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All Journal JURNAL SISTEM INFORMASI BISNIS Ilmu Administrasi Publik Tadris: Jurnal keguruan dan Ilmu Tarbiyah ANDHARUPA Jurnal Teori dan Praksis Pembelajaran IPS Jurnal Sosiologi Pendidikan Humanis JOIV : International Journal on Informatics Visualization PRISMA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Applied Information System and Management Martabe : Jurnal Pengabdian Kepada Masyarakat JURNAL PENDIDIKAN TAMBUSAI Ensiklopedia of Journal Minda Baharu JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Menara Ilmu International Journal for Educational and Vocational Studies Jurnal Studi Guru dan Pembelajaran Social, Humanities, and Educational Studies (SHEs): Conference Series Jurnal Menara Ekonomi : Penelitian dan Kajian Ilmiah Bidang Ekonomi Building of Informatics, Technology and Science The Indonesian Journal of Social Studies The Journal of Society and Media JURNAL GEOGRAFI Geografi dan Pengajarannya JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Herbal, Clinical and Pharmaceutical Science (HERCLIPS) Jurnal Partisipatoris IJIIS: International Journal of Informatics and Information Systems Abdimasku : Jurnal Pengabdian Masyarakat Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat International Journal of Environmental, Sustainability, and Social Science Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat Indonesia International Journal of Social Learning (IJSL) Journal La Lifesci International Journal of Social Science Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) International Journal of Engagement and Empowerment (IJE2) SOCIAL : Jurnal Inovasi Pendidikan IPS Jurnal Pengabdian Kepada Masyarakat Jurnal Abdi Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Jurnal Teknologi Sistem Informasi Jurnal Puan Indonesia Kajian Moral dan Kewarganegaraan Journal of Indonesian Rural and Regional Government (JIRReG) Mudabbir: Journal Research and Education Studies Journal of Comprehensive Science Dedikasi: Jurnal Pengabdian Kepada Masyarakat Zona Manajerial: Program Studi Manajemen (S1) Universitas Batam Jurnal Pendidikan Indonesia (Japendi) Indonesian Research Journal on Education Innovative: Journal Of Social Science Research Jurnal Pengabdian Ibnu Sina International Journal of Emerging Research and Review Jurnal Manajerial dan Bisnis Tanjungpinang WASATHON Khidmat : Jurnal Pendidikan dan Ilmu Sosial Jurnal Pendekar Nusantara Zona Kebidanan : Program Studi Kebidanan Universitas Batam Jurnal Medika: Medika Society Jurnal Pengabdian Masyarakat Inovasi Indonesia Jurnal Dialektika Pendidikan IPS Edumaspul: Jurnal Pendidikan
Sarmini Sarmini
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Journal : Journal of Applied Data Sciences

 1 
Volatility Analysis of Cryptocurrencies using Statistical Approach and GARCH Model a Case Study on Daily Percentage Change Sarmini, Sarmini; Widiawati, Chyntia Raras Ajeng; Febrianti, Diah Ratna; Yuliana, Dwi
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.261

Abstract

Cryptocurrency has become a significant subject in the global financial market, attracting investors and traders with its high volatility and profit potential. This study analyzes the daily volatility and GARCH volatility of six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), USD Coin (USDC), Tether (USDT), and Ripple (XRP). Daily percentage change data and GARCH volatility are analyzed over specific time periods. The analysis reveals that Bitcoin (BTC) has an average daily percentage change of 0.366%, while Ethereum (ETH) has 0.376%. Litecoin (LTC) shows a daily percentage change of 0.166%, whereas USD Coin (USDC) and Tether (USDT) have very low daily percentage changes, nearly approaching zero. In terms of GARCH volatility, Ethereum (ETH) stands out with a volatility of 0.198, followed by Bitcoin (BTC) with a volatility of 0.121. The study's results indicate that cryptocurrencies are vulnerable to extreme price fluctuations, evidenced by their asymmetry distribution and kurtosis. Volatility correlation analysis reveals significant relationships, important for risk management and portfolio diversification. These findings contribute to understanding cryptocurrency volatility characteristics and aid stakeholders in making informed investment decisions.
Novel Predictive Framework for Student Learning Styles Based on Felder-Silverman and Machine Learning Model Maulana Baihaqi, Wiga; Eko Saputro, Rujianto; Setyo Utomo, Fandy; Sarmini, Sarmini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.408

