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INDONESIA
Jurnal Bangkit Indonesia
Published by Sekolah Tinggi Teknologi Indonesia Tanjung Pinang
ISSN : 23374055     EISSN : 27769267     DOI : https://doi.org/10.52771/bangkitindonesia
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Ruang lingkup Bangkit Indonesia adalah sebagai berikut : Domain Specific Frameworks and Applications IT Management dan IT Governance e-Government e-Healthcare, e-Learning, e-Manufacturing, e-Commerce ERP dan Supply Chain Management Business Process Management Smart Systems Smart City Smart Cloud Technology Smart Appliances & Wearable Computing Devices Robotic Systems Smart Sensor Networks Information Infrastructure for Smart Living Spaces Intelligent Transportation Systems Models, Methods and Techniques Conceptual Modeling, Languages and design Software Engineering Information-centric Networking Human Computer Interaction Media, Game and Mobile Technologies Data Mining Big Data Information Retrievel Information Security Image Processing and Pattern Recognition Remote Sensing Natural Language Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 205 Documents
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Implementasi Algoritma Winnowing untuk Deteksi Kemiripan Proposal Skripsi pada Sistem Informasi KP dan Skripsi (SIKPS) STMIK Bandung Rahayu, Mina Ismu; Hidayatullah, Ardiansyah Putra
Jurnal Bangkit Indonesia Vol 14 No 1 (2025): Bulan Maret 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i1.442

Abstract

The Winnowing algorithm is used to identify similar words/sentences (common subsequences) in two or more compared texts. This algorithm converts the text's N-grams into hash values known as fingerprints. This study aims to design and implement a web-based thesis proposal similarity detection system at STMIK Bandung using the Winnowing algorithm. STMIK Bandung provides an online thesis title submission facility through the SIKPS website. However, students often submit identical titles without realizing that these titles have already been submitted. The developed system will assist students in detecting the similarity level of their submitted thesis proposals with existing proposals in the SIKPS website database.
Analisis Kualitas Layanan Website LMS STMIK Bandung Terhadap Kepuasaan Pengguna Menggunakan Metode Webqual 4.0 dan Importance Performance Analysis (IPA) Versanika, Dayanni Vera; Salsabila, Nadiva
Jurnal Bangkit Indonesia Vol 14 No 1 (2025): Bulan Maret 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i1.443

Abstract

In the digital era, technological innovation plays a significant role in the development of various sectors, including education. This study aims to measure the level of user satisfaction among students and to identify the dimensions of Webqual 4.0 and Importance Performance Analysis (IPA) that influence the quality of the Learning Management System (LMS) website at STMIK Bandung. The methodology employed includes Webqual 4.0 and Importance Performance Analysis (IPA). A quantitative approach using questionnaires was applied to students who utilize the website. Data was collected through questionnaires with variables of usability, information quality, and service interaction quality, comprising 22 indicators. Importance Performance Analysis (IPA) was used to identify indicators that require improvement or maintenance based on user performance and expectations. The results of this study reveal an average gap of 0 between the expectations and performance of the LMS website at STMIK Bandung, indicating that the service quality meets expectations. Nevertheless, certain indicators such as USB1 and USB2 exceed user expectations. The hypothesis linking website quality to user satisfaction is accepted, particularly as the variables USB and SIQ demonstrate a significant correlation with user satisfaction. The Webqual Index (WQI) indicates that the website is categorized as good, with a score of 0.87 (87%). Through Importance-Performance Analysis, it is recommended to focus on USB8. The practical implications of this research provide an overview of the service quality of the LMS website at STMIK Bandung and can serve as a guideline for the university administration to enhance the quality of the existing academic platform.
Analisa Metode Strenght Weaknesess Opportunities Threats (SWOT) di Pasaraya Bintan 21 Rahayu, Vita; Daryopi, Resti; Saputra, Fanny
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.404

Abstract

This study aims to analyze the state of Pasaraya Bintan 21 using the Strengths, Weaknesses, Opportunities, and Threats (SWOT) method. This method is used to identify internal and external factors affecting the company’s operations that influence the success of the marketplace in competing in the retail industry. Data were obtained through interviews, observations, and relevant document analysis. The research results show that Pasaraya Bintan 21 has several strengths, such as a strategic location and a diverse range of products, but also faces weaknesses in terms of promotion and innovation. Opportunities for growth are wide open with the increase in population and consumer interest in local products, while threats include intense competition and changing shopping trends.
Prediksi Gagal Jantung Berbasis Deep Learning dengan Algoritma Long Short Term Memory Atho’illah, Ibnu; Emang Smrti, Ni Nyoman; Madani, Annisa Fitri; Sukenada Andisana, I Putu Gd
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.436

