Article Per Year (5 Year)
p-Index From 2021 - 2026
10.681
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI Information System for Educators and Professionals : Journal of Information System Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Sebatik Journal of Information Technology and Computer Engineering JURNAL SIMTIKA (Sistem Informasi dan Informatika) JURTEKSI Informatika : Jurnal Informatika, Manajemen dan Komputer bit-Tech Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Insearch: Information System Research Journal Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Innovative: Journal Of Social Science Research Jurnal Teknologi SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika CSRID Jurnal Responsive Teknik Informatika Methods in Science and Technology Studies
Sumijan
Universitas Putra Indonesia YPTK Padang
Agriculture, Biological Sciences & Forestry Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

Published : 129 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 129 Documents
Search

 9   10   11   12   13 
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

Abstract

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.
Public Sentiment Analysis of Train Services Based on Twitter Opinions Using K-Menas and SVM Methods Dina Selvia; Sumijan; Musli Yanto
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.v13i1.677

Abstract

The development of social media, particularly Twitter, has become a primary means for the public to express opinions, criticisms, and complaints regarding train services, ranging from delays, facility comfort, to ticket policies. The large number of opinions appearing in short, non-standard characters, and containing slang and emoticons makes manual analysis ineffective, resulting in service providers not optimally utilizing valuable information from the public. This study aims to analyze public opinion sentiment on Twitter regarding train services to systematically and structuredly determine public perceptions. The methods used in this study are K-Means Clustering and Support Vector Machine (SVM). K-Means is used to group public opinion based on similarities in language patterns and sentiments to obtain initial labels, while SVM is used to classify opinions into positive and negative sentiments more accurately. The research data comes from the Twitter platform and is obtained through a crawling technique. The maximum limit of tweets retrieved is set at 2005 tweets. The results show that the K-Means method is able to assist the initial labeling process of sentiment data, while the SVM algorithm can classify public opinion with an accuracy level of 99.02%. The combination of clustering and classification methods has proven effective in processing large-scale, unstructured opinion data. Based on the research results, it can be concluded that the sentiment analysis approach using K-Means and Support Vector Machines can provide an objective picture of public perception of train service quality. The results of this analysis are expected to be used by service providers as evaluation material and a basis for decision-making to improve service quality to the public
Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images Fajrul Islami; Sumijan; Sarjon Defit
Jurnal Penelitian Pendidikan IPA Vol 10 No 8 (2024): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i8.7614

Abstract

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.
Utilization of IT Business Management for Marketing Development with the Analytical Hierarchy Process Method Andreas Malau; Sumijan; Muhammad Hafizh
Journal of Computer Scine and Information Technology Volume 9 Issue 3 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i3.75

Abstract

Marketing development is an important factor in a business that must be considered to increase market share. Choosing the right marketing strategy greatly influences the smooth running of sales. This study aims to determine a decision on the right marketing method so that it can be applied by Rozi Bike Shop in expanding its market share. Determination of the marketing strategy at the Rozi bicycle shop is determined based on four criteria, namely organization, product, place and distribution channel. The four criteria are analyzed and processed using the Analytical Hierarchy Process (AHP) method in order to obtain an appropriate marketing decision to implement. method The Analytical Hierarchy Process (AHP) is a multicriteria decision method for solving complex or complex problems, in unstructured situations into parts (variables) which are then formed into functional hierarchies or network structures. The results of calculations using the AHP method show that Strategy A (Technological Innovation) gets the highest value, namely 0.46984 . The results obtained from this study are a decision-making system designed using the AHP method and the application of IT business management in developing the store's marketing.
Poor Family Classification Decision Support System using the Simple Additive Weighting (SAW) Method Lili Amareza Patriani; Sumijan; Sofika Enggari
Journal of Computer Scine and Information Technology Volume 9 Issue 3 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i3.83

