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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Much. Aziz Muslim
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Computer Science & IT

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Pemanfaatan Teknologi IoT untuk Optimalisasi Energi dan Manajemen Fasilitas Berkelanjutan Menggunakan Fuzzy Logic Ceorido Ghalib Wibowo; Jumanto Unjung; Dwika Ananda Agustina Pertiwi; Much. Aziz Muslim
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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The development of the smart airport concept requires the integration of digital technologies to enhance operational efficiency, environmental sustainability, and passenger comfort. One of the key technologies driving this transformation is the Internet of Things (IoT), which enables real-time data collection and analysis from interconnected devices and sensors. This study explores the utilization of IoT for energy optimization and sustainable facility management in smart airports, focusing on systems such as energy consumption monitoring, automated lighting control, sensor-based temperature regulation, and efficient equipment management. The research employs a literature review and data-driven system analysis within the operational context of modern airports. The results indicate that IoT-based implementations can reduce energy consumption by up to 25–30% through dynamic and predictive control of resource usage. Furthermore, IoT contributes to sustainability by reducing carbon emissions and extending infrastructure lifespan. Therefore, IoT technology serves as a fundamental component in realizing airports that are efficient, environmentally friendly, and future-oriented.
Peningkatan Akurasi Prediksi Performa Akademik Siswa Menggunakan Model Stacking Ensemble Ari Nugroho Putro; Much. Aziz Muslim
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Educational data mining has become an effective tool for exploring and predicting student academic performance. Various studies have shown its potential in developing early detection systems for students at risk of dropping out of school. However, the main challenge in such prediction systems is the low performance of conventional classification algorithms in producing high accuracy. This study aims to improve the accuracy of student performance predictions by applying a stacking ensemble model that combines several algorithms. The model developed uses two base learners, namely XGB, LGBM, SVM, and LR, which are then combined through the meta learner LR to produce a final decision. The experiment was conducted using a dataset predicting student dropout and academic success, which included academic paths, demographics, socioeconomic status, and academic performance of students in their first and second semesters. Model validation was performed using 10-fold cross validation to ensure the stability and generalization ability of the model. The test results showed that the stacking ensemble model achieved an accuracy of 0.9168, superior to the single classification model. These findings prove that the stacking ensemble approach is effective in improving student performance predictions.
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.
Co-Authors Ari Nugroho Putro Ceorido Ghalib Wibowo DAA Pertiwi Dwika Ananda Agustina Pertiwi Franki SW Jumanto Unjung