Abstract

Heart failure is one of the leading causes of death in the world. Early detection and accurate analysis are essential for proper treatment. This study proposes the use of Long Short-Term Memory (LSTM) algorithm to analyse and predict the progression of heart failure disease based on patient medical data. The LSTM model developed uses the Python platform with TensorFlow and Keras libraries, as well as the “Heart Failure Prediction” dataset from Kaggle.com. The results showed that the LSTM model with training and testing data ratio of 70:30 (Model B) achieved the best performance with accuracy of 0.869, precision of 0.869, recall of 0.869, and F1-score of 0.869. The model showed consistent ability in identifying positive and negative cases of heart failure and was effective in reducing overfitting. Overall, this research contributes to the development of more accurate and efficient heart failure disease prediction methods.
Website Development as an Information and Material-Sharing Platform for Fiqh of Worship Kartaputra, Dani Pradana; Nugraha, Muhmmad Ramdani; Lukman Nugraha, Ahmad
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.439

Abstract

Majlis Ta'lim Petuah Bandung, established in 2021, faces challenges in expanding its da'wah reach and improving the efficiency of information management. Currently, they rely on conventional methods such as face-to-face lectures and printed materials, which are limited in reach and accessibility. In the contemporary era of globalization, digitalization plays a pivotal role in the distribution and accessibility of information. Study conducted by other researchers shows that digital platforms have positive results in the propagation of religious education. By leveraging a website as a dedicated da’wah platform, Islamic teachings can be systematically organized, efficiently disseminated, and easily retrieved, overcoming the limitations associated with traditional da’wah methods. In this study, we propose to developing a website as an information platform and a medium for sharing Islamic study materials. This website allows access to study materials anytime and provides discussion features to enhance interaction. Utilizing the Rapid Application Development (RAD) method and technologies such as HTML, CSS, JavaScript, PHP, and MySQL, the website is designed to be user-friendly and tailored to the needs of the community. The results of our study indicate that the website we developed has successfully addressed the challenges of da'wah in the era of globalization by utilizing a website as a da'wah platform.
Transformasi Digital Pengelolaan Masjid Berbasis Inovasi Sistem Informasi dan Regulasi Nasional: Studi Evaluatif terhadap Aplikasi Menara Masjid BAZNAS Kusuma, Muhammad Romadhona
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.447

Abstract

Effective and efficient mosque management is a key factor in ensuring operational continuity and enhancing the quality of services provided to congregants. In line with the advancement of the digital era, the adoption of information technology in mosque governance has become an urgent necessity to support transparency, accountability, and administrative efficiency. This study aims to evaluate the effectiveness, transparency, as well as the innovative and regulatory aspects of the Menara Masjid Application developed by BAZNAS RI. The research method employed is exploratory in nature, involving literature review, field observation, and primary data collection from mosque administrators using the application. The results indicate that the application represents a community-based information system innovation that facilitates the management of congregational data, reporting of zakat, infaq, and sadaqah (ZIS), and real-time dissemination of activity information. In addition to promoting internal efficiency and transparency, the application is supported by national regulations through BAZNAS Chairman Regulation No. 005 of 2024, which reinforces its broad legitimacy and adoption. These findings affirm that digitalization through innovative and regulation-based information systems plays a strategic role in establishing modern, participatory, and sustainable mosque governance.
Deteksi Karakter Aksara Jawa Menggunakan YOLO11 Pendekatan Deep Learning untuk Pelestarian Warisan Budaya Digital Darmawan, Eko Rahmad; Ariatmanto, Dhani
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.450

Abstract

Javanese script represents a significant cultural heritage of the Indonesian archipelago that faces extinction threats due to Latin alphabet dominance and minimal users capable of writing with this traditional script. This research aims to develop a Javanese character detection system using You Only Look Once version 11 (YOLO11) algorithm to support cultural preservation efforts through efficient digitalization. The research methodology employs an experimental approach with deep learning, where the Javanese script dataset consisting of 20 basic characters plus background class was obtained from Kaggle and preprocessed using Roboflow with data augmentation techniques. The YOLO11 model was implemented with SGD optimizer, 640px image size, and trained for 500 epochs to achieve optimal convergence. YOLO11 architecture integrates advanced components such as C3K2 blocks, Spatial Pyramid Pooling-Fast (SPPF), and Cross-scale Pixel Spatial Attention (C2PSA) to enhance multiscale feature extraction capabilities. Model performance evaluation utilized confusion matrix with accuracy, precision, recall, and F1-score metrics. Research results demonstrate that the YOLO11 model achieved an overall accuracy of 81.00% with macro-averaged precision of 86.28%, macro-averaged recall of 87.25%, and macro-averaged F1-score of 86.41%. Model performance distribution shows 7 classes with high performance (F1-score ≥ 90%), 9 classes with medium performance (80-90%), and 4 classes with low performance (<80%). The "nga" class achieved perfect performance of 100%, while the "ha" class showed the lowest performance with an F1-score of 68.09%. This research successfully improved accuracy compared to previous methods using backpropagation neural networks (74%) and conventional backpropagation (59.5%), although challenges remain in detecting characters with similar shapes and handling background class. The main contribution is the first implementation of YOLO11 for Javanese script detection, opening opportunities for developing more efficient and accurate ancient literature digitalization systems.
Perbandingan Metode Ekstraksi Fitur LBP, GLCM, dan Canny dalam Klasifikasi Penyakit Daun Padi dengan KNN Jordy, Roy; Ariatmanto, Dhani
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.452

