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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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Prediksi Dinamis Harga Tiket Penerbangan Pesawat Menggunakan Algoritma Regresi Linier Berganda Zacky Muhammad Dinata; Khaerul Anam; Puji Pramudya Marta; Gifthera Dwilestari
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study aims to develop a dynamic prediction model for airline ticket prices using the Multiple Linear Regression algorithm. The research utilizes the public dataset Flight Price Prediction from Kaggle, which originally contained 116,464 rows and 12 columns. After data cleaning by removing missing values (dropna()) and non-predictive columns (such as Flight_ID), the final dataset used for analysis consisted of 116,463 rows and 10 columns. Data preprocessing included handling missing data, encoding categorical variables, feature engineering, standardization, and multicollinearity testing using the Variance Inflation Factor (VIF). The MLR model achieved an R² of 0.882, MAE of 4573.37, and RMSE of 7797.53, indicating strong predictive performance for a linear model. The most influential factors were airline type, service class, number of stops, duration, and booking lead time. Full-service airlines such as Vistara and Air India tend to have higher ticket prices, while early bookings and economy class tickets significantly lower prices. The findings confirm that MLR remains a reliable baseline for interpretable, efficient, and explainable price forecasting systems. Future research may combine MLR with non-linear algorithms (e.g., Random Forest or Neural Network) to enhance accuracy. This study contributes to integrating data science into predictive information systems for dynamic airline pricing and decision support optimization.
Perbandingan Akurasi Machine Learning dan Deep Learning dalam Deteksi Serangan SQL Injection Franki SW; Jumanto Unjung; DAA Pertiwi; Much. Aziz Muslim
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

SQL Injection (SQLi) attacks are among the most common threats to web application security, potentially leading to data breaches and unauthorized manipulation of database systems. The limitations of traditional detection mechanisms, such as Web Application Firewalls (WAF), highlight the need for intelligent approaches capable of adapting to emerging attack patterns. This study aims to develop an effective, accurate, and adaptive SQL Injection detection model by comparing the performance of the Random Forest algorithm as a representation of traditional Machine Learning and the Multilayer Perceptron (MLP) as a representation of Deep Learning. The evaluation focuses on classification accuracy, processing speed, and implementation simplicity using an identical SQL Injection attack dataset. The results of this study are expected to provide recommendations for an optimal detection model to enhance web application security and strengthen defense systems against code injection-based cyber threats.
The selection of appropriate crop types according to agroclimatic conditions is a determining factor in the success of agricultural productivity. This study develops a machine learning-based crop recommendation system to classify 22 crop types based on se Zidan Fahreza; Arwin Datumaya Wahyudi Sumari; Mila Kusuma
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The selection of appropriate crop types according to agroclimatic conditions is a determining factor in the success of agricultural productivity. This study develops a machine learning-based crop recommendation system to classify 22 crop types based on seven agroclimatic parameters (N, P, K, temperature, humidity, pH, and rainfall). Four machine learning algorithms were compared for performance: K-Nearest Neighbors (KNN), Logistic Regression, Artificial Neural Network (ANN), and Decision Tree using a dataset of 2200 samples with an 80:20 split ratio for training and testing. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The research results show that KNN with k=13 achieved optimal performance with 98.18% accuracy, 98.28% precision, 98.18% recall, and 98.17% F1-score. This algorithm outperformed Logistic Regression (97.27%), ANN (96.59%), and Decision Tree (95.23%). Confusion matrix analysis identified that classification errors primarily occurred in crop pairs with similar agroclimatic characteristics such as lentil-chickpea and pigeonpeas-kidneybeans. KNN proved to be the most suitable model for implementing precision agriculture decision support systems in the Indonesian agricultural context by providing high accuracy and good generalization capability.
