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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JSI: Jurnal Sistem Informasi (E-Journal) BACA: Jurnal Dokumentasi dan Informasi
Andria Arisal
Pusat Penelitian Informatika - Lembaga Ilmu Pengetahuan Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Other

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Membangun Cluster dengan Menggunakan Igos Dwi Warna Wiwin Suwarningsih; Nuryani Nuryani; Andria Arisal; Taufik Wirahman; Nurhayati Masthurah
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2009
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Pada tulisan ini akan dijelaskan pembangunan sebuah cluster komputer dengan platform IGOS Nusantara2008 (Dwi Warna). Cluster ini dapat digunakan sebagai fasilitas dalam pemrosesan komputer secara paralel.Setiap node akan diinstal dengan menggunakan sistem operasi IGOS dwi warna. Cara membangun clusterdalam tulisan ini merupakan hasil dari penelitian dan praktek yang dilakukan di laboratorium pusat penelitianInformatika LIPI. Hasil akhir dari penelitian ini adalah terbentuknya sebuah cluster komputer dengan empatnode yang dapat digunakan untuk pemrosesan secara paralel.Kata kunci: cluster node, IGOS, paralel computing
Partisi Data Secara Vertikal Untuk Menentukan Aturan Asosiasi Item Set Data Cuaca Wiwin Suwarningsih; Andria Arisal
Jurnal Sistem Informasi Vol 7, No 2 (2015)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.135 KB) | DOI: 10.36706/jsi.v7i2.2266

Abstract

AbstractThis paper discussed about association rule mining among item sets of weather records, where observation results are distributed from data source and partitioned in order to create an optimal rule pattern. We use decision tree classifiers as method for data partitioning, which each item set has several attributes and these item sets are used to identify the valid global association rule, but did not disclose the items set individual transaction data. The final results of this study was to partition the data to generate a frequency associated items set weather data with the minimal level of support without revealing the value of the item set of individuals. Frequency value associated items set the partition of this data can be used for weather prediction simulations whether there will be rain or no rain. Keywords: association rule mining, item set, weather records, partition,                       decision tree classifiers Abstrak Makalah ini membahas aturan penambangan asosiasi (association rule mining) antar item set data cuaca dimana data hasil pemantauan didistribusikan dari sumber data dan dipartisi untuk memperoleh pola aturan yang optimal. Metoda yang akan digunakan untuk partisi data adalah pengklasifikasian pohon keputusan (decision tree classifiers) yaitu setiap item set memegang beberapa atribut dan item set tersebut mengidentifikasi aturan asosiasi global yang valid, namun item set tidak mengungkapkan data transaksi individu. Hasil akhir dari penelitian ini adalah partisi data untuk menghasilkan frekuensi asosiasi item set data cuaca dengan tingkat dukungan minimal tanpa mengungkapkan nilai item set individu. Nilai frekuensi asosiasi item set hasil partisi data ini dapat digunakan untuk simulasi prediksi cuaca apakah akan terjadi hujan atau tidak hujan. Kata-kuci : aturan penambangan asosiasi, item set, data cuaca,                              partisi, pengklasifikasian pohon
Kumpulan data citra telepon pintar untuk identifikasi varietas cabai merah berbasis daun Suwarningsih, Wiwin; Evandri, Evandri; Kirana, Rinda; Purnomo Husnul Khotimah; Dianadewi Riswantini; Ekasari Nugraheni; Andri Fachrur Rozie; Andria Arisal; Devi Munandar; Noor Roufiq Ahmadi
BACA: Jurnal Dokumentasi dan Informasi 2024: SPECIAL ISSUE - DATA IN BRIEF FOR REPOSITORI ILMIAH NASIONAL
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/baca.2024.7786

Abstract

Chili plants played an important role in human life, serving as a source of income for farmers, as a provider of employment, and as a source of vitamins and minerals for the community. Market demand for red chili continued to increase, encouraging seed producers to provide quality plant seeds. The requirements for selecting plant varieties were based on market demand (taste, color, appearance, size, etc.), high productivity, resistance to plant pest attacks, and suitability for planting in local ecosystem conditions. Based on this, a smart approach was needed to identify plant varieties to maintain seed purity. To facilitate and streamline leaf-based chili variety identification, a comprehensive dataset was compiled. This dataset, consisting of 3877 leaf images divided into 12 variety classes, aimed to determine which plants were parent seeds or seeds that had deviations from their varieties. Leaf images were collected from the BALITSA garden through observations of leaf growth from shoots to 20 days of plant age. Various strict steps were taken to ensure the quality of the dataset and increase its usefulness. Chili leaf images taken from various angles and having high resolution were designed to assist in the development of highly accurate models. By leveraging this curated dataset, it was possible to train a model for real-time leaf-based identification of chili varieties, which significantly helped in the timely identification of such conditions.
Co-Authors Andri Fachrur Rozie Devi Munandar Dianadewi Riswantini Ekasari Nugraheni Evandri, Evandri Noor Roufiq Ahmadi Nurhayati Masthurah Nuryani Nuryani Purnomo Husnul Khotimah Rinda Kirana Taufik Wirahman Wiwin Suwarningsih wiwin suwarningsih Wiwin Suwarningsih