Pesticide Residue Reduction on Curly Chili (Capsicum annum L.) Using Ozone Fine Bubble Technology.

Residu pestisida pada cabai keriting dapat menimbulkan gangguan kesehatan bagi konsumen. Pencucian cabai keriting menggunakan air ozon fine bubble merupakan metode yang menjanjikan untuk mengurangi residu pestisida. Penelitian ini bertujuan untuk mendapatkan dosis dan durasi optimal ozon fine bubble dalam mendegradasi residu pestisida khususnya profenofos, serta mengetahui pengaruhnya terhadap umur simpan dan mutu fisik cabai keriting. Setelah dicuci, cabai keriting disimpan pada suhu ruang dan diamati setiap dua hari sekali. Hasil penelitian menunjukkan bahwa pencucian dengan ozon fine bubble 1 ppm selama 10 menit efektif menurunkan residu profenofos pada cabai keriting hingga 89,8% tanpa mengurangi kualitasnya. Umur simpan cabai keriting diamati dan mulai kehilangan nilai komersialnya setelah 6-8 hari.Pesticide residues in curly chilies may cause health problems in consumers. Washing curly chilies using ozone fine-bubble water is a promising method for reducing pesticide residues. The aim of this study was to determine the optimal dose and duration to degrade pesticide residues, especially for profenofos, and to determine their effect on the shelf life and physical quality of curly chilies. After washing, the curly chilies were stored at room temperature and observed every two days. The results showed that washing with 1 ppm ozone fine bubble water for 10 min was effective in reducing profenofos residue on curly chili by up to 89.8% without reducing its quality. The shelf life of curly chilies was observed, and they started losing their commercial value after 6-8 days. &nbsp

Ergonomic Analysis of Small-Scale Palm Sugar Starch Processing Industry in Rancakalong Village, Sumedang Regency

Rancakalong village, Sumedang Regency, has been known for its small-scale palm sugar starch processing industry since 2013. This industry produces starch from palm trees, which is crucial for food and beverages. Generally, the processes involved in processing palm sugar starch include (i) splitting of the palm, (ii) grating, (iii) coarse fiber screening, (iv) fine fiber screening, (v) harvesting, and (vi) drying. Initial evaluations indicated that the workers experienced physical discomfort during their work. Therefore, this study aimed to analyze the ergonomic aspects of processing palm sugar starch, including the working posture, standard time, workload, noise, and vibration. Based on the analysis, the risk level for each work element in processing palm sugar starch fell into the high-risk (score 8-10) and very high-risk (score 11-15) categories. These risks cause discomfort due to inappropriate work posture and duration, necessitating significant changes. The standard time required for each work element was as follows: 212.88±28.43 seconds/kg (palm splitting), 363.45±12.90 seconds/kg (grating), 95.08±9.74 seconds/kg (coarse fiber screening), 192.05±21.27 seconds/kg (fine fiber screening), 35.57±5.78 seconds/kg (harvesting), and 1821.01±41.09 seconds/kg (drying). Regarding workload analysis, processing palm sugar starch activities fell into the moderate category with total energy cost (TEC) values ranging from 92.66±1.50 265.55±3.88 kcal/hour. Regarding noise and vibration analysis, the grating work element was identified as the station with the highest exposure, i.e., 96.00±0.82 dB and 1.6±0.05 m/s2, respectively. The results of this study could be used as a basis for developing more efficient work procedures, maintaining health, and improving safety in the processing of palm sugar starch.Palm starch processing is a process to produce starch from sugar palm plants, which is used as a raw material in the food and beverage industry. In general, palm starch processing activities include (i) splitting, (ii) grating, (iii) filtering coarse fiber, (iv) filtering fine fiber, (v) harvesting, and (vi) drying. Initial evaluations indicated that workers experienced physical discomfort during work. Therefore, this research aims to analyze the ergonomic aspects of palm starch processing activities, including work posture, standard time, workload, noise and vibration. Based on the analysis, the level of risk in each work element in palm starch processing includes high risk (score 8-10) and very high (score 11-15). This risk causes discomfort because work attitudes and duration do not follow standards, so changes are needed. The standard time required for each work element is as follows: 212.88 s (splitting), 363.45 s (grating), 95.08 s (filtering coarse fiber), 192.05 s (filtering fine fiber), 35 .57 seconds (harvesting), and 1821.01 s (drying). Regarding workload analysis, the Total Energy Cost (TEC) value as the level of exhaustion for sugar palm starch processing activities ranges from 92.66 kcal/hour to 265.55 kcal/hour. Regarding noise and vibration analysis, the grating workstation was identified as the station with the highest exposure values, i.e., 96 dB and 1.6 m/s2. The results of this research can be used as a basis for developing more efficient work procedures, maintaining health and increasing safety in palm starch processing activities

