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All Journal Jurnal Teknologi dan Manajemen Informatika JTERA (Jurnal Teknologi Rekayasa) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Bakti Masyarakat Indonesia Jurnal Teknologi Informatika dan Komputer Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Computer Science and Information Technology (CoSciTech) Society: Jurnal Pengabdian Masyarakat SmartComp Indonesian Journal of Community Empowerment
Insany, Gina Purnama
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Application of Artificial Neural Network in Estimating Harvest Time of Lettuce and Spinach Plants in Nutrient Film Technique Hydroponic System Munawar, Zilfa Agustina; Insany, Gina Purnama; Kharisma, Ivana Lucia
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3261

Abstract

Hydroponic farming using the Nutrient Film Technique (NFT) system is widely implemented due to its efficiency in nutrient management and water use. Spinach and lettuce are leafy commodities widely cultivated using this system because they have a relatively short growth cycle and high economic value. However, determining harvest time is still often done manually based on experience, potentially leading to inaccurate decisions that impact the quality and quantity of production. This study aims to develop a prediction model for harvest time for hydroponic spinach and lettuce plants based on Artificial Neural Network (ANN) by utilizing environmental and physiological parameters of the plant. The parameters used include water temperature, air humidity, light intensity, pH, Electrical Conductivity (EC), and plant age. The dataset used consists of 1,200 observation data of NFT hydroponic cultivation results from January to July 2025. The data went through a preprocessing stage in the form of cleaning, normalization, and dividing training data and test data with a ratio of 80:20. The ANN model was built using the backpropagation method with training parameter optimization. Data was obtained from plant growth monitoring, then normalized and divided into training and test data. Test results showed a prediction accuracy of 92.8% based on MAPE, MAE, and R-squared. This model was implemented in a Streamlit-based web application to facilitate farmer use, making harvest timing more objective, measurable, and data-driven.
Implementasi Convolutional Neural Network untuk Deteksi Penyakit pada Daun Cengkeh Berbasis Mobile: Bahasa Indonesia Junmulyana, Satria; Fergina, Anggun; Insany, Gina Purnama
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9895

Abstract

Clove (Syzygium aromaticum) is a spice crop that has high economic value, but faces serious threats from various diseases that can reduce yields. Early detection of disease in clove plants is very important to prevent greater losses. This research aims to develop a disease detection system for clove plants using Convolutional Neural Network (CNN) implemented in a mobile application. This method is expected to provide a faster and more accurate solution compared to traditional detection methods that are often inefficient. This research was conducted by collecting datasets of infected and healthy clove leaf images, which were then used to train the CNN model. The results show that the developed CNN model is able to achieve high disease detection accuracy, and can be integrated with mobile technology to facilitate farmers in identifying diseases in real-time. Thus, this research not only contributes to increasing agricultural productivity, but also supports the application of digital technology in the agricultural sector. The results of this research are expected to benefit farmers, researchers, and the agricultural industry as a whole.
Implementasi Teknologi YOLOv8n dan IoT pada Alat Pengusir Hama Burung dengan Raspberry Pi Bertenaga Solar Panel Kharisma, Ivana Lucia; Kamdan, Kamdan; Insany, Gina Purnama; Firdaus, Asep Rizki; Nasrulloh, Dendi
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.35-44