Abstract

Poverty is a problem that continues to be the focus of attention for the government. Poverty has also caused people to be willing to sacrifice anything for their survival. To anticipate this problem, various policies have been adopted by the government to break the chain of poverty. One of them is providing assistance funds to poor families (PKH). This is felt directly by all levels of underprivileged society. One of the efforts of the Koto Ranah Tapan government to eradicate poverty that occurs in Koto Ranah Tapan is to follow the central government program, namely the launch of government financial assistance (PKH). These funds will be distributed to poor residents in Koto Ranah Tapan through the nagari guardian office in Koto Ranah Tapan. However, the distribution of aid funds to poor families is often not on target due to a large level of manual calculation error which makes the aid not on target and also the office of the nagari village of high cliff village has not been able to objectively determine the families who receive the aid. To help determine which families are worthy of receiving poor family assistance funds, a decision support system is needed. With this Decision Support System (DSS), it is hoped that the decision-making process can minimize the occurrence of wrong targets that often arise in the process of selecting poor families who wish to receive aid funds . In this calculation the author uses the Simple Additive Weighting (SAW) method, because this method is suitable for accurate calculations and is very helpful in calculating any data obtained. The results obtained were that Ade Irma Suryani got the highest score with a score of 10.8 and was ranked at the top (Best 1), so she could be considered the best recipient of aid funds.
Expert System for Diagnosing Malnutrition Using the Certainty Factor Method Wijaya Hakim; Sumijan; Dinul Akhiyar
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.95

Abstract

Malnutrition in toddlers causes a negative impact on motor nerve development, inhibits behavioral and cognitive development causing a decrease in academic performance and social skills . In addition, malnutrition during infancy can cause long-term risks that focus on later in life, increasing the risk of disease or disability or even death. With advances in information technology today, it is very helpful in predicting or identifying an event, one of which is an expert system that can help an expert in identifying a disease in the world of medicine. Therefore, an expert system is needed that can help doctors and the public find out the type of malnutrition they are suffering from based on the symptoms they are experiencing. The expert system uses the Certainty Factor method in reasoning to obtain diagnostic results from the symptoms shown. This method uses the value of an expert's belief in the symptoms of a disease. The aim of this research is to apply the certainty factor method in identifying malnutrition and providing definitions and suggestions for the disease suffered. The expert system was built using PHP and MySQL database. The results of applying the Certainty Factor method based on the tested data showed that the disease suffered by the patient was Kwarshiorkor with a Certainty Factor level of 0.958528 or 95%. The results of this test show that the certainty factor method expert system is able to identify a disease based on the symptoms experienced
Implementation of the Topsis and AHP Methods in the Decision Support System for Determining the Best Employees Yolan Ananda Putri; Sumijan; Sofika Enggari
Journal of Computer Scine and Information Technology Volume 10 Issue 2 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i2.103

Abstract

Every company or agency needs Human Resources (HR) in the form of employees who have competence and good performance. Employees are one of the most important assets owned by a company. The West Sumatra Province Transportation Service is the organizer of government affairs in the field of transportation or transportation policy for the West Sumatra Province region where the selection of the best employees is still not optimal using Microsoft Excel. The aim of designing a new system at the Provincial Transportation Service is to create optimization in the assessment of each employee to facilitate the recapitulation of employee data. The data is analyzed and processed according to the research framework, namely using a Decision Support System, especially the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) methods. In this research, 10 alternative employees were taken to be assessed. Based on formula calculations using the AHP method, it is used to determine the weighted value of each existing criterion, then the resulting values from the weighting are used to carry out rankings using the TOPSIS method. After carrying out calculations using these 2 methods, the result was that the best employee was alternative 9 in the name of Rusdi with a value of 0.9995. So with this calculation the results can show which employees have the right to be the best employees in that agency
Adaptive Marker-Controlled Watershed Combined with Voxel Quantification for Automated Fetal Measurement Febri Hadi; Sumijan Sumijan; Iskandar Fitri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1224

