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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) 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 EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Informatika (JUPITA) Jurnal Informatika: Jurnal Pengembangan IT
Dinda Lestarini
Fakultas Ilmu Komputer Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Humanities 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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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.
Analisis Sentimen Ulasan Pengguna Aplikasi Sociolla Menggunakan Algoritma Support Vector Machine dengan Optimasi Grid Search Fitriani, Suci; 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.v7i3.8686

Abstract

The rapid growth of digital technology has driven innovation in the beauty industry, one of which is the Soco by Sociolla platform that provides online product reviews. The increasing number of user reviews offers opportunities for conducting sentiment analysis to understand users’ perceptions of service quality. The main challenge in modeling sentiment for beauty product reviews lies in the use of highly varied, subjective, and informal language, which results in diverse distribution patterns. Therefore, this study not only applies the Support Vector Machine (SVM) algorithm for sentiment classification but also compares two kernels—Linear and Radial Basis Function (RBF)—and evaluates the effect of hyperparameter optimization using Grid Search in the context of beauty e-commerce data. A total of 3,387 reviews were collected and processed through several stages, including text preprocessing, labeling, feature extraction using TF-IDF, data splitting, model training, and evaluation. The results show that the baseline RBF kernel provides the best performance with an accuracy of 88.5%, while the baseline Linear kernel achieves an accuracy of 87.76%. Meanwhile, Grid Search optimization produces an accuracy of 86.22%, indicating that the explored hyperparameter configurations were unable to exceed the performance of the RBF baseline despite delivering stable results during cross-validation. These findings suggest that the linguistic characteristics of beauty reviews are more effectively addressed by non-linear kernels, making them superior to Linear kernels in recognizing non-linear patterns within user review data. Furthermore, the results indicate that hyperparameter optimization does not always lead to increased model accuracy, particularly when the baseline SVM configuration is already performing near optimally for the characteristics of the dataset used.
Peningkatan literasi digital petani melalui sosialisasi pemanfaatan teknologi digital di Desa Cempaka Ogan Komering Ulu (OKU) Timur Nabila Rizky Oktadini; Naretha Kawadha Pasemah Gumay; Anna Dwi Marjusalinah; Allsela Meiriza; Dinda Lestarini; Dinna Yunika Hardiyanti; Sarifah Putri Raflesia
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November
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.
Pemberdayaan Ekonomi Lokal melalui Pelatihan Toko Online bagi UMKM di Desa Sungai Rebo Lestarini, Dinda; Novianti, Hardini; Raflesia, Sarifah Putri; Ariani, Ardina; Kurniati, Junia; Willy
Jurnal Pengabdian Informatika Vol. 4 No. 2 (2026): JUPITA Volume 4 Nomor 2, Februari 2026
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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

Abstract

Transformasi digital menjadi salah satu kunci dalam pengembangan usaha mikro, kecil, dan menengah (UMKM), terutama di wilayah pedesaan. Namun, masih banyak pelaku UMKM yang belum memiliki keterampilan dasar dalam memanfaatkan teknologi digital, khususnya dalam hal pemasaran produk secara daring. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Desa Sungai Rebo, Kabupaten Banyuasin I, dengan tujuan untuk meningkatkan literasi digital pelaku UMKM melalui pelatihan pembuatan toko online. Metode yang digunakan meliputi penyampaian materi, demonstrasi, dan pendampingan praktik langsung. Fokus utama pendampingan adalah pembuatan akun email sebagai syarat utama pendaftaran, serta pembuatan akun toko online menggunakan platform Shopee sebagai media penjualan. Kegiatan ini diikuti oleh 15 peserta yang seluruhnya merupakan pelaku UMKM di desa tersebut. Hasil pelatihan menunjukkan bahwa seluruh peserta berhasil membuat akun email dan akun toko online dengan bimbingan dari tim pelaksana. Sebagian besar peserta juga telah berhasil mengunggah produk ke toko masing-masing. Antusiasme peserta sangat tinggi, dan kegiatan ini dinilai efektif dalam memberikan keterampilan digital dasar yang aplikatif. Kegiatan ini menjadi langkah awal yang penting dalam mendukung transformasi digital UMKM desa dan membuka peluang pemasaran yang lebih luas melalui platform daring.
Pemanfaatan Teknologi Augmented Reality dengan Marker-Based Tracking sebagai Media Pengenalan Kabupaten Muara Enim Adeliani, Adeliani; Lestarini, Dinda; Seprina, Iin
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10051

