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All Journal Cipasung Techno Pesantren: Scientific Journal
Maora, Resta Maolina
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Computer Science & IT Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Penerapan Natural Language Processing (NLP) dan Convolutional Neural Network (CNN) dalam Pengembangan Asisten Pertanian Berbasis Chatbot Maora, Resta Maolina; Ubaidillah; Mulyati, Ratih; Nugraha, Dendi
Cipasung Techno Pesantren: Jurnal Ilmiah Vol 19 No 1 (2025): Vol. 19 No. 1 (2025): Cipasung Techno Pesantren: Scientific Journal
Publisher : LPPM Sekolah Tinggi Teknologi Cipasung

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Abstract

The agricultural sector in Indonesia plays an important role in the country's economy, but farmers often face various challenges in managing their farmland. One solution is to integrate technologies, such as Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs), to provide more precise and efficient information. This research aims to develop a chatbot-based Agricultural Assistant system that can assist farmers in managing agricultural activities. The system provides features such as weather forecasts, fertilization guides, plant recommendations, and pest and disease management. The method used in this study is software development with a Scrum approach, which allows for rapid iteration and effective collaboration in system development. The test results show that this system has an accuracy rate of 89% in plant disease classification using CNNs, with several classes that Keywords—Agricultural Assistant; Chatbot; Natural Language Processing; Convolutional Neural Network; Classification of Plant Diseases
Optimalisasi Model Machine Learning Dalam Upaya Penyesuaian Kompetensi Tenaga Kerja Indonesia terhadap Tuntutan Pasar Kerja Digital di Era Peralihan Revolusi Industri 4.0 Menuju Society 5.0 Maora, Resta Maolina
Cipasung Techno Pesantren: Jurnal Ilmiah Vol 19 No 2 (2025): Vol. 19 No. 2 (2025): Cipasung Techno Pesantren: Scientific Journal
Publisher : LPPM Sekolah Tinggi Teknologi Cipasung

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Abstract

The transition from the Fourth Industrial Revolution (Industry 4.0) to Society 5.0 has significantly shifted the skills demanded in the labor market, urging Indonesian workers to adapt to more relevant digital competencies. This study aims to develop an optimized machine learning model to map the skills gap between job seekers and the demands of the digital labor market. Clustering using the K-Means algorithm was applied to group applicants based on demographic profiles and skills, followed by an analysis of skill gaps in each cluster. The results identified two primary clusters: experienced applicants needing reskilling and younger applicants requiring upskilling. Training recommendations were formulated based on the most in-demand skills not widely possessed by applicants, such as JavaScript, Django, and UI/UX. These findings serve as a foundation for formulating more precise, adaptive, and data-driven digital human capital development policies.
Co-Authors Dendi Nugraha Mulyati, Ratih Ubaidillah