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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURTEKSI Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Dinda Lestarini
Fakultas Ilmu Komputer Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Peningkatan literasi digital petani melalui sosialisasi pemanfaatan teknologi digital di Desa Cempaka Ogan Komering Ulu (OKU) Timur Oktadini, Nabila Rizky; Gumay, Naretha Kawadha Pasemah; Marjusalinah, Anna Dwi; Meiriza, Allsela; Lestarini, Dinda; Hardiyanti, Dinna Yunika; Raflesia, Sarifah Putri
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November (In Progress)
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i6.34460

Abstract

AbstrakPemanfaatan teknologi digital di sektor pertanian dapat meningkatkan efisiensi dan produktivitas petani. Kegiatan ini bertujuan untuk mensosialisasikan literasi digital dan pengelolaan pengetahuan kepada Kelompok Tani (Poktan) Harapan Kita II di Kecamatan Cempaka Kabupaten Ogan Komering Ulu (OKU) Timur yang berjumlah 29 orang. Adapun petani yang tergabung dalam kelompok tani Harapan Kita II memiliki komoditas pertanian yang beragam seperti padi, tanaman buah seperti pepaya, jambu, dan berbagai jenis sayuran. Pelaksanaan kegiatan pengabdian kepada masyarakat dilaksanakan dengan beberapa tahapan yaitu persiapan, pembuatan materi penyuluhan dan pelatihan, penyuluhan dan pelatihan, pendampingan, serta monitoring dan evaluasi. Hasil kegiatan adalah adanya peningkatan literasi digital petani terkait masalah hama dan penyakit tanaman, yang berimplikasi pada kemampuan mereka dalam mengakses informasi dan berbagi pengetahuan. Diharapkan kegiatan ini dapat terus berlanjut untuk mendukung keberlanjutan pertanian modern. Kata kunci: sosialisasi; literasi digital; pertanian; berbagi pengetahuan. AbstractThe utilization of digital technology in the agricultural sector can enhance farmers’ efficiency and productivity. This activity aims to promote digital literacy and knowledge management among the Harapan Kita II Farmers Group (Poktan) in Cempaka District, Ogan Komering Ulu (OKU) Timur, consisting of 29 members. The farmers in this group cultivate various agricultural commodities such as rice, fruit crops like papaya and guava, as well as a variety of vegetables. The implementation of this community service program was carried out through several stages, including preparation, development of extension and training materials, delivery of extension and training sessions, assistance, and monitoring and evaluation. The results of this activity indicate an improvement in farmers’ digital literacy concerning pest and disease management, which has implications for their ability to access information and share knowledge. It is expected that this program will continue to support the sustainability of modern agriculture. Keywords: socialization; digital literacy; agriculture; knowledge sharing.
Predicting Cryptocurrency Prices Using Machine Learning: A Case Study on Bitcoin Alfarizi, Muhammad; Lestarini, Dinda
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11234

Abstract

The rapid growth of cryptocurrencies, particularly Bitcoin, has drawn significant attention from investors and researchers due to its extreme price volatility. However, predicting the price of Bitcoin against the Indonesian Rupiah (BTC/IDR) remains a major challenge, especially in emerging markets such as Indonesia. This study aims to conduct an empirical comparison among three deep learning models Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (CNN-1D) in forecasting Bitcoin prices based on historical data obtained from the Indodax platform for the period 2018–2025. The dataset consists of five main variables: opening price, highest price, lowest price, closing price, and trading volume. Prior to model training, preprocessing steps were conducted, including handling missing values using the forward fill method, normalization with MinMaxScaler, and constructing time series data with a 60-day look-back window. The models were trained using an 80% training and 20% testing data split, the Adam optimizer, Mean Squared Error (MSE) as the loss function, for 50 epochs with a batch size of 32. Evaluation was performed using five quantitative metrics: MSE, RMSE, MAE, MAPE, and R², along with validation techniques to prevent data leakage. The results indicate that the GRU model achieved the best performance, with a MAPE of 1.77% and an R² of 0.9916, outperforming LSTM (MAPE 3.90%) and CNN-1D (MAPE 6.17%). These findings suggest that GRU is computationally more efficient and better adapted to nonlinear temporal dependencies in highly volatile markets. This research contributes to the academic discourse on the application of deep learning for digital asset price forecasting and provides practical implications for investors and developers of financial predictive systems in Indonesia. Future studies are expected to explore hybrid models or multi-step forecasting approaches to enhance real-time predictive performance.
Analisis Klasterisasi Kualitas Internet Seluler Menggunakan Metode K-Means dan Gaussian Mixture Model Irwansyah, Muhammad Aziiz; Meiriza, Allsela; Lestarini, Dinda
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8615

