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All Journal Jurnal Agrium Sisfo: Jurnal Ilmiah Sistem Informasi
Nazimah Nazimah
Program Studi Agroekoteknologi Fakultas Pertanian Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering

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PENGGUNAAN MEDIA ALTERNATIVE (DIAPERS) TERHADAP PERTUMBUHAN DAN PRODUKSI TANAMAN CABAI Safrizal Safrizal; Nazimah Nazimah; Rina Resssi
Agrium Vol 15, No 1 (2018)
Publisher : Faculty of Agriculture, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/agrium.v15i1.687

Abstract

Red chili is a major vegetable both in Indonesia and abroad due to its benefits to the needs of nutrition and health through cooking spices. Utilization of home garden yard is an effective way to cultivate pepper plants. On the other hand, every household produces household waste which is difficult to decompose for instance diapers, so that it can cause environmental pollution in long-term. This study was conducted to investigate the role of planting media such as diapers of on chili growth. The results showed that the use of diapers of diapers as planting media gave no significant different to plant height at 1, 4, 7, 10, 13 and 16 week after application.  It also did not increase the number of leaves at the age of 1, 7, 10, 13 and 16 week after application.  There were no changes in leaf length at ages 1, 4, 7, 10 and 16 week after application. It also happened to also its leaf width at ages 1, 4, 7, 10, 13 and 16 week after application and number of branches at ages 1, 4, 7, 13 and 16 week after aplication. However, it showed a significant different to the number of leaves at age 4 week after planting and its leaf length at age 13 week after planting.   The interaction of using diapers as planting media gave significantly different response to root number, root length, wet root weight and plant canopy, and dry weight roots and plant canopy.
Predictive Analysis of Palm Oil Biodiesel Production Using a Naive Bayes Algorithm Based on Data Mining Techniques: A Machine Learning Approach for Renewable Energy Production Prediction Khaidir Khaidir; Nelly Fridayanti Fridayanti; Muhamad Yusuf; Nazimah Nazimah; Safrizal Safrizal; Teuku Multazam
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27291

Abstract

The increasing demand for renewable energy has encouraged the optimization of palm oil biodiesel production to improve product quality and process efficiency. Biodiesel production is strongly influenced by operational parameters, including Free Fatty Acid (FFA) content, moisture content, methanol-to-oil molar ratio, catalyst concentration, reaction temperature, and reaction time, which may lead to quality variability and off-spec products. This study aimed to develop a predictive analysis model for palm oil biodiesel production using the Gaussian Naive Bayes algorithm based on a data mining approach. The study employed the Knowledge Discovery in Databases (KDD) framework using a secondary dataset consisting of 250 observations and six operational variables. Data preprocessing included missing value handling, Min-Max normalization, and Random Over-Sampling (ROS) to address class imbalance. The results showed that the model achieved an accuracy of 86.0%, weighted F1-score of 0.86, and cross-validation accuracy of 86.3 ± 2.4%. The analysis identified FFA, reaction temperature, and moisture content as the main factors influencing biodiesel quality. In addition, the model demonstrated high computational efficiency with a total processing time of 0.070 seconds, indicating its potential for real-time quality monitoring applications in biodiesel production systems.
Co-Authors Khaidir Khaidir Muhamad Yusuf Nelly Fridayanti Fridayanti Rina Resssi Safrizal Safrizal Teuku Multazam