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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 471 Documents
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Pengembangan Sistem IoT Berbasis Sensor untuk Analisis Kesuburan Tanah pada Lahan Pertanian Ery Muchyar Hasiri; Fahmi; Mohamad Arif Suryawan; Marselfa Nasir
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The development of Internet of Things (IoT) technology has had a significant impact in various fields, including the agricultural sector. One of the main challenges in modern agriculture is the efficient and accurate measurement of soil fertility, including temperature, humidity, and nutrient content parameters such as nitrogen (N), phosphorus (P), and potassium (K). Manual measurements take considerable time, effort, and cost, and often result in less accurate data because they are subjective and not real-time. This research aims to design and build an IoT-based soil fertility measuring device that integrates NPK sensors, Soil Moisture sensors, and DHT22 sensors with ESP32 microcontrollers as the system control center. The methods used include hardware and software design, ESP32 programming using Arduino IDE, and integration with the Firebase platform for online data storage. It reads the soil conditions in real-time and displays the measurement results on the LCD, as well as transmitting data to a smartphone application over the internet. The test results show that the tool can distinguish fertile and infertile soil conditions well. In fertile soils, a temperature of 29°C, humidity of 89%, and NPK content of Nitrogen 20–23 ppm, Phosphorus 32 ppm, and Potassium 190–195 ppm, respectively. Meanwhile, in infertile soils, a temperature of 23–32°C, humidity below 75%, and a Nitrogen content of 12 ppm, Phosphorus 22 ppm, and Potassium 118–120 ppm. This system provides benefits in remote monitoring, resource efficiency, and increased agricultural productivity.
Peningkatan Klasifikasi Penyakit Tanaman Menggunakan Kombinasi Anisotropic Diffusion dan Bilateral Filtering Bayu Perasetio; Dede Nurhalimah; Achmad Ardiana; Azzahra Aysya Nugrahini
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Image preprocessing is a critical stage in computer vision systems as it directly influences analysis and classification performance. Anisotropic Diffusion (AD) and Bilateral Filtering (BF) are widely used preprocessing techniques proven effective in reducing noise while preserving edge information, though each has limitations—AD may cause oversmoothing, and BF can be less effective against impulsive noise. This study employs three preprocessing approaches: (1) AD with a blending ratio of 70% original image and 30% diffused result, (2) BF with the same ratio, and (3) a hybrid AD+BF method combining both sequentially, where AD is applied before BF. At each stage, the processed image is proportionally blended (70%:30%) with the original to balance edge preservation and noise reduction. Experiments were conducted on plant disease classification using the MobileNetV2 architecture with the New Plant Diseases dataset containing 10 classes (560 training and 140 validation images per class). Results show that the hybrid AD+BF approach achieved the highest accuracy (99.21%) and F1-score (99.21%), outperforming standalone AD (98.86%) and BF (99.14%). The optimal parameters ( mm dan SUV) offer practical guidance for implementation. These findings provide empirical evidence supporting proportional blending as an effective preprocessing strategy for deep learning-based plant disease classification.
Implementasi Sistem Informasi Manajemen Inventaris Berbasis ERP Odoo 17 pada PT XYZ Lim Jong Su
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

PT XYZ is an agribusiness company engaged in the provision of cassava products and fertilizers, with a commitment to supporting sustainable agricultural practices. The main problems faced include stock recording, fertilizer procurement, and coordination of cultivation activities, which are still carried out manually using notebooks and basic spreadsheets. This condition often leads to data duplication, delays in stock updates, and discrepancies between physical stock data and administrative records. This study aims to design and develop an inventory management information system based on ERP Odoo 17 using the Prototyping Model approach. The system is designed to integrate three main modules—Inventory, Purchase, and Agriculture Management—which are interconnected to support centralized, automated, and real-time business processes. The methods used include data collection techniques such as in-depth interviews with the Warehouse Head, Procurement Staff, and Operations Manager, direct observation of stock management processes, and literature review on ERP implementation in the agribusiness sector. The resulting system prototype is capable of recording goods in and out, performing stock adjustments, supporting purchase requests through to goods receipt from vendors, and digitally recording cassava cultivation activities. With inter-module relationships, every data input in one module is automatically updated in the other modules, thereby minimizing the risk of data redundancy.
