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Pelatihan Pengolahan Hasil Pertanian dan Pemasaran Digital sebagai Produktivitas UMKM untuk Mengurangi Tingkat Pengangguran di SMK Pertanian Lubuklinggau Analisa, Widya; Syabawaihi, Syabawaihi; Aulia, Rizka; Fadli, Muhamad; Aprilisa, Shinta; Sidqi, M. Nejatullah
JURNAL CEMERLANG: Pengabdian pada Masyarakat Vol 7 No 2 (2025): JURNAL CEMERLANG: Pengabdian Pada Masyarakat
Publisher : LP4MK STKIP PGRI Lubuklinggau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31540/jpm.v7i2.3519

Abstract

This community service activity aims to enhance the entrepreneurial skills of students at SMK Pertanian Lubuklinggau through training in agricultural product processing and digital marketing. It is expected to reduce the unemployment rate by creating self-employment opportunities based on local potential. The activity was carried out in three stages: preparation, implementation, and evaluation. The implementation phase lasted for two days and included training on processing local agricultural products and digital marketing using social media and marketplace platforms. Evaluation was conducted through pre-tests, post-tests, and interviews. The training resulted in a significant improvement in students’ knowledge and skills. Participants were able to produce processed agricultural products such as mackerel bone crackers and durian seed flour, and market them through digital media. High enthusiasm and increased entrepreneurial motivation were evident throughout the program. The training program proved effective in increasing the productivity and competitiveness of vocational school graduates. This activity supports the development of agroindustry-based MSMEs and opens up self-employment opportunities for students. The school is committed to continuing the program by integrating it into the curriculum and forming student entrepreneurship groups.
Penerapan Metode Prototype dalam Pengembangan Sistem Informasi Inventory Barang Berbasis Web Aprilisa, Shinta; Aulia, Rizka
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 1 (2024): January
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i1.24749

Abstract

An inventory information system is a system used to determine the stock of goods in a place or company. CV. Multikom is a company that operates in the field of computer equipment. In this company there are problems that occur in the current system, namely the processing of incoming and outgoing goods data, goods delivery data, and customer data that has not been integrated, as well as travel document data collection which is done only on a piece of paper and then copied to the computer by the warehouse admin, so that those who experience difficulties with the goods, the information provided is in accordance with the existing goods. The aim of this research is to design a Goods Inventory Information System to make it easier to collect data on incoming and outgoing goods, stock of goods, customer data, shipping, order transactions using the Prototype method and using Data Flow Diagrams (DFD) for structured modeling.
Job Shop Scheduling Problem menggunakan Ant Colony Optimization dan Algoritme Genetika Aulia, Rizka; Aprilisa, Shinta
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 3 (2024): July
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i3.31591

Abstract

Problem (JSSP) is a problem to determine the sequence of operations carried out on existing machines with the aim of minimizing the total processing time required. The development of optimization methods to achieve solutions to machine operation sequence problems has encouraged the emergence of many new solution methods. This research wants to compare two solution methods using Ant Colony Optimization (ACO) and Genetic Algorithms. The two methods are compared to find out which optimization is best used to solve the JSSP problem. The results of this research show that the ACO algorithm is better with mean squared error of 72.99%, compared to the Genetic Algorithm with mean squared error of 11.71%.