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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknoif Teknik Informatika Institut Teknologi Padang
Ratmana, Danny Oka
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN) Fafaza, Safira Alya; Rohman, Muhammad Syaifur; Pramunendar, Ricardus Anggi; Sri Winarsih, Nurul Anisa; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Ratmana, Danny Oka; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7098

Abstract

Floods are natural disasters that often occur and are among the most destructive because they have significant economic and social impacts. Accurate flood predictions are essential to manage risk and organize emergency response planning effectively. This research uses Deep Neural Network (DNN) to build a flood forecasting model that relies on rainfall index indicators and captures complex and ever-changing patterns obtained from rainfall index data. Using historical information from flood disaster events in Kerala, India, an analysis was conducted to assess the impact of various factors, particularly in learning rate and optimizer type, on model performance. The experimental results show that the type of optimizer is a crucial factor in determining the model's effectiveness, as shown in the ANOVA statistics with a P-value of 0.008493, much lower than the general threshold of 0.05. This is because this type of optimizer can significantly improve prediction accuracy. With the Adam optimizer type, the learning rate range is between 0.1 and 0.4, showing an accuracy level of up to 100%. However, the choice of learning rate does not significantly impact, indicating that the main emphasis on parameter adjustment should be determined accurately. Therefore, by carrying out appropriate parameter adjustments and thorough validation to find the optimal configuration that can increase accuracy in predicting flood disasters based on rainfall indices, the DNN model has the potential to become a tool that can assist in flood risk planning and management.
Perbandingan Efektivitas Nave Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube Azzahra, Tarissa Aura; Winarsih, Nurul Anisa Sri; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7186

Abstract

Advancements in the field of Natural Language Processing (NLP) have opened significant opportunities in sentiment analysis, particularly in the context of disaster response. In today's digital era, YouTube has emerged as a primary source for the public to acquire information regarding critical events. This study explores and compares two dominant sentiment analysis techniques, namely Naive Bayes and Support Vector Machine (SVM). It utilizes YouTube comment data related to natural disasters to test the effectiveness of these algorithms in identifying and classifying public sentiment as neutral, positive, or negative. The process involves collecting comment data, pre-processing the data, and applying Term-Frequency-Inverse Document Frequency (TF-IDF) weighting to prepare the data for analysis. Subsequently, the performance of both models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results indicate that while both algorithms have their strengths and weaknesses, SVM tends to show better performance in sentiment classification, especially in terms of accuracy and precision, with an accuracy result of 92% and precision of 89% for negative predictions and 94% for positive predictions. On the other hand, Naive Bayes only achieved an accuracy of 79% and a precision of 91% for negative predictions and 73% for positive predictions. This study provides significant insights into the application of machine learning algorithms in sentiment analysis.
Forecasting Air Quality Indeks Using Long Short Term Memory Ramadhani, Irfan Wahyu; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Shidik, Guruh Fajar
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7402

Abstract

Exercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values "‹"‹and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757.
Pembentukan Etika Digital melalui Program Belajar dan Bermain dalam Pemanfaatan Internet di SD Islam Bilingual Annisa Ratmana, Danny Oka; Sri Winarsih, Nurul Anisa; Pramunendar, Ricardus Anggi; Rohman, Muhammad Syaifur; Alvin, Fris
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2122

