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All Journal ILKOM Jurnal Ilmiah Journal Pharmacy and Application of Computer Sciences
Fuadi Syam, Rahmat
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Computer Science & IT Medicine & Pharmacology

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ANALISIS APLIKASI PENGAJUAN SURAT KETERANGAN PENDAMPING IJAZAH (APP-SKPI) MENGGUNAKAN ISO/IEC 25010 Asis, Muhammad Arfah; Ilmawan, Lutfi Budi; jeffry; Aziz, Firman; Usman, Syahrul; Fuadi Syam, Rahmat
Journal Pharmacy and Application of Computer Sciences Vol. 1 No. 2: Agustus: 2023: JOPACS
Publisher : Arlisaka Madani Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59823/jopacs.v1i2.27

Abstract

Penerapan Surat Keterangan Pendamping Ijazah (SKPI) atau diploma supplement merupakan amanat kurikulum berdasarkan Kerangka Kualifikasi Nasional Indonesia (KKNI) bagi setiap calon sarjana baru atau lulusan perguruan tinggi. SKPI memuat informasi prestasi dan kegiatan mahasiswa selama menjadi mahasiswa aktif di perguruan tinggi. Program studi Farmasi mengembangkan aplikasi untuk mengajukan SKPI yang disebut App-SKPI. Untuk membantu pengembangan aplikasi, telah dilakukan evaluasi dengan menggunakan model ISO 25010 untuk lima jenis kategori yaitu Functional Suitability, Performance Efficiency, Usability, Portability, dan Maintainability. Hasil pada kategori Functional Suitability, semua proses pada setiap fitur berjalan dengan baik dengan nilai 1 atau maksimal. Performance Efficiency, hasil kinerja dan struktur pada aplikasi mendapatkan Grade B dengan nilai kinerja 89% dan nilai struktural 91%. Usability, tingkat kepuasan mahasiswa terhadap sistem adalah 0,83. Portability, kemampuan adaptasi sistem pada browser yang berbeda mendapat nilai 1 atau maksimal. Maintainability, aplikasi dikembangkan dengan framework yang mendukung kemudahan perawatan
Detection of Persistent vs. Non-Persistent Drugs in Pharmacy Using Decision Tree Classification Based on Gini, Entropy, and Log Loss Criteria Mardewi, Mardewi; Aziz, Firman; Usman, Syahrul; Fuadi Syam, Rahmat
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2585.186-195

Abstract

This study evaluates the performance of Decision Tree methods in classification, utilizing three different criteria: Entropy, Gini, and Log Loss. The objective is to determine which criterion is most effective in achieving high classification accuracy using prescription data from the UCI repository, comprising 3,424 prescription records with 67 variables. The analysis results show that the Entropy criterion delivers the best performance with an accuracy of 79.1%, followed by the Gini criterion at 78%, and the Log Loss criterion at 77.9%. These findings indicate that the Entropy criterion is superior in reducing uncertainty and capturing the underlying data structure, while both Gini and Log Loss criteria also provide competitive, though slightly lower, results. The main contribution of this research is a comparative evaluation of decision tree criteria using real-world prescription data to support accurate classification of medication adherence, which can be beneficial for developing intelligent pharmacy systems. This research offers valuable insights into the effectiveness of various criteria within the Decision Tree method and can aid in selecting the most appropriate criterion for future classification applications.
Co-Authors Firman Aziz Jeffry Lutfi Budi Ilmawan, Lutfi Budi Mardewi, Mardewi Muhammad Arfah Asis Syahrul Usman