cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yuhefizar
Contact Email
ephi.lintau@gmail.com
Phone
+628126777956
Journal Mail Official
ephi.lintau@gmail.com
Editorial Address
Jalan Jati Padang Raya No. 41 Jati Padang Pasar Minggu Jakarta Selatan Kode Pos 12540
Location
,
INDONESIA
Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : -     EISSN : 25973584     DOI : -
Core Subject : Science,
Computer Science & IT
Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) merupakan ajang pertemuan ilmiah, sarana diskusi dan publikasi hasil penelitian maupun penerapan teknologi terkini dari para praktisi, peneliti, akademisi dan umum di bidang sistem informasi dan teknologi dalam artian luas.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 472 Documents
 36   37   38   39   40 
Prototipe Sistem Deteksi Dini Banjir Menggunakan Maket Aliran Sungai dan Sensor Ketinggian Air Berbasis Internet of Things Hasiri, Ery Muchyar; Asniati, Asniati; Fahmi, Fahmi
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Floods are a natural phenomenon that occurs when water from rivers, lakes, or seas exceeds its boundaries and inundates surrounding areas, posing threats to humans and animals and causing environmental and infrastructure damage. This study aims to develop an effective early flood detection tool for river areas utilizing IoT technology. The research methodology begins with identifying needs through problem formulation and a literature review on the latest flood detection technologies and the application of IoT in environmental monitoring systems. The designed system involves integrating sensors to monitor flood parameters such as water level and rainfall, with data transmitted in real-time to an IoT-based monitoring center. Development includes designing data analysis algorithms and developing hardware (resistive sensors, NodeMCU) and Android software. Implementation involves installing sensors at strategic locations and testing the system to validate the accuracy and performance of early warnings. System response evaluation is conducted through flood simulations and real conditions, with results disseminated to authorities and the public. Using river coastal models in the simulation system facilitates understanding of field conditions and serves as an educational tool to raise public awareness. On September 17-20, 2023, testing was conducted on the early flood detection simulation tool and the river flow model using the IoT system. Test results indicated that water levels at all locations had a minimum value of 0 cm and a maximum of 10.73 cm on September 18, 2023. Location 4 consistently showed the presence of flooding. This data integration provides a strong basis for authorities in flood disaster planning and management, enabling a faster and more effective response to detected conditions.
Sistem Pendukung Keputusan untuk Penentuan Klasifikasi Status Hotel Asrul, Billy Eden William; Zuhriyah, Sitti; Anatasya, A. Edeth Fuari
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

The Hotel industry is a key component of the tourism sector that is required to obtain business certification. This certification determines the star classification of a hotel. The determination of 1, 2, 3, 4, and 5-star classifications for hotels, particularly in the city of Makassar, involves the Tourism Business Certification Agency (LSUP) as the auditing body, the Makassar City Tourism Office as the regulatory body responsible for supervision and guidance, and the industry as the auditee. The conventional and manual processes for determining star ratings reduce the effectiveness and efficiency of audit implementation, as well as hinder the supervision of business certification by the Makassar City government.
Bibliometrik Analisis: Utilization of Machine Learning Technology in the Management of Healthcare Database System Suarna, Nana
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Healthcare databases store various types of data, including patient records, medical imaging, and real-time monitoring data. Efficient data management is crucial for improving patient outcomes and operational efficiency. Traditional methods face limitations in terms of scalability, data heterogeneity, and real-time processing. The main challenge in healthcare database management is the ability to efficiently process and analyze large volumes of heterogeneous data. Existing systems struggle with scalability, data integration, and real-time analytics, leading to delays in decision-making and potential errors in patient care. Methodology this research uses machine learning algorithms to enhance the performance and capabilities of healthcare database systems. Techniques such as data mining, predictive analytics, and anomaly detection are applied to optimize data storage, retrieval, and analysis processes. A comparative analysis is conducted between traditional database management systems and ML-enhanced systems to evaluate improvements in efficiency, accuracy, and scalability. The main objective is to demonstrate how ML can be leveraged to overcome existing challenges in healthcare database management. This includes improving data processing speeds, enhancing data integration from various sources, and enabling real-time analytics for better clinical decision-making. Results the findings show that the integration of ML technology significantly enhances the performance of healthcare database systems. The ML-enhanced systems demonstrated improved scalability, faster data retrieval, and more accurate predictive analytics compared to traditional systems. These improvements facilitate timely and informed decision-making in clinical settings, ultimately leading to better patient outcomes.
Transformasi Sistem Informasi Akuntansi: Optimalisasi Efisiensi dengan AI dan Keamanan Siber Sekti, Binastya Anggara; Ramadhan, Akmil Maulana
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Understanding the value of an effective and efficient Accounting Information System
Representasi Pengetahuan Sistem Pakar Menggunakan Struktur Data Redis Sammir, Haddad; Hamdi, Khairil; Sunaryo, Budi
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

