Articles
<![CDATA[Application of Object Mask Detection Using the Convolution Neural Network (CNN) ]]>
<![CDATA[Yuhandri]]>;
<![CDATA[Musli Yanto]]>;
<![CDATA[Eka Naufaldi Novri]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v7i4.5059
<![CDATA[The spread of Coronavirus Disease (Covid-19) is still a serious problem that we are currently facing. Spread occurred very quickly through the face-to-face interaction process. The face-to-face interaction process that occurs both in public spaces and in closed spaces has a great risk of transmitting the Covid-19 virus. One of the efforts to deal with the spread of the Covid-19 virus is to increase the use of masks in both public and closed spaces. On the basis of this, this study aims to develop an object detection process in image processing techniques. Object detection development using the convolution neural network (CNN) method to provide optimal output. CNN can process the input image, which is converted into a pixel matrix and then sent to the convolution layer. The research data set consists of 2000 images of masks and not masks. The images were obtained from open sources, github.com and kaggle.com. The results of the study present a system capable of detecting masks in real time. CNN provides very good performance with an accuracy rate of 99.05%. With these results, the contribution of this research can be used in the monitoring of public services for the community to increase the use of masks.]]>
<![CDATA[Machine learning classification analysis model community satisfaction with traditional market facilities as public service ]]>
<![CDATA[Hadi Syahputra]]>;
<![CDATA[Musli Yanto]]>;
<![CDATA[Muhammad Reza Putra]]>;
<![CDATA[Aulia Fitrul Hadi]]>;
<![CDATA[Selvi Zola Fenia]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v12.i4.pp1744-1754
<![CDATA[Traditional markets are public service facilities that can be utilized by thecommunity. The market function is used place where sellers and buyers meetin conducting transactions. This study aims to build a machine learningclassification analysis model in measuring community satisfaction withtraditional market facilities. The analytical methods used include Fuzzy.multiple linear regression (MRL), artificial neural network (ANN), anddecision tree (DT). Fuzzy is used to generate a pattern of rules in determiningthe level of satisfaction. MRL serves to measure and test the correlation ofrules that have been formed. The ANN method is used to carry out theclassification analysis process based on learning. In the final stage. DT is usedto describe the decision tree of the analysis process. This study presents theresults of machine learning analysis which is very good in determiningsatisfaction with an accuracy rate of 99.99%. This result is influenced by fuzzylogic which can develop a classification rule pattern of 32 patterns. MRL alsoshows a significant correlation level of 81.1% based on the indicator variables.Overall, the machine learning classification analysis model can provideknowledge to be considered in the management of traditional markets aspublic service facilities.]]>
<![CDATA[Determination of children's nutritional status with machine learning classification analysis approach ]]>
<![CDATA[Musli Yanto]]>;
<![CDATA[Febri Hadi]]>;
<![CDATA[Syafri Arlis]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v33.i1.pp303-313
<![CDATA[Malnutrition is a problem that is often faced by every country around the world. Various facts show that malnutrition is of particular concern to many researchers. To can overcome this problem, every effort has been made such as developing analytical models in identification, classification, and prediction. This study aims to determine the nutritional status of children using the machine learning (ML) classification analysis approach. The methods used in the ML analysis process consist of cluster K-Means, artificial neural network (ANN), sum square error (SSE), pearson correlation (PC), and decision tree (DT). The dataset for this study uses data on child nutrition cases that occurred in the previous and was sourced from the provincial general hospital (RSUP) M. Djamil, Padang, West Sumatera. Based on the research presented, ML performance in the nutritional status classification analysis gave maximum results. These results are reported based on the level of precision with an accuracy of 99.23%. The results of the analysis can also present a knowledge-based nutritional status classification. This research can contribute to and update the analytical model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.]]>
<![CDATA[Augmented Reality dengan Model Generate Target dalam Visualisasi Objek Digital pada Media Pembelajaran ]]>
<![CDATA[Randy Permana]]>;
<![CDATA[Eka Praja Wiyata Mandala]]>;
