Analisis Segmentasi Pasar Perusahaan Penyedia Jasa Transportasi (Studi Kasus PO Titian Mas Kota Bima) Supply Company Market Segmentation Analysis Transportation Services (Case Study of PO Titian Mas in Bima City)
Bibliographic record
Abstract
Strategi segmentasi pasar merupakan kegiatan yang bertujuan untuk meningkatkan omzet penjualan. Setiap perusahaan telah berupaya untuk mewujudkan segmentasi pasar yang tepat yang dibutuhkan perusahaan transportasi, namun fakta di lapangan menujukan masih terdapat beberapa perusahaan transportasi yaang mempunyai segmentasi yang menunjukan hasil yang belum optimal. Penelitian ini bertujuan untuk mengetahui dan menganalisis segmentasi pasar perusahaan jasa transprotasi (studi kasus PO Titian Mas Kota Bima). Jenis penelitian ini adalah deskriptif dengan menggunakan data kuantitatif dari sumber data primer yang berasal dari kuesioner berskala likert sebagai instrumen dalam penelitian ini. Populasi dalam peneliian ini adalah masyarakat yang menggunakan jasa transpotasi PO TITIAN MAS Kota Bima yang tidak dketahui secara pasti jumlahnya. Hasil perhitungan sampel berdasarkan rumus unkknwon population diketahui besar sampel pada PO TITIAN MAS Kota Bima adalah 96 responden dengan menggunakan teknik accidental sampling. Teknik pengumpulan data yang digunakan dalam penelitian ini yaitu observasi, kuesioner dan studi pustaka. Teknik analisa data yang digunakan dalam penelitian ini adalah uji validitas, uji relibilitas dan analisis statistik (t-test one sample). Berdasarkan analisa data yang telah dilakukan hasil penelitian ini menunjukan bahwa segmentasi pasar PO TITIAN MAS Kota Bima lebih dari 70% dari yang diharapkan (baik).
 
 Keywords: Segmentasi Pasar, Jasa Transportasi
 ABSTRACT
 Market segmentation strategy is an activity that aims to increase sales turnover. Every company has tried to realize the right market segmentation needed by transportation companies, but the facts on the ground show that there are still some transportation companies that have segmentation that shows results that are not optimal. This study aims to identify and analyze the market segmentation of transportation service companies (case study of PO Titian Mas Kota Bima). This type of research is descriptive using quantitative data from primary data sources derived from Likert scale questionnaires as an instrument in this study. The population in this study is the people who use the transportation service of PO TITIAN MAS in Bima City, whose exact number is not known. The results of the sample calculation based on the Unkknwon Population formula, it is known that the sample size at PO TITIAN MAS, Bima City is 96 respondents using accidental sampling technique. Data collection techniques used in this study are observation, questionnaires and literature study. The data analysis technique used in this research is validity test, reliability test and statistical analysis (one sample t-test). Based on data analysis that has been carried out, the results of this study indicate that the market segmentation of PO TITIAN MAS in Bima City is more than 70% of the expected (good).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".