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Perancangan Aplikasi Pengolahan Data Konsumen Pada Perusahaan Jukut Fotografi Darsiti Darsiti
Prosiding FRIMA (Festival Riset Ilmiah Manajemen dan Akuntansi) No 2 (2019): Prosiding FRIMA
Publisher : Sekolah Tinggi Ilmu Ekonomi STEMBI Bandung

Tujuan_Tujuan dari penelitian ini adalah untuk membuat aplikasi sistem pengolahan data konsumen yang mendukung proses pengolahan data konsumen baik dalam penginputan data maupun pencairan data konsumen sehingga bisa lebih efektif dan efisien. Desain/Metode_Pada penelitian ini peneliti menggunakan metode prototyping . Temuan_Proses pengolahan data konsumen sudah menggunakan sistem sehingga dalam pencarian data konsumen memerlukan waktu yang lebih singkat, serta dalam pembuatan laporan data konsumen sudah tidak terjadi kesalahan Implikasi_Untuk menjaga keamanan data konsumen, sebaiknya proses backup data dilakukan secara rutin, dan diharapkan adanya pengembangan sistem agar lebih optimal. Originalitas_Materi yang digunakan dalam penelitian ini adalah hasil dari wawancara dengan pengguna (objek penelitian) dan bersumber dari buku dan jurnal penelitian yang serupa. Tipe Penelitian_Studi Kasus

Pengembangan Model Sistem E-Library Studi Kasus AMIK HASS Darsiti .; Budiman .
SisInfo Vol 2 No 2 (2020): SisInfo
Publisher : Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (918.699 KB)

AMIK HASS Bandung is a private college that has an Informatics Management study program. AMIK HASS Bandung is divided into several parts of the work, one of which is the library section. The process of borrowing and searching books in this library is still done by recording and searching on the documents that have been provided so that the process of borrowing and searching books takes less time than the need for an application that helps the loan process and search books more effectively and the results of reports are quick, precise and accurate. In this study, the analysis and design of the system used the approach of object models namely Use Case Diagram, Activity Diagram, Sequence Diagram, and Class Diagram. In addition, the authors also used data collection techniques in the form of observations, interviews, and library studies related to this research and the software used is PHP and MySQL. Development results can assist the library in managing transaction data in the library.

Studi Kelayakan Bisnis Pengembangan Inovasi Varian Rasa Bakso Aci Pada Badan Usaha Tercabaikan Darsiti Darsiti; Asep Mahmudin; Cahya Putri Julyandaru; Putri Meilani Gustian; Salsa Sabila
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 4 No. 1 (2024): Maret : Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v4i1.2812

Tercabaikan is one of the MSMEs in the city of Bandung, West Java, which innovates the taste of Bakso Aci into a flavor that is favored by many people. Tercabaikan Company was first initiated by Mr. Inggra Dwipo Prayogo as the owner at this time in 2018 which is located at Jl. Sukagalih number 193 RT 05 / RW 06 Pasirjati Ujungberung, Bandung City, West Java. The purpose of this study was to test the feasibility of neglected business entities by adding variations to existing product flavors. Descriptive method, the techniques used are interviews and field observations on March 15, 2023. The results of this study indicate that the Tercabaikan business entity has the potential to be feasible to develop with innovation in Bakso Aci products. The feasibility analysis shows business sustainability with positive profit projections. However, it is important to continue to monitor and evaluate financial performance in accordance with the projections that have been generated. The feasibility results determined on this neglected business entity through a series of comprehensive observations carried out to obtain a feasible conclusion, especially in the financial elements of the neglected business entity.

Implementasi Website Company Profile Berbasis PHP dan Bootstrap untuk Optimalisasi Akses Informasi Pariwisata Fadlah Aulia; Darsiti Darsiti
Journal of Information Technology Vol 4 No 2 (2024): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v4i2.919

Aplikasi website company profile Kompepar Giri Harja merupakan sebuah platform yang dirancang untuk mengatasi masalah keterbatasan akses dan validasi data manual yang saat ini menghambat penyebaran informasi terkait destinasi wisata, jadwal acara, dan layanan. Dengan menggabungkan fitur-fitur yang disesuaikan untuk pengguna publik, pengguna terdaftar, dan admin, aplikasi ini bertujuan untuk memberikan pengalaman yang optimal bagi pengguna dalam mencari informasi tentang perusahaan, mengakses konten terkini, berinteraksi dengan formulir kontak, dan mengelola akun mereka. Metode penyelesaian masalah yang digunakan adalah implementasi website berbasis PHP dan Bootstrap, dengan memanfaatkan metode System Development Life Cycle (SDLC) Waterfall. Pendekatan ini memastikan proses pengembangan yang sistematis dan sekuensial. Hasilnya, aplikasi ini berhasil meningkatkan efisiensi penyebaran informasi secara signifikan, dengan studi menunjukkan peningkatan efisiensi hingga 40% dan peningkatan kepuasan pengunjung sebesar 35%. Sebagai kesimpulan, dengan fokus pada responsivitas, keamanan, dan manajemen konten yang efisien, aplikasi ini terbukti mendukung Kompepar Giri Harja dalam memperluas jangkauan, meningkatkan interaksi dengan masyarakat, dan mempromosikan pariwisata lokal secara efektif.

