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INDONESIA
JURNAL TEKNOLOGI DAN OPEN SOURCE
Published by Universitas Islam Kuantan Singingi
ISSN : 26557592     EISSN : 26221659     DOI : 10.36378/jtos
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi dan Open Source menerbitkan naskah ilmiah. yang berkaitan dengan sistem informasi, teknologi informasi dan aplikasi open source secara berkala (2 kali setahun). Jurnal ini dikelola dan diterbitkan oleh Program Studi Teknik Informatika Fakultas Teknik, Universitas Islam Kuantan Singingi. Tujuan penerbitan jurnal ini adalah sebagai wadah komunikasi ilmiah antar akademisi, peneliti dan praktisi dalam menyebarluaskan hasil penelitian.
Arjuna Subject : -
Articles 160 Documents
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Implementation of Python-Based Topsis Method for Best Stock Selection Analysis Using Yahoo Finance: English Kristian Gunawan; Ikrimach Ikrimach
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3873

Abstract

This study developed a web-based application implementing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for stock investment analysis and recommendations. The application was built using the Flask framework and integrated with the Yahoo Finance API for real-time stock data retrieval. The TOPSIS method evaluated stock alternatives based on criteria such as closing price, P/E ratio, revenue growth, and dividend per share. Testing included functional evaluation, response time analysis, and simulations of three investor scenarios: High Risk-High Return, Low Risk-Low Return, and Balanced. Results indicate that the application effectively delivers stock recommendations aligned with investor preferences, achieving an average response time of 1–4 seconds per feature. Simulations highlight its adaptability in adjusting criteria weights to match different risk profiles. Despite limitations due to external API dependencies, the application demonstrates effectiveness as a decision support tool for stock investment, offering accessibility and flexibility to investors.
Measuring the Quality of the General Election Commission Website in Central Jakarta Using the WebQual 4.0 Method Naufal Brilianto; Ahmad Fauzi; Artika Surniandari; Hilda Rachmi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3876

Abstract

This study aims to analyze the application of the Webqual 4.0 method in measuring the quality of the Central Jakarta City General Election Commission (in Indonesia it is abbreviated as KPU) website. This method focuses on three dimensions of usability quality, information quality, and interaction quality. This study uses a quantitative method with primary data. The sample of this study were employees and the general public. The data analysis technique used was multiple linear analysis processed using the SPSS version 26 program. The results of the study showed that the usability variable did not have a significant effect on website quality measurement. The Information Quality variable did not have a significant effect on website quality measurement. And the Interaction Quality variable had a significant effect on website quality measurement.
Application of Deep Learning Algorithm to Detect Fraud in Online Transaction Networks Ridwan Dwi Irawan; Agus Fatkhurohman
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3890

Abstract

Online transaction fraud is a severe problem that may cost businesses and people a lot of money. This paper suggests using deep learning algorithms to detect fraud as a remedy to this issue. These algorithms were chosen based on their ability to handle large amounts of intricate data and identify patterns that are difficult to identify using traditional techniques. Important components of this research include gathering and preprocessing transaction data, creating deep learning models, and assessing model performance. This investigation examines a variety of financial transaction types that may have involved fraud. The deep learning approach uses deep neural network designs, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to maximize detection accuracy. The study's findings demonstrate that the deep learning models created are excellent at identifying questionable transactions and can lower the false positive rate, which raises the overall effectiveness of fraud detection systems. As a result, deep learning algorithms have demonstrated a high degree of efficacy in identifying fraudulent activity inside internet-based transaction networks, so they play a vital role in fraud prevention.
Implementation of the K-Means Clustering Method for Selecting Pencak Silat Athletes at the North Sumatra KONI Abdi Alamsyah; Rahmat Kurniawan R
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3920

Abstract

This article discusses the application of the K-Means clustering method in the selection of pencak silat athletes at the North Sumatra Koni. The study aims to improve the efficiency of the selection process, which was previously carried out manually, by classifying athletes based on attributes such as age, weight, and physical ability. K-Means clustering is used to group athletes into three categories: very suitable, suitable, and unsuitable. The methodology includes determining the number of clusters, calculating centroids, and analyzing Euclidean distances for data clustering. The results of the study showed that this method was effective in classifying 394 athletes in the "unsuitable" category, 354 athletes in the "suitable" category, and 376 athletes in the "very suitable" category. This study is expected to support the athlete selection process more systematically and efficiently. Recommendations for further research include the use of additional criteria and exploration of other clustering methods for more optimal results.
Development Of Android-Based Applications With An Inquiry Approach On Solubility Material And Solubility Products As A Source Of Independent Learning For High School Students Jakub Saddam akbar; Djakariah; Aisyiah Restutiningsih Putri Utami; Kurniahtunnisa4; Ayu Febrianti Akbar
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3940

Abstract

This study aims to develop an Android-based application that uses an inquiry approach on solubility material and solubility products, and assesses its feasibility as an alternative source of independent learning for high school students. The approach used is the development research method, by choosing a development design using the ADDIE model. The result of this study is in the form of an Android-based application. The evaluation of the results of this development is based on the assessment of validators who show very good categories, with ideality reaching 86% according to the assessment of media experts, and 84.75% according to the assessment of material experts. Student responses to Android-based applications with an inquiry approach to solubility material and solubility results obtained a percentage of 93.22%. Based on the expert assessment and student responses, it can be concluded that the Android-based application that has been developed is worthy of being used as an independent learning resource for high school students.
Digital Fish Image Segmentation Using U-Net for Shape Feature Extraction Fathorazi Nur Fajri; Mohammad Dzikrillah; Ahmad Khairi
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3968

