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All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL ILMIAH INFORMATIKA JATI (Jurnal Mahasiswa Teknik Informatika) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Journal of Information Technology (JIfoTech) FRIMA: Festival Riset Ilmiah Manajemen & Akuntansi Jurnal Manajemen Informatika & Teknologi SisInfo : Jurnal Sistem Informasi dan Informatika Metta: Jurnal Penelitian Multidisiplin Ilmu Nuansa Informatika Scientica: Jurnal Ilmiah Sains dan Teknologi Faedah: Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia Journal of Islamic Studies Jurnal Riset Ilmiah TEKNO : Jurnal Penelitian Teknologi dan Peradilan Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat KREATIF: Jurnal Pengabdian Masyarakat Nusantara Bulletin of Intelligent Machines and Algorithms Manexia Jurnal Nasional Komputasi dan Teknologi Informasi
Darsiti Darsiti
Universitas Teknologi Digital
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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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

Abstract

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

Abstract

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.
Data Localization and Competitive Reconfiguration: Strategic Adaptation in Emerging Digital Economies Darsiti
Manexia: Journal of Business, Management, and Creative Economy Vol. 1 No. 3 (2025): Governing Digital Markets: Structure and Power
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.01304

Abstract

Data localization has emerged as a defining feature of digital governance in emerging economies, reshaping the institutional environment within which firms compete. While existing research largely frames data localization as a compliance burden or trade barrier, its strategic implications for competitive positioning remain underexplored. This article develops a mechanism-based conceptual framework explaining how data localization induces competitive reconfiguration through four interrelated mechanisms: cost asymmetry creation, reinforcement of territorial embeddedness, architectural modularization, and adaptive capability differentiation. Drawing on institutional theory, international business scholarship, dynamic capabilities, and platform ecosystem research, the study argues that localization policies transform data from a globally scalable resource into a jurisdiction-bound strategic asset. This territorialization alters location advantages, redefines scalability logic from global integration to regional clustering, and differentiates firms based on infrastructural flexibility and adaptive capacity. Competitive outcomes are therefore conditional rather than deterministic: firms with strong domestic embeddedness and high dynamic capabilities are better positioned to convert regulatory segmentation into strategic advantage, whereas centrally integrated and rigid architectures face heightened erosion risks. By reframing data localization as a driver of competitive reordering rather than mere regulatory constraint, the article advances understanding of how institutional boundary-making reshapes digital market dynamics in emerging economies.
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

Abstract

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.
YOLO26n-Based Apple Leaf Disease Detection for Precision Agriculture Using Lightweight Deep Learning and Object Detection Darsiti Darsiti; Budiman; Dhika Wdiyanto; Tarsinah Sumarni
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.22

Abstract

Early detection of apple leaf diseases is a critical factor in supporting agricultural productivity and minimizing losses caused by plant disease outbreaks. However, manual identification processes still have limitations in terms of accuracy, consistency, and time efficiency. This study aims to develop an apple leaf disease detection model based on object detection using YOLO26n to identify four main classes: Apple__BlackRot, Apple__CedarRust, Apple__Healthy, and Apple__Scab. The dataset was obtained from Kaggle in YOLO format, consisting of 2,754 training images and 687 validation images. The study employs a transfer learning approach with various data augmentation techniques, such as mosaic, mixup, copy-paste, rotation, translation, and HSV transformation, to enhance the model’s generalization ability. Evaluation was conducted using the Precision, Recall, mAP50, and mAP50-95 metrics. The results showed that the YOLO26n model achieved a Precision of 0.968, a Recall of 0.887, an mAP50 of 0.958, and an mAP50-95 of 0.880. The best performance was achieved on the Apple__BlackRot class with an mAP50-95 value of 0.987. The inference results also show that the model is capable of accurately localizing diseases through bounding boxes with a high level of confidence. These findings indicate that YOLO26n has great potential as an efficient and accurate lightweight model for the implementation of real-time precision agriculture-based plant disease detection systems.
Implementasi Aplikasi Laporan Kegiatan Magang Berbasis Web pada Bidang Pelayanan Perpustakaan dan Kearsipan Dispusipda Jawa Barat Nanda Alia Destiara; Darsiti; Mamok Andri Senubekti
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/pvw42x38

Abstract

Abstrak - Pengelolaan laporan kegiatan magang pada Bidang Pelayanan Perpustakaan dan Kearsipan (PPK) Dispusipda Jawa Barat sebelumnya masih menggunakan Google Form sehingga proses pencatatan dan rekap laporan kurang efektif. Penelitian ini bertujuan merancang dan membangun aplikasi laporan kegiatan magang berbasis web untuk memudahkan peserta magang dalam melakukan pelaporan harian serta membantu admin dalam mengelola data laporan. Metode pengembangan sistem yang digunakan adalah Rapid Application Development (RAD) karena mampu mendukung proses pengembangan sistem secara cepat sesuai kebutuhan pengguna. Aplikasi dibangun menggunakan PHP dan MySQL. Hasil penelitian menunjukkan bahwa sistem yang dibangun dapat membantu proses pelaporan menjadi lebih rapi, terstruktur, dan mudah dikelola. Selain itu, sistem dapat membantu admin memantau aktivitas peserta magang serta menghasilkan laporan dalam format PDF dan Excel secara lebih cepat dan efisien. Berdasarkan hasil pengujian Black Box Testing, seluruh fitur utama sistem dapat berjalan sesuai dengan fungsi yang diharapkan. Kata kunci : Digitaslisasi; Laporan Magang; Website; Rapid Application Development (RAD); Black Box Testing; Abstract - The management of internship activity reports at the Library and Archives Service Division (PPK) of Dispusipda West Java previously still used Google Forms, making the recording and report recapitulation process less effective. This study aims to design and develop a web-based internship activity reporting application to facilitate interns in submitting daily reports and assist administrators in managing report data. The system development method used is Rapid Application Development (RAD) because it supports rapid system development according to user requirements. The application was developed using PHP and MySQL. The results show that the system can make the reporting process more organized, structured, and easier to manage. In addition, the system helps administrators monitor internship activities and generate reports in PDF and Excel formats more quickly and efficiently. Based on Black Box Testing results, all main system features function properly according to the expected requirements. Keywords: Digitalization; Internship Report; Website; Rapid Application Development (RAD); Black Box Testing;
Co-Authors Abdurrazaq, Muhammad N. Adinda Salsabila Ahmad Asrof Fitri Andri Maulana Yusuf Asep Mahmudin Ashari, Salma Budiman Cahya Putri Julyandaru Dhika Wdiyanto Doni Ramdani Fadlah Aulia Fadzhar Rusghana Fahmi Abdullah Fauzi Amelia, Widya Fujiwati Lestari, Dina Sari Gufron Siregar, Alfat Ikbal Saepul Ramdan Ilma, Ilma Amalia Jaza Iqlima Octavianidori M. Randi Rukmana Mamok Andri Senubekti Maulina, Tiara Putri Maya Safitri Meilani Purwanti Muhamad Nazar Fadilah Muhammad, Thomas Lazuardi Murniati Murniati Naila Syafitri Nanda Alia Destiara Novita Anggraini Oktaviani, Defani Putri Meilani Gustian Ridha Adjie Eryadi Robansah, Sendi Rusghana, Fadzar Sadiah Permata Sari Seanno Shaggy Irawansyah Shafar, Muhammad Shafar Alfarizi Surya Kurnia Alamsyah Tarsinah Sumarni Widi Aulia Lestari Wilda Jamila Suryaman Winda Sulastri  Salsa Sabila