Abstract

Accurate and consistent fetal biometric measurement is essential for assessing fetal growth and gestational age in prenatal care. However, ultrasound (US) imaging presents several challenges, including speckle noise, shadowing artifacts, and low tissue contrast, which often degrade segmentation accuracy. Classical watershed algorithms, though effective for edge detection, tend to produce over-segmentation in such complex textures. The dataset used in this study consisted of 272 ultrasound images of patients from M. Djamil Hospital, Padang, West Sumatra. The dataset covers various phases of fetal development, from the first trimester to the third trimester. All images correspond exclusively to fetal ultrasound examinations and were used solely for automated fetal biometric analysis. To overcome these issues, this study introduced an Adaptive Marker-Controlled Watershed (AMCW) algorithm combined with Voxel Quantification (VQ) to achieve more reliable and automated fetal measurements. The proposed AMCW method integrates adaptive marker generation based on morphological gradient and local intensity statistics, enabling dynamic control of internal and external markers across varying fetal regions. After segmentation, spatially calibrated pixel-based quantification was employed to estimate the dimensional properties of segmented fetal structures. The method was applied exclusively to 2D B-mode ultrasound datasets across multiple gestational ages, targeting four key fetal parameters: Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL). Although the present study is limited to 2D ultrasound images, the proposed framework may be extendable to 3D ultrasound data in future research. The combination of adaptive marker-controlled watershed segmentation and voxel-based quantification presents a robust, interpretable, and computationally efficient framework for automated fetal measurement. The CNN achieved a classification accuracy of 98.75% on the independent testing dataset, indicating that the extracted biometric features contain strong discriminative information for automated fetal condition assessment. This hybrid approach minimizes operator dependency and measurement variability aligning with clinical measurement trends.
Improvement of Interpolation Performance with Statistical Method in Total Suspended Solid Identification Hadi Syahputra; Yuhandri Yuhandri; Sumijan sumijan
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1190

Abstract

Total Suspended Solids (TSS) is one of the key parameters used to determine water quality, which can be observed through the density level of suspended particles. The determination of TSS aims to ensure that river pollution levels can be controlled to maintain good environmental quality. However, the identification of TSS is still performed manually, which requires a relatively long processing time. This condition highlights the need for an effective and efficient identification process. Based on these considerations, this study aims to develop an extraction technique to identify TSS in river water using the Interpolation Mean Square (IMS) algorithm. The development of the extraction technique within the IMS algorithm is crucial for improving the performance of linear interpolation methods. Mean Square is proposed as a parameter in the interpolation process to optimize the extraction algorithm. The segmentation process based on the performance of the IMS algorithm involves exploring and grouping image intensity values. The resulting segmented image clusters are subsequently selected based on the values produced by the Mean Square computation, which are then processed as the final segmentation output. The experimental results show an improvement in the performance evaluation results of the IMS algorithm providing an increase of 7% to 10% over the previous linear interpolation method. The evaluation results produced by the IMS algorithm are 90.19% accuracy, 99.99% sensitivity, and 83.33% specificity. These results indicate that the improved interpolation method presented in the IMS algorithm produces optimal results in determining TSS. Improving the performance of the interpolation method through the development of an IMS-based extraction technique has succeeded in producing optimal identification results. The superiority of the IMS algorithm provides novelty in the development of interpolation techniques for automated segmentation. Furthermore, the findings of this study can effectively support the West Sumatra Environmental Agency in addressing river water pollution issues.
Implementasi Metode Yolov10 Untuk Mendeteksi Penyakit Melalui Analisis Citra Daun Pada Tanaman Padi Encik Yoega Renaldi; Sumijan Sumijan; Rini Sovia
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8486

