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INDONESIA
TEKNIK INFORMATIKA
Published by Universitas Islam Negeri Syarif Hidayatullah Jakarta
ISSN : 19799160     EISSN : 25497901     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika merupakan wadah bagi insan peneliti, dosen, praktisi, mahasiswa dan masyarakat ilmiah lainnya untuk mempublikasikan artikel hasil penelitian, rekayasa dan kajian di bidang Teknologi Informasi. Jurnal Teknik Informatika diterbitkan 2 (dua) kali dalam setahun.
Arjuna Subject : -
Articles 262 Documents
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IMU-Based Early Warning System for Driver Drowsiness Detection via Head Movement Analysis Nurnaningsih, Desi; Kuswowo Adi; Bayu Surarso
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.45271

Abstract

The high incidence of road accidents caused by human error—accounting for approximately 69.7% of all motor vehicle accidents in Indonesia—demonstrates the urgent need for an effective driver monitoring system. One critical factor contributing to human error is driver drowsiness, which can be observed through behavioral indicators such as abrupt changes in head position. This study aims to develop a real-time early warning system for detecting driver drowsiness based on head movement patterns using a wearable device equipped with the MPU-6050 GY-521 accelerometer sensor. The system monitors acceleration on the X, Y, and Z axes and identifies drowsiness when simultaneous changes exceed predefined thresholds. A drowsiness event is characterized by a rapid head displacement, occurring within approximately 18–20 milliseconds. The thresholds applied for detection are 1.0g for the X axis, 3.5g for the Y axis, and 0.5g for the Z axis. In ten test scenarios simulating drowsy head movements, the system successfully identified seven instances, resulting in a detection accuracy of 70%. The novelty of this approach lies in its lightweight, non-intrusive design and its ability to function independently of lighting conditions, making it a practical solution for real-time driver safety enhancement.
Addressing Class Imbalance in Machine Learning for Predicting On-Time Student Graduation at The Islamic University of Riau Efendi, Akmar; Defit, Sarjon
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.45913

Abstract

Timely graduation is an important indicator of academic performance in higher education. However, many students still fail to graduate on time, prompting the need for predictive models to support academic decision-making. This study aims to analyze the impact of class imbalance on machine learning algorithm performance in predicting student graduation at the Islamic University of Riau. Data were obtained through questionnaires and labeled into “graduated on time” and “not on time” classes, which were initially imbalanced. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied during preprocessing to balance the dataset. Four machine learning algorithms were compared: Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, and Support Vector Machine. The evaluation was conducted with and without SMOTE, using accuracy, precision, recall, F1-score, and confusion matrix. Results showed significant performance improvements after applying SMOTE, with all models achieving around 99% accuracy. SVM achieved the most stable results across both conditions. The study highlights the effectiveness of SMOTE in improving classification fairness and reliability, especially in datasets with class imbalance. This work may assist universities in early intervention for students at risk of late graduation.
Public Sentiment and GoTo Stock Price Movement in Indonesia: A Null-Relationship Study Using Naïve Bayes and Non-Parametric Measures Pramesti, Dita; Fakhrurroja, Hanif; Karina M., Rahma
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46447

Abstract

The expiration of the lock-up period for PT GoTo Gojek Tokopedia Tbk's shares led to a sharp stock price decline and public discourse on Twitter. This study aims to examine the statistical relationship between public sentiment and GoTo’s stock price movement in Indonesia. Tweets were classified into positive or negative sentiment using the Naïve Bayes classifier, selected for its computational efficiency on large-scale textual data. The model achieved 70% accuracy, with a precision of 82% and F1-score of 75%. The sentiment polarity was then compared with stock trends across 39 distinct trading periods using four non-parametric statistical tests: Chi-Square (p = 0.6398), Cramer’s V (0.014), Goodman-Kruskal’s Lambda (0.053), and Mann-Whitney U test (p = 0.8994). None of these tests showed a statistically significant association between sentiment polarity and stock price movement. These findings highlight that while public sentiment may reflect short-term public interest, it does not reliably capture the market’s behavioral dynamics—especially in cases of investor decisions driven by broader macroeconomic or institutional factors. Sentiment data, therefore, should be considered as a complementary, rather than primary indicators in stock price analysis.
Real-Time Retail Shelf-Stock Detection with YOLOv7 Alquratu SeptriaPS, Annies; Silvia Handayani, Ade; Nasron, Nasron
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46448

