Purpose This paper aims to assess the importance of maximizing resources in an institution to promote knowledge management (KM) practices, namely, leadership, information technology (IT) and KM. The relationship among them was analyzed. Previous studies’ relating aspects of KM were concerned about the industry; however, the academic institution has not received much attention. Therefore, to address this in an academic setting, the authors developed research model by focusing on an academic institution. Design/methodology/approach The authors used structural equation modeling to check the research prototype with a sample of 160 respondents. The respondents were heads of departments, lecturers and general employees. In addition, the authors used SPSS to measure demographic, non-response bias and generate descriptive statistics. Findings The findings of this research show that the leadership style with path goal theory and IT are elements that support KM program in university setting. The results of hypothesis are displayed in Figure 2, including examining factors that influence of path goal theory, technology and KM program. In other hand, path goal theory had a positive influence on KM program (c = 0.13, p < 0.05), and IT had a positive influence on KM program (c = 0.20, p < 0.05). Research limitations/implications Finally, the authors are not to claim that this will be suitable in many academic institutions and organization types. In this study, the authors tested or checked existing leadership style in university, then suggest/explain to University what style of leadership currently they have and suggest to them how this style may support knowledge sharing practice in University. While the strength of this study provides an opportunity to explore the KM program of an academic institution, limitations do exist above. Therefore, this statement needs to be investigated and validated further. Practical implications The findings of this research may help companies and workers to initiate sharing knowledge or to encourage knowledge sharing in University. In addition, managerial staffs/officers are supposed to make standardization or regulation to encourage workers’ participation for transferring their knowledge. In this aspect, company needs create such as training or formal/informal meeting to make their workers more confidence to communicate each other. Originality/value The authors have combined various aspects, namely, KM, leadership style and social media tools, to solve the obstacle of knowledge sharing practices.
Sentral Tukang Indonesia adalah perusahaan retail yang menjual aksesoris bangunan yang beralamat di Jl. Riau No.131 C-D, Pekanbaru. Sentral Tukang saat ini menghadapi masalah dalam manajemen aset dari proses perawatan aset karena masih menggunakan ingatan perorangan saja sedangkan asetnya banyak. Jika perorangan tersebut terlupa akan menjadi masalah apalagi terkait jenis aset yang melakukan pembayaran dan jika terlambat akan dikenakan denda. Penelitian ini untuk memudahkan sentral tukang untuk melakukan pencatatan perawatan aset perusahaan tersebut. Penelitian ini dibuat dengan menggunakan Visual Basic 6.0 sebagai program aplikasi desktop dan MySQL sebagai aplikasi databasenya. Proses penelitian dilakukan observasi dikarena penulis juga bekerja di sentral tukang selama setahun dan penulis mendapatkan masalah aset ini. Tahap pembuatan aplikasi ini yaitu analisa kelemahan sistem lama, pencarian data, perancangan, pembuatan, pengetesan, dan implementasi dari perancangan sistem informasi manajemen aset di sentral tukang. Hasil dari penelitian ini program desktop yang mengelola pencatatan perawatan aset dan juga sebagai pengingat akan perawatan aset-aset yang berada di Sentral Tukang. Kata Kunci – Sistem Infomasi, Manajemen Aset, Perawatan Aset
Abstrak - Meningkatnya minat masyarakat dalam mengakses berita, khususnya berita online, menuntut redaktur dan situs portal berita untuk memberikan liputan dan berita yang berkualitas. Selain itu, klasifikas berita yang ada masih tergolong umum dapat menjadi kendala yang dialami pembaca. jika pembaca ingin melihat kategori berita yang lebih spesifik, mereka harus menyaring berita tersebut secara manual. Hal ini juga terjadi di bidang sosial Badan Pusat Statistik Provinsi Riau yang kesulitan mencari berita tentang Provinsi Riau. Oleh karena itu, proses klasifikasi berita menggunakan metode k-nearest neighbor menjadi hal yang krusial untuk dilakukan. Jumlah berita yang digunakan dalam penelitian ini berjumlah 510 data dengan tiga kategori yaitu demokrasi, kemiskinan, dan ketenagakerjaan. Proses klasifikasi berita dalam penelitian ini meliputi: pengumpulan data, pelabelan manual, preprocessing teks, pembobotan kata, dan klasifikasi memakai metode k-nearest