Abstract

This study analyzes data from the Open University Learning Analytics Dataset to evaluate how students' interactions with Virtual Learning Environment (VLE) materials influence their final outcomes. This research aims to formulate and build a novel predictive framework based on the Felder-Silverman and Machine Learning Model for student learning styles. Based on these objectives, this research provides novelty and contributions since it enhances student data analysis, uses a learning model using Felder-Silverman Learning Style Model (FSLSM) to give a more comprehensive understanding of students' learning styles, and improves prediction accuracy by introducing Artificial Neural Network (ANN) and feature selection using Random Forest. The data used includes 3 main files: vle.csv, which contains information about the materials and activities in the VLE; studentVle.csv, which records students' interactions with the materials; and studentInfo.csv, which provides demographic information of students and their final outcomes. The analysis process involved data merging and processing, including handling of missing values, data type conversion, as well as mapping activity types to learning style features based on the FSLSM. We use the Random Forest feature selection method, as well as data imbalance handling techniques such as oversampling, to improve model performance. The applied classification models include Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and ANN. The analysis results showed that after tuning, the Random Forest model achieved 97% accuracy, while SVM achieved 97% accuracy as well, with better performance than previous studies. This research highlights the importance of comprehensive data integration and appropriate processing techniques in improving the accuracy of student learning style prediction. Based on the increase in accuracy results, it can be beneficial for more effective personalized learning and improve our understanding of students' learning style preferences. The research advances knowledge and provides practical applications for educators to tailor their teaching strategies.
Predicting Network Performance Degradation in Wireless and Ethernet Connections Using Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron Models Widiawati, Chyntia Raras Ajeng; Sarmini, Sarmini; Yuliana, Dwi
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.519

Abstract

This study explores predicting network performance degradation in wireless and Ethernet connections using three machine learning algorithms: XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP). Key metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were employed to evaluate model performance. The MLP classifier achieved the highest accuracy (98.7%) and AUC-ROC (0.9998), with a precision of 1.0000 and recall of 0.8622, resulting in an F1-score of 0.9260. Logistic Regression provided reasonable baseline performance, with an accuracy of 93.67%, AUC-ROC of 0.9565, and an F1-score of 0.5992, but struggled with non-linear dependencies. XGBoost showed limited utility in detecting degradation events, achieving an F1-score of 0 despite a perfect AUC-ROC (1.0), indicating sensitivity to imbalanced data. Through hyperparameter tuning, MLP demonstrated robustness in capturing complex patterns in network latency metrics (local_avg and remote_avg), with remote_avg emerging as the most predictive feature for identifying degradation across both network types. Visualizations of latency dynamics demonstrate the higher predictive relevance of remote latency (remote_avg) in both network types, where spikes in this metric are closely associated with degradation. The findings underscore the effectiveness of using latency metrics and machine learning to anticipate network issues, suggesting that MLP is particularly well-suited for real-time, predictive network monitoring. Integrating such models could enhance network reliability by enabling proactive intervention, crucial for sectors reliant on continuous connectivity. Future work could expand on feature sets, explore adaptive thresholding, and implement these predictive models in live network environments for real-time monitoring and automated response.
Health and Socio-Demographic Risk Factors of Childhood Stunting: Assessing the Role of Factor Interactions Through the Development of an AI Predictive Model Hariguna, Taqwa; Sarmini, Sarmini; Azis, Abdul
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.612