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy

Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6)

Pembangunan Model Mask R-CNN untuk Identifikasi Daun dan Cabang Tanaman Melon

The quality of melons can be enhanced and optimized by pruning melon plants. Pruning is a removal process carried out on specific parts of the plant. Currently, melon plants are still pruned manually by farmers, but there are many drawbacks to this method. In this research, pruning is conducted on the branches and leaves of melon plants. Pruning can be facilitated with the assistance of a robot capable of recognizing leaves and branches. In this study, the method used to detect branches and leaves is the Mask Region-based Convolutional Neural Network (Mask R-CNN). Hyperparameter tuning technique is employed to obtain the best parameter values, including learning rate, weight decay, and learning momentum. Two scenarios are considered in this research, one with 10 epochs and the other with 30 epochs. The obtained Average Precision (AP) values at 10 epochs are 32.2% for leaf objects and 0% for branches. At 30 epochs, the AP values are 56.8% for leaf objects and 4.1% for branches. The mean Average Precision (mAP) is 16.1% for 10 epochs and 28.4% for 30 epochs.Mutu buah melon dapat ditingkatkan dan dioptimalkan dengan melakukan pemangkasan pada tanaman melon. Pemangkasan merupakan proses penghilangan yang dilakukan pada bagian tanaman tertentu. Saat ini tanaman melon masih dipangkas secara manual oleh petani, namun cara ini mempunyai banyak kekurangan. Pada penelitian ini pemangkasan dilakukan pada cabang dan daun tanaman melon. Pemangkasan dapat dilakukan dengan bantuan robot yang mampu mengenali daun dan dahan. Pada penelitian ini metode yang digunakan untuk mendeteksi cabang dan daun adalah Mask Region-based Convolutional Neural Network (Mask R-CNN). Teknik tuning hyperparameter digunakan untuk mendapatkan nilai parameter terbaik, termasuk learning rate, peluruhan bobot, dan momentum pembelajaran. Dua skenario dipertimbangkan dalam penelitian ini, satu dengan 10 epoch dan yang lainnya dengan 30 epoch. Nilai Average Precision (AP) yang diperoleh pada 10 epoch sebesar 32,2% untuk objek daun dan 0% untuk objek cabang. Pada 30 epoch, nilai AP adalah 56,8% untuk objek daun dan 4,1% untuk cabang. Rata-rata Presisi Rata-rata (mAP) adalah 16,1% untuk 10 epoch dan 28,4% untuk 30 epoc

Physical Characteristics of Flakes with Variations Kepok Banana Bud (Musa paradisiaca Linn.) and Mocaf Flour