<![CDATA[Dewi Eka Putri]]>;
<![CDATA[Musli Yanto]]>
<![CDATA[ Majalah Ilmiah UPI YPTK Vol. 30 (2023) No. 1 ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/jmi.v30i1.143
<![CDATA[Augmented Reality (AR) is a digital technology that allows the creation of a combination between the real world and digital content projections to produce additional valuable information for users. This technology has begun to implement in various fields of human life like industry, health, military, entertainment, and education. AR technology will be applied to the education sector in this community service activity. This activity aims to introduce AR technology and a development model based on the Model Generate Target (MGT) to service partners, namely SMA INS Kayu Tanam. The Generate Target model is an AR application development concept by adopting the concept of Digital Twins, where digital content projections will be made similar to real objects. The projected digital content will serve as a descriptive object from the real world, so users can interact more flexibly with objects from the real world. The activity was carried out by providing an introduction to AR technology, installing AR design software, and practicing making simple AR applications using the MGT concept for partner service to the community. In this activity, the designed AR will use spherical objects in the real world and the resulting digital projection is a virus. Projection of digital content onto spherical objects will provide better information and learning experiences in viral object recognition because 3D objects will appear and be attached to real objects. The expected results of this community service activity are introducing AR technology to partners engaged in education and providing a new solution in implementing new digital content-based teaching media that can be applied to several subjects such as biology, chemistry, and glasses to community service partners.]]>
<![CDATA[Improved Image Segmentation using Adaptive Threshold Morphology on CT-Scan Images for Brain Tumor Detection ]]>
<![CDATA[Syafri Arlis]]>;
<![CDATA[Muhammad Reza Putra]]>;
<![CDATA[Musli Yanto]]>
<![CDATA[ MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/matrik.v23i3.3619
<![CDATA[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.]]>
<![CDATA[Implementation of K-Means Algorithm and C4.5 Classification in the Analysis of Determinants of Student Timely Graduation ]]>
<![CDATA[Rahma Yanti]]>;
<![CDATA[Musli Yanto]]>;
<![CDATA[Syafri Arlis]]>
<![CDATA[ Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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<![CDATA[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.]]>
<![CDATA[Public Sentiment Analysis of Train Services Based on Twitter Opinions Using K-Menas and SVM Methods ]]>
<![CDATA[Dina Selvia]]>;
<![CDATA[Sumijan]]>;
<![CDATA[Musli Yanto]]>
<![CDATA[ Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo ]]>
Publisher : <![CDATA[Universitas Putra Indonesia YPTK Padang ]]>
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DOI: 10.35134/komtekinfo.v13i1.677
<![CDATA[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]]>
<![CDATA[Pengenalan Teknologi Artificial Intellegence Pada Panti Asuhan Al Hidayah Padang ]]>
<![CDATA[Hadi Syahputra]]>;
<![CDATA[Musli Yanto]]>;
<![CDATA[Selvi Zola Fenia]]>
<![CDATA[ Jurnal Pengabdian Masyarakat Dharma Andalas Vol 4 No 2 (2026): Jurnal Pengabdian Masyarakat Dharma Andalas ]]>
Publisher : <![CDATA[LPPM Universitas Dharma Andalas ]]>
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DOI: 10.47233/jpmda.v4i2.2478
<![CDATA[The rapid development of information technology in the era of the Industrial Revolution 4.0 has had a major impact on various aspects of life, including the world of education and the daily lives of the younger generation. One of the most prominent technologies today is Artificial Intelligence (AI). Understanding of AI is still very limited, especially among teenagers who come from economically disadvantaged backgrounds, including children in orphanages. Children at the AL-Hidayah Padang Orphanage often face limited access to information and cutting-edge technology, including knowledge about AI. As part of the role of the tri dharma of higher education, lecturers have a responsibility to participate in educating the nation's life through community service activities. Through the PKM program entitled "Introduction to Artificial Intelligence Technology at the Al Hidayah Padang Orphanage", lecturers and students want to make a real contribution by introducing the basic concepts of AI to foster children and the orphanage's guidance. This socialization is expected to spark curiosity, enthusiasm for learning technology, and open their insights to future opportunities in the digital and artificial intelligence fields. This activity aims not only to provide a theoretical understanding of AI, but also to provide practical and interactive experiences through simple demonstrations, AI-based educational games, and inspiring discussions. With a fun and easy-to-understand approach, this activity is expected to be the first step in building technological literacy among the foster children and their caregivers at the Al Hidayah Orphanage in Padang.]]>
