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All Journal Techno.Com: Jurnal Teknologi Informasi Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika Systemic: Information System and Informatics Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Indonesian Journal of Business Intelligence (IJUBI) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Infortech Jurnal Scientia Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Sistem Informasi RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Insan Hexagon Jurnal Ticom: Technology of Information and Communication Jurnal Infortech
Omar Pahlevi
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences

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Sistem Pendukung Keputusan Pemilihan Platform Digital untuk Meningkatkan Brand Awareness Produk UMKM Menggunakan Metode TOPSIS Enok Tuti Alawiah; Sunarti Sunarti; Omar Pahlevi
JOINS (Journal of Information System) Vol 11 No 1 (2026): Edisi Mei 2026
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v11i1.15787

Abstract

The development of digital technology has encouraged Micro, Small, and Medium Enterprises (MSMEs) to utilize digital platforms as a marketing tool to increase product brand awareness. However, the large number of digital platform options makes it difficult for MSMEs to determine the most effective platform. This study aims to develop a Decision Support System (DSS) in selecting digital platforms to increase MSME product brand awareness using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. This study uses a quantitative approach by collecting data through questionnaires from MSMEs in Bogor Regency. The criteria used include customer reach, engagement level, promotion costs, ease of use, ease of features, and payment methods. The TOPSIS calculation results show that TikTok obtained the highest preference value of 0.657, followed by Shopee Marketplace at 0.574, Instagram at 0.473, Tokopedia at 0.441, and Facebook Pro at 0.392. These findings indicate that TikTok is the most recommended digital platform in increasing MSME brand awareness because it has a wide reach and high level of interaction. Thus, the implementation of TOPSIS-based DSS has been proven to be able to provide objective and systematic digital platform recommendations, thereby helping MSMEs formulate more effective and data-driven digital marketing strategies.
ANALISA PERFORMA ARSITEKTUR MOBILENETV1 DAN RESNET MENGGUNAKAN META-LEARNING DALAM MENDETEKSI OBJEK HEWAN KUCING Faiz Octa Reynaldi; Omar Pahlevi; Indah Suryani
Indonesian Journal of Business Intelligence (IJUBI) Vol 4 No 1 (2021): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v4i1.1686

Abstract

Object Detection memiliki beberapa kendala saat proses training seperti banyaknya data yang harus dilatih, menggunakan waktu cukup lama untuk dilatih dan lain-lain. Pada penelitian ini, peneliti melakukan komparasi akurasi dan average loss training arsitektur SSD MobileNetV1 dan SSD ResNet menggunakan Pre-Trained model dengan metode Few-Shot Learning menggunakan Hold-Out Cross Validation untuk mendeteksi Objek Hewan Kucing Hitam dan Objek Hewan Kucing Putih dengan pengambilan data secara rill dari metode observasi Jakarta Vet Shop dan hanya membutuhkan sedikit data untuk dilakukannya proses training. Penelitian ini dilakukan dengan cara menggunakan Cloud Computing seperti Google Colab sebagai media untuk membandingkan akurasi arsitektur SSD MobileNetV1 dan SSD ResNet. Hasil analisa dalam penelitian ini adalah SSD ResNet memiliki akurasi yang tinggi dengan nilai rata-rata 100% pada kucing hitam dan nilai rata-rata 97.9% pada kucing putih sementara untuk SSD MobileNetV1 memiliki nilai rata-rata 99.66666667% pada kucing hitam dan 78.733% pada kucing putih. Kemudian SD MobileNetV1 memiliki Train Loss lebih besar dengan nilai rata-rata 0.003923 pada Kucing Hitam dan nilai rata-rata 0.0059 Kucing Putih jika dibandingkan dengan SSD ResNet dengan nilai rata-rata 0.030263 pada Kucing Hitam dan nilai rata-rata 0.00413 pada Kucing Putih. 
Klasifikasi Kanker Paru pada Citra CT Scan menggunakan Extreme Learning Machine dan Histogram Equalization Omar Pahlevi; Yopi Handrianto; Dewi Ayu Nur Wulandari; Henny Leidiyana; Luci Kanti Rahayu
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 1 (2025): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

Lung cancer is one of the deadliest types of cancer worldwide, making early detection crucial to improving patient survival rates. One of the primary methods for detecting lung cancer is through Computed Tomography (CT) scan images. However, automated analysis of these images faces challenges due to image quality being affected by noise and low contrast. This study aims to develop a lung cancer classification model from CT scan images using the Extreme Learning Machine (ELM) algorithm and Gray Level Co-occurrence Matrix (GLCM) feature extraction, supported by Histogram Equalization techniques to enhance image quality. Histogram Equalization is employed to improve image contrast, facilitating the extraction of texture features from GLCM, such as contrast, homogeneity, energy, and entropy. ELM was chosen for its speed and accuracy in handling complex medical image classification tasks. The study results demonstrate that the proposed model successfully enhances classification performance with an accuracy of 91.06%. The combination of ELM and Histogram Equalization techniques produces an efficient and accurate classification system for detecting lung cancer from CT scan images.
Co-Authors Agni Isador Harsapranata Amrin Amrin Amrin Amrin Astriana Mulyani Astriana Mulyani Atmojo, Muhamad Kusumo Bachtiar, Dimas Agung Budi Supriyadi Budiman, Ade Surya Cucu Ika Agustyaningrum Danny Ong Desmulyati Dewi Ayu Nur Wulandari Dony Ardiyansah Enok Tuti Alawiah Enok Tuti Alawiah Fahrizal Fahrizal Faiz Octa Reynaldi Fryda Fatmayati Handini Widyastuti Harmen Harmen Harriansyah Harriansyah Harsih Rianto Henny Leidiyana Himawan Wijaya Imelda Sari Indah Suryani Ita Dewi Sintawati Ita Dewi Sintawati Luci Kanti Rahayu Mateus Hantoro Winarsa Miftahul Khoir Muhammad Syafrizal Rahmadian, Jefri Renal Verdiyan Erlangga Reni Widyastuti Riyanto, Umbar Rizka Dahlia Rony, Zahara Tussoleha Satriadi, Irawan Sismadi, Sismadi Sriyadi Sriyadi, Sriyadi Sugandi, Anton Sugiharja, Danur Sunarti sunarti sunarti Syamsiar, Syamsiar Tri Santoso Tri Santoso Yopi Handrianto, Yopi Yuni Fitriani