Yellow Kepok banana buds (KBB) are well-known for their high dietary fiber content and prospective use as functional food additives. High fiber consumption has been linked to diabetes prevention. The production of flakes derived from KBB has the potential to facilitate individuals in incorporating high-fiber food items into their daily diets. Nevertheless, the utilization of just Yellow KBB flour yields flakes that are deemed undesirable by consumers due to their firm consistency and deep hue. The substitution of Yellow KBB flour with a combination of wheat flour and mocaf has the potential to enhance the physical characteristics of the resulting flakes. The objective of this study is to assess the physical attributes of flakes derived from KBB. This particular flake was produced using a series of five distinct formulations. The present study employed a completely randomized design (CRD) with three replications. The formulas consist the proportions of wheat flour (WF), mocaf flour (MF), and Yellow Kepok banana bud (KBB). The following are the five ratios: (100%:0%:0%), (50%:50%:0%), (50%:37.5%:12.5%), (50%:25%:25%), and (50%:37.5%:12.5%). The water absorption, swelling ability, texture, and color of flakes produced from wheat, mocaf, and KBB flour were examined. The F2 sample, consisting of a composition of 50% wheat flour, 37.5% mocaf flour, and 12.5% yellow KBB flour, exhibited the highest water absorption value (63.19%) among all the samples. In addition to this, F2 can be characterized as a specimen exhibiting a relatively low level of hardness (1.63 N) and a correspondingly low fracture value (2.08 N). The F2 flakes had a much higher brightness value (37.06) in comparison to the other samples used yellow KBB flour

Rapid Prediction of Moisture and Ash Content in Sungkai Leaves Herbal Tea (Peronema canescens Jack.) using NIR Spectroscopy

It is imperative to measure the chemical composition of Sungkai leaf herbal tea in order to produce high-quality goods that promote human health. The moisture and ash content of Sungkai leaf herbal tea are critical parameters for assessing the quality of herbal tea. This study aimed to evaluate an NIR spectroscopy method for quickly determining the moisture and ash content of Sungkai leaf herbal tea. Sungkai leaf herbal tea has a moisture content between 3.93% and 7.59%, and an ash content between 3.94% and 5.51%. We developed a calibration model using partial least squares (PLS) with several pretreatment methods. We split the data into calibration and prediction sets and performed an internal random cross-validation. A PLS calibration model with Rp2 = 0.86, a root means square error of prediction (RMSEP) of 0.30 (%), and a residual predictive deviation (RPD) of 2.76, performed exceptionally well at predicting the moisture content when the standard normal variate (SNV) pre-treatment was applied to the NIR spectra. The Savitzky-Golay derivative (a 9-point smoothing window, second-order polynomial, dg2) pre-treatment method also generated the best PLS calibration model for ash content determination, with Rp2 = 0.70, RMSEP = 0.16 (%), and RPD = 1.86. NIR spectroscopy can quickly determine the moisture and ash content of Sungkai leaf herbal tea, as suggested by these findings

Sifat Fisiko Kimia Tepung Gembili (Dioscorea esculenta L.) dari Umbi Berdaging Putih dan Berdaging Putih-Keunguan

