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"Penyakit jerawat merupakan sebuah kondisi kulit yang umum yang ditandai dengan folikel rambut yang tersumbat, tidak hanya mempengaruhi penampilan fisik tetapi juga mempengaruhi kepercayaan diri individu. Metode diagnostik yang maju sangat penting untuk menentukan tingkat keparahan jerawat, yang dapat membimbing strategi pengobatan yang efektif. Penelitian ini memperkenalkan pendekatan inovatif untuk mengklasifikasikan tingkat keparahan jerawat pada citra wajah menggunakan Diagnostic Evidence Distillation yang memanfaatkan multi teacher knowledge distillation. Studi ini meningkatkan model single teacher konvensional dengan menggabungkan beberapa arsitektur teacher, memungkinkan transfer pengetahuan yang lebih kuat dan akurat ke student model. Penulis mengembangkan dan menguji model yang mengintegrasikan Convolutional Neural Network (CNN) dengan kerangka kerja multi teacher untuk meningkatkan akurasi prediktif. Pendekatan yang dilakukan menggabungkan secara unik diagnostic evidence dengan teknik deep learning untuk mengoptimalkan proses klasifikasi. Evaluasi dilakukan menggunakan dataset ACNE04, yang dianotasi menurut kriteria Hayashi, memastikan representasi komprehensif dari berbagai tingkat keparahan jerawat. Hasilnya menunjukkan bahwa model multi teacher knowledge distillation mencapai akurasi yang lebih unggul dibandingkan model single teacher sebelumnya, dengan peningkatan akurasi menjadi 90.00%, melampaui benchmark sebelumnya sebesar 86.06%. Ini menunjukkan kemajuan signifikan dalam klasifikasi otomatis tingkat keparahan jerawat. Studi penulis tidak hanya memberikan kemajuan metodologis dalam bidang pemrosesan gambar medis tetapi juga berkontribusi pada penilaian tingkat keparahan jerawat yang lebih akurat dan dapat diandalkan, berpotensi meningkatkan hasil pengobatan dan perawatan pasien.

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Acne is a common skin condition characterized by clogged hair follicles, affecting not only physical appearance but also an individual's self-confidence. Advanced diagnostic methods are crucial for determining the severity of acne, which can guide effective treatment strategies. This research introduces an innovative approach to classifying the severity of acne in facial images using Diagnostic Evidence Distillation that utilizes multi teacher knowledge distillation. This study enhances the conventional single teacher model by integrating multiple teacher architectures, allowing for a more robust and accurate knowledge transfer to the student model. The authors developed and tested a model that integrates a Convolutional Neural Network (CNN) with a multi-teacher framework to improve predictive accuracy. The approach uniquely combines diagnostic evidence with deep learning techniques to optimize the classification process. The evaluation was conducted using the ACNE04 dataset, annotated according to the Hayashi criteria, ensuring a comprehensive representation of various acne severities. The results show that the multi-teacher knowledge distillation model achieves superior accuracy compared to the previous single-teacher model, with an improved accuracy of 90.00%, surpassing the previous benchmark of 86.06%. This indicates significant advancement in the automated classification of acne severity. The authors' study not only provides methodological advancement in the field of medical image processing but also contributes to more accurate and reliable assessments of acne severity, potentially enhancing treatment outcomes and patient care."

"ABSTRAKThe productivity of water treatment through distillation method was studied by varying the size of the zinc heat absorber in a solar-based distillation unit. An additional zinc heat absorber was proposed to improve the efficiency of the distillation unit. This research investigates the usage of zinc heat absorber with size 10 to 90% of water surface area. The temperature at various locations inside the distillation unit was monitored throughout the operation in order to obtain data necessary for the equation engineering solver method, which was conducted to calculate the efficie orber that is 10% of the water area produced 1.43 liters of condensed product per day providing efficiency of 25.99%. The efficiency reduced significantly to 15.02% when the sizeof the heat absorber was increased to 90%. "

"Telah dilakukan penelitian mengenai analisis efisiensi termal pada proses distilasi low grade bioetanol pada bulan Maret hingga Juni 2010. Penelitian tersebut bertujuan untuk mengetahui jumlah kalor yang diterima oleh bioethanol, sehingga dapat diketahui efisiensinya. Hal tersebut penting sebagai pertimbangan untuk penelitian lebih lanjut mengenai pengintegrasian portable mini distillator pada mesin statis dan dinamis. Untuk mengetahuhi jumlah kalor yang diserap oleh bioetanol serta efisiensi termal portable mini distillator, maka dilakukan pengujian berdasarkan variasi konsentrasi (40%, 50%, 60%, dan 65%) dan variasi volume (300 ml, 400 ml, dan 500 ml) bioetanol. Hasil yang diperoleh menunjukkan bahwa efisiensi termal berkorelasi negatif terhadap konsentrasi sampel, namun berkorelasi positif terhadap volume sampel. Sementara itu, efisiensi termal menunjukkan rentang 13% hingga 22%, tergantung dari komposisi variabel dan konsentrasi bioetanol.

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Research emphasized on the analysis of thermal efficiency in low grade bioethanol distillation process had conducted on March until June 2010. This research aims to observe the amount of heat reserved by bioethanol, therefore it will show the thermal efficiency. It is important as consideration in advanced research concerned in how to integrate the portable mini distillator (PMD) on static and dynamic engine.

In order to know the amount of heat reserved by bioethanol and the thermal efficiency of PMD, we have to do some test based on variations in concentration (40%, 50%, 60%, 65%) and volume (300 ml, 400 ml, 500 ml) of bioethanol.

The result indicates that the thermal efficiency has a negative correlation with bioethanol concentration, but has a positive correlation of sample volume. meanwhile the thermal efficiency indicates in range from 13% to 22% that depends on variable composition and bioethanol concentration."

"ABSTRAKTelah dilakukan isolasi minyak atsiri daun surian (Toona sureni (Bl.) Merr) dengan metoda destilasi uap air. Hasildestilasi berwarna kuning kecoklatan, bau yang sangat menyengat dengan rendemen rata-rata 0,23%. Analisis GC-MSmemperlihatkan beberapa senyawa terpenoid (turunan naftalen) yang cocok dengan spektrum. Salah satunya adalahcopaene (C15H24) dengan berat molekul (m/z = 204). Krim dasar dari formulasi Rajin et al., memberikan krim yangstabil dalam penyimpanan dengan warna putih pucat dan pH yang netral. Penambahan minyak atsiri pada krim dasarmenyebabkan terjadinya perubahan warna dan aroma krim. Kestabilan krim juga mulai terganggu pada komposisiminyak atsiri 10%

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AbstractIsolation of volatile oil of leaf surian (Toona sureni(Bl.) Merr.) have been done by distillationaqueous vapour method. Distillation result had the chocolatecolored and stinging aroma,and rendementwas 0.23%.GC-MS analysis shown of some compound of terpenoid (naftalen derivated) which correspondence to mass spectrum.One of them is copaene (C15H24), m/z = 204. Cream based of Rajin et al. formula giving stable cream, white colouredand neutral pH. Volatile oil of surian leaf in cream based cause color and aroma cream were changed. Cream stabilityalso was annoyed at oil of atsiri 10%"