This research discusses knowledge representation in expert systems using the Redis data structure, with a focus on the knowledge-based inference process. Inference in an expert system involves comparing existing facts with an existing database. When the elements in the rule match the database, the rule is considered TRUE and processed further in the database. For efficiency and speed in the inference process, this research utilizes the Redis data structure, especially the "sets" data structure type, which allows set operations to be carried out quickly and effectively. This research also suggests a convention for writing keys and values in Redis so that data consistency and integrity is maintained. Through this implementation, it is hoped that the expert system can process and make decisions more efficiently, utilizing Redis' advantages in set-based data management and access.
Pengenalan Pola pada Batik Lontara berbasis Kecerdasan Buatan Mohammad Yazdi Pusadan; Fuad Mahfud; Anisa Yulandari; Sabarudin Saputra
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Batik is an Indonesian cultural heritage in which almost every region has its own distinctive batik with diverse motifs. UNESCO designated batik as a world cultural heritage created by the Indonesian people in 2009. In South Sulawesi, there is also batik called Batik Lontara. Batik Lontara itself is a type of Bugis-Makassar batik unique to South Sulawesi that features motifs of the Lontara script. The purpose of this research is to implement the extraction of woven Batik Lontara and stamped Batik Lontara using the GLCM (Gray Level Co-occurrence Matrix) method and the KNN (K-Nearest Neighbor) algorithm to recognize the types of Batik Lontara. The Gray Level Co-occurrence Matrix (GLCM) is a feature extraction method that uses second-order texture calculations, considering pairs of two pixels from the original image. This research employs the K-Nearest Neighbor (KNN) algorithm, which is a method for classifying objects based on training data with the closest distance to the test data. The research material used is images of Batik Lontara with various motifs, namely woven Batik Lontara and non-woven Batik Lontara. Based on the Batik Lontara images, a process of converting the images from RGB to Grayscale will be carried out. The expected output of this research is a reputable international journal publication.
Analisis Kualitas Layanan Aplikasi ASN-G Kabupaten Tangerang Dengan Metode Servqual Muhammad Ridwan Maulana; Qori Halimatul Hidayah
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