Flour quality in the form of physicochemical and sensory properties is an essential parameter that will influence the design, process, and results of the processing of gembili flour derivative products. This study aims to evaluate flour's physical and chemical properties from white-fleshed gembili tubers (Yawal Porei) and purplish-white-fleshed gembili tubers (Thai) and determine consumer preferences for the flour produced. The physical properties of gembili flour were analyzed, including yield, whiteness, and fineness modulus (FM). Meanwhile, the chemical composition of the flour analyzed includes moisture content, carbohydrates, protein, ash content and crude fibre. 35 untrained panellists were used in organoleptic tests to assess consumer preferences. The results show that the two gembili tubers produce flour with different physical and chemical properties. White-fleshed gembili flour has several advantages in terms of physical and chemical properties, namely yield (15.62% ± 0.41), whiteness (79.55 ± 0.98), carbohydrates (82.86% ± 0.21) and crude fibre (5.28% ± 0.61) which has the potential as a rice analogue, noodles, cake, fillers, and cookies. Meanwhile, purplish-white-fleshed gembili flour has a high protein content (5.40% ± 0.16) and ash content (6.75% ± 0.05), which has the potential as a bakery product. The FM of the two types of gembili flour was not much different and has a moisture content that meets the Indonesian National Standard, below 14.5%. The sensory assessment showed that the panellists preferred white-fleshed gembili flour to purplish-white-fleshed gembili flour. The sensory assessment showed that the panellists preferred white-fleshed gembili flour to purplish-white-fleshed gembili flour.Mutu tepung berupa sifat fisikokimia dan sensoris merupakan parameter yang sangat penting yang akan mempengaruhi desain, proses, dan hasil dalam pengolahan produk turunan tepung gembili. Penelitian ini bertujuan untuk mengevaluasi sifat fisik dan kimia tepung dari umbi gembili berdaging putih (Yawal Porei) dan tepung gembili berdaging putih-keunguan (Thai) serta mengetahui preferensi konsumen terhadap tepung yang dihasilkan. Sifat fisik tepung gembili dianalisis antara lain rendemen, derajat putih, dan modulus kehalusan (FM). Sedangkan, komposisi kimia tepung yang dianalisis meliputi kadar air, karbohidrat, protein, kadar abu dan serat kasar. Sebanyak 35 orang panelis tidak terlatih digunakan pada uji organoleptik untuk menilai preferensi konsumen. Hasil yang diperoleh menujukkan bahwa kedua jenis umbi gembili menghasilkan tepung dengan sifat fisik dan kimia yang berbeda. Tepung gembili putih mempunyai beberapa keunggulan dari segi sifat fisik dan kimia yakni rendemen (15.62%±0.41), derajat putih (79.55±0.98), karbohidrat (82.86%±0.21) dan serat kasar (5.28% ± 0.61) yang berpotensi sebagai beras analog, mie, bahan campuran cake, filler, maupun cookies. Sedangkan, tepung gembili putih-keunguan tinggi kadar protein (5.40%±0.16) dan kadar abu (6.75%±0.05) yang berpotensi dibuat sebagai produk roti. FM kedua jenis tepung gembili tidak jauh berbeda (1.10 ± 0.13 dan 1.11 ± 0.0) serta memiliki kadar air telah memenuhi Standar Nasional Indonesia (SNI) yaitu di bawah 14,5%. Penilaian sensoris menunjukkan bahwa tepung gembili putih lebih disukai oleh panelis dibandingkan tepung gembili putih-keunguan. Tepung gembili yang diperoleh dari penelitian ini berpotensi untuk dikembangkan sebagai tepung komposit untuk produk pangan

Determination of Sustainable Factory Locations for the Lemon Agroindustry using AHP, Mapping and Water Management

This research was conducted in Suntenjaya Village, Lembang, and West Bandung Regency, focusing on lemon agro-industrial development. Research was conducted using the Analytical Hierarchy Process (AHP) approach, area mapping, and water management to determine a sustainable factory location. The main objective is the selection of factory sites by integrating lemon production, considering sustainable agriculture aspects, product aspects, and water conservation programs. The results of this study provide a strong foundation for sustainable agro-industrial development that will support sustainable agriculture, local economy, and environmental protection. The research also combined qualitative and quantitative elements with a mixed approach that included Focus Group Discussion (FGD) and AHP, and the results showed that integrated drainage management was the top priority, followed by sanitation, clean water, reforestation, and sustainable agriculture. Mapping of areas based on geographical characteristics, such as rainfall, slope, and soil type, provided a map of water infiltration rates, which became a key guide in planning water conservation programs. The ultimate location for the lemon agroindustry is half of Desa Suntenjaya, mostly from the center to the northern area, which needs to consider proximity to markets in Bandung Regency and City, easy access to sources of raw materials for lemons, water availability, adequate transportation infrastructure, access to energy sources, suitable climate for lemon growth, and the availability of adequate labor in the region.This research was conducted in Suntenjaya Village, Lembang, West Bandung Regency, focusing on lemon agro-industry development through the Analytical Hierarchy Process (AHP) method approach, area mapping, and water management to determine a sustainable factory location. The main objective is the selection of factory site by integrating lemon production considering sustainable agriculture aspects, product aspects, and water conservation programs. The results of the study provide a strong foundation for sustainable agro-industrial development will support sustainable agriculture, local economy, and environmental protection. The research also combined qualitative and quantitative elements with a mixed approach that included Focus Group Discussion (FGD) and AHP with the results showing that integrated drainage management was the top priority, followed by sanitation, clean water, reforestation, and sustainable agriculture. Mapping of areas based on geographical characteristics such as rainfall, slope, and soil type provided a map of water infiltration rates that became a key guide in planning water conservation programs. This research has positive implications in supporting sustainable agricultural practices and local economic empowerment. The results of the AHP analysis and area mapping form a strong framework for the development of a sustainable lemon agro-industry with positive economic and environmental impacts