Peningkatan Signifikan Kualitas Klaster K-Means Berbasis DBI: Integrasi UMAP-K-Means Restu Normalasari; Siti Sopiyah; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research focuses on improving the quality of high-dimensional data clustering results through the integration of Uniform Manifold Approximation and Projection (UMAP) and the K-Means algorithm. The main objective is to evaluate how UMAP, when used as a preprocessing stage, enhances cluster compactness and separation produced by K-Means. The experiment compares two approaches—standard K-Means and the UMAP + K-Means combination—using the Davies–Bouldin Index (DBI) as the primary evaluation metric. Empirical findings indicate that UMAP integration significantly reduces the DBI value from 0.704 to 0.094, representing an 86.6% improvement in clustering quality. Furthermore, visual analysis shows that UMAP enables K-Means to form more compact and clearly separated clusters. These results confirm that manifold-based embeddings like UMAP effectively overcome K-Means limitations in handling nonlinear, high-dimensional data. This study contributes to the development of more accurate and efficient clustering approaches applicable to various domains, including bioinformatics, medical imaging, and socio-economic data analysis.
Predictive Maintenance pada Kapal Tanker Mid-Range Menggunakan Machine Learning (XGBoost Algorithm) Meschac Timothee Silalahi; Veryawan Nanda Perkasa; Ita Wijayanti; Hanifah Widiastuti
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study develops a machine learning-based predictive maintenance model for chemical tankers with capacities of 11,000–25,000 DWT using synthetic log-book data representing manual engine-room records without additional sensors. The XGBoost model predicts potential system failures within 14 days, achieving an Area Under Curve (AUC) of 0.9, recall of 0.82, and precision of 0.87. SHAP interpretability analysis identifies exhaust gas temperature differentials between cylinders, scavenge air pressure, and iron content in lubricating oil as the most influential predictors of failure. Implementation of the predictive system improves Mean Time Between Failure (MTBF) by 25.5% and system availability from 94.6% to 97.8%. Economic evaluation yields a Net Present Value (NPV) of USD 2.45 million per vessel with a Payback Period of 11 months. The findings confirm the reliability of machine learning-based predictive maintenance using operational data without expensive sensor infrastructure, supporting both efficiency gains and digital transformation within the maritime industry.
Prediksi Kelulusan Mahasiswa Berdasarkan Nilai Akademik Menggunakan Algoritma KNN Ilham Firmansyah; Adelia Rizky Cantika; Nizirwan Anwar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The prediction of student graduation has become an essential component in academic performance analysis, especially in identifying factors that influence student success. This study aims to develop a simple prediction model for student graduation status using the K-Nearest Neighbor (KNN) algorithm. The dataset used consists of student academic records, including Grade Point Average (GPA) and total completed credits (SKS). The data were processed and divided into training and testing sets to evaluate the model’s performance. The KNN algorithm was applied with various K values (1, 3, 5, 7, and 9) to determine the most optimal classification result. The experiment showed that the KNN model achieved the highest accuracy of 90% when K=3, indicating that the algorithm performs effectively in classifying students based on academic achievement. The results suggest that KNN can be utilized as an initial analytical tool for predicting student graduation likelihood, which can later support decision-making in academic management systems.
Prototype Sistem Informasi Presensi Karyawan Dengan Pendeteksi Lokasi Berbasis Web Pada PT Space Indonesia Danendra Saskara; Budi, Budi; Rahmadi
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

PT Space Indonesia, located in the EJIP area of South Cikarang, Bekasi Regency, currently uses a fingerprint-based employee attendance system. This system faces several issues, including dependence on electricity, frequent maintenance, and malfunction due to dirty or wet sensors. Moreover, it does not support digital submission of leave, sick, or permission requests, making attendance administration less efficient. This study aims to design a web-based employee attendance information system equipped with location detection features using Geotagging and GPS technology. The system enables employees to check in online via PC, laptop, or smartphone, whether working on-site or remotely (Work From Anywhere). It also includes features for submitting leave, sick, and permission requests, as well as exporting attendance reports in Excel and PDF formats. Data collection was conducted through observation and interviews with the HR and IT staff. The system was developed using the Prototype method with a UML design approach and built using Laravel 11, Laravel Filament, PHP, HTML, CSS, JavaScript, and MySQL, running on Laragon. The result is a web-based attendance system prototype that offers greater flexibility and efficiency compared to conventional fingerprint systems.
