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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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Analisis Faktor-faktor yang Mempengaruhi Adopsi Teknologi 5G di Kota Bandung Alhafizh, Dzaki; Tritoasmoro, Iwan Iwut; Nur, Levy Olivia
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.v18i2.976

Abstract

This research aims to examine determinants influencing the adoption of 5G technology among users in Bandung City. As Indonesia begins its transition toward advanced communication infrastructure, understanding user behavior and adoption level becomes important, especially in Bandung city. Use a quantitative approach, this research collected data from 206 respondents through a structured questionnaire designed to capture various technical and non-technical factors. Eight independent variables were analyzed infrastructure availability, technology awareness and knowledge, network service quality, service and device cost, perception of security and privacy, potential and benefits offered by 5G, user experience, and social influence. Data analysis was performed using ordinal logistic regression to determine which factor adopted 5G technology. The results reveal that network service quality, perception of security and privacy, and social influence have a statistically significant negative effect on adoption levels. Meanwhile, the remaining variables showed no significant influence. Based on the research findings, the article provides recommendations such as improving network service quality, optimizing security and privacy, and increasing public awareness campaigns to address user concerns and support broader adoption of 5G technology. These efforts are expected to enhance user experience and accelerate the implementation of 5G in support of smart city development in Bandung.
Decision Support System Internet Disruption Using ORESTE and Geolocation PT Cloud Solution Zakaria Mubarok, Adhi; Supriyono, Supriyono; Latifah, Noor
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.v18i2.980

Abstract

PT Cloud Solution is a cloud-based internet service provider located in Kudus, Central Java, and serves the Kudus, Pati, and Jepara areas. The main problem in PT Cloud Solution's operations is the process of reporting and handling internet service disruptions which are still done manually, without an integrated system and without utilizing customer location data. This causes delays in technician responses, inaccuracies in technician distribution, and the absence of a prioritization system based on the level of urgency of the disruption. This study aims to develop a web-based Decision Support System (DSS) that integrates the Organization, Rangement Et Synthèse De Données Relationnelles (ORESTE) method and geolocation technology to support the decision-making process in determining the priority of damage handling. The ORESTE method is used because it is able to process ordinal data and perform an objective ranking of alternatives without a complex weighting process. Geolocation technology is implemented to map the position of customers and technicians in real-time, so that the system is able to recommend handling based on location proximity and priority scale. The system was developed using the Waterfall model with stages of needs analysis, design using Use Case diagrams, implementation with PHP language and MySQL database, and tested using the Black Box method. Test results show that the system is capable of generating accurate and efficient handling priorities. This system is expected to improve the speed and accuracy of technician distribution, and customer satisfaction
Application of Bagging Ensemble Learning on Naïve Bayes Algorithm to Predict Coronary Heart Disease Nugraha, I Gusti Agung Satria; Gunadi, I Gede Aris; Dewi, Luh Joni Erawati
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.v18i2.981

Abstract

Cardiovascular health is vital, with heart disease, particularly Coronary Heart Disease (CHD), being a significant health concern in Indonesia. The 2023 Indonesian Health Survey reported 877,531 cases of heart disease. Traditional CHD diagnosis is often costly and invasive. Therefore, machine learning-based classification has emerged as a promising alternative for enhancing the accuracy and efficiency of detection. This study aims to predict CHD using a hybrid approach combining the Naïve Bayes algorithm with the Bagging ensemble method. Naïve Bayes was selected for its computational efficiency and effectiveness with high-dimensional data, while Bagging was employed to mitigate its inherent weaknesses by reducing variance and increasing prediction stability. The CRISP-DM methodology was applied to a secondary dataset of 462 rows from Kaggle. The research process included data preprocessing, method implementation, and evaluation using a confusion matrix. Results show the Bagging method with n=2 estimators achieved optimal performance, with 76.34% accuracy, 65.00% precision, and an f1-score of 70.27%. This study demonstrates that ensemble techniques can effectively improve the accuracy and stability of CHD prediction models, offering a reliable and low-cost solution for initial screening.
Sentiment Analysis of 2024 Election Reviews In Twitter Using BERT with Emoticon Feature Extraction Vitria Anggraeni; Yuliant Sibaroni; Sri Suryani Prasetyowati
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.v18i2.993

