Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

5.203

P-Index

This Author published in this journals

All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Jurnal Ekobistek Sebatik Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Informatika Terpadu Journal of Computer Scine and Information Technology Bulletin of Computer Science Research 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 TECHNOMEDIA : Informatics and Computer Science Jurnal TAM (Technology Acceptance Model)

Syafri Arlis

Universitas Putra Indonesia YPTK

Author-ID : 640735

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

Published : 43 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4 5

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

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.

Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median Sumijan, Sumijan Sumijan; Purnama, Ayu Widya; Arlis, Syafri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 6: Desember 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Perkembangan alat teknologi akuisisi citra medis, satu diantaranya adalah teknologi yang lazim disebut CT-scan. CT-Scan (Computed Tomography Scan) adalah prosedur untuk mendapatkan gambaran dari berbagai area kecil dari tulang termasuk tengkorak kepala dan otak. Citra hasil akuisisi atau rekaman CT-Scan dapat mebantu memperjelas adanya dugaan yang kuat tentang kelainan yang terjadi pada otak. Kualitas citra dapat dilakukan dengan proses mengubah citra menjadi citra baru sesuai kebutuhan, salah satu cara seperti fungsi transformasi, operasi matematis dan pemfilteran. Peningkatan kualitas citra CT-Scan diperlukan untuk objek keputusan medis yang mempunyai kualitas tidak baik, misalnya citra mengalami derau (noise), citra terlalu terang atau gelap, citra kurang tajam, dan kabur. Proses Peningkatan kualitas citra dapat dilakukan dengan menerapkan salah satu metode pemfilteran, untuk memperbaiki kualitas citra agar dihasilkan citra yang lebih baik dari citra aslinya. Metode gaussian filter untuk mengurangi noise speckle dan poisson pada citra otak pada CT-Scan. Pada citra noise gaussian, standar deviasi yang terbaik dalam mengurangi noise bernilai satu. Namun untuk citra noise speckle dan poisson nilai standar tidak dapat mengurangi noise tersebut. Hal ini dikarenakan standar deviasi adalah parameter dalam proses gaussian filter hanya dapat untuk noise Gaussian normal, untuk mengurangi noise sebaran tidak normal (non-linier) digunakan median filter. Kelemahan gaussian filter pada noise nilai parameter tidak stabil (non-linier) dapat diatasi pada filter median. Dari hasil penggabungan filter gaussian dan filter median filter dapat meningkatkan kualitas citra dan menguranggi noise lebih baik sebaran normal dan tidak normal. AbstractThe development of medical image acquisition technology tools, one of which is the technology commonly called CT scan. CT-Scan (Computed Tomography Scan) is a procedure to get a picture of various small areas of bone including the skull and brain. Image acquisition results or CT-Scan recordings can help clarify the existence of strong suspicions about abnormalities that occur in the brain. Image quality can be done by the process of changing the image into a new image as needed, one way such as the transformation function, mathematical operations and filtering. Increasing the quality of CT-Scan images is needed for medical decision objects that have poor quality, for example images experience noise (noise), images are too bright or dark, images are less sharp, and blurred. The process of improving image quality can be done by applying one of the filtering methods, to improve image quality to produce a better image than the original image. Gaussian filter method to reduce speckle and poison noise in brain images on CT scan. In the Gaussian noise image, the best standard deviation in reducing noise is one. However, for speckle noise images and standard poison values it cannot reduce the noise. This is because the standard deviation is a parameter in the Gaussian filter process that can only be used for normal Gaussian noise, to reduce the abnormal noise distribution (non-linear) the median filter is used. The weakness of the Gaussian filter on the noise value of an unstable (non-linear) parameter can be overcome in the median filter. From the results of combining the Gaussian filter and median filter, it can improve image quality and reduce noise better than normal and abnormal distribution.

