RANCANG BANGUN SISTEM PEMANTAUAN PARAMETER LINGKUNGAN BERBASIS INTERNET OF THINGS (IoT) DI GUDANG PENYIMPANAN UNTUK PABRIK GULA

Gula merupakan salah satu komoditas kebutuhan pokok. Proses produksi untuk menghasilkan gula di pabrik terdiri dari beberapa tahapan yaitu penggiingan, pemurnian, penguapan, kristalisasi,  pemisahan dan penyelesaian. Setelah itu, gula yang telah dikemas, disimpan dalam gudang sebelum di kirim ke pasar. Kondisi lingkungan gudang penyimpan gula harus sesuai dengan standar. Faktor lingkungan yang tidak sesuai, mampu merusak gula. Beberapa faktor lingkungan penting dalam gudang penyimpanan yaitu suhu, kelembaban, CO2, dan api. Ketersediaan alat ukur portable berbasis internet of things merupakan salah satu bentuk pengawasan keamanan pabrik gula di dalam gudang, sehingga gula yang disimpan tidak menggumpal, meleleh dan menjadi rusak. Tujuan dari penelitian ini adalah untuk membuat alat monitoring gudang pabrik gula menggunakan sistem internet of things (IoT). Alat monitoring ruang yang dibuat terdiri dari komponen utama Wemos D1 R2, sensor MQ-135 untuk deteksi CO2, sensor DHT22 untuk deteksi suhu dan kelembaban dan sensor api. Semua komponen disusun di dalam kotak elektronik berwarna hitam dengan ukuran 15 cm x 9,5 cm x 5 cm. Terdapat 5 alat yang digunakan dalam penelitian ini. Uji kinerja sistem meliputi, tingkat stabilitas, Reliabilitas, respon sistem, akurasi pengiriman data. perhitungan penggunaan daya listrik dan biaya operasional alat diukur dalam penelitian ini. Uji kinerja dilakukan selama 7 hari dengan jarak antar ruang 50-100 meter. Dari hasil uji kinerja, seluruh sistem telah bekerja stabil. Hasil pengujian dengan menggunakan Cronbach Alpha taraf 5% menunjukan alat 1 sampai 5 menghasilkan nilai reliabilitas tinggi. Rerata respon sistem ke 5 alat yaitu 8,28 detik. Rerata akurasi transmisi data dari 5 alat meliputi data suhu adalah 0,00124, kelembaban 0,00434, dan Nilai CO2 adalah 0,00678. Hasil uji kinerja menunjukkan bahwa alat telah bekerja dengan baik sesuai harapan.Gula merupakan salah satu komoditas kebutuhan pokok. Proses produksi untuk menghasilkan gula di pabrik terdiri dari beberapa tahapan yaitu penggiingan, pemurnian, penguapan, kristalisasi,  pemisahan dan penyelesaian. Setelah itu, gula yang telah dikemas, disimpan dalam gudang sebelum di kirim ke pasar. Kondisi lingkungan gudang penyimpan gula harus sesuai dengan standar. Faktor lingkungan yang tidak sesuai, mampu merusak gula. Beberapa faktor lingkungan penting dalam gudang penyimpanan yaitu suhu, kelembaban, CO2, dan api. Ketersediaan alat ukur portable berbasis internet of things merupakan salah satu bentuk pengawasan keamanan pabrik gula di dalam gudang, sehingga gula yang disimpan tidak menggumpal, meleleh dan menjadi rusak. Tujuan dari penelitian ini adalah untuk membuat alat monitoring gudang pabrik gula menggunakan sistem internet of things (IoT). Alat monitoring ruang yang dibuat terdiri dari komponen utama Wemos D1 R2, sensor MQ-135 untuk deteksi CO2, sensor DHT22 untuk deteksi suhu dan kelembaban dan sensor api. Semua komponen disusun di dalam kotak elektronik berwarna hitam dengan ukuran 15 cm x 9,5 cm x 5 cm. Terdapat 5 alat yang digunakan dalam penelitian ini. Uji kinerja sistem meliputi, tingkat stabilitas, Reliabilitas, respon sistem, akurasi pengiriman data. perhitungan penggunaan daya listrik dan biaya operasional alat diukur dalam penelitian ini. Uji kinerja dilakukan selama 7 hari dengan jarak antar ruang 50-100 meter. Dari hasil uji kinerja, seluruh sistem telah bekerja stabil. Hasil pengujian dengan menggunakan Cronbach Alpha taraf 5% menunjukan alat 1 sampai 5 menghasilkan nilai reliabilitas tinggi. Rerata respon sistem ke 5 alat yaitu 8,28 detik. Rerata akurasi transmisi data dari 5 alat meliputi data suhu adalah 0,00124, kelembaban 0,00434, dan Nilai CO2 adalah 0,00678. Hasil uji kinerja menunjukkan bahwa alat telah bekerja dengan baik sesuai harapan