Abstract

The development of digital technology has increased the demand for more interactive information media, including those used to present regional potential. Muara Enim Regency is rich in culture, industry, and tourism, all of which need to be introduced through more engaging media for both younger generations and the wider community. This study aims to develop an Augmented Reality–based application for introducing Muara Enim Regency using the Marker-Based Tracking method as a response to the need for more immersive and accessible information media. The development process follows the Multimedia Development Life Cycle (MDLC) method, which includes the Concept, Design, Material Collecting, Assembly, Testing, and Distribution phases. The application is implemented using Unity and Vuforia, integrating 3D objects, information panels, and an interactive quiz feature. Functional testing through Black-box Testing shows that all features operate according to specifications without significant issues. User Acceptance Testing (UAT) produced results categorized as very good, indicating that the application is positively received in terms of operational ease, informational clarity, stability, and interaction experience. Therefore, this application is considered suitable as an alternative medium for introducing the potential of Muara Enim Regency and has promising opportunities for further development through additional content and enhanced interactivity.
Beyond Traditional Models: Integrating EUCS, SERVQUAL, and UEQ in SeaBank Nabilatulrahmah, Raihana; Lestarini, Dinda; Seprina, Iin; Wedhasmara, Ari; Hardiyanti, Dinna Yunika
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34297

Abstract

Research on digital banking satisfaction remains conceptually fragmented, as End-User Computing Satisfaction (EUCS), SERVQUAL, and User Experience Questionnaire (UEQ) reflect distinct evaluative paradigms that often produce inconsistent results when applied independently. This study develops and validates an integrated model capturing informational, service, system, and experiential determinants of satisfaction in a branchless digital banking context. Data from 212 active SeaBank users were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a Two-Stage approach. The model explains 76.5% of the variance in user satisfaction. User experience is the strongest predictor, followed by system quality and information quality, while service quality is not statistically significant. The findings indicate an experience-centric evaluation structure, where affective and system-related factors outweigh traditional service dimensions. This study provides a unified empirical validation of EUCS, SERVQUAL, and UEQ, clarifying their hierarchical relationships and indicating the reduced relevance of service quality in fully digital environments. Practically, the results highlight the importance of UI/UX optimization, system reliability, and experiential design for enhancing user retention. The Gen Z-dominated sample and cross-sectional design limit broader generalization, suggesting the need for longitudinal and cross-context validation.
Deep learning for mental health analysis: long short-term memory approach to text-based condition classification Yamani, Zaqqi; Lestarini, Dinda; Raflesia, Sarifah Putri; Sari, Purwita; Athalina, Ghita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1762-1770

Abstract

The increasing prevalence of mental health disorders highlights the need for scalable and automated approaches to early detection. This study proposes a deep learning–based text classification framework using a long short-term memory (LSTM) network to identify mental health conditions from user generated textual data. A corpus of 103,488 labeled texts representing anxiety, stress, bipolar disorder, depression, personality disorder, suicidal ideation, and normal states was preprocessed through tokenization, padding, and word embedding. The proposed LSTM model achieved overall accuracy of 87% on test set, with strong class-wise performance reflected by precision, recall, and F1-scores, particularly for anxiety, personality disorder, and normal classes. Comparative error analysis using a confusion matrix revealed challenges in distinguishing depression from suicidal ideation, indicating semantic overlap between these conditions. The results demonstrate that LSTM-based models can effectively capture sequential linguistic patterns relevant to mental health classification. This framework shows potential as a decision-support tool for early screening and digital mental health applications, complementing clinical assessment rather than replacing it.
Co-Authors Adelia Azahra Adelia Rizki Putri Adeliani, Adeliani Ahmad Rifai Alfarizi, Muhammad Ali Bardadi Allsela Meiriza, Allsela Andesta Angelina Anna Dwi Marjusalinah Ari Wedhasmara Ariani, Ardina Athalina, Ghita Darmawahyuni, Annisa Darwin, Amelia Desty Rodiah Dinna Yunika Hardiyanti Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Fransiska Prihatini Sihotang, Fransiska Hardini Novianti Hardini Novianti Iriyani, Silfi Irwansyah, Muhammad Aziiz Junia Kurniati, Junia Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Muhammad Azmi Zaky Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muliana, Ahmad Syakir Nabila Rizki Oktadini Nabilatulrahmah, Raihana Naretha Kawadha Pasemah Gumay Oktadini, Nabila Rizky Pacu Putra Pibriana, Desi Purwita Sari, Purwita 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 Suci Fitriani, Suci Taufiqurrahman Rusdy Taufiqurrahman Taufiqurrahman Via Mariska, Inneke Willy Yamani, Zaqqi