Analisis Algoritma K-Means Clustering Dalam Pengelompokan Prestasi Belajar Siswa Menengah Atas (SMA) Dila, Rahmah; Defit, Sarjon; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.751

The increased use of social media among high school students has a positive and negative impact on academic achievement. This can be seen from changes in learning patterns, concentration levels, and students' motivation in participating in learning activities. This study aims to classify student learning achievement based on the level of social media use using the K-Means Clustering algorithm. K-Means Clustering is one of the main methods in data mining. which is a technique of grouping data based on the similarity of its characteristics. The parameters used in analyzing this study are Social Media Duration (X1), Active Time (X2), Main Platform (X3), Main Goal (X4), Social Media Access Time While Learning (X5), Social Media Addiction (X6), Social Media Addiction Level (X7), Number of Study Groups (X8) and Academic Average (X9). Based on the K-Means Clustering method, it has been proven to be able to group students based on the level of social media use. These results can be seen from the cluster category C0 (High) with 46 students, C1 (medium) with 80 students, and C2 (Low) with 72 students. The contribution of this research benefits students by helping them understand the relationship between social media usage habits and learning achievement, so as to encourage more effective time management.

Analisis Cluster Algoritma K-Means Untuk Pengelompokan Kondisi Gizi Balita Pada Posyandu Roza, Yesi Betriana; Defit, Sarjon; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.752

Toddler health is a crucial indicator of community and national development. Integrated Service Posts (Posyandu) play a key role in monitoring the nutritional status of toddlers through routine weight and height checks. This study aims to analyze toddler nutritional status using the K-Means Clustering algorithm, a non-hierarchical method that groups data based on centroid proximity. The data came from 98 toddlers at the Posyandu in Manggung Village, North Pariaman District, Pariaman City, including weight, height, weight-for-age, height-for-age, weight-for-height, and weight gain. The K-Means results showed a distribution of three clusters: C0 (undernourished) with 37 toddlers, C1 (severely malnourished) with 17 toddlers, and C2 (well-nourished) with 44 toddlers. The majority of toddlers were categorized as well-nourished. This research contributes to the rapid identification of toddler nutritional problems, enabling Posyandu staff to take appropriate preventive and corrective measures.

Identifikasi Varietas Kopi Berdasarkan Analisis Warna dan Tekstur Menggunakan Metode Convolutional Neural Network Utama Putra, Kharisma; Ramadhanu, Agung; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.759

Coffee is a plantation commodity with high economic value in Indonesia, with various varieties such as Arabica, Robusta, and Liberica. Differences in coffee varieties can generally be identified through the physical characteristics of the beans, especially color and texture. Based on this, this study aims to develop a digital image-based coffee variety identification system using the Convolutional Neural Network (CNN) method with color and texture analysis as the main features. The research stages include coffee bean image acquisition, pre-processing including color segmentation and image conversion to grayscale, and color and texture feature extraction. This research dataset comes from images of unroasted coffee beans, commonly called green beans, taken using a high-resolution smartphone camera and also using secondary data taken from the Kaggle site. Both types of datasets have the same characteristics and resolution to maintain data consistency. The image dataset is divided into training data and test data, then used to train and test the Convolutional Neural Network (CNN) model. Based on this study, the Convolutional Neural Network (CNN) method can identify coffee varieties based on color and texture analysis. By using 210 training data and 90 test data of coffee bean images, the CNN method can produce an accuracy rate of 94,44%. This research contribution has the potential to be a supporting solution in the process of identifying coffee varieties quickly, accurately, and consistently, so that it can help the coffee industry in the sorting and quality control process.

Penerapan Acunetix Vulnerability Scanner dari Serangan Siber pada Keamanan Website Kampus Rusydi, Rezki; Yuhandri; Arlis, Syafri
Jurnal KomtekInfo Vol. 11 No. 3 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i3.569

