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All Journal bit-Tech Jurnal Teknologi Dan Sistem Informasi Bisnis International Journal of Artificial Intelligence and Robotics (IJAIR)
unggara, Ilham
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Water Quality Monitoring for Smart Farming Using Machine Learning Approach Hendriana, Yana; Taruno, Restiadi Bayu; Zulkhairi, Zulkhairi; Bashir, Nur Azmi Ainul; Ipmawati, Joang; Unggara, Ilham
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7499

Abstract

Water quality in fish farming environments has been a topic of research investigation for numerous years. While most studies have concentrated on managing water quality in fish ponds, there is a lack of research on implementing these practices on a commercial scale. Maintaining good water quality helps prevent disease, stress, and death in fish, resulting in higher yields and profits in fish farming operations. In our study, we gathered weekly data from two fish ponds in the Lintangsongo smart farming area over six months. To deal with the limited dataset, we utilized methods for reducing dimensionality, like the pairwise comparison of correlation matrices to eliminate the highest correlated predictors. We used techniques of feature selection, including XGBoost classification, and apart from that, we used Recursive Feature Elimination (RFE) to determine the importance of features. This analysis identified ammonium and calcium as the top two predictors. These nutrients played a vital role in maintaining the paired cultivation system and promoting the robust development of Nile tilapia fish and water spinach. This process of detecting and distributing nutrients persists until the desired quantities of ammonium and calcium are reached. During each cycle, 0.7 g of ammonium sulfate and calcium nitrate are distributed, and the nutrient levels are assessed. Vernier sensors were employed for assessing nutrient values, and a system of actuators was integrated to supply the necessary nutrients to the smart farming environment using the closed-loop concept. This research investigates water quality management practices in fish farming, assesses their impact on fish health and profitability, identifies key water quality predictors, and implements a closed-loop system for nutrient delivery.
Analisis Status Gizi Anak Menggunakan Metode Klastering pada Dataset Anthropometri Ipmawati, Joang; Unggara, Ilham
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1869

Abstract

Stunting merupakan salah satu masalah kesehatan masyarakat yang serius di Indonesia, memengaruhi pertumbuhan fisik dan kognitif anak-anak. Penelitian ini bertujuan untuk mengelompokkan status gizi anak-anak berdasarkan data antropometri untuk mengidentifikasi kelompok risiko tinggi stunting. Dataset yang digunakan terdiri dari 120.000 entri dengan variabel umur (bulan), tinggi badan (cm), jenis kelamin, dan status gizi, yang diperoleh dari sumber sekunder. Metode penelitian menggunakan algoritma K-Means untuk klasterisasi data dengan jumlah klaster optimal ditentukan melalui metode Elbow dan Silhouette Score. Proses analisis melibatkan tahap preprocessing, klasterisasi, dan validasi serta evaluasi eksternal. Hasil penelitian menunjukkan bahwa data dapat dikelompokkan ke dalam empat klaster dengan karakteristik yang berbeda. Klaster 0 dan Klaster 2 didominasi oleh anak usia muda (0–35 bulan) dengan rata-rata tinggi badan masing-masing 74.34 cm dan 73.13 cm, yang mencerminkan kelompok risiko tinggi stunting. Sebaliknya, Klaster 1 dan Klaster 3 mencakup anak-anak dengan pertumbuhan optimal, dengan rata-rata tinggi badan di atas 100 cm. Analisis korelasi menunjukkan hubungan signifikan antara tinggi badan, umur, dan status gizi, mendukung pentingnya intervensi gizi pada kelompok risiko tinggi. Penelitian ini memberikan kontribusi penting dalam upaya pencegahan stunting melalui identifikasi kelompok risiko tinggi secara lebih terarah. Temuan ini relevan untuk mendukung program kesehatan masyarakat di Indonesia, khususnya dalam merancang intervensi berbasis data untuk meningkatkan status gizi anak-anak. Pendekatan klasterisasi berbasis machine learning yang digunakan membuktikan efektifitasnya dalam memetakan pola pertumbuhan anak, sehingga dapat digunakan untuk mendukung perencanaan kebijakan yang lebih efisien.
Sistem Rapor Online di Madrasah Tsanawiyah Al Mumtaz Berbasis Web Maimanah, Khamidah Toriq; Unggara, Ilham
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 1 (2025): Januari 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i1.1665

Abstract

The rapid advancement of information technology has encouraged companies and institutions to enhance data management more effectively and efficiently to support productivity. The online report card system at MTs Al Mumtaz is designed to manage student grades digitally. Currently, grade processing at MTs Al Mumtaz is still done manually using Microsoft Excel, without a specialized application. With a web-based system, it is expected that grade management will improve, allowing students and parents easy access to grades, while also simplifying data entry for teachers, making the process more efficient. This research uses the Waterfall method, with UML, PHP, MySQL, and the CodeIgniter (CI) framework as tools. The results show that the developed online report card system can enhance efficiency in grade management, reduce the risk of data entry errors, and speed up delivering grade information to students and parents. Additionally, the system facilitates teachers in managing grades, both in terms of data entry and final grade processing. The author recommends further development of this system, with additional features such as automatic notifications and mobile application integration, making user access easier and the report card system more optimized.
Co-Authors Heni Pujiastuti Joang Ipmawati Maimanah, Khamidah Toriq Nur Azmi Ainul Bashir Taruno, Restiadi Bayu Yana Hendriana