Article Per Year (5 Year)
p-Index From 2021 - 2026
7.007
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Nur Ghaniaviyanto Ramadhan
Institut Teknologi Telkom Purwokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Data Mining Techniques in Handling Personality Analysis for Ideal Customers Nur Ghaniaviyanto Ramadhan; Adiwijaya Adiwijaya
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.175-181

Abstract

Background: Personality distinguishes individuals from one another, guides their actions and reactions, and dictates their preferences in many aspects of life, including shopping. Objective: This study determines the characteristics of an ideal customer based on individual personality. Methods: Data mining techniques used in this study are K-nearest neighbour (KNN), linear support vector machine (SVM), and random forest. This study also applies the synthetic minority oversampling technique (SMOTE) to overcome the imbalance in the amount of data. Results: This study shows that the application of the SMOTE and random forest models resulted in 88% accuracy, 79% precision, and 70% recall, which are the highest compared to other models. Conclusion: SMOTE in this research is unsuitable for use in the KNN and linear SVM classification models. Ensemble-based models such as random forest can produce high accuracy when SMOTE is applied for data pre-processing.
Klasifikasi Gerakan Yoga dengan Model Convolutional Neural Network Menggunakan Framework Streamlit Mohammad Fikri Nur Syahbani; Nur Ghaniaviyanto Ramadhan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5520

Abstract

Indonesian people are not fit and lack sports activities, therefore one of the alternative sports activities is yoga. Yoga is a type of exercise that has two important components, namely breathing and movement. Yoga movements also vary and can be distinguished from body curves, but ordinary people may not be familiar with yoga movements. With advances in technology and computer performance intelligence, it is now possible for computers to recognize an image for object recognition, namely detecting yoga movements using the digital image classification method. To make it easier to classify yoga movements, you can use the CNN model. Convolutional Neural Networks (CNN) are a combination of artificial neural networks with deep learning methods. The CNN process will carry out a training and testing process for yoga movements so that an image classification can be determined from the type of yoga movement. The image of the yoga movement is divided into 80% for training and 20% for testing. The training process is carried out using two different scenarios by differentiating the input image size, batch size, optimizer. The dataset consists of goddess, plank, tree, warrior2, downdog movements. The highest accuracy results are 94.10% using 170 x 170 image input, batch size 32, RMSprop optimizer. The results of testing a total of 40 images of yoga movements, 37 images were correctly guessed. The model that has been trained is implemented into the website using the Streamlit framework.
Perancangan Website Company Profile Perusahaan CV. CUP10INDO Menggunakan Metode Design Thinking Abdurrahman Ibnul Rasidi; Nur Ghaniaviyanto Ramadhan
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 5 (2023): April 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i5.690

Abstract

CV.CUP10INDO is a newly pioneering company in the field of export of local products. Just like other business people, CV.CUP10INDO certainly needs media to convey information both from the product to be exported and information about the company's profile. In export activities, it is certain to rely on valid information because export activities involve many parties. This is due to the lack of valid information about existing products so that there are some consumers who cancel their orders. In order for this incident not to recur, it is necessary to create a company profile website that will be very profitable for CV.CUP10INDO because information will be quickly and precisely conveyed to consumers. Researchers use the Design thinking method to design a company profile website because the thinking process of this method is human centered design which means it is centered on the user itself. After the website has been completed, it is necessary to test whether the web is feasible or not. At the web testing stage, researchers use usability testing in a moderate remote manner and use SUS as a benchmark which gets a final score of 78 and gets a good grade which means the web is acceptable
Analisis SMOTE Pada Klasifikasi Hepatitis C Berbasis Random Forest dan Naïve Bayes Nabilah Sharfina; Nur Ghaniaviyanto Ramadhan
JOINTECS (Journal of Information Technology and Computer Science) Vol 8, No 1 (2023)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v8i1.4456

