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All Journal Katalogis Jurnal Mandiri IT
Podungge, Emil Salim
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Computer Science & IT Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics

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PENGARUH BAURAN PEMASARAN JASA TERHADAP KEPUTUSAN MAHASISWA MEMILIH STMIK BINA MULIA PALU Podungge, Emil Salim
Katalogis Vol 3, No 10 (2015)
Publisher : Katalogis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.631 KB)

Abstract

The research aims to explore and analyze the influence of service marketing mix that includes: product, price, promotion, place, people, process, physical evidence and productivity and quality, on students’ decisions to select STMIK Bina Mulia, Palu. The study is descriptive quantitative and it consists of 73 students who were selected as respondents thorough Probability Proportional to Size sampling method. Based on analysis using Multiple Linear Regressions, it is found that service mix that consist of product, price, promotion, place, people, process, physical evidence and productivity and quality, has significant influence on students’ descision to select STMIK Bina Mulia Palu. Partially, promotion (X3) has insignificant influence on students’ decision to select STMIK Bina Mulia Palu.
Classification of diseases in chili plants using the convolutional neural network method with MobileNetV2 architecture Fatmawati, Ika; Podungge, Emil Salim; Rahman, Abd
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.476

Abstract

Chili peppers are a strategic horticultural commodity in Indonesia, but their productivity is often hampered by plant diseases that are difficult to identify accurately and in a timely manner using conventional methods. This study aims to develop an accurate and efficient system for classifying chili pepper plant diseases using Convolutional Neural Networks (CNN). The MobileNetV2 architecture was used due to its computational efficiency, making it suitable for implementation on devices with limited resources. A dataset containing 4,000 images of chili leaves was used, categorized into four classes: healthy leaves, leaves infected with fruit flies, curled leaves, and anthracnose. The model was trained and verified, achieving an overall accuracy of 89.62%. Evaluation results showed strong performance with precision of 89.93%, recall of 89.62%, and an F1 score of 89.54%. The trained model was successfully integrated into a web-based application to facilitate use by farmers and agricultural officers. The findings conclude that the proposed MobileNetV2-based system provides an effective and practical solution for early detection of chili diseases, supporting precision agriculture and regional food security initiatives in Morowali Regency.
Co-Authors Abd Rahman Ika Fatmawati Pramasari