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All Journal Jurnal Riset Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics
Citra Mirna Wati
Universitas Nahdlatul Ulama Blitar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Mekanisme Fetch dan Execute pada Sistem Kinerja Control Processing Unit M Maulana Ikhsan; Citra Mirna Wati; Muhammad Yusuf Ashari; Abd. Charis Fauzan
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 1 No 1 (2019): Volume 1, Nomor 1, Agustus 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (680.405 KB) | DOI: 10.28926/ilkomnika.v1i1.6

Abstract

Tujuan pembuatan Jurnal ini adalah untuk mengetahui fungsi komponen-komponen CPU dan untuk mengetahui mekanisme CPU serta kinerja komputer saat melakukan proses instruksi. Dengan menggunakan metode fetch dan execute. Fungsi komputer di bentuk oleh eksekusi progam, progam yang di eksekusi berisi intruksi yang disimpan di memori, CPU melakukan tugas dengan cara mengeksekusi progam, sederhananya dengan mengolah intruksi yang terdiri dari 2 yaitu, intruksi fetch CPU, lalu CPU mengksekusi intruksi. Proses fetch dan eksekusi itulah cara untuk mengeksekusi progam, Untuk intruksi tunggal di perlukan pengolahan yang disebut siklus intruksi, dari riset yang dilakukan dan dari sumber-sumber yang telah di dapatkan memperoleh cara atau metode untuk melakukan eksekusi memory dan untuk melakukannya menggunakan tabel eksekusi program untuk mengatahuinya. Hasil dari metode yang di dapatkan itu sendiri adalah dari eksekusi progam yang terdapat pada komputer saat ini. CPU adalah komponen yang penting sebagai pengolah data berdasarkan intruksi di sistem komputer. Kesimpulannya fungsi CPU menjalankan progam yang disimpan dari dalam memori utama caranya adalah intruksi diambil, di uji dan dieksekusi satu persatu sesuai alurnya, sederhananya saja dari proses eksekusi progam dengan mengambil intruksi pengolah data yang terdiri dari dua langkah, yaitu operasi pembacaan instruksi (fetch) dan operasi pelaksanaan instruksi (execute).
PERFORMANCE COMPARISON OF MUSHROOM TYPE CLASSIFICATION BASED ON MULTI-SCENARIO DATASET USING DECISION TREE C4.5 AND C5.0 Citra Mirna Wati; Abd. Charis Fauzan; Harliana Harliana
Jurnal Riset Informatika Vol 4 No 3 (2022): Period of June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1833.048 KB) | DOI: 10.34288/jri.v4i3.383

Abstract

Indonesia has a tropical climate that supports mushroom growth. Mushroom classification into poisonous and non-poisonous mushrooms. Identification of the type of mushroom is vital because mushrooms, especially poisonous mushrooms, risk causing potential hazards to humans, such as causing serious illness and even death. This study aimed to identify the fungus type using a computational approach, namely the Decision Tree C4.5 and C5.0 Algorithms. This research contributes to using multi-scenario datasets and comparing the performance of the C4.5 and C5.0 decision tree algorithms. The dataset used is a fungal classification dataset obtained from kaggle.com. The method stages in this research are literature study, data collection, and data preprocessing, which includes a data cleaning process and a partitioning process for multi-scenario datasets. Afterwards, the Decision Tree Algorithms C4.5 and C5.0 were implemented using the sci-kit-learn library. The last step is to do a performance comparison using the confusion matrix. The results showed that identifying poisonous mushrooms using the Decision Tree C5.0 Algorithm obtained an accuracy of 97.05% for scenario 1, 97.00% for scenario 2, and 97.11% for scenario 3. At the same time, the Decision Tre C4.5 algorithm yielded an accuracy. by 96.92% for scenario 1, 96.90% for scenario 2, and 97.05% for scenario 3. Based on the comparison of the performance of the classification results, we conclude that the Decision Tree C5.0 algorithm in scenario 3 has the highest accuracy for fungal identification poisonous.
PERFORMANCE COMPARISON OF MUSHROOM TYPE CLASSIFICATION BASED ON MULTI-SCENARIO DATASET USING DECISION TREE C4.5 AND C5.0 Citra Mirna Wati; Abd. Charis Fauzan; Harliana Harliana
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.173

Abstract

Indonesia has a tropical climate that supports mushroom growth. Mushroom classification into poisonous and non-poisonous mushrooms. Identification of the type of mushroom is vital because mushrooms, especially poisonous mushrooms, risk causing potential hazards to humans, such as causing serious illness and even death. This study aimed to identify the fungus type using a computational approach, namely the Decision Tree C4.5 and C5.0 Algorithms. This research contributes to using multi-scenario datasets and comparing the performance of the C4.5 and C5.0 decision tree algorithms. The dataset used is a fungal classification dataset obtained from kaggle.com. The method stages in this research are literature study, data collection, and data preprocessing, which includes a data cleaning process and a partitioning process for multi-scenario datasets. Afterwards, the Decision Tree Algorithms C4.5 and C5.0 were implemented using the sci-kit-learn library. The last step is to do a performance comparison using the confusion matrix. The results showed that identifying poisonous mushrooms using the Decision Tree C5.0 Algorithm obtained an accuracy of 97.05% for scenario 1, 97.00% for scenario 2, and 97.11% for scenario 3. At the same time, the Decision Tre C4.5 algorithm yielded an accuracy. by 96.92% for scenario 1, 96.90% for scenario 2, and 97.05% for scenario 3. Based on the comparison of the performance of the classification results, we conclude that the Decision Tree C5.0 algorithm in scenario 3 has the highest accuracy for fungal identification poisonous.
Co-Authors Abd. Charis Fauzan Harliana Harliana M Maulana Ikhsan Muhammad Yusuf Ashari