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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 282 Documents
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Usability Evaluation of SIMAK at Udayana University Using Outlier Identification with Webuse and Heuristic Methods Deppy librata; Wulaning Ayu, Putu Desiana; Huizen , Roy Rudolf
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.48807

Abstract

Universities require reliable and user-friendly academic information systems to support teaching, learning, and administrative processes. However, the usability of such systems often encounters obstacles that affect user satisfaction and operational efficiency. This study evaluates the usability of the Sistem Informasi Manajemen Akademik (SIMAK) at Udayana University using a combined Webuse questionnaire and Heuristic Evaluation approach. A total of 100 student responses were collected, and outlier identification using standard deviation analysis removed 34 inconsistent responses. This step was essential for preventing inflated Webuse scores and ensuring that the final dataset (n = 66) more accurately reflected typical user experiences.The Webuse results classify all usability dimensions in the “Excellent” category Content Organization & Readability (0.86), Navigation & Links (0.83), User Interface Design (0.84), and Performance & Effectiveness (0.81). However, the heuristic evaluation conducted by four expert evaluators identified 19 moderate to high-severity issues, revealing critical weaknesses in system responsiveness, interface consistency, and error prevention. These contrasting outcomes highlight that high perceived satisfaction does not necessarily align with expert-validated usability standards.The main contribution of this study lies in integrating outlier detection to refine questionnaire-based usability data, resulting in more valid interpretations. The findings offer practical recommendations for improving SIMAK’s performance, interface clarity, and error-handling mechanisms, while providing methodological insights for future usability evaluations.
Explainable Ensemble Learning for Urban Flood Risk Mapping in Jakarta Using Multi-Source Geospatial and Hydrometeorological Data Wibowo, Arief; Achadi, Abdul Haris
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49010

Abstract

Urban flooding is a frequent hydrometeorological hazard in Indonesia, particularly in Jakarta, driven by rapid urbanization, limited drainage capacity, land cover change, and extreme rainfall. This study develops an explainable ensemble learning framework for urban flood risk mapping in Jakarta using multi-source geospatial and hydrometeorological data, including satellite-based rainfall, topography, land use/land cover, NDVI, and IoT-based river water level observations from 2023–2025. Flood occurrence labels were constructed by integrating municipal flood records with satellite-based inundation data. The framework integrates Random Forest, Gradient Boosting, and XGBoost models, with SHAP applied for interpretability and identification of dominant flood drivers. Model evaluation using ROC-AUC and RMSE indicates that XGBoost achieved the highest performance (AUC = 0.91, RMSE = 0.184), outperforming Random Forest (AUC = 0.87, RMSE = 0.221) and Gradient Boosting (AUC = 0.89, RMSE = 0.203). SHAP analysis identifies rainfall intensity, elevation, proximity to river channels, and built-up area percentage as the most influential factors. Despite uncertainties in flood labeling and the lack of high-resolution drainage data, the results demonstrate the potential of explainable ensemble learning for urban flood risk assessment and resilience planning. 
Impact of Wavelet Denoising on LSTM-Based Greeting Sentence Recognition Using the IndSpeech Teldialog SVCR Dataset Shabira Zhillan; Wardhani, Luh Kesuma; Anggraini, Nenny; Nashrul Hakiem; Imam Marzuki Shofi
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49040

Abstract

Speech signals play a crucial role in human communication, particularly in speech recognition systems. However, speech recognition performance is often compromised by noise in the audio signal. This study aims to examine the effect of wavelet denoising technique on greeting sentence data containing artificial white noise before performing speech recognition using Long Short-Term Memory (LSTM). Mel Frequency Cepstral Coefficient (MFCC) is used as speech feature extraction. The results show that speech recognition accuracy reaches 90% on clean data. Accuracy drops to 51% when tested on data with noise, indicating a significant decrease of 39 percentage points. After applying the wavelet denoising method, accuracy improved using the two best parameter combinations. The combination with the highest SNR value resulted in an improvement of 18 percentage points, while the combination with the highest PESQ value resulted in an improvement of 13 percentage points. These findings indicate that the wavelet denoising method is capable of improving the performance of LSTM-based speech recognition in noisy environments.
Geographical Indication Classification Based on Stingless Bee Honey Samples Using a Generative Model on Spectral Data for Mobile Device Applications Maulana, Hata; Purwanto, Yohanes Aris; Diding Suhandy; Sony Hartono Wijaya; Heru Sukoco
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49280