<![CDATA[Teacher Performance Evaluation Analysis Using K-Means Clustering Algorithm and Random Forest Classification ]]>
<![CDATA[Dito Jurinaldo]]>;
<![CDATA[Musli Yanto]]>;
<![CDATA[Syafri Arlis]]>
<![CDATA[ Sebatik Vol. 30 No. 1 (2026): June 2026 ]]>
Publisher : <![CDATA[STMIK Widya Cipta Dharma ]]>
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DOI: 10.46984/sebatik.v30i1.2744
<![CDATA[Teacher performance assessment is a primary parameter in determining the quality of educational institutions. Evaluation systems in many elementary schools still rely on descriptive qualitative approaches. Abundant school administrative data often remain as unprocessed archival records without further analytical utilization. This condition results in school management decision-making that lacks a strong empirical foundation. This study applies data mining technology to transform administrative data into strategic information. The research focuses on SD Negeri 12 Padang Besi and involves all active teaching staff during the current academic year. The research dataset is entirely derived from internal school records. This study excludes the use of questionnaire instruments, and in-depth interview methods are not employed in the data collection process. The analysis is strictly limited to administrative aspects, without including assessments of in-class pedagogical competence. The technical implementation utilizes the K-Means Clustering algorithm to automatically identify patterns in teacher performance grouping. This process is followed by the application of the Random Forest algorithm to measure classification accuracy based on the available administrative features. The combination of these methods produces a performance mapping that is free from human subjectivity. The analytical results provide clear performance labels for each individual teacher. This study contributes to the development of a data-driven digital evaluation model. School management can use the outputs of this research as a basis for reward allocation or targeted professional development programs. This approach ensures transparency in human resource governance within the educational environment.]]>
<![CDATA[Deep Learning Analysis for Predicting the Approval Time of Clinical Practice Guidelines (CPG) Based on Historical Administrative Data ]]>
<![CDATA[Yuliana Pertiwi]]>;
<![CDATA[Musli Yanto]]>;
<![CDATA[Billy Hendrik]]>
<![CDATA[ Sebatik Vol. 30 No. 1 (2026): June 2026 ]]>
Publisher : <![CDATA[STMIK Widya Cipta Dharma ]]>
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DOI: 10.46984/sebatik.v30i1.2745
<![CDATA[This study aims to predict the processing time of the approval of Clinical Practice Guidelines (CPG), which exhibits considerable variation in duration and is difficult to predict accurately. In addition, the utilization of historical hospital administrative data to build effective predictive models for estimating the duration of the CPG approval process has not yet been optimized. Therefore, this research seeks to develop a predictive model to estimate the processing time of the CPG approval process.The proposed approach employs deep learning techniques by leveraging historical administrative data as the basis for modeling. The methods applied include K-Means Clustering, Decision Tree, and Long Short-Term Memory (LSTM). K-Means Clustering is used to group CPG data based on similar administrative characteristics, enabling the identification of approval time patterns. Subsequently, the Decision Tree method is utilized to analyze the relationships among variables and to generate classification rules that explain the factors influencing the duration of the CPG approval process. Meanwhile, LSTM serves as the primary model for predicting the processing time of CPG approval.This study uses 487 CPG records collected over the period from 2020 to 2024. The evaluation results indicate that the K-Means Clustering method achieves an accuracy rate of 87,36%. This level of accuracy reflects strong clustering performance and a high degree of conformity with actual conditions, indicating that the results are suitable to be used as a foundation for further analysis in the classification and prediction stages of the CPG approval process.]]>