Komparasi TOPSIS dan Weighted Product Dalam Pengambilan Keputusan Pada Penentuan Susunan Personalia Organisasi Ahmadi Irmansyah Lubis; Rezi Elsya Putra; Supinah
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The determination of the composition of personnel in an organization, especially in the Branch Meeting (Musran) of the Muhammadiyah Branch Executive (PRM) of Sungai Panas Village, is still carried out manually and tends to be subjective. This has the potential to cause inefficiencies and internal conflicts in the decision-making process. This research aims to develop a web-based Decision Support System (SPK) that can help the management selection process objectively and systematically through the comparison of two Multicriteria Decision Making (MCDM) methods. The process of weighting criteria is carried out objectively using the Rank Order Centroid (ROC) method. Furthermore, the ranking of prospective administrators was carried out using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Weighted Product (WP) methods to test the accuracy and suitability of the results. The five main criteria used include: organizational experience, level of education, loyalty to the organization, leadership, and availability of time. The system is designed using the PHP programming language and MySQL database. The results of the implementation show that the system is able to produce recommendations for a more systematic, transparent, and accountable management structure, as well as provide a clear comparison of results between TOPSIS and taxpayers. With this SPK, the deliberation process can run faster and more efficiently, as well as strengthen democratic data-based organizational governance.
Analisis Kinerja Algoritma Machine Learning untuk Klasifikasi Prestasi Mahasiswa pada Mata Kuliah Bahasa Inggris Riri Narasati; Dadang Sudrajat; Ahmad Faqih; Indra Wiguna Marthanu; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study analyzes the performance of several machine learning algorithms in classifying student achievement in English language courses. The research focuses on comparing the performance of K-Nearest Neighbors (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM) using the K-Fold Cross Validation approach to evaluate accuracy, F1-score, and fairness. The dataset, consisting of students’ final grades, was processed through data pre-processing and feature scaling. Results show that the KNN model with K=5 achieved the highest accuracy of 100%, followed by Naïve Bayes with 95.59%. Statistical tests indicated a significant performance difference between Random Forest and SVM, while fairness evaluation revealed that Random Forest provided the most balanced error distribution. These findings confirm that KNN and Random Forest algorithms are highly effective for academic performance classification based on numerical data. The study highlights the potential of machine learning to enhance adaptive, objective, and equitable educational evaluation systems.
Klasifikasi Telur Fertil dan Infertil Berbasis Hybrid MobileNetV3 dengan Mekanisme Attention dan Texture Fusion Bani Nurhakim; Dadang Sudrajat; Tati Suprapti; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Accurate fertile-infertile egg classification is crucial to improve hatching productivity and sorting efficiency. This study proposes MobileFusionV3, a MobileNetV3 architecture enriched with CBAM (Convolutional Block Attention Module) and Hybrid Texture Fusion (LBP and GLCM) to combine deep and texture features to be more robust to candling illumination variations. A dataset of 1,275 candling images (675 fertile, 600 infertile) was subjected to preprocessing (resizing, normalization, background enhancement) and realistic data augmentation (rotation, brightness/contrast changes, Gaussian noise, illumination variations). The model was trained using transfer learning, early stopping, and an evaluation scheme based on accuracy, precision, recall, F1-score, and AUC. The test results showed an accuracy of 97.2%, precision of 96.8%, recall of 97.5%, F1 of 97.1%, and AUC of 0.99, surpassing previous designs that did not use attention mechanisms and texture fusion. Grad-CAM++ analysis confirms the model's focus on physiologically relevant regions (embryonic shadow and air-cell), thus improving the reliability of interpretation. These findings indicate that lightweight, efficient designs based on attention and texture fusion have the potential to be implemented in smart hatchery systems and edge/mobile devices while maintaining high accuracy.
