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Jurnal Teknologi Informasi dan Pendidikan
Published by Universitas Negeri Padang
ISSN : 20864981     EISSN : 26206390     DOI : https://doi.org/10.24036/jtip
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Engineering
Jurnal Teknologi Informasi dan Pendidikan (JTIP) is a scientific journal managed by Universitas Negeri Padang and in collaboration with APTEKINDO, born from 2008. JTIP publishes scientific research articles that discuss all fields of computer science and all related to computers. JTIP is published twice a year. The editorial board comes from the lecturer board in the Department of Electronics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 340 Documents
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New Student Admission Forecasting Model with Support Vector Machine Method: Case Study of Bali State Polytechnic Arya Pradnyana, I Putu Bagus; Wisnawa, I Putu Oka; Puspita, Ni Nyoman Harini
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.956

Abstract

Every educational institution, both formal and non-formal, organizes new student admissions every year. This process requires institutions to improve the quality of education, services, and accreditation, both in terms of student competence, facilities, and infrastructure. Therefore, effective and efficient planning is needed, especially in making strategic decisions. This research aims to forecast the number of new student admissions using the Support Vector Machine (SVM) method. SVM is one of the artificial intelligence techniques known to have a high level of accuracy in data analysis and forecasting. The results showed that the SVM method was able to produce predictions with a low error rate. The test results using Root Mean Square Error (RMSE) show that the Electrical Engineering study program has the best RMSE value of 7.292, making it the study program with the highest level of forecasting accuracy in this study. This finding proves that the SVM method can be effectively implemented in forecasting new student admissions, so that it can help institutions in developing better and data-based admission strategies.
Combustion Characteristics of Biodiesel Droplets from Nyamplung Seeds with Eggshell Catalyst Using PLX-DAQ Software Sukma, Kinanti Wilujeng; Sanata, Andi; Jatisukamto, Gaguk; Kustanto, Nurkoyim; Darsin, Mahros
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.829

Abstract

The purpose of this study was to determine the effect of eggshell catalyst (CaO) on the combustion characteristics of nyamplung oil biodiesel droplets (Calophyllum inophyllum L) on flame visualization, ignition delay, and temperature. At present, Indonesia still uses fossil fuels, namely diesel. However, over time there are alternative fuels using palm oil raw materials. However, palm oil is also used as a source of food so that it will have an impact on the scarcity of palm oil. In this study, we looked for alternative energy using nyamplung seeds (Calophyllum inophyllum L.) with catalyst weight variations of 1%, 3% and 5%. The tested droplet volume of 1 ml was placed in a type K thermocouple. The results of this study showed that the percentage of catalyst weight increased temperature and shortened ignition delay. The highest temperature and lowest ignition delay were found at a variation of 5% with the highest temperature of 679.5 °C and ignition delay of 3000 ms.
Design of Android-Based Augmented Reality Media for Teaching Computer Network Models Novaliendry, Dony; Irnanda, Muhammad Fakhri; Budayawan, Khairi; Asmara, Delvi
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.961

Abstract

The development of digital technology has a major impact on the education sector, especially since the Covid-19 pandemic changed the learning system to online. One technology that has great potential in supporting the learning process is Augmented Reality (AR). This technology allows the integration of two or three-dimensional virtual objects into the real environment interactively. This study aims to design and build Android-based learning media with marker-based AR technology for the introduction of computer network models for class XI at SMA Negeri 4 Padang. Based on the results of observations and interviews with informatics teachers, it is known that learning is still dominated by theory without direct visualization of computer network devices due to limited practical tools. The learning media developed uses AR technology with markers as markers to display 3D objects, utilizing the Vuforia platform, 3D models are created using Blender, interface design using Figma, and system integration is carried out with Unity. The visualized material includes network topology (bus, star, ring, mesh) and the OSI Layer model. With this media, students can understand the concept of computer networks more interactively and deeply, and increase their involvement in the learning process.
Potensi Metode Regresi Kuat dalam Pengukuran Skew Jam Setiawan, Putu Ayu Citra; Saputra, Komang Oka; Wiharta, Dewa Made
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.962