Abstract

Menurut WHO, orang yang terinfeksi virus Hepatitis C tercatat sekitar 71 juta orang pada 2019. Hanya 49,7% orang yang menyadari adanya penyakit Hepatitis C. Pencegahan dini penting dilakukan untuk meminimalisir kemungkinan buruk terjadi. Untuk memaksimalkan upaya ahli medis dalam meminimalisir risiko penularan, dibuat program yang mampu mengklasifikasikan penyakit Hepatitis C dengan sistem deteksi otomatis menggunakan model machine learning. Random Forest dipilih karena mampu menangani outlier dan imbalance data sehingga mampu menghasilkan nilai akurasi yang tinggi serta mampu mengidentifikasi fitur-fitur yang penting. Naïve Bayes dipilih karena algoritmanya yang sederhana, namun mampu menghasilkan nilai akurasi tinggi. Setelah dilakukan pengujian pada kedua model, dilakukan perhitungan terhadap hasil prediksi menggunakan formula confusion matrix. Hasil pengujian menunjukkan dengan menerapkan model Random Forest tanpa SMOTE sebesar 93% dan Naïve Bayes tanpa SMOTE sebesar 88%. Sehubungan dengan adanya imbalance data pada dataset, maka dilakukan teknik oversampling menggunakan metode SMOTE. Hasil pengujian yang diperoleh dari menerapkan model Random Forest dengan SMOTE sebesar 98% dan Naïve Bayes dengan SMOTE sebesar 89%.
Opinion mining indonesian presidential election on twitter data based on decision tree method Nur Ghaniaviyanto Ramadhan; Merlinda Wibowo; Nur Fatin Liyana Mohd Rosely; Christoph Quix
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.v14i4.832

Abstract

Indonesia is a country led by a president. The term of the leadership of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold a direct general election to determine the president between 2024 and 2029. Before the general election was held in Indonesia itself, it was thick related to the campaign for each presidential candidate carried out by his supporters. The campaign is carried out directly to village locations and on social media Twitter/Facebook/YouTube. His campaign writing on Twitter is exciting to analyze. Even now, many tweets related to the 2024 presidential election contain various opinions from the public. This study will examine the sentiment of someone's tweet to see the public's statement regarding the 2024 presidential election. The resulting sentiment categories are positive, negative, and neutral, and the word tweet related to the sentiment category will be visualized. The results of the sentiment category will then be classified using a tree-based method, namely a decision tree. The accuracy generated by applying the decision tree method is 99.3%. The decision tree method is also superior to the regression-based way by 2.5%.
Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy Azka Khoirunnisa; Nur Ghaniaviyanto Ramadhan
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1056

Abstract

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.
Klasifikasi Berita Palsu Menggunakan Model Bidirectional Encoder Representations From Transformers (BERT) Enjeli Cistia Sukmawati; Lintang Suryaningrum; Diva Angelica; Nur Ghaniaviyanto Ramadhan
SisInfo Vol 6 No 2 (2024): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/sisinfo.v6i2.934

Abstract

Penyebaran informasi palsu menjadi tantangan serius dalam era digital yang terus berkembang, terutama melalui internet dan platform sosial media. Akses mudah terhadap informasi tidak terverifikasi menciptakan tantangan membedakan antara fakta dan hoaks. Salah satu aspek utama yang perlu diatasi adalah mengklasifikasikan berita palsu dengan tingkat akurasi yang tinggi. Berita sebagai sumber informasi aktual memerlukan pengelompokan untuk memfasilitasi akses, namun tidak semua berita dari berbagai sumber memiliki kredibilitas tinggi, terutama dengan adanya fake news. Fake news dapat merugikan individu dan berpotensi memanipulasi persepsi masyarakat, terutama melalui media sosial. Mengidentifikasi informasi palsu menjadi tantangan dalam Natural Language Processing (NLP) dengan pertumbuhan pesat platform media sosial. Meskipun ada beberapa metode untuk mendeteksi berita palsu, belum ada platform yang secara luas dikenal menerapkan algoritma terfokus pada sumber berita spesifik. Oleh karena itu, penelitian ini bertujuan untuk mengatasi masalah penyebaran informasi palsu dengan mengelompokkan berita palsu berdasarkan ciri-ciri bahasa, mengadopsi metode klasifikasi menggunakan teknologi Bidirectional Encoder Representations from Transformers (BERT). BERT, sebagai model bahasa yang dilatih mendalam, memahami konteks kata dalam teks dengan baik. Kami mengadopsi teknologi BERT untuk meningkatkan akurasi deteksi berita palsu. Meskipun BERT memiliki kompleksitas, sumber model yang sudah dilatih oleh Google memudahkan penggunaan tanpa perlu membuat model dari awal. Dengan langkah pretraining dan fine tuning, BERT dianggap lebih akurat dalam mendeteksi berita palsu dibandingkan metode lainnya. Penelitian ini memberikan kontribusi dalam menghadapi tantangan penyebaran informasi palsu dengan memanfaatkan keunggulan teknologi BERT dalam mengklasifikasikan berita.
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.
Expert System to Diagnose Diseases in Durian Plants using Naïve Bayes Nugraha, Narantyo Maulana Adhi; Rahardian, Reva; Kridabayu, Adam Nur; Adhinata, Faisal Dharma; Ramadhan, Nur Ghaniaviyanto
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.891 KB) | DOI: 10.47065/bits.v3i3.1077

