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All Journal International Journal of Renewable Energy Development JKPK (Jurnal Kimia dan Pendidikan Kimia) Molekul: Jurnal Ilmiah Kimia
Tjahjono, Martin
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Energy Materials Science & Nanotechnology

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Assessing the feasibility of gray, blue, and green ammonia productions in Indonesia: A techno-economic and environmental perspective Tjahjono, Martin; Stevani, Isabella; Siswanto, Gracheilla A; Adhitya, Arief; Halim, Iskandar
International Journal of Renewable Energy Development Vol 12, No 6 (2023): November 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.58035

Abstract

Ammonia, owing to its carbon-free attributes, stands as a promising alternative for replacing fossil-based fuels. This study investigates the techno-economic and environmental aspects of gray, blue, and green ammonia production in Indonesia. In this regard, a spreadsheet-based decision support system has been developed to analyze the levelized cost of each mode of ammonia production and their cost sensitivity across various parameters. The results of the analysis show a levelized cost of gray ammonia of $297 (USD) per ton, which is strongly affected by natural gas prices and carbon taxation. Blue ammonia emerges as the most stable production option with a levelized cost of $390 per ton, impacted by natural gas prices and the expenses associated with carbon sequestration. On the other hand, the levelized cost of green ammonia varies between $696 to $1,024 per ton and is predominantly influenced by the choice of electrolyzers, the cost of renewable energy sources, and maintenance and operational expenditures. Furthermore, the study reveals that gray and blue ammonia production result in emissions of 2.73 and 0.28 tons of CO2 equivalent per ton of ammonia, respectively, while in-situ carbon emissions from green ammonia can be considered negligible. Overall, this study underscores the potential of implementing green ammonia production utilizing geothermal or hydropower renewable energy resources as viable solutions for decarbonizing the power, industry, and transport sectors in Indonesia. Several policy recommendations aimed at overcoming existing barriers to the development of green ammonia plants in the country are also provided.
Endoscope-Based Digital Image Colorimetry for Nitrite Quantification in Processed Meats Halim, Giovania; Wangsawijaya, Valerie; Gondowardojo, Grace Renata; Tjahjono, Martin
Molekul Vol 21 No 1 (2026)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jm.2026.21.1.14528

Abstract

ABSTRACT. This study introduces a novel digital image colorimetry (DIC) approach for quantifying nitrites in processed meats, specifically sausages, to meet the demand for more accessible and cost-effective testing methods in food safety. By utilizing a custom-designed, 3D-printed imaging box to ensure standardized experimental conditions and incorporating an endoscope as the image sensor to reduce variations caused by smartphone camera specifications, the DIC method enhances the accuracy and reliability of analysis. The method achieved comparable accuracy to the Griess spectroscopy method (average error <5%) while providing significant advantages in portability, affordability, and accessibility for on-site nitrite concentration monitoring. Validation against the Griess spectroscopic technique demonstrated consistent results in quantifying nitrites in both commercial sausages and standard samples. This study highlights the utility of the DIC method as a viable tool for routine nitrite testing in the food industry, supporting consumer safety and regulatory compliance. Furthermore, the proposed approach paves the way for broader applications in food safety and analytical chemistry. Keywords: 3D-printed device, digital image colorimetry, endoscope sensor, food safety monitoring, Griess analysis, nitrite quantification
Smartphone-Based Digital Image Analysis for Qualitative Classification of Food Dyes Using Machine Learning: Effects of Color Space and Lighting Conditions Tjandra, Yozef; Halim, Giovania; Briano, Jansen; Tjahjono, Martin
JKPK (Jurnal Kimia dan Pendidikan Kimia) Vol 10, No 2 (2025): JKPK (Jurnal Kimia dan Pendidikan Kimia)
Publisher : Program Studi Pendidikan Kimia FKIP Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkpk.v10i2.107520

Abstract

Smartphone-based digital image analysis (DIA) has emerged as an affordable and accessible method for chemical analysis, particularly in colorimetry. While most existing studies have focused on quantitative applications, this study explores a machine learning–assisted DIA approach for the qualitative classification of synthetic food dyes. Digital images of nine food dyes solutions (Carmoisine, Sunset Yellow, Allura Red, Ponceau 4R, Tartrazine, Fast Green FCF, Brilliant Blue FCF, Quinoline Yellow WS, and Indigo Carmine), were captured under both controlled (closed) and open lighting conditions using a smartphone camera. The images were subsequently processed to extract color values in different color spaces, namely RGB, normalized RGB (rgb), HSL, and CIELAB. These values served as input features for a k-nearest neighbors (KNN) classifier trained to identify the dye present in each solution. The KNN model performed well on model solutions, with at least 86% accuracy across all color spaces and lighting conditions. To assess practical applicability, the classifier was also tested on seven commercial food and health products. The results show that HSL color space yielded the highest classification accuracy in the commercial sample testing, across both lighting setups, with the open condition consistently producing better performance. These findings demonstrate the potential use of smartphone-based DIA combined with machine learning for low-cost, portable, and reliable solutions for qualitative colorimetric analysis. 
Co-Authors Adhitya, Arief Briano, Jansen Gondowardojo, Grace Renata Halim, Giovania Halim, Iskandar Siswanto, Gracheilla A Stevani, Isabella Tjandra, Yozef Wangsawijaya, Valerie