The Kinetics of Ethanol Volume Change With Variation of Input Volume and Heating Temperature in The Re-Distillation Process of Glucomannan Extraction Residue Using Batch Distillator

Ethanol is commonly used as a solvent in extracting glucomannan from Porang. However, the extraction process often leaves ethanol. The remaining ethanol can be re-distilled to save the use of it. The remaining ethanol is used in the re-distillation process with input volumes of 50L and 100L with variations in heating temperatures of 80°C, 85°C, and 90°C. This study aimed to analyze the effect of the ethanol input volume and temperature on the output volume of re-distilled ethanol and determine the constant change in volume of re-distilled ethanol using kinetics and Arrhenius equations. The results showed that the input volume and heating temperature variation differed significantly from the ethanol output volume. The k value changes in the ethanol output volume from re-distillation with an input volume of 50L and a temperature variation of 80°C, 85°C, and 90°C respectively were 0.0016, 0.0023, and 0.0027 L/min, while the input volume of 100L was 0.0009, 0.001, and 0.0014 L/min. The results of the k value as a function of temperature using the Arrhenius equation showed that the re-distillation process with an input volume of 50L and 100L produces activation energy (Ea) of 55.83 kJ/mol and 46.94 kJ/mol, while the collision frequency value (A) of 3.03x105/min and 7.7x103/min

Mathematical Model of Drying Edamame (Glycine max (L.) Merill) Using Food Dehydrator Technology Based on Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)

Edamame is included in perishable products or products that have a fairly short shelf life if post-harvest processing is not carried out. One of the post-harvest processing methods commonly used by the community is drying. The purpose of this study was to analyze the drying process of edamame related to the MLRL and ANN models. This study used a completely randomized design (CRD) with three variations of air velocity, namely 1 m/s, 3 m/s, and 5 m/s. Data collection was repeated three times every 30 minutes until 330 minutes. Multiple linear regression (MLR) model training and validation produce accuracy values of 88.03 and 82.23, and the value of R2 of 0.93 and 0.90. While the training and validation of the artificial neural network (ANN) model resulted in accuracy values of 88.34 and 82.15, and R2 values of 0.93 and 0.90

Performance and Prospects of Mobile Rice Mill Unit

Rice milling is one of the important steps in post-harvest operations to get good quality white rice. As an innovation in improving customer service, a mobile rice mill unit (MRMU) has recently been operated in various rice-producing areas. The objective of this study was to evaluate the performance and prospects of MRMU based on technical and economic analysis. The research was conducted in East Lampung Regency, Lampung, Indonesia, by observing three MRMUs. Each mill was observed for three working days to obtain MRMU performance data, namely grain quality, milled yield, working time, fuel consumption, working capacity, and quality of white rice produced. Other information includes machine price, the machine age, estimated economic life, investment, interest rate, fuel consumption, operator wage, milling charge, and repair and maintenance costs. Results showed that MRMU had an actual capacity between 63.29–98.82 kg/hour with a milled yield between 60.41–64.96%. The white rice produced has a proportion of head rice 58.26–61.42%, with a whiteness index less than the SNI for rice quality standards. The unit cost of the rice milling process using MRMU was an average of 457.91 IDR/kg. At a milling charge of 666.67 IDR/kg, the operation of MRMU is economically feasible at an annual working hour higher than 1000 h. In addition, the MRMU operation was not economically acceptable at a milling charge of 500 IDR/kg. With the rapid growth in the rice milling numbers, an unbiased regulation is required to avoid unhealthy competition among the MRMU enterprises

Use of corncob biochar and urea for pakchoi (Brassica rapa L.) cultivation: Short-term impact of pyrolysis temperature and fertiliser dose on plant growth and yield

This study aimed to evaluate the effect of pyrolysis temperature of corncob biochar as a soil amendment and urea fertiliser on the growth and yield of pakchoi. Pakchoi was cultivated in pots (13 cm height, 19 cm upper diameter, and 13.5 cm bottom diameter). Two factors including pyrolysis temperature of biochar and urea dose were combined with four levels each. Pyrolysis temperature factor consisted of B0 (no biochar), B1 (250 °C), B2 (300 °C), and B3 (350 °C). Urea dose consisted of F0 (no urea), F1 (0.6 g pot–1), F2 (1.2 g pot–1), and F3 (1.8 g pot–1). All treatment combinations were randomly designed in triplicates. The amount of biochar was 90 g with total growing media of 3000 g. The results showed that pyrolysis temperature influenced significantly (a = 0.05) growth parameters, fresh yield, and water productivity. Pyrolysis temperature of 350 °C resulted in the highest growth and production with average yield of 30.6 g pot–1, water productivity of 10.09 g cm–3, and fertiliser productivity of 27.59-53.39 g g–1 depending on the dose. In order to have optimal benefits, biochar application should be combined with fertiliser application