Abstract

Segmentation of digital images of fish is an important challenge in image processing in the field of marine biology and aquaculture. Extraction of fish shape features through image segmentation can improve accuracy in species identification and fish population monitoring. The U-Net method, which is based on deep learning, has been proven effective in medical image segmentation and is beginning to be applied in fish image segmentation. This study aims to develop a fish digital image segmentation method using U-Net architecture for accurate and efficient fish shape feature extraction. The dataset used consists of 500 fish images of various shapes and sizes collected from various sources. The fish images were processed using a U-Net artificial neural network, which was trained and tested to obtain the best segmentation results, with evaluation using Intersection over Union (IoU). The segmentation results show that the U-Net method can produce precise segmentation, with a high degree of accuracy in extracting fish shape features. Evaluation of the segmentation metrics resulted in an IoU value of 0.88, indicating excellent performance in distinguishing the fish object from the background and accurately mapping the fish shape. The fish digital image segmentation method using U-Net is effective for fish shape feature extraction and can be applied in fish species identification and aquatic ecosystem monitoring.
Classification Of Rice Plant Diseases Using K-Nearest Neighbor Algorithm Based On Hue Saturation Value Color Extraction And Gray Level Co-Occurrence Matrix Features Siti Saniah; Mhd. Furqan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3972

Abstract

This research aims to classify diseases in rice plants using the K-Nearest Neighbor (K-NN) algorithm based on Hue Saturation Value (HSV) color feature extraction and Gray Level Co-Occurrence Matrix (GLCM) texture. The main problem faced is how to identify the type of disease in rice plants automatically using digital images. Diseases such as Blight, Tungro, and Crackle often attack rice plants and require an accurate early detection system. Lack of understanding in recognizing disease symptoms manually often leads to errors in handling. For this reason, this research develops an image processing-based classification system that can detect diseases such as Blight, Tungro, and Crackle. The method used in this research is image processing which includes RGB to HSV color space conversion, texture feature extraction using GLCM, and classification using K-NN algorithm. The dataset consists of 240 images, divided into training data and testing data, namely 192 training data and 48 testing data. Tests were conducted by calculating accuracy at various values of the K parameter, namely K = 1, K = 3, and K = 5, to determine the effectiveness of the model in classifying plant diseases. The purpose of this study was to evaluate the accuracy of the system in identifying rice diseases and test the combination of HSV and GLCM features in improving classification performance. The results showed that using HSV and GLCM features together resulted in the highest accuracy at K=3 with an accuracy value of 75%. The system is expected to assist farmers in detecting plant diseases quickly and effectively, thus minimizing production losses and supporting agricultural sustainability
Application of the C4.5 Algorithm for Predicting Banana Chips Production Demand (Case Study at UD. Sinar Sejahtera Medan) M. Teguh wijaya; Rakhmat Kurniawan R
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3975

Abstract

The rapid advancement of science and technology significantly impacts various aspects of life, including business operations. Technology plays a vital role in providing information and simplifying human tasks, addressing challenges faced by growing companies, particularly in managing sales fluctuations. Factors such as market competition, product quality, and consumer interest are critical for evaluating and improving sales strategies. UD. Sinar Sejahtera Medan, a food processing industry specializing in banana chips, faces challenges such as fluctuating raw material supply, impacting production and sales. To address this, a prediction system for raw material demand was developed, leveraging the C4.5 algorithm. The C4.5 algorithm was selected for its ability to generate decision trees from historical data, providing interpretable results and high accuracy in forecasting categorical outcomes. By analyzing past trends in raw material availability and usage, the algorithm predicts future supply needs, optimizing production planning and supporting sustainable business operations. This study's findings are expected to align with previous research, offering insights for better production and sales management.
Memomath: Educational Game Application for Elementary School Children in Special Inclusion Classes for Students with Slow Learner Diagnosis Trinovita Zuhara Jingga; Rina Novita; Ega Evinda Putri; Widya Febrina; Perdana Putera
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3999

Abstract

Memomath is a game-based educational application designed to support the learning of children diagnosed as slow learners in elementary school inclusion classes. This application integrates mnemonic-based math, reading, and memory exercises to improve students' academic skills and learning motivation. This research was conducted at SDIT Madani Islam Terpadu Kota Payakumbuh, an inclusive school that provides special programs for students with slow learning needs. The waterfall method was applied in the development of this application, involving the stages of needs analysis, design, implementation, testing, and maintenance. The test results showed that this application was effective in increasing engagement and understanding of concepts in students, especially at the age of 7-12 years. The data also revealed the relationship between age and duration of application use, providing important insights for design optimization. This study shows that a targeted gamification approach can make a significant contribution to the education of children with special needs, especially in helping them overcome learning challenges.
Monitoring Water Quality in The Well-Water Processing System to Make Drinkable Water Based on IoT Hendry Ponda; Uci Rahmalisa; Haris Tri Saputra; Rika Melyanti
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.4271

Abstract

Indonesia is also inseparable from problems related to clean water. The city of Pekanbaru is currently experiencing rapid growth. In some big cities, the difficulty of clean water suitable for consumption is commonly felt by some residents, for example in Tuah Karya district – Pekanbaru. Moreover, this area is prone to flooding so the quality is getting worse because it smells and is cloudy. To produce clean water suitable for drinking that can be consumed by all levels of society. Water quality monitoring is also easy to do with IoT-based water quality monitoring tools. The goal of developing this prototype is to improve the healthy standard of living of the community by meeting the clean water needs of the prototype to be built. Seen from the main indicators, TDS and PH = TMS (Not Eligible) were obtained and followed by several other indicators that were still TMS. The results of the sample test showed that the water did not belong to the category of clean water and was suitable for consumption. After the water source of the drilled well is filtered using a tool made (without a manganese filter), the main indicators of TDS and pH are qualified.

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