This study aims to evaluate the quality of the ASN-G (Aparatur Sipil Negara Gemilang) application service used by the Tangerang Regency Housing, Settlement, and Cemetery Agency using the SERVQUAL method. The five main dimensions of service quality analyzed include Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Data collection was conducted through the distribution of an online questionnaire using Google Forms to 60 civil servants as respondents, and the data was analyzed descriptively using SPSS software for validity and reliability testing of the instrument. The results of the study indicate that all dimensions have a negative gap with an average gap of -0.5927, indicating that the application's performance has not been able to meet user expectations. The most problematic dimensions are Empathy (gap -0.753) and Responsiveness (gap -0.6467), with the weakest attributes being device compatibility (TA_4), guidance for new users (EM_3, gap -1.20), and application performance consistency (RE_1, gap -1.13). The main identified barriers are technical issues such as connection timeouts (reported by 37% of respondents) and insufficient guidance for older users (43.33% of respondents are over 50 years old). Based on these findings, immediate improvements are needed in the application's user interface, system response speed, and the provision of more adaptive technical support. Strategic recommendations include adding interactive video-based or icon-based guidance.
Analisis Citra MRI Untuk Deteksi Tumor Otak Menggunakan Random Forest Muhammad Akmal; Muhamad Fajar Al Muslih; Sendi Agung Setiyadi; Taufik
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Magnetic Resonance Imaging (MRI) is one of the most widely used imaging modalities for diagnosing brain abnormalities due to its high spatial resolution and ability to visualize soft tissues in detail. However, manual interpretation of MRI images is time-consuming and subjective. This research aims to analyze the performance of the Random Forest algorithm in detecting brain tumors from MRI images. The dataset used consists of 253 brain MRI images obtained from the Kaggle Brain Tumor MRI Dataset, divided into two classes: tumor and non-tumor. The research stages include preprocessing, feature extraction using Gray Level Co-occurrence Matrix (GLCM), model training with Random Forest, and performance evaluation. Preprocessing steps such as grayscale conversion, noise reduction, contrast enhancement, and normalization were applied to improve image quality. The model achieved an accuracy of 86.27%, precision of 90%, recall of 87.1%, and F1-score of 88.52%, indicating strong classification capability. The results show that the Random Forest algorithm can effectively identify tumor patterns based on texture and intensity features, making it a reliable approach for supporting early brain tumor diagnosis and potentially applicable in developing automated computer-aided diagnostic systems in medical imaging.
Analisis Komparatif Model Klasifikasi Penyakit Daun Padi Berbasis MobileNetV2 Azharangga Kusuma; Nurul Hikmah; Salman Abdullah Al Baaqir
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

Manual identification of rice leaf diseases is often inefficient. The purpose of this study is to conduct a comparative analysis of classification models for detecting four types of rice leaf diseases. The method involves feature extraction from a MobileNetV2 architecture on the data from the Rice Leaf Disease Dataset, containing 5,932 images. Four models were tested: a Prototype Classifier, MLP-Softmax, Support Vector Machine (SVM) with an RBF kernel, and a Hybrid Ensemble. The evaluation results showed that the SVM-RBF and Hybrid Ensemble models achieved the best performance with a perfect accuracy of 100%, outperforming the MLP-Softmax (99.24%) and the Prototype Classifier (79.58%). This study concludes that the synergy between MobileNetV2 features and SVM classification provides a highly accurate solution for automated rice leaf disease detection.
Analisis Perbandingan Waktu Respons Model Chatbot AI Generatif (GPT, Gemini, dan DeepSeek) Abhy Maulana Fadli
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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

Abstract

This study analyzed the response time performance of three generative AI chatbot models GPT-3.5 Turbo, Gemini 2.5 Flash, and DeepSeek Chat V3.1 using a controlled experimental approach. A total of 450 data points were collected through three replicated test sessions involving five categories of questions related to the Laravel framework. Normality and homogeneity tests showed that the data did not meet parametric assumptions, leading to the use of the Friedman test for further analysis. The results indicate a significant difference in response time among the three models, with GPT-3.5 Turbo demonstrating the fastest and most consistent performance, followed by Gemini 2.5 Flash, while DeepSeek Chat V3.1 showed the slowest and most variable responses. Analysis of question types revealed that only GPT-3.5 Turbo exhibited significant performance differences across categories, with Knowledge-Based questions receiving the fastest responses. In contrast, Gemini and DeepSeek maintained stable response times across all question types. These findings highlight that GPT-3.5 Turbo is better suited for real-time applications requiring low latency, while Gemini and DeepSeek offer more consistent performance across varying input complexities. Future research may include newer model versions, broader domains of questions, and evaluation of answer quality for more comprehensive assessment.

Page 38 of 48 | Total Record : 472

 36   37   38   39   40 

Filter by Year

2017 2025


Reset
Filter By Issues
All Issue Vol 9 No 1 (2025): SISFOTEK IX 2025 Vol 8 No 1 (2024): SISFOTEK VIII 2024 Vol 7 No 1 (2023): SISFOTEK VII 2023 Vol 5 No 1 (2021): SISFOTEK V 2021 Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020 Vol 3 No 1 (2019): SISFOTEK 2019 Vol 2 No 1 (2018): SISFOTEK 2018 Vol 1 No 1 (2017): SISFOTEK 2017