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All Journal Jurnal Buana Informatika Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Komputer dan Agri-Informatika Jurnal CoreIT Jurnal Teknoinfo Petir Kilat TEKNOLOGIA Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Journal Social Science And Technology For Community Service Reputasi: Jurnal Rekayasa Perangkat Lunak BEES: Bulletin of Electrical and Electronics Engineering Jurnal Sisfotek Global Journal of Computing and Informatics Research Bulletin of Informatics and Data Science Jurnal Teknoinfo
Palupiningsih, Pritasari
Institut Teknologi Perusahaan Listrik Negara
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Development of a Plant Weed Detection Model Using the Mask R-CNN Algorithm for Smart Farming Budi Prayitno; Pritasari Palupiningsih; Atam Rifai Sujiwanto
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.873

Abstract

A more efficient and sustainable agricultural system is urgently needed during world population growth and global climate change. One of the main challenges is that ineffective weed management can significantly reduce crop yields. Conventional farming methods, such as large-scale herbicide application, also negatively impact the environment. Therefore, the development of smart farming technology based on artificial intelligence (AI) is a crucial innovative solution. This research is urgent in the context of developing AI-based systems that significantly contribute to agricultural technology. The urgency of this study is the creation of a plant weed detection model using deep learning to determine the readiness of planting land with high accuracy values. The importance of this research lies not only in the development of technology, but also in its contribution to the farmer economy and the progress of the agricultural sector in Indonesia. This research aims to build and develop a plant weed detection model using deep learning to determine the readiness of planting land, as well as evaluate the detection model built to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preprocessing, modelling, and evaluation. The deep learning method used is object detection by applying the Mask R-CNN algorithm with the ResNet-50 architecture as the backbone. The evaluation of model performance was carried out using Mean Average Precision (MAP). The results of this study demonstrated the development of a deep learning-based weed detection model using the Mask R-CNN algorithm, which achieved a MAP of 37.32 and was able to overcome the challenges of varying weed types, lighting conditions, and complex field conditions.
Employee Performance Evaluation Using RECA-based Weighting and RAWEC: Evidence from Textile Manufacturing: Evaluasi Kinerja Karyawan Menggunakan Pembobotan Berbasis RECA dan RAWEC: Studi Empiris pada Industri Manufaktur Tekstil Setiawansyah, Setiawansyah; Wang, Junhai; Maryana, Sufiatul; Palupiningsih, Pritasari
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.13709

Abstract

Employee performance evaluation in the textile industry production division still faces issues of subjectivity, limited indicators, and inconsistency in ranking that do not yet reflect the real contribution of employees. This study aims to assess employee performance using a multi-criteria decision-making approach by integrating the RECA method for determining objective criterion weights and the RAWEC method for generating performance rankings. Performance data is collected based on several key criteria, namely work productivity, production quality, timeliness, work discipline, and production error rates, which reflect the operational conditions in the textile manufacturing environment. The analysis results indicate that the applied approach clearly distinguishes employee performance and produces a stable ranking, with Gina taking first place with a final score of 0.483 and Citra with a score of 0.2933. These findings indicate that RECA and RAWEC support more reliable and data-driven managerial decisions in the textile industry.   Evaluasi kinerja karyawan di divisi produksi industri tekstil masih menghadapi masalah subjektivitas, keterbatasan indikator, dan ketidakkonsistenan pemeringkatan yang belum mencerminkan kontribusi nyata karyawan. Penelitian ini bertujuan untuk menilai kinerja karyawan menggunakan pendekatan pengambilan keputusan multi-kriteria dengan mengintegrasikan metode RECA untuk menentukan bobot kriteria objektif dan metode RAWEC untuk menghasilkan peringkat kinerja. Data kinerja dikumpulkan berdasarkan beberapa kriteria utama, yaitu produktivitas kerja, kualitas produksi, ketepatan waktu, disiplin kerja, dan tingkat kesalahan produksi, yang mencerminkan kondisi operasional pada lingkungan manufaktur tekstil. Hasil analisis menunjukkan bahwa pendekatan yang diterapkan mampu membedakan kinerja karyawan secara jelas dan menghasilkan pemeringkatan yang stabil, di mana Gina menempati peringkat pertama dengan nilai akhir 0.483 Citra dengan nilai 0,2933. Temuan ini menunjukkan RECA dan RAWEC mendukung keputusan manajerial yang lebih andal dan berbasis data di industri tekstil.
Integration of CRISUS Weighting and ROV Method for Division Head Performance Evaluation in a Manufacturing Company Junhai Wang; Setiawansyah Setiawansyah; Pritasari Palupiningsih
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v7i1.12511

Abstract

The performance evaluation of division heads in manufacturing companies often faces various problems, such as high subjectivity in assessment, the absence of clear criteria weighting standards, and instability in ranking results due to data variation. This situation causes the evaluation results to be less consistent and less able to accurately represent performance. Therefore, this study aims to develop a more objective performance evaluation model by integrating the CRISUS and ROV methods. The CRISUS method is used to determine the criteria weights objectively based on the characteristics of data distribution, while the ROV method is used to rank alternatives by considering variations in performance values through upper and lower bound approaches. The criteria used include leadership, productivity, innovation, operational costs, and error rates. The research results indicate that the proposed model is able to produce more stable and consistent preference values in representing candidate performance. Based on the calculation results, CDT-03 obtained a preference value of 0.4316 and ranked first, followed by CDT-06 with a value of 0.4212 in second place, and CDT-01 in third place with a value of 0.3180. Meanwhile, CDT-02 was in the last position with a value of 0.0739. These findings show that the integration of the CRISUS and ROV methods is able to provide a more objective, comprehensive, and reliable evaluation in supporting managerial decision-making. This research provides several important contributions; this combination is able to overcome the weaknesses of conventional methods by presenting objective criteria weighting as well as a ranking mechanism that takes into account variations in performance conditions.
Co-Authors Aditia Yudhistira Ahmad Ari Aldino Alwi Muhammad Andi Dahroni Andi Muhammad Fadel Omar Pasha Andi Nikma Sri Handayani Andika, Rio Ari Sulistiyawati Arshad, Muhammad Waqas Atam Rifai Sujiwanto Budi Prayitno Dahroni, Andi Dody Dody Eka Putra Farhan Muhamad Ikhsan Ferico Octaviansyah Pasaribu, Ahmad Firmansyah Apryadhi Gibtha Fitri Laxmi Hamdan Sobirin, Muhammad Haryono Haryono Heni Sulistiani Junhai Wang Kusuma, Anndya Dyah Marcelindo, Fitto Megawaty, Dyah Ayu Mehta, Abhishek Meiwasandi, Putu Niar Muhammad Fadli Pratama Muhammad fadli Prathama Muhammad Fadli Prathama Nasril Sany Pratomo Prawirodirjo, Raden Ronggo Bintang Prawirodirjo, Raden Ronggo Bintang Pratomo Putri Ayu Mardhiyah Rakhmadi Irfansyah Putra Rd.M.Dimas Burhanudin Akbar Riki Ruli A. Siregar, Riki Ruli A. Setiawansyah Setiawansyah Sitna Hajar Hadad Sufiatul Maryana Sujiwanto, Atam Rifa'i Wang, Junhai Yessy Asri Yuri Rahmanto