggunaan PCA berhasil mengurangi waktu komputasi dari 1 menit menjadi 30 detik. Kombinasi dengan teknik reduksi dimensi Principal Component Analysis (PCA) menunjukkan kinerja yang lebih baik dalam mengklasifikasikan data pada sistem Intrusion Detection System (IDS). PCA dan Naïve Bayes terbukti menjanjikan dalam mendeteksi ancaman modern, meskipun masih diperlukan perbaikan untuk mencapai kinerja yang lebih optimal.Kata kunci: Intrusion Detection System, Naïve Bayes, PCA, Keamanan JaringanAbstract:An Intrusion Detection System (IDS) is used to detect attacks or suspicious activities in the network. With the increase of modern cyber threats, this research proposes a combination of Naïve Bayes and Principal Component Analysis (PCA) methods to improve detection accuracy and efficiency. The additional PCA method can reduce the dataset dimension to 30 principal components without losing important information, using the UNSW-NB15 dataset. The process involves data standardization with Standard-Scaler, dimensionality reduction using PCA, and Naïve Bayes model evaluation on the dataset with and without PCA. This analysis used a Python program executed with Google Collab, with the results showing that the model with PCA achieved an accuracy of 96.65% with a recall of 1.00 for the threat class. However, the precision was still low (0.49). In contrast, without PCA, the accuracy only reached 92.72% with a precision of 0.31 for the same class. In addition, the use of PCA successfully reduced the computation time from 1 minute to 30 seconds combination with the Principal Component Analysis (PCA) dimension reduction technique shows better performance in classifying data in the Intrusion Detection System (IDS). PCA and Naïve Bayes proved promising in detecting modern threats, although improvements are still needed to achieve more optimal performance.Keywords: Intrusion Detection System, Naïve Bayes, PCA, Network Security
Implementasi Data Mining Pada Proses Seleksi Beasiswa Menggunakan Naive Bayes Dan Backward Elimination Agustina, Irma; Dwilestari, Gifthera; Rinaldi, Ade Rizki
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol. 10 No. 1 (2025): JII Volume 10, Number 1, Januari 2025
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Proses seleksi penerima beasiswa sering kali menghadapi tantangan dalam mengelola data yang kompleks dan memastikan keakuratan seleksi. Penelitian ini bertujuan mengoptimalkan algoritma Naive Bayes melalui teknik Backward Elimination untuk efisiensi proses seleksi. Dataset penelitian terdiri dari 1.042 data penerima beasiswa, mencakup variabel seperti Indeks Prestasi Kumulatif (IPK), penghasilan, jumlah tanggungan, dan status beasiswa. Penelitian dilakukan menggunakan platform RapidMiner versi 10.2 dengan tahapan meliputi preprocessing, transformasi data, pembagian data latih dan uji melalui Split Data. Teknik Backward Elimination diterapkan untuk menyederhanakan model dengan menghapus variabel yang kurang signifikan. Hasil penelitian menunjukkan bahwa penerapan Naive Bayes dengan teknik Backward Elimination menghasilkan tingkat akurasi sebesar 74,62%. Variabel utama yang paling berpengaruh adalah tanggungan orang tua dan penghasilan, yang secara signifikan memengaruhi keputusan seleksi. Selain itu, teknik ini juga berhasil mengurangi kompleksitas model, meningkatkan efisiensi proses analisis, dan meminimalkan waktu serta sumber daya yang dibutuhkan. Penelitian ini mendukung pengembangan sistem seleksi berbasis data yang lebih transparan dan efisien. Implementasi teknik Backward Elimination mempermudah interpretasi model. Dengan demikian, hasil ini diharapkan dapat menjadi landasan bagi pengembangan sistem seleksi beasiswa berbasis machine learning yang lebih efektif, serta membuka peluang untuk penelitian lanjutan yang berfokus pada optimalisasi algoritma dan seleksi fitur di berbagai sektor.
House Price Prediction Analysis Using a Comparison of Machine Learning Algorithms in the Jabodetabek Area Ningsih, Indah Ratna; Faqih, Ahmad; Rinaldi, Ade Rizki
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.733

