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All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Informatika Dan Tekonologi Komputer
Neysa Talitha Jehian
Universitan Negeri Medan
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Social Sciences Other

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Sistem Jemuran Otomatis Berbasis Deteksi Visual dan Sensor Hujan dengan ESP32 Muhammad Rizki Andrian Fitra; Neysa Talitha Jehian; Sybil Auzi; Thania Dealva Arsyad; Hermawan Syahputra
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 2 (2025): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i2.6597

Abstract

Unpredictable weather in tropical regions often disrupts clothes drying activities, as sudden rain can cause clothes to become wet again. To address this issue, this study developed an automatic clothes drying system based on ESP32-CAM that can detect weather conditions using two main methods: sky image analysis and rain sensors. The system periodically captures sky images using the ESP32-CAM camera, then analyzes the brightness and contrast of the images to determine weather conditions, with brightness thresholds < 100 and contrast > 30 indicating cloudy weather. Data from the rain sensor is used as additional verification to enhance system accuracy. The decision-making logic combines both data sources to determine whether the clothesline should be retracted or left open. Offline image classification results show an accuracy of 93.67%, while direct testing against 10 weather scenarios yields a system accuracy of 100%. With its high performance and adaptive response to weather changes, this system demonstrates significant potential for implementation as an Internet of Things (IoT)-based home automation solution.
KLASIFIKASI STATUS MAHASISWA MENGGUNAKAN ALGORITMA XGBOOST PADA DATASET AKADEMIK Muhammad Rizki Andrian Fitra; Neysa Talitha Jehian; Thania Dealva Arsyad; Ayman Human Sukma; Arnita; Fanny Ramadhani
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 2 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Oktober 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i2.3997

Abstract

Monitoring student academic status is an important aspect of supporting the effectiveness of higher education systems. This study aims to classify student status—Dropout, Enrolled, and Graduate—using the XGBoost algorithm on an academic dataset from Kaggle consisting of 4424 entries and 37 features. The research was conducted using a quantitative approach and computational experiment methods. The stages included data pre-processing, training and testing data split (80:20 ratio), model training, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The results showed that the XGBoost model was able to classify student status with an accuracy of 79.9%. Further evaluation revealed that the model performed well on the majority class (Graduate), but had low performance on the minority class (Enrolled), especially with recall of 0,48. This indicates challenges in dealing with imbalanced class distributions. Therefore, the use of data balancing techniques must be implemented, thus the accuracy by the model reached 82,65%, for further refining and improving its performace, hyperparameter tuning is recommended for it. The results of this study are expected to assist educational institutions in detecting potential dropouts earlier and supporting data-driven decision making.
Co-Authors Arnita Ayman Human Sukma Hermawan Syahputra Muhammad Rizki Andrian Fitra Ramadhani, Fanny Sybil Auzi Thania Dealva Arsyad