neighbor. Selain itu, cosinus similarity juga digunakan untuk meningkatkan nilai akurasi. Nilai akurasi tertinggi yang diperoleh pada penelitian ini adalah 87% menggunakan nilai k = 3 dengan distribusi data uji 20% dan data latih dari 80%. Dari penelitian ini dapat diambil kesimpulan bahwa metode K-Nearest Neighbor dapat bekerja dengan baik dalam proses klasifikasi berita.Kata kunci: Badan Pusat Statistik, Berita, Cosine Similarity, Klasifikasi, K-Nearest Neighbor Abstract - The increasing of public interest in accessing news, especially online news, requires editors and news portal sites to provide quality coverage and news. In addition, the grouping of news that still classified as a general can be an obstacle experienced by readers. if the reader wants to see a more specific category of news, they must filter the news manually. This is also happened in the social sector of Badan Pusat Statistik Provinsi Riau, which has trouble when finding news about Riau Province. Therefore, the news classification process using the k-nearest neighbor method is a crucial thing to do. The number of news stories used in this study amounted to 510 data with three categories, democracy, poverty, and employment. The news classification process in this study includes: data collection, manual labeling, text preprocessing, word weighting, and classification using k-nearest neighbor method. Besides that, cosine similarity is also used to increase the accuracy value. The highest accuracy values obtained in this study were 87% using a values of k = 3 with distribution of test data of 20% and training data of 80%. From this research, it can be concluded that the K-Nearest Neighbor method works well in the news classification process.Keywords: Badan Pusat Statistik, Cosine Similarity, Classification, K-Nearest Neighbor, News
The thesis is one of the scientific works based on the conclusions of field research or observations compiled and developed by students as well as research carried out according to the topic containing the study program which is carried out as a final project compiled in the last stage of formal study. A large number of theses, of course, will be sought in looking for categories of thesis topics, or the titles raised have different relevance. However, the student thesis can be by topics that are almost relevant to other topics so that it can make it easier to find topics that are relevant to the group. One of the uses of techniques in machine learning is to find text processing (Text Mining). In-text mining, there is a method that can be used, namely the Clustering method. Clustering processing techniques can group objects into the number of clusters formed. In addition, there are several methods used in clustering processing. This study aims to compare 2 cluster algorithms, namely the K-Means and K-Medoids algorithms to obtain an appropriate evaluation in the case of thesis grouping so that the relevant topics in the formed groups have better accuracy. The evaluation stage used is the Davies Bouldin Index (DBI) evaluation which is one of the testing techniques on the cluster. In addition, another indicator for comparison is the computation time of the two algorithms. According to the DBI value test carried out on algorithm 2, the K-Medoids algorithm is superior to K-Means, where the average DBI value produced by K-Medoids is 1,56 while K-Means is 2,79. In addition, the computational time required in classifying documents is also a reference. In testing the computational time required to group 50 documents, K-Means is superior to K-Medoids. K-Means has an average computation time for grouping documents, which is 1 second, while K-Medoids provide a computation time of 26,7778 seconds.
Perpustakaan STAI Auliaurrasyidin memiliki kurang lebih 8000 buku yang terdiri dari berbagai jenis topik pembahasan. Buku tersebut disusun rapi didalam rak buku agar pengunjug menemukan buku yang diinginkan dengan mudah. Rak buku tersebut dilengkapi dengan abjad yang mewakili awal judul buku karena perpustakaan tersebut hanya memiliki sistem temu informasi buku atau OPAC (Online Public Access Catalog) yang berbasis local sehingga sangat menyulitkan jika mencari buku pada saat kita tidak berada dimeja komputer pencarian. Oleh sebab itu maka dikembangkanlah Aplikasi Online Public Access Catalog Perpustakaan berbasis Website menggunakan Bahasa pemrograman PHP dan dirancang menggunakan database MySQL guna untuk memudahkan dalam permasalahan tersebut. Aplikasi ini dikembangkan dengan menggunkana pendekatan metode RAD (Rapid Application Deployment) Serta Aplikasi OPAC yang dikembangkan telah melalui pengujian Black Box dan pengujian UAT dengan memperoleh hasil rentang kategori setuju.
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