Abstract

Jabodetabek, as the largest metropolitan area in Indonesia, has complex property price dynamics, making it difficult for developers and buyers to determine house prices. This study aims to analyze and compare the performance of the Multiple Linear Regression and Random Forest Regression algorithms in predicting house prices in the region. The data was obtained through scraping techniques from the rumah123.com website in October 2024, covering 999 data points with variables such as price, location, building area, land area, number of bedrooms, bathrooms, and garages. A comparative approach with cross-validation was applied to evaluate the performance of both algorithms using the metrics MAE, MSE, RMSE, MAPE, and R². The research results show that Random Forest Regression using GridsearchCV has better predictive performance, with an MAE value of Rp.645,764,815, MAPE of 28.12%, and R² of 0.864. The main factors influencing house prices in Jabodetabek include building size, land size, number of bedrooms, bathrooms, garages, and location. This finding emphasizes the superiority of Random Forest Regression in capturing complex data patterns and the significant role of these variables in determining house prices.
Application of Neural Network to Predict Rupiah Exchange Rate Against Korean Won Saeful, Agung; Dwilestari, Gifthera; Rinaldi, Ade Rizki
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.734

Abstract

This study investigates the application of neural networks for predicting the exchange rate of the Indonesian Rupiah against the Korean Won, addressing the challenges posed by currency fluctuations in international trade and investment. The research employs a data mining approach utilizing historical exchange rate data, which allows the neural network to identify complex patterns that traditional forecasting methods may miss. The model is developed using RapidMiner software, facilitating data preprocessing, transformation, and evaluation. The outcomes show that the predictions were quite accurate, as indicated by a low prediction error rate. The findings suggest that the neural network model not only provides reliable forecasts but also maintains consistent performance over time. This research contributes to the growing field of artificial intelligence in finance, highlighting the potential of advanced predictive models to enhance decision-making processes in the context of global economic interactions. The study underscores the importance of integrating technology with economic analysis to better navigate the complexities of currency exchange and its implications for financial risk management.
Co-Authors Ade Irma Purnama Sari Ade Irma Purnamasari agus bahtiar Ahmad Faqih Ahmad Syaeful Ma’arief Alfi Rizqi Falih Amelia, Mita Amida, Anggi Fitria Aria Pratama Arif Rinaldi Dikananda Arlandy, Kevin Salsabil Asep Budiyanto Aziz Chairul Imam Basysyar, Fadhil Muhammad Bima Anurgraha Djajnegara Cep Lukman Rohmat Cep Lukman Rohmat Dadang Sudrajat Dea Miftahul Huda Dian Ade Kurnia Dikananda, Arif Rinaldi Dodi Solihudin Edi Tohidi Fathrurrahman Fathurrohman Fathurrohman Fidya Arie Pratama Gifthera Dwilestari Hayati, Umi Hery Widijanto Iin Iin Solihin Iin, Iin Irfan Ali Irma Agustina Jayawarsa, A.A. Ketut Kurnia, Dian Ade Lana Surlanto Muhamad Abdurohman Muhamad Aditya Sunaryo Mulyawan Mulyawan Mulyawan, Mulyawan Narasati, Riri Narasati Ningsih, Indah Ratna Nining Rahaningsih Noviati Odi Nurdiawan Odi Nurdiawan Permana, Sandy Eka Pratama, Denni Pratama, Fidya Arie Raditya Danar Dana Rahma, Aliya Anisa Ryan Hamonangan Saeful, Agung Sandy Eka Permana Septianto, Muhamad Arif Syarif Maulana Yaasin Tineka Handayani Tohidi, Edi Tuti Hartati Wahyu Saputra