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All Journal CommIT (Communication & Information Technology) Jurnal Hospitality dan Manajemen Jasa Sinkron : Jurnal dan Penelitian Teknik Informatika
Billy Chandra
Jurusan Komputerisasi Akuntansi, Fakultas Ilmu Komputer, Universitas Bina Nusantara, Jl. K.H. Syahdan No. 9, Kemanggisan/Palmerah, Jakarta Barat
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions

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ANALISIS PENGARUH SKILL, KNOWLEDGE DAN JOB DESCRIPTION TERHADAP EFISIENSI KERJA KARYAWAN F&B DEPARTMENT DI RESTAURANT KEMANGI HOTEL SANTIKA PANDEGILING SURABAYA Budianto, Ferdinand Pramudita; Chandra, Billy; Kartika, Endo Wijaya
Jurnal Hospitality dan Manajemen Jasa Vol 2 (2014): Jurnal Hospitality dan Manajemen Jasa
Publisher : Jurnal Hospitality dan Manajemen Jasa

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Abstract

Penelitian ini dilakukan untuk mengetahui faktor-faktor yang mendorong efisiensi kerja karyawan di Restoran Kemangi Hotel Santika Pandegiling Surabaya. Data - data dalam penelitian ini telah memenuhi persyaratan uji validitas dan reliabilitas, uji asumsi klasik. Berdasarkan hasil penelitian, diperoleh pengaruh yang signifikan secara serempak pada varibel skill, knowledge, job description terhadap efisiensi kerja. Sedangkan pengaruh yang signifikan secara parsial terdapat pada variabel knowledge terhadap efisiensi kerja karyawan di Restoran Kemangi Hitel Santika Pandegiling Surabaya. 
STUDI KORELASI ANTARA BINUSMAYA DENGAN KEPUASAN MAHASISWA UNIVERSITAS BINA NUSANTARA: STUDI KASUS PROGRAM STUDI KOMPUTERISASI AKUNTANSI Sukawati, Anak Agung Nyoman; Tania, Wandari; Putri, Tina Carolina Antoro; Chandra, Billy
CommIT (Communication and Information Technology) Journal Vol 1, No 1 (2007): CommIT Vol. 1 No. 1 Tahun 2007
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v1i1.468

Abstract

The article purpose was to know the relation between BinusMaya with student satisfaction of Bina NusantaraUniversity. The used research method was survey method, corelational technique with collecting data using questioner.The research result was there is correlation coefficient + (0,4336) which mean there is positive relation and includingmedium category between BinusMaya with student satisfaction. Determination coefficient was 0,188 which means 18,8% of student satisfaction variation can be explain by BinusMaya variable. The conclusion was there is positive correlationbetween BinusMaya with the student satisfaction, which means the more effective of BinusMaya so the student satisfactionis getting higher.Keywords: BinusMaya, student satisfaction, computerize accounting
Evaluation of Clustering Algorithms for Identifying Shoe Characteristics Patterns at XYZ Footwear Watasendjaja, William; Chandra, Billy; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14332

Abstract

As the third-largest shoe-exporting country in the world, Indonesia faced a 25% decline in shoe exports in 2023 compared to the year before, both in terms of net weight and sales value. This decline in shoe exports occurred due to the increase of complexity and variety in customer orders to shoe manufacturers. These reasons require shoe manufacturers to enhance their production planning systems to become more efficient and competitive. To address this problem, this study explores the application of clustering algorithms to optimize the production planning process in shoe manufacturing companies. Using a case study at XYZ Footwear, clustering algorithms such as K-Means, Support Vector Clustering (SVC), and Deep Autoencoder were evaluated and compared to find the most effective algorithms in identifying patterns in shoe characteristics, thereby improving shoe manufacturers' production planning process. The datasets consist of the 2024 production season data, categorized into shoe categories, models, and variants, and purchase orders. The result shows that the combination of Deep Autoencoder and K-Means has better performance than just K-Means or Support Vector Clustering (SVC), achieving a silhouette score of 0.4822 and a Davies-Bouldin Index (DBI) of 0.6741. These findings highlight the effectiveness of combining deep learning (Deep Autoencoder) with clustering algorithms (K-Means) in identifying patterns in shoe characteristics.
Co-Authors Anak Agung Nyoman Sukawati, Anak Agung Nyoman Endo Wijaya Kartika Ferdinand Pramudita Budianto Ito Wasito Tina Carolina Antoro Putri, Tina Carolina Antoro Wandari Tania, Wandari Watasendjaja, William