Abstract

This study utilizes internet network data from Ookla Open Data (Speedtest Global Performance), comprising three main variables: download speed, upload speed, and latency. The aim is to analyze the condition and performance of mobile internet networks across 17 regencies/cities in South Sumatera Province in 2025 and to provide data-driven recommendations for the Department of Communication and Informatics to promote equitable and improved digital infrastructure through a Knowledge Discovery in Databases (KDD) approach. The applied methods include RobustScaler for data normalization, Principal Component Analysis (PCA) for dimensionality reduction, and K-Means and Gaussian Mixture Model (GMM) algorithms for clustering regions based on network characteristics. The analysis shows that both algorithms form three clusters (K=3) with distinct patterns. GMM demonstrates higher stability than K-Means, achieving a Silhouette score of 0.426 and Davies–Bouldin Index of 0.284, compared to K-Means with 0.351 and 0.688, while the lower Calinski–Harabasz score of GMM (9.960) indicates a trade-off between cluster compactness and stability, highlighting its adaptive behavior to data variation. Urban areas such as Palembang and Prabumulih belong to the high-performance cluster, whereas Ogan Komering Ulu Selatan lies in the low-performance cluster (18.87 Mbps; 33 ms), revealing a digital gap of approximately 18 Mbps across regions. These findings emphasize the need for equitable digital infrastructure strategies through fiber-optic expansion, BTS capacity enhancement, and multi-stakeholder collaboration toward Indonesia’s Digital Vision 2045.
Perbandingan Kinerja Naive Bayes, Support Vector Machine dan Random Forest Untuk Analisis Sentimen Aplikasi Brimo Darwin, Amelia; Lestarini, Dinda; Seprina, Iin
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8697

Abstract

The development of financial technology has driven the increasing use of mobile banking, including BRImo, owned by Bank Rakyat Indonesia (BRI). However, user reviews on the Google Play Store show various complaints such as login difficulties, system errors, and failed transactions. This study aims to analyze BRImo user sentiment using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Random Forest. Data were obtained from 4,996 reviews through web scraping and labeled based on ratings with categories 1-3 negative and 4-5 positive. The labeling process obtained 4,123 positive reviews and 873 negative reviews, which were then balanced using the Synthetic Minority Oversampling Technique (SMOTE). Feature extraction was performed using TF-IDF. Test results showed that Random Forest provided the best performance with an accuracy of 0.87, a recall of 0.70, and an F1-score of 0.65 in the negative class, and an F1-score of 0.92 in the positive class. The macro F1-score reached 0.79, higher than SVM (0.69) and Naive Bayes (0.70). This finding indicates that Random Forest is more effective in classifying BRImo user sentiment, especially after data balancing, and can serve as a reference for developers in improving the quality of application services.
Co-Authors Adelia Azahra Adelia Rizki Putri Ahmad Rifai Alfarizi, Muhammad Ali Bardadi Allsela Meiriza, Allsela Andesta Angelina Darmawahyuni, Annisa Darwin, Amelia Desty Rodiah Dinna Yunika Hardiyanti Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Fransiska Prihatini Sihotang, Fransiska Prihatini Gumay, Naretha Kawadha Pasemah Hardini Novianti Iriyani, Silfi Irwansyah, Muhammad Aziiz Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Marjusalinah, Anna Dwi Muhammad Azmi Zaky Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muliana, Ahmad Syakir Nabila Rizki Oktadini Oktadini, Nabila Rizky Pacu Putra Pibriana, Desi Putri, Dila Okta Dwi Rahmadiana, Reva Nur Richa Pratiwi Rizka Dhini Kurnia Rosdiyanah, Rosdiyanah Rusdy, Taufiqurrahman Sarifah Putri Raflesia Sarifah Putri Raflesia Sarifah Putri Raflesia Sarifah Putri Raflesia, Sarifah Putri Seprina, Iin Silfi Iriyani Siti Nurmaini Taufiqurrahman Rusdy Taufiqurrahman Taufiqurrahman Via Mariska, Inneke