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All Journal International Journal of Renewable Energy Development JOIV : International Journal on Informatics Visualization Communications in Science and Technology Jurnal Pasopati : Pengabdian Masyarakat dan Inovasi Pengembangan Teknologi
Moh Djaeni
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering

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Enhancement of Energy Efficiency and Food Product Quality Using Adsorption Dryer with Zeolite Djaeni, Moh; Sasongko, S.B.; van Boxtel, A.J.B.
International Journal of Renewable Energy Development Vol 2, No 2 (2013): July 2013
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2.2.81-86

Abstract

Drying is a basic operation in wood, food, pharmaceutical and chemical industry. Currently, several drying methods are often not efficient in terms of energy consumption (energy efficiency of 20-60%) and have an impact on product quality degradation due to the introduction of operational temperature upper 80oC. This work discusses the development of adsorption drying with zeolite to improve the energy efficiency as well as product quality. In this process, air as drying medium is dehumidified by zeolite. As a result humidity of air can be reduced up to 0.1 ppm. So, for heat sensitive products, the drying process can be performed in low or medium temperature with high driving force. The study has been conducted in three steps: designing the dryer, performing laboratory scale equipment (tray, spray, and fluidised bed dryers with zeolite), and evaluating the dryer performance based on energy efficiency and product quality. Results showed that the energy efficiency of drying process is 15-20% higher than that of conventional dryer. In additon, the dryer can speed up drying time as well as retaining product quality.
Optimization of ultrasound assisted extraction of sappan (Caesalpinia sappan L) wood for preparation of high quality extract Djaeni, Moh; Budi Sasongko, Setia; Yuni Susanti, Devi; Mahatmanti, F Widhi; Cahyo Kumoro, Andri; Kurniasari, Laeli
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1710

Abstract

This study optimized the Ultrasound-Assisted Extraction (UAE) of sappan wood (Caesalpinia sappan L.) using Central Composite Design-Response Surface Methodology (CCD-RSM) and investigated its kinetics. Temperature, solvent-to-solid ratio, and extraction time were selected as independent variables with extract yield as the response. Analysis of Variance (ANOVA) showed that the solvent-to-solid ratio significantly affected yield. Optimal extraction conditions were 69.9°C, 29.9 mL/g, and 20.2 min, producing approximately yield of 0,293 mg GAE/g sample. High Performance Liquid Chromatography (HPLC) confirmed the presence of brazilin, while Fourier Transform InfraRed (FTIR) analysis indicated the retention of functional groups. UAE was shown to enhance extraction efficiency and preserve phenolic compounds. Additionally, the extraction process was modeled, resulting in a validated effective diffusivity (De) of 1.8 × 10?? cm²/s, The kinetic study was useful in industrial application especially to determine appropriate extraction time.
EDUKASI PENGELOLAAN DAN KONSERVASI SUMBER DAYA AIR SEBAGAI UPAYA PENGEMBANGAN POTENSI DAERAH WISATA DESA COKRO Dwi Hatmoko, Jati Utomo; Djaeni, Moh; Islam Filardli, Abdullah Malik; Muhammad, Rizdian Arsyal; Budi Santosa, Ari Wibawa; Azalia, Nashwa
Jurnal Pasopati Vol 7, No 2 (2025): Vol 7, No 2 (2025)
Publisher : Fakultas Teknik Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/pasopati.2025.29145

Abstract

Program Kuliah Kerja Nyata (KKN) Tematik yang dilaksanakan di Desa Cokro bertujuan untuk mendorong potensi desa menjadi kawasan wisata berbasis kelestarian lingkungan. Desa ini memiliki kekayaan alam berupa Umbul Ingas dan aliran Sungai Pusur, yang telah dimanfaatkan sebagai objek wisata, namun masih menyimpan potensi untuk dikembangkan lebih lanjut. Pelibatan masyarakat dalam menjaga keberlanjutan kawasan tersebut menjadi hal yang krusial melalui upaya konservasi lingkungan. Salah satu pendekatan yang digunakan dalam kegiatan ini adalah edukasi mengenai pentingnya pengelolaan air bersih secara berkelanjutan serta teknologi pengolahan limbah dengan metode presentasi dan poster. Kegiatan ini juga disertai dengan usulan pemanfaatan limbah tersebut menjadi pupuk organik cair sebagai bentuk penguatan sistem pengolahan limbah. Selain itu, dirumuskan pula rancangan pengembangan wilayah wisata air baru pada bagian aliran Sungai Pusur yang belum dimanfaatkan, dengan menerapkan prinsip prinsip pengelolaan sumber daya air yang berwawasan lingkungan. Seluruh rangkaian kegiatan ini diharapkan dapat menjadi kontribusi nyata dalam mendukung Desa Cokro sebagai desa wisata yang berkelanjutan.Kata kunci : Wisata, Pengelolaan, Konservasi, Air, Limbah tahu cair
Deep Learning Models Performance for Classifying Dried Chili Based on Digital Image Analysis Yudantoro, Tri; Prayitno, Prayitno; Rizal Isnanto, Rizal; Djaeni, Moh
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2919

Abstract

Artificial Intelligence and agriculture have been combined to create significant advancements in smart agricultural analysis that have improved both output and quality. This approach has completely changed conventional farming operations by utilizing image processing technologies. To assess the dryness levels of red chili peppers—a crucial component of crop quality and market value—the study set out to compare the efficacy of several CNN architectures. A dataset with 600 training images and 150 testing images spread over three classes was used to evaluate four CNN models (MobileNetV2, DenseNet121, InceptionV3, NASNetMobile). With a validation accuracy of 99%, DenseNet121 outperformed MobileNetV2 (which had a validation accuracy of 97%). The findings demonstrate how deep learning models can improve sorting procedures for agriculture by increasing accuracy and productivity. A scalable, impartial, and economical way to uphold crop standards and promote industry sustainability is by incorporating CNNs into the classification of agricultural products. The results of this study represent a breakthrough in the application of deep learning to agriculture, opening the door to automated systems that guarantee constant product quality. By optimizing yield and quality through image processing technology, the findings highlight the revolutionary influence of AI in smart agriculture. To increase production and improve competitiveness in the market, future research efforts may focus on developing automated sorting systems and further enhancing CNN models for agricultural applications. The research adds to the increasing corpus of work using AI in agriculture to enhance crop management and quality control.  
Co-Authors A.J.B. van Boxtel Andri Cahyo Kumoro Ari Wibawa Budi Santosa Azalia, Nashwa F Widhi Mahatmanti Islam Filardli, Abdullah Malik Jati Utomo Dwi Hatmoko Laeli Kurniasari Muhammad, Rizdian Arsyal Prayitno Prayitno Rizal Isnanto, Rizal S.B. Sasongko Setia Budi Sasongko Yudantoro, Tri Yuni Susanti, Devi