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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Jurnal Ekobistek Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Journal Of Informatics And Busisnes Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science Jurnal TAM (Technology Acceptance Model)
Syafri Arlis
Universitas Putra Indonesia YPTK
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Penentuan Hasil Ujian Karateka Inkanas Menggunakan Metode WSM Berbasis WEB Prayuda, Muhammad Hozi; Arlis, Syafri; Andrianof, Harkamsyah
Journal Of Informatics And Busisnes Vol. 3 No. 3 (2025): Oktober - Desember
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i3.3230

Abstract

The INKANAS Karate School of West Sumatra routinely conducts promotion tests for its karateka. However, the assessment process is still carried out manually, which risks subjectivity and inefficiency. This research aims to design a Decision Support System (DSS) based on the Weighted Sum Model (WSM) method to determine promotion eligibility more objectively and systematically. The system uses three criteria: Kihon, Kata, and Kumite, and was developed using PHP and MySQL. This DSS is expected to support a more transparent and efficient decision-making process.
MULTIPLE LINEAR REGRESSI PADA FUZZY NEURAL NETWORK (FNN) PENENTUAN KUALITAS DAGING SAPI Yanto, Musli; Arlis, Syafri; Putra, Deri Marse
JST (Jurnal Sains dan Teknologi) Vol. 11 No. 1 (2022)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (520.682 KB) | DOI: 10.23887/jstundiksha.v11i1.38267

Abstract

Tujuan penelitian ini membahas proses identifikasi kualitas daging sapi dengan implementasi metode multiple linear regressi (MLR) pada fuzzy neural network (FNN). Metode ini dikembangkan untuk menyempurnakan proses identifikasi yang sudah ada sebelumnya. MLR mampu melakukan proses pengukuran korelasi variable (X) dengan hasil keluaran (Y). Pendekatan dalam proses analisis tersebut menggunakan pendekatan kuantitatif untuk melakukan pengukuran dari beberapa aspek indikator yang digunakan dalam penentuan kualitas daging sapi.  Berdasarkan hasil uji korelasi dengan MLR membuktikan bahwa variabel kandungan zat kimia (X1), bau (X2), warna (X3), dan tekstur daging (X4) menghasilkan hubungan yang signifikan terhadap kualitas daging sapi (Y) dengan nilai sebesar 96.5%. Hasil analisis MLR mampu memberikan gambaran indikator variable yang tepat dalam proses analisis. Keluaran FNN juga menyajikan hasil yang cukup akurat dengan nilai sebesar 99.88%. Dengan hasil keluaran yang didapat, maka secara keseluruhan dapat disimpulkan bahwa model analisis MLR dan FNN memberikan hasil analisis dengan tingkat akurasi yang lebih baik dan efektif. Hasil tersebut mampu memberikan implikasi berupa sebuah rekomendasi dalam bentuk pengetahuan dan informasi yang didapat kepada masyarakat guna menentukan daging sapi yang baik dikonsumsi.
Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection Arlis, Syafri; Putra, Muhammad Reza; Yanto, Musli
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3619

Abstract

Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.
Co-Authors Agung Ramadhanu Andrian, Reski Aswandi, Nopan Candra Putra Darma Syahrullah Ekajaya Dodi Guswandi Doli Raharjo, Tio Edwar, Eggy Febyanti Fajrul Islami Febri Hadi Feri Pratama Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Guslendra Hanum, Luthfia Harkamsyah Andrianof Hendrik, Billy Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad, Abulwafa Musli Yanto Nabila Frisca Oktavia Nur, Rofil M Nurcahyo, Yuhandri Olivia, Ladyka Febby Permana, Nabilah Putri Prayuda, Muhammad Hozi Putra, Deri Marse Putra, Muhammad Reza Ridwan, Ridwan Rini Sovia Rofil M. Nur Sahari Sahari Sahari Sahari Sahari Sarjon Defit Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sumijan Sumijan Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Wahyu, Fungki Winda Sutra Amanda Yanto, Musli Yuhandri Yuhandri Yunus Yuhandri, Y Yulasmi