Abstract

This study developed a real-time shelf stock monitoring system for retail environments, leveraging the You Only Look Once version 7 (YOLOv7) deep learning-based object detection framework. The system effectively addresses the inefficiencies, delays, and errors inherent in manual stock auditing processes. The underlying model was trained on a comprehensive dataset comprising 15,397 annotated object labels across fifteen distinct retail product categories. The fully trained model was then integrated into a web-based platform designed to capture real-time shelf images via a webcam. These captured images undergo automated processing for product detection and counting. The detection results are dynamically displayed on an interactive dashboard and securely stored in a backend database. The system also incorporates voice alerts, which are triggered automatically when stock levels fall below predefined thresholds, thereby facilitating immediate restocking. Experimental validation indicates high performance, with both precision and recall exceeding 96%, and an average processing latency of less than one second per frame. The model achieved an mAP@0.5 of 0.996 and an mAP@0.5:0.95 of 0.86. These findings underscore the system's effectiveness in providing a rapid, accurate, and efficient monitoring solution specifically tailored for small to medium-sized retail businesses. The primary contribution of this research lies in its comprehensive, end-to-end system integration, combining robust YOLOv7-based object detection with real-time web visualization and automated voice alerts, successfully addressing existing gaps in prior implementations.
Food Image Classification and Recipe Recommendation for South Sumatran Cuisine Using EfficientNetB1 Salsabillah, Farhah; Silvia Handayani, Ade; Anugraha, Nurhajar
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46449

Abstract

Visual-based food classification and recipe recommendation systems remain underexplored in the context of local culinary traditions. To address this gap, a system was developed using the EfficientNetB1 architecture of Convolutional Neural Networks (CNN), integrated with a Large Language Model (LLM) to generate South Sumatran recipes from food images, adapting suggestions to classification results. The model was trained using transfer learning on eight food ingredient classes selected for their prevalence in local cuisine. It achieved a validation accuracy of 98.2% and a test accuracy of 98%, with average precision, recall, and F1-score all exceeding 98%, indicating consistent and reliable performance. The system was deployed as a web-based application, DapoerKito, allowing users to upload food images, receive classification results, and obtain generated recipe suggestions. LLM-generated recipes are produced instantly, matched to ingredients, and shown in a clear format. These findings demonstrate the value of integrating computer vision and language generation in an AI-based platform that supports usability and cultural relevance. In addition to its technical capabilities, the system contributes to the digital preservation of regional culinary heritage through interactive AI. This CNN–LLM integration offers a novel approach for advancing food AI with diverse ingredients, personalized nutrition, and multilingual support.
Back-End Development of an Interactive Dashboard with Real-Time API Integration for Chili Plant Monitoring in Precision Agriculture Azwar Farrel Wirasena; Hanif Fakhrurroja; Dita Pramesti
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46450

Abstract

  This research focuses on the development of an interactive web-based dashboard to support a precision agriculture system for chili plants. The primary focus of this research is on the back-end development of the system. The system integrates several internal and external APIs, including the Flask API (internal) for plant disease classification and growth prediction, and the Google Gemini API for the AI-powered chatbot that provides consultation to farmers (external). These features allow farmers to receive automatic disease diagnosis and growth predictions, improving decision-making and crop management. The dashboard also presents weather information, environmental data, and nanobubble data, along with Echarts gauge charts for seven essential metrics: Electrical Conductivity (EC), temperature, humidity, pH, nitrogen, phosphorus, and potassium. Data for the environmental and nanobubble data is retrieved from the ThingSpeak API (external), while weather information is fetched from the OpenWeatherMap API (external). The system was thoroughly tested using Postman to ensure all API endpoints function correctly. The results confirmed that all endpoints responded with status code 200 OK, indicating stable back-end performance. Performance testing showed response times stabilizing at 2000 ms after initial 4500 ms peaks, confirming efficient handling, reliable endpoints, and deployment readiness.
Sentiment Classification in Imbalanced Data: Trade-Offs Between Metrics and Real-World Relevance Indra Swanto Ritonga; Wanayumini; Dedy Hartama
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46652

