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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Adillion, Ilham Gurat
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Computer Science & IT Dentistry Education Engineering Library & Information Science

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AN IOT-BASED AUTOMATED WATERING SYSTEM FOR PLANTS USING INTEGRATED FUZZY LOGIC AND TELEGRAM BOT Shiddiqi, Ary; Anindita, Muhammad Raihan; Suadi, Wahyu; Soelaiman, Rully; Lili, Suhadi; Adillion, Ilham Gurat
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 2, July 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i2.a1191

Abstract

The development of automatic plant watering systems has recently gained popularity due to the need to conserve water and ensure healthy plant growth. This study focuses on integrating fuzzy logic, sensors, and algorithms to provide an automatic watering system. Fuzzy logic is a powerful tool that allows the system to interpret sensor data and make informed decisions. The sensors measure soil moisture, humidity, temperature, and light intensity. The data collected from these sensors is analyzed using algorithms to determine the appropriate watering schedule. The system’s ability to analyze and interpret data ensures that the plants receive the necessary moisture without over-watering or under-watering. Integrating the Telegram Bot is a significant feature of the system, enabling users to monitor and control the system remotely. The Telegram Bot sends users notifications when the system is activated, or the plants require attention. The system can also be controlled remotely through the Bot, enabling users to adjust the watering schedule or turn the system on or off. This research shows that the designed features of the system function effectively and can be used on a daily household scale. The system’s automated features reduce the need for constant monitoring and manual watering, making it ideal for those who engage in gardening at home. This innovation is particularly relevant in increasing the productivity of plants. In addition, the system’s ability to be controlled remotely through the Telegram Bot is a significant advantage, making it accessible and convenient for users.
A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting Shiddiqi, Ary Mazharuddin; Ardi, Bagaskoro Kuncoro; Amaliah, Bilqis; Mogi, I Komang Ari; Rizki, Agung Mustika; Nuralamsyah, Bintang; Adillion, Ilham Gurat; Alzamzami, Moch. Nafkhan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1264

Abstract

Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.
Document Matching for Contradiction Detection in Low-Resource Legislative Texts With Self-Training and Augmentation Using Transformer Model Navastara, Dini Adni; Abdillah, Surya; Benito, Davian; Adillion, Ilham Gurat; Purwitasari, Diana
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.95954

Abstract

Detecting contradictions within low-resource legislative texts presents significant challenges due to limited labeled data, complex legal language, and the vast number of verses contained within legal documents. These contradictions can lead to legal ambiguities and disputes if not addressed effectively. To tackle this problem, this study proposes a comprehensive system that combines document matching with contradiction detection. Legal documents are first clustered based on contextual similarity, enabling a more targeted analysis of potentially contradictory verses. Among several clustering approaches tested, keyword similarity-based clustering using KeyBERT produced the highest MatchingScore of 0.6111. To overcome the scarcity of labeled data, we employed a multi-step strategy involving manual annotation, generative AI-based data augmentation, and self-training techniques. The contradiction detection model was developed using the XLM-RoBERTa architecture, trained on TPU V2 with a batch size of 64. The model achieved strong performance, with 0.978 recall, 0.9356 precision, 0.982 accuracy, and a 0.9566 F1-score, completing each epoch in 82 seconds. This integrated approach significantly reduces the complexity of contradiction detection in legislative documents while ensuring high accuracy and robustness.
Co-Authors Abdillah, Surya Agung Mustika Rizki, Agung Mustika Alzamzami, Moch. Nafkhan Anindita, Muhammad Raihan Ardi, Bagaskoro Kuncoro Ary Mazharuddin Shiddiqi Benito, Davian Bilqis Amaliah Diana Purwitasari Dini Adni Navastara, Dini Adni I Komang Ari Mogi, I Komang Ari Nuralamsyah, Bintang Rully Soelaiman Shiddiqi, Ary Suhadi Lili Wahyu Suadi