Abstract

Clock skew, defined as the difference in clock rates between digital devices, serves as a unique and stable fingerprint for device identification and authentication, particularly in distributed network environments. Traditional clock skew estimation techniques, such as linear regression, are effective under stable conditions but often fail in the presence of data disturbances, such as latency, jitter, and asymmetric delays, which introduce outliers. This study explores the application of robust regression methods to enhance the accuracy and stability of clock skew estimation under such conditions. Three robust techniques are comparatively analyzed: Least Median of Squares (LMedS), Random Sample Consensus (RANSAC), and S-Estimators. LMedS offers high resistance to outliers by minimizing the median of squared residuals, though it is computationally demanding for large datasets. RANSAC achieves a practical balance between robustness and efficiency through iterative model fitting and inlier maximization, while S-Estimators provide strong statistical resistance to both outliers and high-leverage points, albeit with increased implementation complexity. The comparative evaluation considers key parameters such as estimation accuracy, computational cost, and robustness to anomalies. Results indicate that RANSAC is generally preferred for clock skew measurement in distributed systems due to its efficient performance and explicit outlier detection capabilities. However, LMedS and S-Estimators remain valuable in scenarios with more complex anomaly structures or higher noise levels. This study contributes to the selection of appropriate robust regression methods for reliable clock skew estimation in dynamic and error-prone network environments.
Optimization of Web Server Load Balancing Using HAProxy with the Weighted Round Robin Algorithm Amalia, Zahrina; Dadi Riskiono, Sampurna
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.965

Abstract

The advancement of information and communication technology (ICT) in Indonesia has driven the widespread use of web-based services across various sectors, including education, public services, and digital administration. However, the growing traffic demand is often not supported by adequate server infrastructure, leading to issues such as slow access and system failures. These problems are primarily caused by unbalanced server workloads, especially when relying on a single server or lacking effective load distribution strategies. Load balancing technology is therefore essential to ensure stable and efficient web service performance. This study explores the implementation of the Weighted Round Robin (WRR) algorithm using HAProxy software to distribute loads proportionally based on each server's capacity. The research uses a quantitative experimental method by building a test environment consisting of one HAProxy server and multiple backend servers with different specifications. Traffic simulations are conducted using Apache JMeter to evaluate system performance based on technical metrics such as response time, and request success rate. The results of this study are expected to provide practical solutions for improving the reliability and efficiency of web services, particularly in supporting Indonesia's digital transformation initiatives that are often constrained by limited infrastructure resources.
Optimalisasi Klasifikasi Warna Badan Air Dengan Convolutional Neural Network Melalui Reduksi Kelas Skala Forel-Ule Prasetyo, Budi; Novaliendry, Dony; Sriwahyuni, Titi; Syafrijon, Syafrijon
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 1 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i1.970

Abstract

This study presents a method to optimize water color classification based on the Forel-Ule scale using a Convolutional Neural Network (CNN). The original 21-class system presents challenges such as high computational complexity, overlapping spectral characteristics, and class imbalance. A class reduction approach is proposed to group similar spectral categories into three ecologically meaningful classes: oligotrophic (clear blue), mesotrophic (greenish), and eutrophic (brownish). Using a dataset of 3,018 labeled water body images from EyeOnWater and implementing a CNN architecture trained on both the original and the reduced class schemes, the experimental results show that the reduced 3-class model achieved significantly higher accuracy (94.0%) compared to the original 21-class model (44.3%). These findings demonstrate that class reduction improves classification robustness, simplifies interpretation, and enhances practicality for real-world environmental monitoring.
Sentiment Analysis About Acquisition and Policy of X (Twitter) by Elon Musk Fatimatuzzahro, Adelia; Larasati, Aisyah; Dwiastuti, Anik
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.901