Abstract

Stunting is a significant global health problem, especially in developing countries such as Indonesia. This study aims to develop and evaluate an artificial intelligence (AI)-based predictive model to identify the risk of stunting in children using the CatBoost algorithm which is a combination of Weighted Apriori and XGBoost. This model is designed to utilize the advantages of each algorithm in handling data with variable weights to improve prediction accuracy. Feature analysis shows that "Height (cm) Age (months)" are the main indicators in classifying children's nutritional status. Model evaluation shows high accuracy of 94.85%, precision of 95%, recall of 94.85%, and F1 Score of 94.84%. Kappa Coefficient and Matthews Correlation Coefficient (MCC) reached 93.13% and 93.19%, respectively, while ROC-AUC reached 99.70%. These findings indicate that the CatBoost model can provide highly accurate results in detecting the risk of stunting and offer in-depth insights into risk factors that can improve the effectiveness of health interventions. This study fills the gap in the literature by integrating the Weighted Apriori and XGBoost algorithms, providing a significant contribution to early detection of stunting and supporting government efforts to reduce the prevalence of stunting in Indonesia and other regions.
Co-Authors Abdul Azis Abdul Fadlil Abednego Dwi Septiadi Aditiya, Eka Candra Rachmad Adiya, Az Zahra Dwi Nur Afianti, Nur Azizah Agung Dwi Bahtiar El Rizaq agung setiawan Agus Suprijono Agus Taufik Mulyono AHMAD MUZAKKI Ali Imron Amalia Fatma Pitaloka Amelia, Cevy Andhanie, Shafa Andi Hidayatul Fadlilah Andika Prasetya Nugraha Anindya, Salsa ANNA NOORDIA Antartika, Abrianty Arif Rahman Hakim Ariyanti Ariyanti Ariyanti Ariyanti Artono Artono, Artono Astri Wahyuningsih Aura Afan Shabrina Ayudya Nova Puspaningtyas BAYU SEGARA PUTRA, GEDE Bela, Sita Bisri, Mashur Hasan Bonda Sisephaputra Budiarto, Mochamad Kamil Catra Y, Wayan Cevy Amelia Chyntia Raras Ajeng Widiawati Daniel Happy Putra Diah Ainin Budiarti Dinata, Candra DWI GATI, NITA Dwi Dwi Krisbiantoro, Dwi Dzakkiyah, Alya Khansa Dzikri, Muhammad Zulfikar Eka Tripustikasari, Eka Elminaliya Sandra Elsa Komala, Elsa Ely Kurniawati Erina Rahmadyanti Ervina Halit Fandi Fatoni Fandy Setyo Utomo Fauziyyah, Ulfah Febri Edward Febrianti, Diah Ratna Ferdila, Ferdila Fira, Choly Septa Fitriya, Ulthufna Kausarul FX Sri Sadewo Gading Gamaputra Galih Setyawan, Katon Galih Wahyu Pradana Gomgom Samosir, Marinus Gunawan, Dahlan Harmanto Harmanto Hendra Budiman Hidayati, Armawati hidayatulloh, hanif Ilham, Rifqi Arifin Imam Tahyudin Indriyani, Ria Irvani, Zendika Ita Mardiani Zain Jacky, M Katon Setyawan Ketut Prasetyo Khadijah Khadijah Kharisma, Marcellina Tiara Putri Khasanah, Fitrotul Khoerida, Nur Isnaeni Kristanti, Fania Putri Kurnia Imtichatus Sholichah Kusmanto, Hari Kusnul Khotimah Kusnul Khotimah Laila Vika Safitri Lailiyah, Faridatul Lediawati, Teni Siti Linayati Lestari, Linayati Listyaningsih Listyaningsih LUTFAIDAH, ANNA M. Jacky, M. Jacky M., Jacky Ma'arifah, Windiya Mahat, Hanifah Maulana Baihaqi, Wiga Maya Richmayati Mengkepe, Amy Dara Istikoma MUHAMMAD JACKY Muhammad Turhan Yani Mujahidin, Muhammad Diwanul Mulyadi Mulyadi Mustika, Ita Nabilah, Shafa Rizqi NAGALIMAN, Nagaliman Nanda, Risma Nasution Nasution Nasution Nasution Ngaliman, Ngaliman Ningrum, Diah Luckyta Niswatin Nuansa Bayu Segara Nugroho Hari Purnomo Nur Habibah Nurdewanti, Nilam Puspita Nurjanah, Rita Nurul Hidayati Oksiana Jatiningsih Pahlevi, Rahma Shintya Pambayun, Niken Lia Prihatiningtias Pamor Gunoto Prameswari, Karina Puspita Prastuti, Ajeng Eka Pratama, Cindy Arinda Diah Pratama, Irfan Pratama, Satrya Fajri Pratama, Wildan Razzaq Puspaningtyas, Ayudya Nova Rahmah, Anisa Aulia Rahmawati, Rizqi Rahmi Nurhaini, Rahmi Ramadhan, Rio Fadly Rasyid, Suparta Ratna Dewi Silalahi Rendianto, Fakrul Aldi Rini Elfina Risa Ayu Aktavia Riski Darma Santi Risnawati Risnawati Riswandhi Ismail Rujianto Eko Saputro Sabri Sabri Salma, Karina Salsabila, Firdausi Irma Sanuri, Ranti Saputra, Aina Aldi Saputri, Inka Sasmita Timur, Elshinta Agustin Satriawan, Bambang SEPTINA ALRIANINGRUM Setiyono, Rizal Setyawan, Katon Galih Silalahi, Ratna Dewi Siraj Siraj Siti Maizul Habibah Sri Hartini Sri Yanti Sriwahyuni, Tutik Subarkah, Pungkas Sugandi, Zain Arif Wildan SUGENG HARIANTO Suhaimi Suhaimi Sukartiningsih, Sri Sukma Perdana Prasetya Sumantri, Sumatri Sunardi Sunardi Sunarto Sunarto Supriyanto, Muhammad Susanti, Martini D.E Suyono Suyono Syahrizaldy, Hikmalul A'la Syawaldi, Rizky Bilal Syifa Fauziah Taqwa Hariguna Tarwoto, Tarwoto Turhan, Muhammad Ulthufna Kausarul Fitriya Uswatun Hasanah Wahid, Arif Mu'amar Wahyu, Herta Tri Waluyo, Retno Warsono Warsono Widiawati, Chyntia Windayati, Dian Titik Windayati, Diana Titik wisnu wisnu, wisnu Yahya, Saifudin Yenny Aryaneta Yi, Ding Yuanita FD Sidabutar Yuliana, Dwi Yuliarti, Agnes Pradini Yulvinda, Rossa Yuniar, Indhiawan Yunita, Ika Romadoni Zamzami, Mohammad Aqil Misbach Zein, Ita Mardiani