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PELUANG DAN TANTANGAN DINAS PERPUSTAKAAN DAN KEARSIPAN KABUPATEN TANAH DATAR DALAM MENGEMBANGKAN KONSEP GLAM SEBAGAI UPAYA UNTUK MELESTARIKAN KOLEKSI KEARIFAN LOKAL Fadhli, Muhammad; Wahyuni, Sri; Jufriazia Manita, Rika; Nofri Yoliadi, Dodi; Nur Arifin, Haniva; Meiliana, Isra
Literatify: Trends in Library Developments Vol 5 No 1 (2024): MARCH
Publisher : UPT Perpustakaan UIN Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/literatify.v5i1.45720

Abstract

Penelitian ini bertujuan untuk menguraikan peluang dan tantangan yang dihadapi oleh Dinas Perpustakaan dan Kearsipan Kabupaten Tanah Datar dalam mengembangkan konsep GLAM (Gallery, Library, Archive, Museum) sebagai salah satu upaya dalam melestarikan koleksi kearifan lokal Minangkabau serta untuk mewujudkan layanan satu pintu (one stop service) dalam menelusur suatu informasi. Dalam penelitian ini peneliti menggunakan metode penelitian kualitatif yang dalam pengumpulan datanya menggunakan wawancara, observasi, dokumentasi terhadap pustakawan, pengelola galeri, arsiparis serta kurator di Kabupaten Tanah Datar. Penelitian ini dilaksanakan dalam kurun waktu 10 bulan, dimulai pada Bulan Januari hingga Bulan Oktober 2023. Hasil penelitian menunjukkan bahwa implementasi GLAM blum efektif diterapkan dikarenakan belum adanya satu kebijakan yang mampu mempersatukan fungsi dari masing-masing lembaga dalam rangka pelestarian informasi. Dalam upaya untuk pelestarian informasi Dinas Perpustakaan dan Kearsipan Kabupaten Tanah Datar telah menemui bupati guna mengefektifkan pengumpulan informasi yang ada. Tantangan kedepan adalah bagaimana mendigitalisasikan informasi yang telah didapat sehingga dapat diakses oleh masyarakat luas tanpa terhalang oleh waktu serta jarak dan lebih interaktif.
ANALYSIS OF STUDENT ACADEMIC ACHIEVEMENT LEVELS USING FUZZY LOGIC Nofri Yoliadi, Dodi
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.201

Abstract

The purpose of this study is to use fuzzy logic to analyze and retrieve data about student performance levels. This data will later be used to facilitate the analysis of the performance of SMP Negeri 5 Batusangkar students. Data for this study were obtained through observation and literature review from a variety of sources. The results of this survey analysis make it easier for teachers to analyze student performance without manual searches, and the ability to quickly process values ​​using the provided media such as Microsoft Excel, Access, etc. We provide accurate and accurate data. When judging the characteristics of a student's level of academic performance, she complies with the Order of the Minister of Education, Culture, Research and Technology (Permendikbudristok) No. 21 of 2022 on Educational Evaluation Standards. Analyzing student performance levels is performed using a fuzzy logic database by mapping student results based on variables. Ethics of use, knowledge and aspects of information (attitudes). This technique is tested using Microsoft Excel to query the designed data.
K-MEANS ALGORITHM DATA MINING IN SALES LEVEL ANALYSIS Nofri Yoliadi, Dodi
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.3535

Abstract

In this research, data collection and processing of sales of electronic goods were carried out with CV. Berkah Elektronik. The data obtained is then aggregated with the K-Means algorithm to gain knowledge about which electronic products are selling well on the market and which are not. In this study, the clustering method was used with Tanagra 1.4.48 software, and the K-Means algorithm was used as an algorithm to draw conclusions about which items were selling well and which were not inputted, namely product prices. goods and sale of goods. And from the results of testing and manual testing with the Tanagra application, the same clusters are produced, namely, products that do not sell well (Cluster_KMeans_1) and products that sell well (Cluster_KMeans_2).