Abstract

Accurate and timely identification of rice leaf diseases plays a crucial role in supporting early disease control efforts in agriculture. This study aims to compare the performance of three image feature extraction methods—Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Canny Edge Detection—in classifying three types of rice leaf diseases: Bacterial leaf blight, Brown spot, and Leaf smut. Each method was evaluated based on its confusion matrix as well as key performance metrics, including accuracy, precision, recall, and F1-score. Experimental results show that LBP achieved the highest classification performance with an accuracy of 92.06%, followed by GLCM at 78.57% and Canny at 66.67%. In addition to accuracy, LBP also outperformed the other methods across all evaluation metrics. These findings indicate that the local texture features captured by LBP are more effective in distinguishing disease types compared to the global texture features from GLCM and edge-based features from Canny. Therefore, LBP is recommended as a superior feature extraction method for automated classification systems of rice leaf diseases based on digital imagery.
Business Process Model And Notation Untuk Memodelkan Proses Pengingat Pinjaman Pada Koperasi Diamanta, David; Muhammad, Alva Hendi
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.458

Abstract

Savings and loan cooperatives are strategic microfinance institutions facing challenges in managing loan reminder processes. XYZ Savings and Loan Cooperative operates manual reminder processes without standard documentation, creating risks of human error and operational inefficiency. This study aims to design Business Process Model and Notation (BPMN) to model and standardize loan reminder processes at XYZ Savings and Loan Cooperative. The research employed a qualitative approach with descriptive analytical methods. Data collection was conducted through direct observation for one month, interviews with the Secretary Department Cooperative Employee, and internal document studies. Business process analysis was performed to understand existing workflows, then modeled into BPMN elements using Bizagi Modeler software. Model validation was conducted through structured questionnaires with 20 validation aspects. BPMN model was successfully designed with two main scenarios namely Friday reminder process as the main process and Monday reminder process with follow-up mechanisms. The model involves three main actors (Cooperative Members, Cooperative Employees, and Cooperative Head) with clear swimlane divisions. The process starts from attendance checking, WhatsApp messaging, phone calls, to coordination for direct visit scheduling. Validation shows perfect conformity of 100% from 20 evaluated aspects. The BPMN model successfully transformed manual processes without documentation into structured and standardized visualization. The study concludes that BPMN implementation can effectively standardize previously manual and undocumented loan reminder processes, producing standard documentation that can be implemented for procedure standardization and new employee literacy, thereby improving operational effectiveness and reducing human error risks in cooperative loan reminder processes.
Analisis Perbandingan Kinerja Model YOLO11 dan YOLOv8 dalam Identifikasi Penyakit pada Daun Tomat Majid, Muhammad Arif Kholis; Ariatmanto, Dhani
Jurnal Bangkit Indonesia Vol 14 No 2 (2025): Bulan Oktober 2025
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52771/bangkitindonesia.v14i2.459

Abstract

Diseases on tomato leaves can reduce the quality and quantity of agricultural yields, as well as affect market prices. This study compares the effectiveness of the YOLO11 and YOLOv8 models in detecting diseases on tomato leaves with traditional CNN-based models such as VGG-16 and Inception-V3. The results show that the YOLO11 model provides the best accuracy of 99.4%, followed by YOLOv8 with 98.5%, both excelling in real-time detection. CNN-based models like VGG-16 and Inception-V3 have high accuracy (99% and 93.8%), but are slower in computation. The ensemble model of VGG-16 and NASNet Mobile achieves an accuracy of 98.7%, but is slightly lower than YOLO11. The YOLO model is more efficient in detection speed, making it a better choice for field applications. This study shows that YOLO11 offers the best combination of accuracy and detection speed for a real-time plant disease detection system.

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