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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Komtekinfo
Yanto, Musli
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Rekomendasi Penerima Remisi Menggunakan Metode AHP dan TOPSIS pada Lapas IIB Solok Syalsabilla, Adinda; Yanto, Musli; Ariandi, Vicky
Jurnal KomtekInfo Vol. 11 No. 1 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i1.499

Abstract

Criminal law plays a crucial role in maintaining public order, and the correctional system is a crucial element in the administration of criminal law. Remission is a right for prisoners and becomes an essential aspect of corrections that provides incentives for good behavior during the rehabilitation period. Class IIB Solok Penitentiary faces challenges in the process of granting remission to prisoners without decision support systems. This study aims to develop a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to provide recommendations for granting remission to prisoners. The research method involves using AHP to assign weights to relevant criteria in the remission process and TOPSIS to rank each alternative prisoner. This method is implemented by constructing a website using PHP programming language and MySQL database.The research dataset includes information about prisoners and criteria used in the remission decision-making process. The results show that the implementation of the DSS with AHP and TOPSIS methods can effectively provide recommendations for granting remission to prisoners. The system is capable of prioritizing prisoners based on their level of priority for receiving remission, enhancing efficiency in decision-making at Class IIB Solok Penitentiary. This study contributes to improving the objectivity and efficiency in the remission decision-making process, supporting effective criminal law administration
Sectoral vulnerabilities and adaptations to climate change: insights from a systematic literature review Prihandoko, Prihandoko; Windarto, Agus Perdana; Yanto, Musli; Yuhandri, Muhammad Habib
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6944-6957

Abstract

Climate change is an urgent global issue impacting various life sectors, including health, agriculture, and infrastructure. This systematic literature review (SLR) aims to provide a comprehensive synthesis of research on sectoral vulnerabilities and adaptation strategies to climate change. Utilizing bibliometric analysis, the review identifies key themes and research gaps, highlighting the successes and challenges in implementing adaptation strategies. Key findings reveal that topics such as climate change, adaptive management, agriculture, public health, and food security are central to the research discourse. However, areas like health equity, sanitation, and agricultural worker adaptation remain under-researched. The analysis underscores the necessity for holistic, context-specific, and innovative approaches to policy-making, Scopus integrating sustainable development and public health to enhance resilience and adaptive capacity in vulnerable regions. This review offers valuable insights for researchers and policymakers aiming to develop effective adaptation strategies and address the multifaceted challenges of climate change.
Effectiveness of VGG19 in deep learning for brain tumor detection Arlis, Syafri; Putra, Muhammad Reza; Yanto, Musli
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1210-1218

Abstract

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.
Optimizing the gallstone detection process with feature selection statistical analysis algorithm Yanto, Musli; Yuhandri, Yuhandri; Tajuddin, Muhammad; Septiana, Vina Tri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1183-1191

Abstract

Early detection is one form of early anticipation in treating gallstone disease patients using medical images. However, the problem that exists is that there are still many shortcomings in medical images, such as noise in the image that causes the detection process to not run optimally. Based on this, this study aims to carry out the process of detecting gallstone objects in magnetic resonance cholangiopancreatography (MRCP) images by optimizing the performance of extraction techniques for feature selection. Optimization of extraction techniques in feature selection is carried out using the performance of the feature selection statistics analysis (FSSA) algorithm. The performance of the FSSA algorithm can provide improvements in the feature selection process by excelling in the performance of classification methods such as k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), and the Pearson correlation (PC) method. Based on the tests that have been carried out, the performance of the FSSA algorithm in the detection process provides an accuracy level of 95.69%, a sensitivity of 89.65%, and a specificity of 98.43%. Overall, this study can contribute to the development of extraction and provide a significant technical impact on optimizing the gallstone detection process.
Machine Learning Analisis Klasifikasi dalam Penentuan Status Gizi Anak Yanto, Musli; Febri Hadi; Syafri Arlis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5278

Abstract

Malnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.
MULTIPLE LINEAR REGRESSI PADA FUZZY NEURAL NETWORK (FNN) PENENTUAN KUALITAS DAGING SAPI Yanto, Musli; Arlis, Syafri; Putra, Deri Marse
JST (Jurnal Sains dan Teknologi) Vol. 11 No. 1 (2022)
Publisher : Universitas Pendidikan Ganesha

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

Abstract

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

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

Abstract

Diagnosing disease by playing the role of image processing is one form of current medical technology development. The results of image processing performance have been able to provide accurate diagnoses to be used as material for decision-making. This research aims to carry out the process of detecting brain tumor objects in Computed Tomography (CT-Scan) images by developing a segmentation technique using the Adaptive Threshold Morphology (ATM) algorithm. The performance of the ATM algorithm in the segmentation process involves the Extended Adaptive Global Treshold (eAGT) function to produce an optimal threshold value. This research method involves several stages of the process in detecting tumor objects. The preprocessing stage is carried out using the cropping and filtering process which is optimized using the eAGT function. The next stage is the morphological segmentation process involving erosion and dilation operations. The final stage of the segmentation process using the ATM algorithm is labeling objects that have been detected. The research dataset used 187 Computed Tomography-Scan images from 10 brain tumor patients. The results of this study show that the accuracy rate for detecting brain tumor objects in Computed Tomography-Scan images is 93.47%. These results can provide an automatic and effective detection process based on the optimal threshold value that has been generated. Overall, this research contributes to the development of segmentation algorithms in image processing and can be used as an alternative solution in the treatment of brain tumor patients.
Co-Authors Agus Perdana Windarto Ariandi, Vicky Febri Hadi Prihandoko Prihandoko Putra, Deri Marse Putra, Muhammad Reza Septiana, Vina Tri Syafri Arlis Syalsabilla, Adinda Tajuddin, Muhammad Yuhandri Yuhandri, Yuhandri Yuhandri, Muhammad Habib