Abstract

National elections are critical components of a functioning democracy, as they empower individuals to influence their nation's trajectory through active participation. In the contemporary digital landscape, social media platforms such as Twitter have emerged as pivotal forums for political discourse, enabling the expeditious dissemination of public sentiments. Nevertheless, assessing sentiment on Twitter poses various difficulties, primarily due to the casual nature of the language, the prevalent use of slang, sarcasm, and emoticons that often convey implicit emotional undertones. The present study puts forth a sentiment analysis framework that utilizes the Bidirectional Encoder Representations from Transformers (BERT) model, particularly IndoBERT, augmented with insights derived from emoticons. Emoticons are classified into three sentiment categories: positive, negative, and neutral. The dataset under consideration is composed of Indonesian tweets that have been pre-labeled and that pertain to the 2024 national election. Two configurations of the model were evaluated: a foundational IndoBERT model that relies solely on text, and an enhanced model that includes binary emoticon features. The experimental findings reveal that the emoticon-inclusive model attained a higher accuracy (78.5%) as opposed to the baseline model (77.7%) and demonstrated enhanced sensitivity in distinguishing neutral and negative sentiments. This finding suggests that emoticons offer valuable contextual information, thereby enhancing the accuracy of sentiment classification. The strategic integration of emoticons and BERT for Indonesian political sentiment analysis has received scant attention, rendering this method a novel addition to the field. The findings underscore the potential benefits of integrating text-based deep learning systems with emoticon characteristics to more effectively capture intricate emotional expressions in social media, particularly during political campaigns.
Critical Success Factors of ICT Implementation in Vocational High Schools Alam, RG Guntur; Hidayah, Agung Kharisma
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.v18i2.958

Abstract

Implementing Information and Communication Technology (ICT) in Vocational High Schools (SMK) plays a crucial role in enhancing the quality of learning, improving administrative efficiency, and preparing students for a technology-driven workforce. However, its implementation still faces various challenges, such as limited infrastructure, the readiness of educators, and uneven policy support. This study aims to identify the Critical Success Factors (CSFs) in ICT implementation in SMKs and analyze the challenges and opportunities for its development. A qualitative approach was employed, utilizing interviews with teachers, school principals, and students from various vocational schools to obtain a comprehensive perspective on ICT adoption in vocational education. The findings reveal eight key factors contributing to the success of ICT implementation: infrastructure availability, teacher competence, the role of school principals, government policy support, student engagement, curriculum integration, digital ethics, and continuous evaluation and monitoring. The study concludes that the success of ICT implementation heavily depends on the synergy between schools, the government, and industry. Therefore, more effective strategies are needed to ensure equitable access to technology, enhance teacher competencies, and strengthen digital-based curricula to optimize ICT to produce competitive SMK graduates in the Industry 4.0 era
A Web-Based SIBI Sign Language Translator Application with Speech-to-Text Feature Using CNN and MediaPipe Rahman, Fathur; Hadi, Ahmaddul; Novaliendry, Dony; Dwinggo Samala, Agariadne
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.v18i2.971

Abstract

This study developed a web-based application to facilitate two-way communication between individuals with hearing impairments and the general public. The application translated hand gestures based on the Indonesian Sign System into text using a Convolutional Neural Network model and real-time landmark detection. Additionally, it converted spoken language into text through speech recognition technology, which was then displayed alongside the corresponding sign language images. The system used a camera to capture hand gestures, which were processed into landmark data and classified into letters A to Z. Voice input was processed directly in the browser without additional installations. The application was designed to be lightweight, interactive, and compatible with various devices. Testing results showed that the gesture recognition feature achieved high accuracy, ranging from 98.71% to 100%. The speech-to-text feature also provided accurate transcription results, both for individual letters and complete sentences. Accuracy decreased at distances beyond 30 cm and in noisy environments. The integration of gesture recognition and speech-to-text conversion in a single web platform offered an effective, accessible, and inclusive communication solution for users with special needs.
Decision Support System Application for Determining Nutritional Status of Toddlers at Winong Community Health Center Using the KNN Method Arifviando, Muhammad Villa; Irawan, Yudie; Setiawan, R. Rhoedy
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.v18i2.982

