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All Journal Dinamik Jurnal Informatika dan Teknik Elektro Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Antivirus : Jurnal Ilmiah Teknik Informatika Generation Journal Jurnal Ilmiah Wahana Pendidikan Jurnal Penelitian Rumpun Ilmu Teknik Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Jurnal Informatika Polinema (JIP) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yuyun Umaidah
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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Analysis of Student Reading Interest in UNSIKA Library with K-Means Algorithm Naibaho, Syela Herdina; Nailufar Farha Afifah; Yuyun Umaidah; Nono Heryana
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 18 No 1 (2024): Mei 2024
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/antivirus.v18i1.2926

Abstract

This study aims to analyze students' reading interest at the Singaperbangsa Karawang University Library using the K-Means algorithm. The K-Means method is used to group books based on borrowing patterns, so as to provide better insight into students' reading preferences and assist libraries in managing book collections. Book borrowing data was obtained from the university librarian database, while literature study was carried out by studying and looking for references in related journals and literature. After the data is clean and can be processed, the clustering process is carried out using the K-Means algorithm by determining the value of k. The clustering results are displayed in the form of a table showing the groups of books that are frequently borrowed, books with a moderate borrowing frequency, and books that are rarely borrowed by students. Cluster quality evaluation was carried out using the Davies Bouldin value. The findings of this study indicate that the K-Means algorithm is effective in classifying books based on borrowing patterns. With a better understanding of students' reading interest, libraries can optimize the placement of books, increase the availability of books of interest, and develop strategies to increase overall student reading interest. The results of this study make a significant contribution to the development of university libraries and provide useful guidelines for decision-making in library management.
Perbandingan Kinerja Model RNN, LSTM, dan BLSTM dalam Memprediksi Jumlah Gempa Bulanan di Indonesia Roni Merdiansah; Khofifah Wulandari; Mentari Hasibuan; Yuyun Umaidah
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 3 No. 1 (2024): Februari : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v3i1.3466

Abstract

Earthquakes are natural phenomena that frequently occur in Indonesia. To identify and predict the level of earthquake activity, effective prediction methods are needed. In this study, we employed a Recurrent Neural Network (RNN) to predict the average number of earthquakes that occur each month in Indonesia. This research utilized a large amount of historical earthquake data in Indonesia. We divided this data into training and testing sets to train and evaluate our prediction model. Additionally, we used Mean Absolute Error (MAE) and Mean Squared Error (MSE) as evaluation metrics to measure the accuracy of our model's predictions. The results showed that using Long Short-Term Memory (LSTM) units with a Bidirectional (BLSTM) configuration, which is a part of RNN, provided accurate predictions regarding the average number of earthquakes per month in Indonesia. We achieved an MAE of 0.0668 and RMSE of 0.0858, indicating a good level of accuracy in predicting the average number of earthquakes. This research contributes significantly to the understanding and prediction of earthquake activity in Indonesia. The use of deep learning techniques in RNN can provide accurate and reliable prediction outcomes for earthquake mitigation and risk reduction efforts in Indonesia.
Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19 Euis Saraswati; Yuyun Umaidah; Apriade Voutama
Generation Journal Vol 5 No 2 (2021): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v5i2.16125

Abstract

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.
K-Nearest Neighbor Berbasis Particle Swarm Optimization untuk Analisis Sentimen Terhadap Tokopedia Dicki Pajri; Yuyun Umaidah; Tesa Nur Padilah
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2658

Abstract

Tokopedia is a popular marketplace used by e-commerce in Indonesia. Customers’ perception of Twitter towards Tokopedia can be used as an important source of information and can be processed into useful insights. Sentiment analysis is a solution that can be used to process the customers’ perception using K-Nearest Neighbor based on Particle Swarm Optimization. The purpose of this study is to classify customers’ perception based on positive, neutral, and negative classes. The test is carried out with four different scenarios and k values which are evaluated using a confusion matrix. Evaluation results showed the distribution of the dataset is 90:10 and the value of k = 1 is the best evaluation result, which is 88.11%. The feature selection was used for results by using Particle Swarm Optimization. The Particle Swarm Optimization used 20 iterations and 10 particles. It produced 97.9% the best evaluation accuracy, 96.17% precision, 96.62% recall, and 96.39% f-measure.
RANCANG BANGUN SISTEM TANYA JAWAB AKADEMIK DOSEN DENGAN METODE RETRIEVAL AUGMENTED GENERATION BERBASIS WEBSITE Rafli Pasya; Yuyun Umaidah; Mohamad Jajuli
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.7614

