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All Journal Tech-E Nusantara Hasana Journal PROFICIO: Jurnal Pengabdian Masyarakat
Amat Basri
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Nursing Social Sciences Other

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Journal : Tech-E

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Tongue Detection For Identification Of Syndrome Diagnosis In Heart Disease Using Convolutional Neural Network Niko Suwaryo; Koniasari; Amat Basri
Tech-E Vol. 8 No. 2 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i2.3285

Abstract

Convolutional Neural Network (CNN) which is one of the Deep Learning methods for Image identification and CNN models can identify images well but in this case it requires higher accuracy because the case is very crucial to determine the risk of heart disease. The initial stage in this study was the collection of tongue image data, 4836 training data and 1209 testing data. The image data used were the front, right side, left side of the tongue and under the tongue. The dataset was obtained from taking pictures using a smartphone camera centimeters above the object. The distribution of data in each class is shown in the following figure. The model from the two CNN algorithm experiments has accuracy performance. Based on the training results the model from the algorithm gets an accuracy value and Testing by identifying 20% of the total dataset as test data. The identification results are formed in a Confusion Matrix to then be poured into a classification report and obtain: train loss 0.301446, train accuracy 0.862696, test loss 0.314132 and test accuracy 0.850290 so that from the results of the tongue data test it can be concluded that the accuracy value is quite good, above 80%.
Rebuilding Public Trust in Islamic Banking Through the Utilization of Technology: Conceptual Review and Strategies Antasari, Novira Dian; Zulhamdani; Amat Basri; Triana Salma Dewi
Tech-E Vol. 9 No. 1 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i1.3231

Abstract

Public trust in Islamic banking has been a significant challenge in recent years. This is influenced by several factors such as lack of technology literature, technology distribution, cybersecurity, regulatory frameworks, customer awareness, and financial inclusion. This research aims to highlight the technology in restoring public trust in Islamic banking and present practical strategies for digital Sharia banks. This research aims to identify public trust in Islamic banking in Indonesia through technology. This research design is descriptive qualitative and the type of data used is secondary data. Sources and data were obtained from OJK websites, books, the internet, social media, journals, and other relevant digital format. Data is processed using the Python programming language. The results of this research indicate that digital literacy for Bank Hijra is less well-known among the public. From the mobile banking application, the rating of the sample banks shows a number above 4. User reviews show that the use of these applications has not yet achieved maximum customer satisfaction, as there are still complaints about data security concerns. It concluded that customer trust in digital Sharia banks has indeed increased, but there is still a need to improve data security and digital literacy, and user experience for the public.
Type 2 Diabetes Mellitus Diagnosis Model Using the C4.5 Algorithm Ruaida Susanti; Dewi Marini Umi Atmaja; Arif Rahman Hakim; Amat Basri
Tech-E Vol. 7 No. 2 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i2.2676

Abstract

Type 2 Diabetes Mellitus (DM) is a metabolic disorder characterized by elevated blood sugar resulting from decreased insulin secretion by pancreatic beta cells and/or impaired insulin function (insulin resistance). Over the last 50 years, there has been a rapid increase in the prevalence of diabetes, paralleling the rise in obesity rates. This study aims to develop a diagnostic model for type 2 DM using C4.5, incorporating feature selection and analyzing age and gender parameters of Type II DM patients. The research employs the Cross-Industry Standard Process for Data Mining (CRISP-DM). Based on the dataset used, the C4.5 model demonstrated superior performance compared to SVM and Random Forest, achieving an AUC value of 72.5%, indicating a reasonably good classification level. The predominant gender among Type II DM patients is female, comprising 210 patients or 54.8% in the age range of 18-94 years, while 173 male patients or 45.2% fall within the age range of 23-80 years.
Performance Analysis of Classification and Regression Tree (CART) Algorithm in Classifying Male Fertility Levels with Mobile-Based Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Andri Ariyanto
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2110

Abstract

Fertility is the ability to produce offspring in a man or the ability of the reproductive organs to work optimally in fertilization. Fertility rates have declined drastically in the last fifty years. Machine Learning is a field devoted to understanding and building learning methods. This study will use machine learning algorithms to classify male fertility levels, namely the Classification and Regression Tree (CART) algorithm and the K-Fold Cross Validation validation method. The fertility dataset used in this study was obtained from the UCI Machine Learning website, with a total of 100 data and the variables used are Age, Childish diseases, Accident or serious trauma, Surgical intervention, High fevers in the last year, Frequency of alcohol consumption, Smoking habit, Number of hours spent sitting per day and Diagnosis. K-Fold Cross Validation can be used together with CART to measure the performance of the CART model on different data, so as to avoid overfitting or underfitting the CART model. Based on the calculation of the CART algorithm and the K-Fold Cross Validation validation method (K = 1 to K = 9), the average accuracy value for training data is 98.70% and the average accuracy value for testing data is 81.16%. The results of this study have proven that the CART algorithm can be used to classify the level of fertility in men well. In addition, the classification model formed can be implemented into a mobile application (android) so that it is easy to use and understand.
Co-Authors Alexius Hendra Gunawan Andri Ariyanto Andri Wijaya Antasari, Novira Dian Arif Rahman Hakim Dewi Marini Umi Atmaja Gunawan Andi Leo Koniasari Lily Damayanti Murniasih, Intan Raditya Rimbawan Ruaida Susanti Salma, Triana Dewi Suwaryo, Niko Verri Kuswanto Zulhamdani Zulhamdani, Zulhamdani