Abstract

Toddler nutrition issues are an important issue in public health because they affect children's physical and cognitive growth and development. The process of assessing nutritional status which is still done manually in health centers, such as in Winong Community Health Center, is often inefficient and prone to errors. This study aims to develop a web-based decision support system that can automatically classify toddler nutritional status based on age, weight, and height data. This system is built using the CRISP-DM approach as a development methodology and the KNN algorithm as a classification method. The benchmark data for nutritional status refers to the standards of the Ministry of Health of the Republic of Indonesia. The test results show that the system is able to classify nutritional status into categories of good nutrition, malnutrition, and excess nutrition with a satisfactory level of accuracy. The system also provides easy access and speed in the decision-making process for health workers. In conclusion, this system is effective in helping health centers monitor toddler nutritional status quickly, accurately, and efficiently based on valid data.
Sistem Pakar Diagnosa Penyakit Gigi Menggunakan Metode Certainty Factor Berbasis Web Studi Kasus Klinik Pratama Al-Fatah Pradata, Aria Bima Putra; Supriyono; Laily Fithri, Diana
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.v18i2.988

Abstract

Dental health has an important role in supporting overall body health. However, public awareness of the importance of treatment and early detection of dental diseases is still low. This study aims to design and develop a web-based expert system that is able to help early diagnosis of dental diseases using the Certainty Factor (CF) method. The CF method was chosen because it is able to handle uncertainty and produce probability estimates based on the symptoms inputted by the patient. The system was developed using the Waterfall approach and implemented in the Al-Fatah Primary Clinic case study. Evaluation of the system's performance shows that the CF method has an accuracy of 89%, outperforming the Naïve Bayes comparison method which only reaches 82%. The system is also equipped with an interactive interface for patients and administrators, as well as features for managing data on symptoms, diseases, and diagnosis results. The results show that this CF-based expert system is able to provide efficient solutions for early consultation of dental diseases independently and accelerate diagnosis services in clinics.
Implementation of the Crisp-Dm Methodology and Naive Bayes Algorithm on A Raw Material Requirement Prediction System to Reduce Food Waste (Case Study: Adamsafee Bakery, Resto, & Cafe) Hakim, Adam Fathul; Irawan, Yudie; Setiawan, R. Rhoedy
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.v18i2.990

Abstract

Accurate forecasting of raw material requirements is critical for culinary businesses to reduce food waste and optimize costs. In the case of Adamsafee Bakery, Resto, & Cafe, high levels of waste have been caused by reliance on intuition-based forecasting, resulting in both overstocking and understocking. This study develops a web-based predictive system using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Naive Bayes algorithm to classify demand patterns into three categories: high, medium, and low. Historical sales data were transformed into categorical attributes and processed through the Naive Bayes model to generate demand predictions. The system was evaluated by comparing predicted sales with actual outcomes. Results show that the model achieved an accuracy of 98.7% and a mean absolute percentage error (MAPE) of 1.31%, indicating that the forecasts closely aligned with real sales performance. These findings demonstrate the effectiveness of the Naive Bayes algorithm in supporting data-driven decision-making for inventory management. This data-driven approach replaces subjective decision-making, enabling management to optimize inventory, minimize food waste, and enhance operational efficiency and business sustainability, while also offering a baseline for future research using alternative machine learning algorithms.
Web-Based Sales Prediction at PT Menara Kudus Indonesia Using the Double Exponential Smoothing Method Bayu Hidayat, Hilmi; Darmanto, Eko; Supriyono
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.v18i2.992

Abstract

PT Menara Kudus Indonesia is a printing, publishing, and bookstore company that still relies on manual sales and paper-based transaction recording, causing inefficiencies and dead stock due to unbalanced production and demand. This study develops a responsive web-based sales prediction system that integrates the Double Exponential Smoothing (DES) method to improve accuracy in forecasting sales trends. The system was developed using the PHP programming language, MySQL database, and the Waterfall SDLC model. The novelty of this research lies in the direct integration of the DES forecasting algorithm into a web-based system, enabling real-time predictions and automated reporting. The implementation results show that the system can process historical sales data, generate accurate forecasts, and provide tabular and graphical reports. The forecast for the 11th month resulted in 174 units, which can serve as a production planning reference. This system improves efficiency, reduces the risk of overstock and understock, and accelerates managerial decision-making.

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