Abstract

Pada era kontemporer ini, internet telah menjadi suatu elemen yang tak terpisahkan dan menjadi kebutuhan esensial bagi seluruh lapisan masyarakat, tak terkecuali dari kalangan anak-anak hingga orang tua. Dalam konteks kehidupan sehari-hari, internet digunakan untuk berbagai keperluan seperti aktivitas pekerjaan, proses pembelajaran, transaksi berbelanja, dan beragam fungsi lainnya. Meskipun terdapat berbagai manfaat yang dapat diperoleh melalui pemanfaatan internet, perlu diakui bahwa penggunaannya juga membawa risiko tertentu, seperti potensi kecanduan dan penyebaran informasi yang tidak valid. Oleh karena itu, menjadi suatu keharusan bagi para pengguna internet untuk menjalankan aktivitasnya dengan penuh kebijaksanaan dan tanggung jawab. Dalam konteks ini, perlu ditekankan bahwa pengguna internet, terutama anak-anak usia sekolah dasar, mungkin belum sepenuhnya mampu menggunakan internet secara bijak. Hal ini disebabkan oleh keterbatasan dalam hal kematangan mental, pola pikir, dan kedewasaan dalam berperilaku. Sebagai contoh, di lingkungan Sekolah Dasar Islam Bilingual Annisa, sebagian besar siswa telah memiliki eksposur terhadap internet, namun ada pula yang belum memahami cara menggunakan internet dengan bijak. Penting untuk dicermati bahwa penggunaan internet yang tidak terkontrol dapat berpotensi mengekspos anak-anak terhadap konten yang tidak pantas, bahkan dapat mempengaruhi mereka melalui penyebaran informasi yang ambigu. Mengobservasi situasi dan permasalahan yang timbul, kami, sebagai penulis, bermaksud memberikan bantuan kepada anak-anak Sekolah Dasar Islam Bilingual Annisa dalam meningkatkan pemahaman mereka terkait penggunaan internet secara bijak
PERANCANGAN APLIKASI PERHITUNGAN BEBAN KERJA DOSEN TERINTEGRASI DENGAN PENDEKATAN WATERFALL Ratmana, Danny Oka; Syaifur Rohman, Muhammad; Firdausillah, Fahri; Wilujeng Saraswati, Galuh
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 12 No 2 (2024): TEKNOIF OKTOBER 2024
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2024.V12.2.139-148

Abstract

Digital transformation in higher education emphasizes the importance of information technology in enhancing management efficiency, including the management of lecturers' workloads. This study aims to design a Full-Time Equivalent Teaching Load (EWMP) calculation system integrated with the Integrated Resource Information System (SISTER), implemented by the Ministry of Education, Culture, Research, and Technology (KEMDIKBUDRISTEK). The application was developed using the Waterfall methodology and leverages the SISTER Application Programming Interface (API) to automate the collection of lecturer activity data at Universitas Dian Nuswantoro Semarang (UDINUS). By integrating the workload calculation application into the internal management system, this solution streamlines data recording, reduces manual errors, and enhances accuracy in the evaluation of lecturer performance. The test results indicate that the application successfully synchronizes data with SISTER in an accurate and real-time manner, supporting more effective workload management for lecturers. Additionally, the system provides reports and analyses of lecturer workloads, facilitating resource planning and allocation. This application is expected to contribute to a more transparent, accurate, and quality-driven human resource management process in higher education.
Pelatihan Logika Dasar Pemrograman menggunakan Code.org pada SMA Negeri 1 Bergas Winarsih, Nurul Anisa Sri; Pramunendar, Ricardus Anggi; Saputra, Filmada Ocky; Rohman, Muhammad Syaifur; Ratmana, Danny Oka; Hamid, Maulana As’an; Kartika, Gita
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2779

Abstract

Program "Pelatihan Logika Dasar Pemrograman Menggunakan Situs Web Code.org" bertujuan untuk menyediakan platform yang komprehensif dan mudah diakses bagi individu yang ingin meningkatkan keterampilan dasar logika pemrograman mereka. Program ini menggunakan situs web Code.org, sumber daya online yang ramah pengguna, untuk menyampaikan modul pelatihan yang menarik dan interaktif. Peserta akan dibimbing melalui konsep dasar logika pemrograman, membentuk pemahaman yang kuat tentang prinsip-prinsip kunci yang menjadi dasar berbagai bahasa pemrograman. Integrasi platform Code.org memastikan pengalaman belajar yang intuitif, menjadikannya cocok untuk pemula sambil menawarkan wawasan berharga bagi mereka yang memiliki latar belakang pemrograman tertentu. Pendekatan terstruktur dan latihan praktis program memberdayakan peserta untuk mengembangkan keterampilan dasar pemecahan masalah dan berpikir algoritmik, yang pada akhirnya mempersiapkan mereka untuk upaya pemrograman yang lebih canggih.
Co-Authors Alvin, Fris Azzahra, Tarissa Aura Fafaza, Safira Alya Fahri Firdausillah Guruh Fajar Shidik Hamid, Maulana As’an Kartika, Gita Ramadhani, Irfan Wahyu Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Syaifur Rohman, Muhammad Wilujeng Saraswati, Galuh Winarsih, Nurul Anisa Sri