Keamanan website telah menjadi salah satu aspek yang paling penting dalam menjaga integritas, kerahasiaan, dan ketersediaan informasi serta data dari ancaman serangan siber. Sebagai institusi akademis yang mengelola berbagai data penting, Institusi menghadapi tantangan signifikan dalam memastikan bahwa website mereka terlindungi dari berbagai ancaman keamanan yang semakin kompleks dan canggih. Keamanan website tidak hanya penting untuk menjaga data institusi, tetapi juga untuk melindungi privasi dan informasi pribadi pengguna yang berinteraksi dengan platform tersebut. Penelitian ini berfokus pada analisis dan peningkatan sistem keamanan website Fakultas Teknik UM Sumatera Barat dengan menggunakan Acunetix Vulnerability Scanner. Alat ini adalah salah satu solusi otomatis yang dirancang untuk mengidentifikasi kerentanan keamanan pada aplikasi web. Acunetix memungkinkan pendeteksian kerentanan secara cepat dan menyeluruh, sehingga memberikan gambaran yang jelas mengenai potensi risiko yang mungkin dihadapi oleh website tersebut. Metode penelitian yang diterapkan dalam studi ini melibatkan pengujian penetrasi menggunakan Acunetix untuk mendeteksi berbagai celah keamanan yang ada pada website. Pengujian ini mencakup identifikasi terhadap celah yang mungkin dieksploitasi oleh pihak tidak bertanggung jawab, termasuk serangan cross-site scripting (XSS), SQL injection, dan kerentanan terhadap serangan Distributed Denial of Service (DDoS). Hasil analisis menunjukkan bahwa terdapat beberapa kerentanan kritis yang harus segera diatasi untuk mencegah potensi eksploitasi. Berdasarkan temuan ini, peneliti menyusun rekomendasi perbaikan dan mitigasi yang bertujuan untuk mengurangi risiko serangan siber terhadap website. Berdasarkan hasil scanning literasi pertama, website Fakultas Teknik UM Sumatera Barat dikategorikan pada tingkat ancaman 3 yang termasuk tinggi, dengan terdapat 245 peringatan atau kerentanan yang teridentifikasi, di antaranya, 8 dianggap berada pada tingkat high, 2 berada pada tingkat medium, 13 berada ditingkat Low dan selebihnya Informational Berdasarkan evaluasi yang telah dilakukan, tingkat keamanan yang tercapai berada pada level 0. Pada level ini, tidak terdapat kerentanan yang teridentifikasi (nol kerentanan) dan dukungan keamanan juga mencapai tingkat optimal (nol dukungan). Oleh karena itu, dapat disimpulkan bahwa situs web Fakultas Teknik UM Sumatera Barat saat ini, dengan status level 0, tidak memiliki kerentanan keamanan. Hasil penelitian bisa menjadi acuan bagi pengelola website di lingkungan akademis, dalam melindungi website dari ancaman siber.

PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) Permana, Nabilah Putri; Arlis, Syafri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.7388

The recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms.

IMPLEMENTASI MODEL CONVOLUTIONAL NEURAL NETWORK RESNET50 PADA PENYAKIT MATA DARI CITRA FUNDUS Waruwu, Kalfinus; Syafri Arlis
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 3 No 1 (2026): Januari
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v3i1.160

Kondisi mata seperti katarak, glaukoma, dan retinopati diabetik merupakan penyebab utama kebutaan di dunia. Deteksi dini melalui analisis citra fundus retina sangat penting untuk mencegah komplikasi lebih lanjut. Studi ini merancang sistem klasifikasi penyakit mata berbasis citra retina menggunakan arsitektur Convolutional Neural Network (CNN) ResNet50 dengan pendekatan transfer learning. Dataset yang digunakan berasal dari Kaggle, berjumlah 4.217 citra fundus, dan diklasifikasikan ke dalam empat kategori: normal, katarak, glaukoma, dan retinopati diabetik. Proses pelatihan dilakukan dengan rasio data 80:10:10 untuk training, validasi, dan pengujian. Hasil evaluasi menunjukkan performa tinggi dengan akurasi keseluruhan mencapai 94%, didukung oleh nilai precision, recall, dan F1-score yang optimal, khususnya pada kelas diabetic retinopathy dengan hasil sempurna (1.00). Meskipun demikian, kelas glaukoma masih menghadapi kesulitan klasifikasi akibat kemiripan visual dengan kelas normal. Kontribusi utama penelitian ini terletak pada implementasi ResNet50 yang terbukti efektif dalam mendeteksi penyakit mata berbasis citra fundus secara otomatis, sekaligus memberikan dasar ilmiah untuk pengembangan sistem pendukung keputusan klinis di bidang oftalmologi. Dengan demikian, studi ini tidak hanya menunjukkan keandalan model deep learning dalam diagnosis medis, tetapi juga membuka peluang penerapan lebih luas pada deteksi dini penyakit mata untuk meningkatkan kualitas layanan kesehatan.