Penerapan AI untuk Sistem HVAC Bangunan Pintar: Integrasi Prediksi Spasio-Temporal, MARL, dan Contrastive Learning Putu Bagus Adidyana Anugrah Putra; I Made Oka Widyantara
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The building sector accounts for over 40% of global energy consumption, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for nearly 60% of this share. Improving HVAC efficiency while maintaining occupant comfort has therefore become a critical challenge for smart building management. Conventional control strategies, such as rule-based methods and Model Predictive Control (MPC), often fall short when dealing with dynamic, multi-zone environments. In response, recent advances in Artificial Intelligence (AI) have introduced new directions for HVAC prediction and control. This review systematically analyzes 15 recent studies (2023-2025), classified into three main categories: (i) Graph-SpatioTemporal Prediction (C1), focusing on graph neural networks combined with temporal modules for predicting temperature, CO?, occupancy, and energy demand; (ii) Multi-Agent Reinforcement Learning (C2), enabling adaptive and decentralized HVAC control across multiple zones and subsystems; and (iii) Representation & Contrastive Learning (C3), which enhances time-series representation to improve data efficiency and generalization. The synthesis highlights key achievements: high prediction accuracy from graph-temporal models, up to 40% energy savings using MARL, and improved robustness through contrastive learning. However, gaps remain, including the limited adoption of multi-task prediction, insufficient exploration of curriculum learning and policy distillation in MARL, and minimal integration of contrastive learning into HVAC applications. Looking ahead, the review outlines a 5-10 year roadmap, emphasizing hybrid multi-task models, curriculum MARL, contrastive-RL integration, cross-building transferability, federated learning, and the vision of autonomous, self-evolving HVAC systems. By providing a comprehensive mapping of the state of the art and future opportunities, this review aims to guide researchers and practitioners toward developing AI-based HVAC solutions that are more efficient, adaptive, and occupant-centered.
Perancangan dan Implementasi Sistem Informasi Salon Kecantikan Berbasis Website untuk Efisiensi Layanan Nisa Aulia; Binastya Anggara Sekti
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research aims to design a website-based information system to improve operational efficiency and service quality at Shella Beauty Salon. The development process uses the prototype method, including communication, quick planning, modeling, building the prototype, and evaluation. Testing was conducted using black box testing to ensure system functionality. The system was built using HTML, PHP, and MySQL database with features such as service management, online ordering, automatic reporting, and home service booking. The results show that the system helps streamline services, reduce recording errors, and assist both customers and administrators in managing transactions efficiently. The system is expected to strengthen Shella Beauty Salon’s competitiveness in the digital era.
Sistem Informasi Geografis Stasiun Pemantauan Spektrum Frekuensi Radio Di Wilayah Bali I Nyoman Suada; I Made Oka Widyantara
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The Radio Frequency Spectrum Monitoring Center Denpasar must carry out observation and monitoring. In carrying out this monitoring, there are eight fixed and transportable monitoring stations that have been placed in several locations in the Bali Province to carry out monitoring. However, given the scattered distribution of Radio Station Licenses across various locations in cities/regencies in the province of Bali, there are blind spots where some Radio Station Licenses are not covered by fixed and transportable stations. This will affect the achievement of the observation and monitoring performance agreement targets of the Radio Frequency Spectrum Monitoring Center. In practice, observations and monitoring must be carried out in uncovered locations using mobile or portable monitoring devices. The selection of monitoring locations is based on the distribution of ISRs, the coverage radius of monitoring points, and the conditions and contours of the area. This ensures that these locations are effective and efficient in providing optimal results, especially in achieving the observation and monitoring performance agreement targets. The analysis method utilizes the QGIS application, where optimal locations are identified based on the visual representation of locations in Google Earth, the locations of fixed and transportable stations, and the distribution of Radio Station Licenses, which are then visualized in the QGIS application. Based on the results of the analysis and visualization, it was found that the percentage of performance agreement achievement or the number of radio station licenses monitored at the monitoring location was achieved. The nine cities/regencies in Bali Province have the following achievement percentages: Badung 99%, Tabanan 85%, Klungkung 100%, Gianyar 99%, Karangasem 85%, Denpasar 100%, Bangli 100%, Jembrana 86%, and Buleleng 89%.

Page 43 of 48 | Total Record : 471

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