Abstract

Social media Twitter (now X) is quite popular because it offers the ability to communicate between users and accelerate the flow of information obtained. In its development, the acquisition of the company by Elon Musk led to various changes. Some of the new policies had a direct impact on users and caused mixed reactions. This research applies a comparison between the two types of labeling techniques using TextBlob and VADER, a comparison of algorithms using Random Forest and Balanced Random Forest, as well as the use of algorithm parameters by default and Grid Search, to find information on user perceptions of the impact of the acquisition and new policy X by conducting sentiment analysis. The data used is the result of crawling X's post in the period from the emergence of the acquisition issue until the rebranding of the Twitter name and logo to X, namely April 25, 2022 to July 23, 2023. The results show that visually, these three factors have an accuracy level that shows the use of superior factors, namely TextBlob, Balanced Random Forest, and default parameters, whose combination obtained the highest accuracy value of 87%. The results of sentiment classification using two labeling techniques show that positive sentiment is greater than negative sentiment. However, in negative sentiment there are several problems based on the highest frequency of words that appear. So in this study, several recommendations are given that can be done to meet the expectations of user satisfaction with the X platform.
Grouping of Student Achievement Based on Student Names in Class VII of SMPN 28 Sarolangun Using the K-Means Clustering Method Chintia Ningsih, Nur; Novaliendry, Dony; Hendriyani, Yeka; Asmara, Delvi
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.967

Abstract

Although the selection of outstanding students is important to provide awards and recognition for student achievement, the methods currently used by schools are not optimal. The process often takes a long time and requires a lot of manpower to collect and process student data, which can ultimately disrupt daily school operations. This study aims to identify outstanding students in class VII at SMP 28 Sarolangun using the clustering method with the K-Means algorithm. This type of research is quantitative research. The method used in this study is K-Means Clustering , with the determination of the optimal number of clusters using the Elbow Method. The results of the study obtained a grouping of students into four clusters, including Cluster 1 with 10 students (15.2%), Cluster 2 with 16 students (24.2%), Cluster 3 with 25 students (37.9%) and Cluster 4 with 15 students (22.7%). From the resulting Elbow graph, the elbow point is seen at the value of K = 4, which indicates that four clusters are the most effective and efficient number to separate student data into meaningful groups .
Klasifikasi Warna Objek secara Real-Time Menggunakan Optimasi Model CNN MobileNetV2 Apriliyanti, Resti; Kurniadi, Denny; Novaliendry, Dony; Rahmadika, Sandi; Farhan, Muhammad
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.969

Abstract

This research aimed to develop a Convolutional Neural Network (CNN) model for automatic object color classification using MobileNetV2. To determine the optimal configuration, the training process adjusted several hyperparameters, with particular focus on identifying the most suitable learning rate. The dataset consisted of 3,212 images grouped into five color categories: red, green, blue, random (including yellow, orange, and brown), and none (no object detected). Data augmentation techniques were applied to enhance the variety and robustness of the dataset. The model was trained using the Adam optimizer alongside the categorical crossentropy loss function, with various learning rate settings tested during training. Evaluation results showed that the model worked best with a learning rate of 0.0001 and a batch size of 32, with an average accuracy of 94%. To display prediction results in real time, the top-performing model was integrated into a graphical user interface (GUI). These findings demonstrate the effectiveness of the MobileNetV2-based CNN model in recognizing object colors and highlight its suitability for integration into real-time industrial sorting applications
Exploring the Influence of Programming Courses on University Students’ Computational Thinking Skills Darwas, Rahmadini; Sepriana, Rina; Rahimullaily, Rahimullaily; Jalinus, Nizwardi; Ambiyar, Ambiyar; Giatman, Giatman
Jurnal Teknologi Informasi dan Pendidikan Vol. 18 No. 2 (2025): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v18i2.977

Abstract

This study aims to explore the relationship between students' perceptions of programming courses and computational thinking (CT) skills in Information Systems Study Program students at Metamedia University. The sample consisted of 37 semester students in the 2024/2025 academic year. The research method used was quantitative with a correlational approach, and data analysis was carried out using the JASP application. The results of the Spearman's rho correlation test showed a value of 0.103 with a p-value of 0.590. This value indicates a very weak and insignificant relationship between perceptions of programming courses and CT skills, as measured by the Mid-Semester Exam scores. This finding indicates that although students have positive perceptions of programming courses, these perceptions are not necessarily directly proportional to their CT skills. This result contradicts initial expectations that assume a strong relationship between positive perceptions and increased CT skills. The implications of this study indicate the need to develop a learning approach that explicitly integrates CT elements in the learning process, assignments, and assessments in programming courses in higher education

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