An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system

Penyimpangan Iklim ENSO dan IOD di Kalimantan Tengah Serta Kaitannya dengan Produksi Kelapa Sawit

Kelapa sawit merupakan tanaman perkebunan yang membutuhkan curah hujan yang merata sepanjang tahun. Curah hujan diIndonesia memiliki 3 pola yaitu pola monsoonal, equatorial dan lokal. PT. Harapan Hibrida Kalbar Sungai Bila Estate merupakan wilayah kajian penelitian ini memiliki pola curah hujan equatorial. Curah hujan dapat menyimpang dari pola kondisi iklim pada umumnya karena adanya variabilitas iklim El Nino Southern Oscillation dan Indian Ocean Dipole. Hasil koefisien korelasi pearson antara curah hujan musim Juni Juli Agustus dan September Oktober November dengan indeks El Nino Southern Oscillation sebesar -0,78** dan -0,64*. El Nino Southern Oscillation Memiliki hubungan yang kuat dan terbalik dengan curah hujan diwilayah kajian saat musim kemarau dengan nilai signifikan pada selang kepercayaan 0,01 ( Juni, Juli, Agustus) dan 0,05 (September, Oktober, November) Hasil koefisien korelasi pearson antara curah hujan musim Juni Juli Agustus dan September Oktober November dengan indeks Indian Ocean Dipole sebesar -0,4 dan -0,5. Pengaruh El Nino Southern Oscillation lebih kuat dibandingkan dengan Indian Ocean Dipole di wilayah kajian. Fase El Nino (lanina) menyebabkan curah hujan diwilayah kajian menjadi lebih rendah (tinggi) dari kondisi normal, sehingga terjadi kemarau Panjang (Kemarau basah). Produksi kelapa sawit pada jenis tanah Sandy Loam lebih fluktuatif dan lebih rentan saat terjadi kemarau panjang dibandingkan jenis tanah clay. Produksi kelapa sawit lebih dipengaruhi oleh jumlah hari hujan dibandingkan jumlah akumulasi curah hujan dalam setahun.Oil palm is a plantation crop that requires even rainfall throughout the year. Rainfall in Indonesia has 3 patterns, namely monsoonal, equatorial and local patterns. PT Harapan Hybrid Kalbar Sungai Bila Estate is the study area for this research which has an equatorial rainfall pattern. Rainfall can deviate from the general pattern of climatic conditions due to the climate variability of the El Nino Southern Oscillation and the Indian Ocean Dipole. The results of the Pearson correlation coefficient between the June July August and September October November rainfall with the El Nino Southern Oscillation index are -0.78** and -0.64*. El-Nino Southern Oscillation has a strong and inverse relationship with rainfall in the study area during the dry season with a significant value at a confidence interval of 0.01 (June, July, August) and 0.05 (September, October, November). Results of the Pearson correlation coefficient between rainfall monsoon rains June July August and September October November with an Indian Ocean Dipole index of -0.4 and -0.5. The influence of the El Nino Southern Oscillation is stronger than that of the Indian Ocean Dipole in the study area. The El Nino (Lanina) phase causes rainfall in the study area to be lower (higher) than normal conditions, resulting in a long dry season (wet dry season). Palm oil production on Sandy Loam soil types is more volatile and more vulnerable during long periods of drought than clay soil types. Palm oil production is more influenced by the number of rainy days than the amount of accumulated rainfall in a year