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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi 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 Jurnal Informatika Terpadu Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Media Computer Science Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science TECHNOMEDIA : Informatics and Computer Science Jurnal TAM (Technology Acceptance Model) Jurnal Komputer, Informasi dan Teknologi

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

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Application of Forward Chaining and Certainty Factor Methods to Identify Anxiety Disorder Categories Doli Raharjo, Tio; Widi Nurcahyo, Gunadi; Arlis, Syafri
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.663

Anxiety disorders are a form of mental disorders that often occur and have a significant impact on the quality of life of individuals. However, the process of diagnosing this disorder still faces various challenges, especially limited access to professionals and difficulties in identifying the type of disorder based on varying symptoms. This research aims to design and implement an expert-based system to help the early diagnosis process of anxiety disorders quickly and accurately. The system was developed as a web application that allows users to answer a series of questions related to the symptoms experienced, then provide possible types of disorders based on the calculation of confidence levels. The method used is forward chaining as an inference engine to conduct a rule and certainty factor search to calculate the level of confidence in the identification results of the symptoms experienced by the user. Data collected from the literature and interviews with experts were built into a knowledge base consisting of 8 types of anxiety disorders with a total of 41 symptoms. Each rule in the system is formulated using an if-then structure that combines CF values to represent the level of confidence in the symptoms and the results of logical inference with advanced tracking methods. The system was tested using 20 test data in the form of symptom-based case simulations. The results of the evaluation showed that the system was able to produce an initial diagnosis with an accuracy rate of up to 100% based on comparison with manual diagnosis from experts. This system also provides explanatory information in the form of confidence level in each diagnosis result. These findings suggest that the Certainty Factor and Forward Chaining approaches are effective in building expert systems for diagnosing anxiety disorders and have the potential to be further developed as a screening tool in educational or primary health care settings.

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.

Sistem Deteksi Kepuasan Pelanggan dengan Teknik Pengelolaan Citra Menggunakan Convolutional Neural Networks Saputra, Randy; Yuhandri, Yuhandri; Arlis , Syafri
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1219

Advancements in computer vision and facial expression recognition provide a new, objective, and non-intrusive method for measuring customer satisfaction in real time. This study develops a customer satisfaction detection system at Rumah Diskusi ALCO Café using Convolutional Neural Networks (CNN) with a mixed-methods approach, combining quantitative and qualitative analysis. The RAF-DB dataset containing 15,339 labeled images (12,271 for training and 3,068 for testing) across seven emotion classes was processed through image acquisition, preprocessing, and ResNet50 fine-tuning. The resulting model achieved an accuracy of 80.34%, with a Precision of 83.55%, Recall of 81.78%, and F1-Score of 82.32% on the test data. Field implementation over four weeks successfully recorded and analyzed thousands of customer facial expressions in key areas such as the cashier and main seating area in real time. Results showed a customer satisfaction distribution of approximately 72% “Satisfied,” 16% “Quite Satisfied,” and 12% “Not Satisfied,” with a declining trend during peak hours in the afternoon. Cross-validation with customer surveys demonstrated a strong correlation between the system’s predictions and reported satisfaction, proving the effectiveness of this method as a real-time monitoring tool. The study contributes a practical technical and methodological framework that can be replicated in other service industries for objective and real-time customer satisfaction monitoring.

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.

Implementasi Convolutional Neural Network untuk Klasifikasi Penyakit Daun pada Tanaman Jagung hidayat, Ilsa; Arlis, Syafri
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 16, No 2 (2025): Desember
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v16i2.4448

This study aims to evaluate the performance of Convolutional Neural Network (CNN) models in detecting pests on caisim (Chinese mustard) plants using different architectural approaches, namely CNN from scratch, VGG16, and Xception. The dataset used consists of 1,000 images classified into several disease categories and a healthy class. Five experiments were conducted to compare the effectiveness of the models based on evaluation metrics such as accuracy, precision, recall, and F1-score.The results show that CNN models trained from scratch produced varying levels of performance. The first and second experiments experienced underfitting, with accuracies of 30.25% and 62.29%, respectively. A significant improvement was observed in the third experiment, achieving an accuracy of 83.73% and an F1-score of 0.82, indicating that the model began to better recognize data patterns. The best performance was achieved in the fourth (VGG16) and fifth (Xception) experiments, with accuracies of 91.41% and 92.12%, respectively, and balanced precision, recall, and F1-score values above 0.90.Factors contributing to model success include an optimal proportion of training data, appropriate architectural selection, hyperparameter tuning, and the use of callbacks such as early stopping and model checkpoint. This study demonstrates that selecting the appropriate CNN architecture can significantly improve the accuracy of image classification systems for pest detection in plants

Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection Syafri Arlis; Muhammad Reza Putra; Musli Yanto
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

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.

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.

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.

Implementation of K-Means Algorithm and C4.5 Classification in the Analysis of Determinants of Student Timely Graduation Rahma Yanti; Musli Yanto; 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

This study was motivated by the importance of timely graduation as a key parameter affecting program accreditation. The timely graduation rate reflects the effectiveness of academic management and serves as an indicator of program quality. The purpose of this study was to apply the concept of data mining using the K-means and Decision Tree C4.5 methods to analyze the timely graduation of students in the Information Technology and Computer Education Study Program at UIN Bukittinggi. The research methods used are the K-Means and Decision Tree C4.5 methods. The K-Means algorithm is used to cluster student graduation data, which will then be processed in the next method. The Decision Tree C4.5 algorithm is used to classify student graduation data. The research data was sourced from the 2017 batch of the Information Technology and Computer Education Study Program at UIN Bukittinggi, with a total of 158 data points. The results of this study produced a model that was able to achieve an accuracy rate of 96% in the validation process. The accuracy results were relatively high, so the model produced can be used by the study program to improve academic quality. Based on the results of this study, it contributes as a basis for evaluating student academic performance, monitoring the risk of study delays, and supporting academic decision-making. In addition, this information contributes to maintaining and improving academic quality and supports the achievement and maintenance of the accreditation status of the PTIK UIN Sjech M. Djamil Djambek Bukittinggi Study Program.

Co-Authors Agung Rahmadhanu Ahmad Khomsi Andrian, Reski Aprilian Gevindo Ariza Ikhlas Aswandi, Nopan Bambang Supperianto Billy Hendrik Candra Putra Darma Syahrullah Ekajaya Deri Marse Putra Dito Jurinaldo Dodi Guswandi Doli Raharjo, Tio Eggy Febyanti Edwar Fachriqi Naldes Fadhila Putri Sani Fadhila Putri Sani Fajrul Islami Febri Hadi Febri Hadi Feri Pratama Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Hanum, Luthfia Harkamsyah Andrianof hidayat, Ilsa Imam Fakhri Muhammad Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani, Larissa Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad Reza Putra Muhammad, Abulwafa Nabila Frisca Oktavia Nabilah Putri Permana Olivia, Ladyka Febby Permana, Nabilah Putri Purnama, Ayu Widya Putra, Muhammad Reza Rahma Yanti Ramadhanu, Agung - Ridwan, Ridwan Rofil M. Nur Rusydi, Rezki Sahari Sahari Sahari Sahari Sahari Saputra, Randy Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sovia, Rini Sukardi Sukardi Sumijan Sumijan Sumijan, Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Wahyu, Fungki Waruwu, Kalfinus Winda Sutra Amanda Winda Sutra Amanda Yanto, Musli Yanto, Musli Yuhandri Yuhandri, Yuhandri Yulasmi

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