Abstract

Sentiment analysis plays a crucial role in assessing public perception, particularly in healthcare services like BPJS Kesehatan, Indonesia’s national health insurance program. However, sentiment classification faces a challenge due to class imbalance, where negative feedback dominates positive responses. This study investigates whether sentiment classification should prioritize traditional evaluation or maintain real-world data representation by preserving the original sentiment distribution. Two feature extraction methods, Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), were evaluated using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression with varying maximum feature counts (100–300) to examine the impact of feature dimensionality. Model performance was evaluated using traditional metrics, while sentiment distribution fidelity was assessed by comparing predicted proportions with the dataset. Results show TF-IDF achieves higher precision and recall but fails to capture positive sentiments, leading to a skewed representation of real-world trends, while BoW offers a more balanced distribution with slightly lower accuracy. Paired t-tests and Wilcoxon signed-rank tests confirmed differences in accuracy and recall are significant, but not in precision and sentiment distribution. These findings highlight a trade-off between performance and sentiment diversity, vital in healthcare services and other fields with imbalanced datasets, emphasizing the need to align evaluation metrics with real-world objectives. Future research should investigate advanced models, such as deep learning and transformer-based approaches, to enhance both accuracy and fairness when analyzing imbalanced data.
VARFIS: A Hybrid Neuro-Fuzzy Model for Intelligent Microclimate Control in Black Soldier Fly Farming Systems Yunita Sartika Sari; Kusrini; Ema Utami; Ferry Wahyu Wibowo
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46610

Abstract

Maintaining optimal microclimate conditions is essential for Black Soldier Fly (BSF) cultivation, yet traditional systems often struggle with dynamic environmental changes. This study proposes the Vector Autoregressive-Fuzzy Inference System (VARFIS), a hybrid model combining Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance temperature and humidity control in BSF insectariums. VARFIS adapts to uncertainty using probabilistic learning, achieving a 48% reduction in prediction error (MAPE = 1.36%) and high accuracy (R² = 0.9695), outperforming standalone VAR and ANFIS models. The model effectively captures daily climate fluctuations, improving larval growth efficiency and waste conversion. However, it remains limited in handling extreme events such as sudden heatwaves or humidity spikes, indicating the need for enhancements like adaptive fuzzy rule tuning and integration of physical constraints. VARFIS presents a scalable solution for intelligent microclimate management, supporting sustainable insect farming and circular economy goals. This work contributes to precision agriculture by offering data-driven tools for resilient environmental control.
Uncovering Hidden Themes in Indie Music: Crisp-Dm Guided LDA Topic Modeling on a Kaggle-Based Lyric Generation Dataset Thoyyibah T; Yan Mitha Djaksana
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46643

Abstract

The development of music has produced many works in the form of data, especially lyrical data, which provide insight into the semantic structure of music. This study explores latent thematic patterns in the indie lyric dataset from Kaggle by applying Latent Dirichlet Allocation (LDA), which is the first LDA study of indie music lyrics in the Indonesian context with the interpretation of love, emotional needs, romance, and inner conflict. The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology can be effectively applied to unstructured data, opening up opportunities for better music classification. The methodological stages include business and data understanding, data preparation, modelling, evaluation, and dissemination. In the early stages, the Kaggle dataset implemented Natural Language Processing, which was done with case folding, punctuation removal, stopword removal, stemming, and tokenization. The LDA model is trained by identifying five topics with different interpretations. Visualization in WordClouds, with topic distribution on datasets and title-based topic mapping. This model yielded a coherence value of 0.3044, which indicates limited semantic consistency, which means the words in the topic have a reasonably good relationship, but there is still potential for refinement in subsequent studies. The limitations of this study include the limited size of the dataset, with only 347 rows and slight variation in interpretation. For future research, it is recommended to use larger datasets and more diverse interpretations and apply more machine learning models.
Use Case Point Activity-Based Costing and Adjusted Function Point for Software Cost Estimation Ahmad Fauzan Haryono; Andria Farhan; Dewi Khairani; Supardi Razak; Fitri Mintarsih
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46669

Abstract

Effective software development planning is vital across various industries, and inadequate planning can lead to project failures. A key part of this planning is accurately estimating effort and costs, which is crucial for staying within budget and meeting deadlines. This research compares two methods – Use Case Point Activity Based Costing (with 21 complexity factors) and Adjusted Function Point (with 16 complexity factors) – for estimating costs versus actual values. The analysis reveals that the Use Case Point method had a 23.52% deviation from actual costs, while the Adjusted Function Point method had a 33.35% deviation. These findings provide essential reference points during software project planning, ensuring estimates closely align with actual values based on project-specific attributes. This deviation underscores the importance of precision and selecting appropriate methodologies tailored to each project’s unique characteristics. Ultimately, this research equips businesses and project managers with a robust financial prudence framework and enhances the likelihood of project success.

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