Optimasi Akurasi dan Efisiensi Deteksi Intrusi pada Lingkungan Komputasi Awan dengan Analisis Deret Waktu CNN-LSTM Martanto; Khaerul Anam; Indra Wiguna Marthanu; Puji Pramudya Marta
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study proposes a CNN-LSTM time series analysis-based intrusion detection system (IDS) model to improve accuracy and efficiency in cloud computing environments. With more organizations moving to the cloud, security threats are becoming more sophisticated, rendering traditional detection methods inadequate. The objective of this study is to develop and evaluate a hybrid model that can address these challenges. The methodology used involves an experimental quantitative approach on a representative CSE-CIC-IDS2018 dataset. This dataset underwent rigorous data preprocessing, including data cleaning, conversion to time series format, and feature selection using stationarity and Granger causality tests. The CNN-LSTM model was then trained and evaluated using accuracy and computational efficiency metrics. The results showed superior model performance with an accuracy of 0.910, precision of 0.874, and F1-Score of 0.882. The model also demonstrated good computational efficiency, with a training time of 3.9887 seconds and a prediction time of 0.3607 seconds, making it suitable for real-time detection. This study concludes that the CNN-LSTM model is a viable solution for improving cloud computing security, offering a balance between high accuracy and good computational efficiency. Future research could explore multi-dataset validation and the integration of interpretation methods to improve its application.
Analisis Sentimen Ulasan Pelanggan Terhadap Produk Elektronik Di Marketplace Tokopedia: Studi Kasus Toko Studio Ponsel Menggunakan Metode Naïve Bayes Miranda Nofel; Ryan Putra Laksana; Qori Halimatul Hidayah
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The rapid development of e-commerce in Indonesia has propelled Tokopedia to become one of the leading marketplaces, offering a diverse range of electronic products from various sellers. In this competitive environment, customer reviews are a crucial factor influencing purchasing decisions and public perception of a store's product and service quality. This research aims to analyze the sentiment of customer reviews for electronic products at the Studio Ponsel store using the Naïve Bayes algorithm. Data was collected through web scraping from Tokopedia, totaling 11,943 reviews. The analysis stages included data cleaning, text pre-processing (normalization, stopword removal, tokenization, and stemming), sentiment labeling (positive, neutral, negative), vectorization with TF-IDF, and model evaluation using accuracy, precision, recall, and F1-score metrics. The evaluation results show that the model achieved an accuracy of 90.27%, with positive sentiment dominating the overall reviews. These findings provide valuable insights for businesses in formulating strategies to improve service, product quality, and marketing effectiveness based on customer data
Pengukuran Kualitas Udara Berbasis IoT Menggunakan NODEMCU V3 dan Sensor MQ135 Hemiltin Adilah; Budi Tjahjono
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Air pollution due to factory activities can have negative impacts on the environment and human health, especially from excessive carbon dioxide (CO?) concentrations. This study aims to design and build an Internet of Things (IoT)-based air quality monitoring system using NodeMCU V3 (ESP-12) and MQ-135 sensors to detect CO? concentrations. This system provides real-time air quality monitoring through the Blynk application and is equipped withnotifications and buzzer alarms if the CO? concentration exceeds the threshold of 2,000 ppm. The data obtained helps identify air quality and provide early warnings regarding the dangers of air pollution. The test results show that the system can work effectively in detecting increasing CO? concentrations and providing notifications as needed, so it can be used as a solution to minimize health risks due to air pollution in factory environments.
Perancangan Enterprise Architecture menggunakan TOGAF ADM pada PT. Mandiri Leader Cargo Angelina Mega Leltakaeb; Yulhendri
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The business world has undergone significant changes as a result of digital transformation, including the logistics sector, which requires information system integration and efficiency. Businesses can now optimize their operational processes and become more competitive thanks to advances in information technology. PT. Mandiri Leader Cargo (MLC), a freight forwarding company operating via air and land routes, still faces difficulties in running its operations due to the lack of an integrated information system. Processes such as fleet scheduling, monitoring delivery status, and reporting are still carried out manually through verbal communication, WhatsApp, and Excel. This leads to inefficiency and errors. The purpose of this research is to create an Enterprise Architecture (EA) using the TOGAF ADM approach, starting from the preliminary phase to Opportunities and Solutions. The design results in a blueprint for an integrated business architecture, applications, data, and technology, with a focus on the development of three main modules: Pick-Up, Outgoing Tracking, and Outgoing Report. It is expected that this design will improve operational processes, enhance coordination between divisions (Customer Service, Fleet, Warehouse, Gateway, and Finance), and enhance the quality of service and competitiveness of PT. MLC in the domestic logistics industry

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