Abstract

Hama burung adalah salah satu tantangan serius yang dihadapi pada sektor pertanian. Metode tradisional yang dilakukan untuk mengatasi hama burung hanya memberikan solusi sementara dan juga mengakibatkan petani tidak dapat melakukan aktifitas lainnya, karena harus menjaga sawah secara terus menerus. Untuk optimalisasi serta untuk mengatasi permasalahan yang muncul pada perancangan alat sebelumnya yaitu kendala pada koneksi internet yang tidak stabil, sumber listrik yang terbatas serta permasalahan penyimpanan lokal pada alat , dibutuhkan integrasi antara konsep kecerdasan buatan (AI) dan Internet of Thing (IoT). Pengembangan alat menggunakan model deteksi objek versi YOLOv8nano, Raspberry Pi sebagai Single Board Computer dan penambahan solar panel . Pengujian yang dilakukan secara langsung menunjukkan bahwa fungsionalitas dan kinerja alat telah sesuai dengan yang diharapkan. Alat mampu mendeteksi objek burung dengan kinerja nilai rata-rata confidence score 74,64%, sedangkan pada pengujian solar panel menunjukkan hasil daya listrik yang mampu disimpan dalam durasi waktu 12 rata-rata sebesar 13,31 volt. Penambahan sebuah dashboard monitoring, bermanfaat untuk memberikan informasi kinerja alat.  Terpenuhinya semua proses pengujian menunjukkan bahwa alat pengusir burung ini memiliki potensi besar untuk memudahkan petani dalam mengatasi masalah hama burung secara lebih efektif, sehingga mengurangi ketergantungan pada metode tradisional.
Optimalisasi Smart Watering Berbasis Iot dengan Kontrol Fuzzy Sugeno untuk Kebutuhan Air dan Nutrisi pada Budidaya Melon Hidroponik di Greenhouse insany, gina purnama
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 2: Desember 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i2.2025.69-78

Abstract

Budidaya melon secara hidroponik di greenhouse memiliki tantangan signifikan dalam pengelolaan air dan nutrisi. Sistem irigasi konvensional kurang efisien dalam mendistribusikan air dan nutrisi sesuai dengan kebutuhan tanaman, sehingga mengakibatkan pertumbuhan melon tidak optimal. Berdasarkan permasalahan tersebut, peneliti mengembangkan suatu sistem smart watering berbasis Internet of Things (IoT) untuk memantau kebutuhan air dan nutrisi secara otomatis, mengimplementasikan logika fuzzy sugeno sebagai kontrol sistem cerdas yang mengolah data sensor (pH, suhu dan TDS) untuk takaran optimal pemberian air dan nutrisi. Metode penelitian ini metode kuantitatif-eksperimen dengan pendekatan IoT dan kontrol fuzzy sugeno. Sistem smart watering akan dikembangkan menggunakan sensor pH, suhu, dan Total Dissolved Solids (TDS) yang terhubung ke ESP32 mikrokontroler berbasis IoT. Data yang diperoleh akan diolah menggunakan algoritma fuzzy logic untuk menentukan jumlah air dan nutrisi yang diberikan kepada tanaman melon secara real-time. Pengujian dilakukan dalam lingkungan greenhouse dengan perbandingan antara sistem konvensional dan sistem berbasis fuzzy. Parameter yang diukur meliputi efisiensi penggunaan air, pertumbuhan tanaman (tinggi, jumlah daun, dan bobot buah), serta kestabilan pH dan nutrisi dalam larutan hidroponik.
Co-Authors adang badru jaman,anggun fergina, adang badru jaman,anggun fergina AGUNG, DEDE Al-Basori, Muhamad Cepnur Alamsyah, Zaenal Alawiyah, Sindi Aulia Alun Sujjada Amalia, Phina Putri Bunga, Siti Dede Sukmawan Delia Nur Annisa, Delia Nur Fadilah, Muhammad Sahrul Firdaus, Asep Rizki Futri, Dila Aura Gunawan, Muhamad Sahrul Haq, Muhammad Raihan Izharul Iyusmani Junmulyana, Satria Kamdan Kharisma, Ivana Lucia Kustandi, Agus Lidena, Salwa Dwi Maulana Yusuf Maulina, Siti Farda Medal Wangi, Shanika Layung Munandar, Rifki Arief Munawar, Zilfa Agustina Munggaran, Dava Febrian Nasrulloh, Dendi Natanael, Brillian Aji Natasya, Sandra Naufal, Ripki Tri Noer, Silvia Novianti, Ane Nugraha Nugraha Perkasa, M. Juang Pajri Purba, Raihan Lutfhi Putra, Reza Rama Putri, Azzahra Rahman, Wigi Januar Ramdan, Hari Muhammad Rosmawati Rosmawati Septian, Deni Ramdan Septian, Muhammad Ardhi Serliani, Siska Meyisa Amelia Somantri Somantri, S Somantri, Somantri Sukardi, Evi Septiyani Taqwana, Friendly Jihad Tirawati, Tirawati Wardana, Roihan Kusuma Wilianti, Refi Yansah, Ardi Yustiana, Indra Zakaria, Muhammad Maulana