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All Journal Jurnal Informatika Terpadu Jurnal Teknologi Informasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan
Azwa Guntara
Institut Teknologi dan Bisnis Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Other

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Perancangan Sistem Informasi Penjadwalan Keberangkatan Transportasi Umum Berbasis Mobile Menggunakan Metode Waterfall Nazwa Dwi Cahya Khairunnisa; Azwa Guntara; Ismar Hidayat
LOGIC : Jurnal Ilmu Komputer dan Pendidikan Vol. 3 No. 6 (2025): Logic : Jurnal Ilmu Komputer dan Pendidikan
Publisher : Shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi informasi memberikan dampak yang besar di berbagai sektor, termasuk dalam manajemen transportasi umum. Hingga kini, penjadwalan keberangkatan transportasi umum di Kabupaten Langkat masih dilakukan dengan cara manual dan belum mampu memberikan informasi tentang jadwal keberangkatan dengan cepat dan akurat. Penelitian ini bertujuan untuk merancang sebuah sistem informasi penjadwalan keberangkatan transportasi umum berbasis mobile sebagai solusi untuk masalah ini. Metode yang digunakan dalam penelitian ini adalah metode Waterfall, yang diterapkan secara berurutan dan terstruktur. Hasil dari penelitian ini berupa rancangan sistem informasi yang dipresentasikan dengan menggunakan alat pemodelan seperti Aliran Sistem Informasi (ASI), Data Flow Diagram (DFD), Context Diagram, dan Entity Relationship Diagram (ERD). Diharapkan bahwa perancangan sistem ini dapat menjadi landasan untuk penerapan sistem di masa depan, serta membantu meningkatkan efektivitas dan kualitas layanan dalam penjadwalan keberangkatan transportasi umum.
Sistem Pakar untuk Diagnosis Acne Vulgaris dan Tingkat Keparahannya Berbasis Web Menggunakan Metode Certainty Factor Azwa Guntara; Jesika Citra Ayu
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2772

Abstract

Acne vulgaris is a polymorphic skin disease with various clinical manifestations that can affect both the physical and psychological conditions of sufferers. Limited public knowledge and access to dermatological healthcare services have increased the need for an easily accessible early diagnostic support system. Previous studies have generally focused on identifying types of acne without integrating severity classification as part of the system output, resulting in limited information provided. This study aims to develop a web-based expert system using the Certainty Factor (CF) method that not only identifies types of acne vulgaris but also determines their severity levels. The system’s knowledge base consists of six types of acne, three levels of severity, and 24 symptoms obtained through literature review and expert consultation. The inference mechanism is carried out by calculating the CF value for each symptom based on the multiplication of expert confidence values (MB–MD) and user confidence levels, followed by combining multiple CF values using the CF Combine formula to obtain the final CF value for each type of acne. The diagnosis is determined based on the highest CF value obtained. The results of Black Box Testing indicate that all system functions operate as expected. Usability evaluation using the System Usability Scale (SUS) yielded an average score of 75.25, which falls into the acceptable category with a grade B (Good), indicating that the system is well accepted by users. This system is expected to serve as a supporting tool in obtaining preliminary information about acne conditions in a fast, accessible, and accurate manner.
Implementasi MobileNetV2 untuk Klasifikasi Jenis Acne Vulgaris pada Platform Website Azwa Guntara; Egi Mutiara Br Sitepu
Jurnal Teknologi Informasi Vol 5, No 2 (2026): Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/juti.v5i2.2850

Abstract

Acne vulgaris is one of the most common skin diseases affecting adolescents and adults. The identification of acne types is generally performed through visual examination by medical professionals. However, limited access to dermatology services and the high cost of consultation often lead many individuals to perform self-treatment without knowing the type of acne they have. This study aims to develop a web-based Acne Vulgaris classification system using a CNN with the MobileNetV2 architecture. The dataset used in this study was obtained from Kaggle and consists of 4,617 images divided into five classes: Blackheads, Cyst, Papules, Pustules, and Whiteheads. The dataset was split into 60% training data, 20% validation data, and 20% testing data. The preprocessing stage included image resizing to 224×224 pixels, data normalization, and image augmentation. The model was developed using transfer learning and fine-tuning on the last 30 layers of MobileNetV2. The training process employed the Adam optimizer with a learning rate of 0.00001, a batch size of 32, and 50 epochs. The results showed that the fine-tuned MobileNetV2 model achieved an accuracy of 79.96%, outperforming the transfer learning model, which achieved an accuracy of 75.27%. The model also obtained a precision of 81%, a recall of 80%, and an F1-score of 80%. The best-performing model was then implemented into a web-based application using the Flask framework, enabling automatic classification of Acne Vulgaris based on images uploaded by users.
Co-Authors Egi Mutiara Br Sitepu Ismar Hidayat Jesika Citra Ayu Nazwa Dwi Cahya Khairunnisa