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KARAKTERISTIK TENAGA KERJA PENGALENGAN IKAN DI PT. CITRA RAJA AMPAT CANNING SORONG KOTA SORONG PROVINSI PAPUA BARAT

2013· article· en· W1546333599 on OpenAlexaff
Agustina Kocu, Steelma V. Rantung, Olvie V. Kotambunan

Bibliographic record

VenueAKULTURASI (Jurnal Ilmiah Agrobisnis Perikanan) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsRajaWorkforceProductivityCompetition (biology)Research methodGeographyEngineeringManagementBusinessBusiness administrationEconomic growthEconomicsBiologyEcology

Abstract

fetched live from OpenAlex

Abstract In the days of tight competition, efforts to improve the quality and productivity of labor are needed. Improving the quality of human resources in the labor aspect of the system was developed through the integration of education and training on the basis of developments in science and technology. Labor characteristics are traits that entrenched for a long time. Characteristics of the labor force were age, education, region of origin, religion, expertise, skills and productivity. This study aimed to investigate the characteristics of the workforce in the industry PT. Canning image of Raja Ampat Sorong. Basic research is a case study and the nature of this research is descriptive. Method of data collection is done by sampling techniques were analyzed using descriptive analysis method. The research was carried out for 1 month at PT. Canning image of Raja Ampat Sorong. The company is located in the village of Kampung Baru, Sorong, west Papua. Workforce numbered 631 men, aged 25-30 years at most. Most are elementary school education. Most regions of origin, namely West Papua, while the little originating from Napier. Hindu religion is the least, which many are Protestant Christians. Highest productivity in January, is FP = 3,940,982 per person by the number of 631, while the lowest in December, ie FP = 2,310,085 per person by the number of 129 people. Working time starts at 7:00 a.m. to 15:00, with the basic wage is Rp. 384,000.00. Keywords: characteristics, labor, canning, productivity Abstrak Di era persaingan yang semakin ketat, upaya untuk meningkatkan kualitas dan produktivitas tenaga kerja sangat dibutuhkan. Peningkatatan kualitas sumberdaya manusia dalam aspek ketenagakerjaan dikembangkan melalui sistem keteRp. aduan antara dunia pendidikan dan pelatihan atas dasar perkembangan ilmu pengetahuan dan teknologi. Karakteristik tenaga kerja adalah sifat-sifat yang membudaya sejak lama. Karakteristik tenaga kerja tersebut adalah umur, pendidikan, asal daerah, agama, keahlian, ketrampilan dan produktivitas. Penelitian ini bertujuan untuk mengetahui karakteristik tenaga kerja pada industri PT. Citra Raja Ampat Canning Sorong. Dasar penelitian yang digunakan adalah studi kasus dan sifat penelitian ini adalah deskriptif. Metode pengumpulan data dilakukan dengan teknik sampling yang dianalisis mengunakan metode analisis deskriptif. Penelitian ini dilakukan selama 1 bulan di PT. Citra Raja Ampat Canning Sorong. Perusahaan ini berlokasi di Kelurahan Kampung Baru, Kota Sorong, propinsi Papua Barat. Tenaga kerja berjumlah 631 orang, paling banyak berumur 25-30 tahun. Pendidkan terbanyak adalalah sekolah dasar. Asal daerah paling banyak, yaitu Papua Barat, sedangkan sedikit berasal dari Makasar. Agama paling sedikit adalah hindu, yang banyak adalah Kristen Protestan. Produktivitas tertinggi pada bulan Januari, yaitu FP = 3.940.982/orang dengan jumlah 631, sedangkan terendahpada bulan Desember, yaitu FP = 2.310.085/orang dengan jumlah 129 orang. Waktu kerja dimulai pada pukul 07.00-15.00, dengan upah pokok berjumlah Rp. 384.000,00. Kata Kunci:karakteristik, tenaga kerja, pengalengan, produktivitas

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.265
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2013
Admission routes1
Has abstractyes

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