Abstract

Padi menjadi makanan pokok bagi hampir 80% untuk diseluruh Indonesia, yang penghidupannya sangat bergantung pada hasil panen. Sektor pertanian padi menghadapi tantangan berupa penyakit pada daun tanaman, dengan mayoritas petani masih menggunakan metode konvensional dalam deteksi penyakit, menyebabkan keterlambatan penanganan. Penelitian ini mengembangkan sistem deteksi dini penyakit tanaman padi menggunakan kecerdasan buatan dan computer vision dengan deep learning. Implementasi metode YOLOv10 yang efektif dengan menghilangkan penekanan Non-Maximum Suppression untuk mengurangi komputasi secara signifikan. Data penelitian yang dikumpulkan di Dinas Pertanian Kota Padang mencakup 1.446 citra dari tiga jenis penyakit: hawar daun bakteri, cendawan bercak, dan virus tungro. Pre-processing melalui augmentasi data, dataset diperbesar menjadi 10.122 citra. Pelatihan model selama 100 epoch menghasilkan tingkat kepercayaan untuk penyakit daun bakteri hawar (90%), cendawan bercak (91%), dan virus tungro (98%). Sistem mencapai tingkat kepercayaan mAP 93%, Skor F1 88%, dengan waktu komputasi 0,9 detik per citra. Sistem ini menjadi solusi efektif dan efisien bagi para ahli pertanian dan petani dalam menganalisis tingkat keparahan penyakit daun pada tanaman padi.
Co-Authors A Alfarisdon Abdi Rahim Damanik Adek Putri Adi Gunawan Aflili Sari Ahmad Khomsi Ahmad Zaki Aktavera, Beni Alifia Restu Selvanda Amran Sitohang Andre Agasi Andreas Malau Andres Boni Fakio Andri Nofiar Anjun Dermawan Ardia Ovidius Asep Kurniawan Asri Hidayad Ayu Prima Siska Bias Yulisa Geni Billy Hendrik Budi Jaya Budi Permana Putra Caniago, Deosa Putra Cyntia Lasmi Andesti Daeng Saputra Perdana Darma Yunita Darmawi Dede Pratama Dedi Irawan Deosa Putra C aniago Devi Maryuni Devia Kartika Dhena Marichy Putri Dhio Saputra Dian Cyntia Dewi Dina Ayudia Dina Selvia Dinul Akhiyar Dwiki Aulia Fakhri Edo Rinaldi Rais Eka Praja Wiyata Mandala Encik Yoega Renaldi Eri Haryadi Eri Haryadi eriwandi Esa Kurniawan Fachriqi Naldes Fachrul Ilmawan Fajri Karim Fajrul Islami Febri Aldi Febri Hadi Feri Irawan Ghea Paulina Suri Gunadi Widi Nurcahyo Hadi Syahputra Hadrila P A Hafid Dwi Adha Hafiz Mursalan hamsiah hamsiah Hardiansyah Putra Haris Kurniawan Hengki Juliansa Ibnu Rasyid Munthe Ieannoal Vhallah Ilham Roni Yansyah Indra Gunawan Irzal Arif Wisky Iskandar Fitri Jeri Wandana Jufriadif Na`am, Jufriadif Julius Santony Julius Santony Julius Santony Julius Santony K Kadrahman Kalfinus Waruwu Khairul Azmi Lc Granadi Suhaidir Lili Amareza Patriani M Syahputra M. Arif M. Rasyid M.Hafiz Alfansury Mahdiasa Sholihin Mardayulis, Mardayulis Mardison Monsya Juansen Muhammad Hafizh Muhammad Iqbal MUHAMMAD TAJUDDIN Muhammad Tajuddin Muhammad, Abulwafa Mustopa Husein Lubis Nandra Sunaryo Nella Novrina Doni Nindi Misyahdul Yuzi Nopan Pirsa Nur Aini Nurhidayat Okta Veza Pratama, Muhammad Harits Pratiwi, Fitri Puja M Alca Radillah, Teuku Rahmad Dian Rahmi Fauzana Rakhmad Pribowo Hariputra Rani Yunima Astia Rezki - Riadi, Rahadatul ‘Aisy Rian Kurniawan Riski Randa Hidayatullah Roni Roni Roni Salambue Rubiati, Nur Rusnedy, Hidayati S Salmiati Sabri T Rahman Sahyunan Harahap Salsa Fitiansyah Sarjon Defit Seni Oknora Firza Setiawan, Adil Soeheri Soeheri Sofika Enggari Sovia, Rini Sri Amalia Harahap Sri Handayani Subrianto Chandra Suhefi Oktarian Surmayanti, Surmayanti Surya Aulia Rahman Surya Dwi Putra Syafri Arlis Syahid Hakam Abdul Halim Syaljumairi, Raemon Tio Ramadan Sapto Hari Ulia Ulfa Wahyudi Wahid Wardana, Bendra Wendi Boy Widya Febriani Wijaya Hakim Yanto, Musli Yendi Putra Yoga Ananda Putra Yolan Ananda Putri Yosua Ade Pohan Yuhandri Yuhandri, Yuhandri Yuki Saputra Yusma Elda Z Zulvitri ZH, Lina Alfaridah. Zulfitri Yani