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All Journal Jurnal Penelitian Sains Go-Integratif : Jurnal Teknik Sistem dan Industri
Aisyah, Andini Nur
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Chemistry Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Mathematics Physics

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Pemodelan Prediksi Temperatur Freezer Menggunakan Pendekatan Machine Learning Berbasis Framework TensorFlow Davi, Ahmad; Sidiq, Farkhan Jatmiko; Arrizal, Muhammad Aziz; Wibowo, Fahrizal Agil; Gunawan, Taopik Sendy; Aisyah, Andini Nur; Hidayat, Alif Nur
Go-Integratif : Jurnal Teknik Sistem dan Industri Vol 5 No 02 (2024): Go-Integratif : Jurnal Teknik Sistem dan Industri
Publisher : Engineering Faculty at Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35261/gijtsi.v5i02.12524

Abstract

Temperature control in freezers is crucial to maintaining product quality and safety, particularly in the food and pharmaceutical industries. Uncontrolled temperature fluctuations can lead to product damage, increased waste, and reduced quality. Machine learning technology offers an effective solution for predicting and controlling temperature, enabling more accurate monitoring and rapid responses to changing conditions. This study aims to develop a machine learning model using the TensorFlow framework to predict freezer temperatures. Temperature data were collected from sensors installed inside the freezer and used to train and test several machine learning architectures, including Long Short-Term Memory (LSTM) and 1D Convolutional (Conv1D) networks. Model development leveraged TensorFlow's advanced features, enabling efficient model creation, training, and testing. The results show that the Conv1D model with a data composition of 90% training, 5% validation, and 5% testing achieved the best predictions, with a test RMSE of 0.02085°C and a test MAPE of 0.33522%. This predictive model has the potential to be used as an early warning system to prevent product damage. This research is expected to significantly contribute to the development of more efficient temperature monitoring and control systems in freezers, with potential applications across industries such as food and pharmaceuticals. The findings also reinforce the substantial potential of machine learning in environmental prediction and monitoring.
Studi Optimasi Fotodegradasi Limbah Pestisida Menggunakan Material CaO Melalui Metode Taguchi Aisyah, Andini Nur; Shofie, Auliya; Larrisa, Keisha Ekya; Umam, Hilman Imadul; Pambudi, Teguh
Jurnal Penelitian Sains Vol 28, No 1 (2026)
Publisher : Faculty of Mathtmatics and Natural Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56064/jps.v28i1.1340

Abstract

Penelitian ini bertujuan untuk menguji kemampuan penggunaan ulang (reusability) katalis CaO, serta menentukan kondisi optimum pada proses fotodegradasi limbah pestisida melalui metode taguchi. CaO disintesis dari cangkang telur melalui kalsinasi pada suhu 800°C dan dikarakterisasi menggunakan SEM, UV-Vis, dan FTIR. CaO yang dihasilkan memiliki morfologi berpori dengan band gap 2,75 eV, serta telah mengalami transformasi dari CaCO₃ menjadi CaO. Uji fotodegradasi dilakukan dengan memvarisikan sumber pencahayaan, meluputi cahaya matahari dan cahaya lampu 100 W. Katalis CaO juga menunjukkan kemampuan reusabilitas yang baik hingga tiga siklus dengan penurunan efisiensi yang tidak signifikan. Optimasi menggunakan metode Taguchi mengidentifikasi waktu iradiasi sebagai faktor paling dominan (88,93%), dengan kondisi optimum pada pH 3, dosis 16 mg, daya 80 W, dan waktu 240 menit menghasilkan efisiensi degradasi prediksi 53,26% uji konfirmasi menunjukkan efisiensi degradasi sebesar 47,21% dengan galat 6,05%. Penelitian ini membuktikan bahwa CaO dari cangkang telur berpotensi sebagai fotokatalis efektif, ekonomis, dan ramah lingkungan untuk pengolahan limbah pestisida.
Co-Authors Arrizal, Muhammad Aziz Davi, Ahmad Gunawan, Taopik Sendy Hidayat, Alif Nur Larrisa, Keisha Ekya Shofie, Auliya Sidiq, Farkhan Jatmiko Teguh Pambudi, Teguh Wibowo, Fahrizal Agil