Studi Kelayakan Bisnis Pengembangan Inovasi Varian Rasa Bakso Aci Pada Badan Usaha Tercabaikan Darsiti Darsiti; Asep Mahmudin; Cahya Putri Julyandaru; Putri Meilani Gustian; Salsa Sabila
KREATIF: Jurnal Pengabdian Masyarakat Nusantara Vol. 4 No. 1 (2024): Maret : Jurnal Pengabdian Masyarakat Nusantara
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/kreatif.v4i1.2812

Tercabaikan is one of the MSMEs in the city of Bandung, West Java, which innovates the taste of Bakso Aci into a flavor that is favored by many people. Tercabaikan Company was first initiated by Mr. Inggra Dwipo Prayogo as the owner at this time in 2018 which is located at Jl. Sukagalih number 193 RT 05 / RW 06 Pasirjati Ujungberung, Bandung City, West Java. The purpose of this study was to test the feasibility of neglected business entities by adding variations to existing product flavors. Descriptive method, the techniques used are interviews and field observations on March 15, 2023. The results of this study indicate that the Tercabaikan business entity has the potential to be feasible to develop with innovation in Bakso Aci products. The feasibility analysis shows business sustainability with positive profit projections. However, it is important to continue to monitor and evaluate financial performance in accordance with the projections that have been generated. The feasibility results determined on this neglected business entity through a series of comprehensive observations carried out to obtain a feasible conclusion, especially in the financial elements of the neglected business entity.

A Comprehensive Machine Learning Approach for Predicting Beats Per Minute (BPM) in Music Using Audio Features Darsiti Darsiti
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.2

Predicting Beats Per Minute (BPM) in music is a significant challenge due to the complexity of the relationship between various audio features, such as rhythm, energy, and mood. Traditional methods are often unable to handle the complexity of feature variations and interactions. This study aims to develop a more accurate and reliable machine learning model to predict song BPM based on extracted audio features. We use advanced machine learning algorithms, including LightGBM, XGBoost, and Random Forest, to train models with a dataset covering ten audio features. Evaluation is performed using a k-fold cross-validation scheme with RMSE, MAE, and R² Score metrics. The experimental results show that boosting-based models such as LightGBM produce the best performance, with the lowest RMSE of 10.48, the lowest MAE of 7.62, and the highest R² Score of 0.83. However, these models still show a tendency to regress to the mean, indicating that some more extreme BPM variations are not fully captured. These findings emphasize the importance of improvements in feature engineering techniques and data rebalancing to improve BPM prediction accuracy in practical applications, such as music recommendation systems and tempo analysis.

An LSTM-Based Approach for Short-Term Solar Power Forecasting with Diurnal and Intra-Day Variability Darsiti Darsiti; Tarsinah Sumarni; Fahmi Abdullah; Budiman
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 2 (2026): BIMA January 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i2.7

The increasing penetration of solar photovoltaic (PV) systems into modern power grids demands accurate, reliable short-term power forecasting to ensure operational stability and efficient energy management. However, solar power generation exhibits strong nonlinearity, non-stationarity, and pronounced temporal dependencies, driven by diurnal cycles and rapid environmental variations, which pose significant challenges for conventional forecasting approaches. This study aims to develop an efficient Long Short-Term Memory (LSTM)-based framework for short-term DC power prediction that effectively captures the temporal dynamics of solar power generation while maintaining low computational complexity. The proposed approach utilizes historical power and operational data collected from two utility-scale solar PV plants in India. A comprehensive time-series preprocessing pipeline is applied, including temporal feature extraction, categorical transformation, and Min–Max normalization. Multiple LSTM architectures with varying numbers of hidden units are systematically evaluated to identify an optimal balance between model complexity and predictive performance. Model training is conducted using the Adam optimizer with exponential learning rate decay and early stopping to prevent overfitting. Experimental results demonstrate that the proposed LSTM model with a 25–50 unit configuration achieves the best performance, yielding a test Mean Squared Error of 51.92 and a prediction error of only 0.36%. Visual and quantitative analyses confirm that the model accurately reconstructs diurnal patterns and intra-day fluctuations, with strong generalization capability on unseen data. The findings indicate that a carefully configured LSTM can deliver high forecasting accuracy without relying on complex hybrid architectures or additional weather data, making it suitable for practical solar energy management applications.

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