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All Journal Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Medica Hospitalia bit-Tech
Pratama, Yudistira
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Computer Science & IT Dentistry Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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Pembuatan Briket Pelepah Sawit Menggunakan Proses Torefaksi Pada Variasi Tekanan Dan Penambahan Perekat Tapioka Pratama, Yudistira; Helwani, Zuchra; Komalasari, Komalasari
Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Vol 4, No 1 (2017): Wisuda Februari Tahun 2017
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains

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Abstract

Palm frond so far only used as a source of raw materials for animal feed, compost and organic fertilizer in the plantation. Palm frond has a calorific value of 15184.05 kJ/kg with a density of 0.1383 g/cm3. The calorific value can be increased by densification. Dencification is one method to increase the density of the biomass so that it will increase the calorific value of the biomass. The purpose of this research is to produce solid fuel products from palm fronds uses densification process, to determine the characteristics of the product and determine the effect of tapioca adhesive composition and pressure in the process of densification in density, calorific value and proximate. Tapioca adhesive composition used were10, 20, 30 %. Pressure used were50, 75 and 100 bar. The particle size used were < 20 Mesh . The highest density and calorific value of the product is 1.1375gr/cm3and 5144,94cal/gr was obtained at tapioca adhesive composition 30% and pressure 100 bar.Keywords :Densification, Density, Tapioca Adhesive, Pressure, Palm Fronds.
The Relationship Between Obesity and Allergies with Olfactory Disorders in Covid-19 Patients Pratama, Yudistira; Kusuma Dewi, Anna Mailasari; Muyassaroh; Hariyati, Riece; Yusmawan, Willy
Medica Hospitalia : Journal of Clinical Medicine Vol. 10 No. 3 (2023): Med Hosp
Publisher : RSUP Dr. Kariadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36408/mhjcm.v10i3.949

Abstract

Background: The prevalence of olfactory disorders is around 68% - 85% which occurs in COVID-19 patients with obesity and allergies as risk factors. The abnormalities olfactory pathways can cause by inflammatory response in adipose tissue in obese patients and excessive inflammation due to hyperreactivity of the immune system to allergens in allergic patients. This study aims to analyze the relationship between obesity and allergies to the occurrence of olfactory disorders in COVID-19. Method: It was an observational study during pandemic. Subjects were adult COVID-19 patients in the dr. Kariadi Hospital from June to July 2021. Patient with complete medical record ask for olfactory and allergic questionnaire. Patient with nasal tumor were excluded. Result: We found 100 subjects who meet the criteria. There was a significant difference in smell disturbances between obese and non-obese subjects. (p = 0.019, OR 4.99). There was a significant difference (p=0.001) in complaints of olfactory disorders between allergic and non-allergic subjects, whereas all allergic subjects experienced olfactory disorders. Conclusion: Obesity and allergies are corelated with impaired smell in COVID-19 patients.
Implementation of Content-Based Filtering in a Novel Recommendation System to Enhance User Experience Sanjaya, Imam; Sujjada, Alun; Pratama, Yudistira
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2833

Abstract

This study addresses a critical challenge in digital novel platforms: the difficulty of delivering personalized and accurate recommendations due to limited user interaction data. This limitation often leads to irrelevant or generic suggestions, which can diminish user engagement and hinder content discovery. The significance of solving this issue lies in enhancing user experience by ensuring that readers are presented with novels that truly align with their interests, even in the absence of extensive behavioral data. To overcome this problem, the study proposes an innovative hybrid recommendation system that integrates Content-Based Filtering (CBF) with the Random Forest algorithm. The system generates personalized recommendations by analyzing novel attributes such as title, genre, score, and popularity. The methodology involves extracting features from textual data using Term Frequency-Inverse Document Frequency (TF-IDF), followed by the calculation of cosine similarity to assess title relevance. These similarity scores are then combined with popularity predictions derived from the Random Forest model to produce final recommendations that reflect both content similarity and statistical relevance. The proposed system demonstrates strong performance, achieving an accuracy of 94.0%, precision of 81.4%, recall of 80.3%, and an F1-score of 80.8%. These results underscore the system’s capability to deliver accurate and diverse suggestions. By enhancing personalization and addressing the limitations of conventional CBF systems, this hybrid approach offers practical value for digital novel platforms. It serves as an effective tool for improving content discovery, increasing reader satisfaction, and supporting user retention in content-rich environments.
Co-Authors Alun Sujjada Hariyati, Riece Komalasari Komalasari Kusuma Dewi, Anna Mailasari MUYASSAROH Sanjaya, Imam Willy Yusmawan, Willy Zuchra Helwani, Zuchra