Abstract

Interaksi tanya jawab akademik antara mahasiswa dan dosen seringkali menghadapi tantangan efisiensi, di mana dosen dibebani oleh pertanyaan yang repetitif sementara mahasiswa membutuhkan respons yang cepat. Permasalahan ini mendorong kebutuhan akan solusi otomatis untuk memfasilitasi komunikasi yang lebih efektif. Penelitian ini bertujuan untuk merancang dan membangun sebuah sistem tanya jawab akademik berbasis website dengan mengimplementasikan metode Retrieval-Augmented Generation (RAG) untuk menyediakan jawaban yang relevan dan kontekstual. Pengembangan sistem menggunakan metodologi Agile Scrum yang dilakukan secara iteratif, mencakup tahapan product backlog, sprint planning, implementasi, sprint review, dan sprint retrospective. Sistem ini mengintegrasikan beberapa teknologi yaitu backend portal dikembangkan dengan Laravel 10, antarmuka chatbot menggunakan Streamlit, basis data menggunakan PostgreSQL dengan ekstensi pgvector untuk vector similarity, serta model generative AI Gemini 2.5 Flash untuk menghasilkan respons. Evaluasi sistem dilakukan melalui blackbox testing pada seluruh fungsionalitas. Hasil pengujian menunjukkan bahwa sistem berhasil berfungsi sesuai dengan yang diharapkan, di mana chatbot mampu memberikan jawaban yang akurat dan relevan berdasarkan basis pengetahuan yang tersedia, serta secara efektif mengurangi kebutuhan interaksi langsung antara mahasiswa dan dosen
Implementasi Metode NLP Dalam Pembuatan Chat Bot Telegram Radi Alpiyanto; Muhammad Fitra Fajar Rusamsi; Yuyun Umaidah
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 17 No. 2 (2023): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

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

Abstract

In the current digital era, the use of chat bots has become increasingly popular, especially oninstant messaging platforms like Telegram. To create a better user experience, accurate andresponsive understanding of user messages is necessary. In this regard, Natural LanguageProcessing (NLP) plays a crucial role. The goal of this research is to implement NLP methods inthe development of a Telegram chat bot using an appropriate and effective approach. In thisimplementation, experiments and testing are conducted to ensure the functionality andresponsiveness of the bot. Testing involves sending messages to the bot and evaluating the resultsof NLP processing and bot responses. During this process, enhancements and improvements aremade to enhance the bot's ability to understand and respond to user messages more effectively.NLP methods are applied in message handling. Entity recognition is used to identify importantinformation such as names, locations, or dates from user messages. Intent understanding isperformed to determine the intentions or goals behind received messages. Natural languageprocessing is used to analyze sentiment, keywords, or language structure in messages. Based onthe results of NLP processing, the bot provides appropriate responses to users through theTelegram bot API.
Perbandingan Convolutional Neural Network dan Algoritma Machine Learning Konvensional untuk Klasifikasi Kemiskinan Multidimensional di Indonesia Sarwanti, Ruth Tika; Yuyun Umaidah
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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Abstract

Multidimensional poverty in Indonesia is a complex phenomenon involving various interconnected social, economic, and structural aspects. Conventional approaches to poverty classification often fail to capture non-linear interaction patterns and spatial dependencies inherent in multidimensional socio-economic data. This research aims to compare the performance of Convolutional Neural Networks (CNN) with conventional machine learning algorithms such as Random Forest and XGBoost in classifying multidimensional poverty in Indonesia. The research method employs a comparative quantitative approach using data from the 2023 National Socio-Economic Survey (Susenas) by BPS, covering 8,000 household observations. The target variable is multidimensional poverty status based on the Multidimensional Poverty Index (MPI) with a 1/3 cutoff. Data was split 70:30 for training and testing, with preprocessing including missing value imputation, one-hot encoding, and Min-Max scaler normalization. The CNN model was designed with a two-convolutional-layer architecture, while Random Forest used 200 decision trees and XGBoost with 200 estimators. Research results demonstrate that CNN provides the best performance with 82.4% accuracy, outperforming Random Forest (80.1%) and XGBoost (81.2%). Important variable analysis reveals that housing infrastructure conditions, household head education level, and sanitation access are key factors in determining multidimensional poverty, providing strategic input for formulating more targeted poverty alleviation policies.
Co-Authors Alfanda Novebrian Maharadja Alfina Novi Yanti Andri Dwi Noviandi Apriade Voutama Chella Aprianti Dicki Pajri Euis Saraswati Farras Salsabila Fatma Eka Zulfiakhoir Genta Triyandana Heryana, Nono Ika Fitrianti Jaman, Jajam Haerul Khofifah Wulandari Lala Aprianti Putri Mentari Hasibuan Mohamad Jajuli Muhammad Faishal Muhammad Fitra Fajar Rusamsi Naibaho, Syela Herdina Nailufar Farha Afifah Purwantoro Purwantoro Radi Alpiyanto Rafli Pasya Rijal Abdulhakim Rika Nursyahfitri Rini Mayasari Riva Arsyad Farissa Roni Merdiansah Sarwanti, Ruth Tika Syahrul Dwi Hilda Tesa Nur Padilah Tesa Nur Padillah Wahyuningrum Ayu