Empowering Digital Pedagogy through ICT-Based Deep Learning for Numeracy and Computational Thinking in Elementary Schools Helsa, Yullys; Arlis, Syafri; Zainil, Melva; Zulnaidi, Hutkemri; Rachman, Bayu
Bubungan Tinggi: Jurnal Pengabdian Masyarakat Vol 8, No 1 (2026): FEBRUARY 2026
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/btjpm.v8i1.17314

This community service program aimed to strengthen elementary school teachers’ capacity to integrate ICT-based deep learning pedagogy in improving students’ numeracy and computational thinking (CT) literacy. The program involved 30 teachers within a local Teacher Learning Community and was implemented through participatory workshops and structured mentoring using a one-group pre-test–post-test design to examine program impact. The intervention focused on enhancing teachers’ competence in designing ICT-integrated lesson plans, learning activities, and assessments aligned with numeracy and CT indicators. Data were collected through teacher self-assessment, classroom observation, and students’ pre- and post-tests and analyzed descriptively to determine mean improvement and percentage gain. The results demonstrated a 24% increase in teachers’ ICT pedagogical competence, indicating strengthened instructional capacity. As a follow-up impact of improved teaching practice, students’ numeracy achievement increased by 24%, and computational thinking performance improved by 20%. These findings confirm that empowering teachers through ICT-integrated deep learning contributes to measurable improvements in instructional quality and supports the development of higher-order literacy skills. Overall, the program achieved its objective by reinforcing teacher capacity as the primary outcome, with positive learning gains emerging as a direct pedagogical impact within elementary classrooms.

Analysis of the feasibility level of IT device using K-Means cluster and C4.5 classification: English Fachriqi Naldes; S. Sumijan; Syafri Arlis
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i1.673

The availability of reliable laptops is essential for ensuring smooth business operations; however, decisions regarding device upgrades and replacements in many organizations still rely primarily on device age and subjective user perceptions. This practice often leads to inconsistent IT asset lifecycle decisions, increased security risks, and inefficient cost management. This study proposes a classification model to recommend laptop feasibility levels, namely usable, requires upgrade, and requires replacement, based on a combination of technical specifications and operating system characteristics. K-Means clustering is applied to group laptops into three feasibility categories using processor type, release year, RAM capacity, storage type, and operating system attributes that have undergone performance score–based ordinal encoding and Min–Max normalization. Subsequently, the C4.5 algorithm is employed to construct a decision tree using the K-Means cluster labels as target classes, producing interpretable if–then rules that describe device feasibility patterns. The dataset is obtained from the IT device inventory of PT Semen Indonesia, consisting of 1,905 laptop records, which after data cleaning result in 85 unique specification combinations for analysis. The clustering process classifies 47 laptops as usable, 22 as requiring upgrades, and 16 as requiring replacement. The C4.5 algorithm model achieves accuracy, precision, recall, and F1-score values of 100% on the test data, indicating its ability to effectively replicate the feasibility patterns generated by K-Means algorithm. These findings demonstrate that the proposed approach provides a data-driven framework for supporting upgrade and replacement decisions, contributing to more efficient and measurable IT asset lifecycle management.

Co-Authors Andrian, Reski Aswandi, Nopan Candra Putra Darma Syahrullah Ekajaya Dila, Rahmah Dodi Guswandi Doli Raharjo, Tio Eggy Febyanti Edwar Fachriqi Naldes Fajrul Islami Febri Hadi Feri Pratama Gevindo, Aprilian 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 Purnama, Ayu Widya Putra, Deri Marse Putra, Muhammad Reza Rachman, Bayu Rahma Yanti Ramadhanu, Agung - Ridwan, Ridwan Rini Sovia Rofil M. Nur Roza, Yesi Betriana Rusydi, Rezki S. Sumijan Sahari Sahari Sahari Sahari Sahari Sarjon Defit Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sumijan Sumijan Sumijan Sumijan Sumijan, Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Utama Putra, Kharisma Wahyu, Fungki Waruwu, Kalfinus Winda Sutra Amanda Yanto, Musli Yuhandri Yuhandri Yuhandri Yuhandri Yunus Yuhandri, Y Yulasmi Yullys Helsa Zainil, Melva Zulnaidi, Hutkemri

Title

Found 43 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 43 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 43 Documents
Search