Abstract

Durian is a fruit that is very popular and very easy to find throughout Indonesia. Durian fruit is a thorny fruit with a very pungent smell with a distinctive taste, and for some durian fans, the distinctive taste of durian is what makes durian unique compared to other fruits. However, it is unfortunate that the production and quality of durian fruit in Indonesia is currently still low due to the limited knowledge of farmers in caring for and maintaining durian plants from pests and diseases on durian plants. So far, in detecting pests and diseases, farmers still carry out pest and disease detection manually, and of course, this is very dependent on pest and disease observers/experts. For this reason, so that later the level of production and quality of durian in Indonesia can increase, we create an expert system to diagnose a disease in durian plants to help farmers overcome problems around pests and diseases commonly occur in durian plants. This study uses the Naïve Bayes method as a determinant of durian disease. The experimental results yield an accuracy of 82%, which indicates the proposed method is quite good in diagnosing durian disease.
Pendekatan Algoritma Tree dalam Prediksi Populasi pada Smart Poultry Wahyu Nugroho, Nicolaus Euclides; Ramadhan, Nur Ghaniaviyanto; Wibowo, Merlinda; Pramono, Sigit
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2609

Abstract

Intelligent systems for monitoring poultry in kennels are experiencing an increasing trend in several studies. Monitoring poultry is very important in the cage so that you can find out the chickens' condition and environment in the cage. Conditions that can be monitored include the weight of the chickens, whether or not there is enough water in a day, CO2 levels in the cages, air temperature, and humidity in the cages. Several studies have been conducted studies on monitoring poultry cages using IoT-based sensors. However, people have yet to predict the poultry population for tomorrow. So this study aims to predict the number of poultry populations in kennels based on related parameters. The prediction method used in this research is a decision tree and Support Vector Machine (SVM) to see which prediction method is better. The results evaluation techniques used in this study are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2. The experimental results show that using the decision tree method, and the results are MSE 61987.202, RMSE 248.972, MAE 85.086, and R2 0.969. Overall the results of the decision tree method are superior to SVM.
Co-Authors Abdurrahman Ibnul Rasidi Adil El-Faruqi Adiwijaya Agung Toto Wibowo Agus Supriatman Aji Gautama Putrada Alfian Akbar Gozali Alon Jala Tirta Segara Ananda Aulia Rizky Arief Rais Bahtiar Arif Amrulloh Azka Khoirunnisa Christoph Quix Diovianto Putra Rakhmadani Diva Angelica Enjeli Cistia Sukmawati Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Hashimoto, Takako Hilda Fahlena Imelda Atastina Kridabayu, Adam Nur Kurnianingsih Kurnianingsih, Kurnianingsih Lintang Suryaningrum Maman Abdurohman Merlinda Wibowo Mohammad Fikri Nur Syahbani Nabilah Sharfina Nia Annisa Ferani Tanjung Nicolaus Euclides Wahyu Nugroho Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nur Fatin Liyana Mohd Rosely Purnama Dileon Yamora Nainggolan Purwitasari, Rachma Wukir Rachma Wukir Purwitasari Rahardian, Reva Ramadhan, Teguh Ikhlas Rifqi Akmal Saputra Rifqi Alfinnur Charisma Salsabila, Nisrinia Eka Sayyid Yakan Khomsi Pane Sigit Pramono Sofiyudin Pamungkas Warih Maharani Wibowo, Merlinda Yohani Setiya Rafika Nur