Abstract

Stingless bee honey (SBH) is a food product with a simple and fast production process. Honey production is highly dependent on the condition and ability of the bees. Each region in Indonesia has different botanical and environmental conditions, with different entomological characteristics of bee species based on their geographical origin. This honey classification model is part of the formation of Geographical Indications (GI) based on product characteristics. A generative model is used in the pre-processing stage to produce spectrum data with the best grouping (silhouette score > 0.6). The main process discussed in this article is the application of GI classification model of four types of stingless bee honey based on cultivation location. The results of the study with a 400x745 dataset and 4 classes (Lampung, Bogor, Sukabumi, and Rangkas Bitung) showed that the classification model produced an accuracy above 95% with a precision and recall above 0.99. The SBH GI classification application has been successfully built using the scrum methodology and is cloud-based. The application displays the classification results of origin, type (entomological), feed dominance (botanical) and dominant spectrum values. The application has also been tested for feasibility based on the User Acceptance Test with results above 90%.
Comparing K-Prototypes and K-Medoids with Catboost for Health Profile Clustering of Pesantren Students Moch. Aghisna Hadzikunnuha; Harits Ar Rosyid; Arifin, M. Zainal
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49369

Abstract

Health screening in pesantren is challenging due to communal living conditions, limited health facilities, and the need for early identification of vulnerable student groups. This study compares the performance of K-Prototypes and K-Medoids clustering for grouping student health profiles and evaluates the use of cluster labels as additional features in a CatBoost classification model. The dataset consists of 1,464 new students from Queen Al Falah Islamic Boarding School in the 2025/2026 academic year, collected through the admission system and analyzed after preprocessing. Clustering is performed using K-Prototypes and K-Medoids with three clusters to support interpretability of nutritional and health profiles. Although two clusters yield higher silhouette values, three clusters provide more meaningful distinctions for practical screening. Classification experiments use CatBoost with an 80:20 stratified train-test split, comparing baseline models and hybrid models that integrate cross-algorithm cluster features. The results show an asymmetric pattern. Adding K-Prototypes features improves K-Medoids target accuracy from 99.66 percent to 100 percent, while adding K-Medoids features slightly decreases K-Prototypes target accuracy from 98.98 percent to 98.63 percent. McNemar test results indicate that these differences are not statistically significant. Overall, the proposed framework supports reliable and interpretable health profile clustering for pesantren student monitoring.
FinBERT-Based Sentiment Integration in Hybrid CNN– BiLSTM Models For Stock Price Forecasting Pawitra, Mohammad Tyas; Abdurrahman, Lukman; Fakhrurroja, Hanif
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49466

Abstract

This study investigates sentiment-aware deep learning models for short-term stock price forecasting using NVIDIA (NVDA) as a representative high-volatility technology stock. Four architectures—CNN, LSTM, BiLSTM, and a hybrid CNN–BiLSTM—are evaluated under two configurations: without sentiment and with FinBERT-based financial news sentiment integrated as a continuous contextual feature. Historical OHLV data are combined with sentiment information to enable multimodal learning under a controlled experimental setting. The results demonstrate that recurrent architectures consistently outperform convolution-only models, highlighting the importance of temporal dependency modeling in financial time series. Among all configurations, the hybrid CNN–BiLSTM with FinBERT sentiment achieves the best overall performance, yielding the highest R², the lowest MAE and RMSE, and the smallest overfitting gap. Bootstrap-based confidence intervals indicate stable generalization, while Wilcoxon signed-rank tests confirm that the observed performance improvements are statistically significant. The study also presents a near real-time deployment framework with low inference latency, demonstrating practical applicability for decision-support systems. Overall, the findings show that effective alignment between local feature extraction, bidirectional temporal modeling, and contextual sentiment integration is critical for improving stock price.  forecasting accuracy and robustness.
Design and Implementation of MCP-Web-Curl: A Model Context Protocol Server for Web and API Access in Agentic Coding Assistants Rayhan Zahwan Saleh; Muharman Lubis
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49625

Abstract

Many contemporary agentic coding assistants expose large language models through API wrappers but still lack a generic, reliable way to read the live web or invoke arbitrary REST endpoints, when relevant documentation or error explanations fall outside their internal context, these agents often stop rather than extend the search space. To address this gap, this paper presents MCP-Web-Curl, a Node.js/TypeScript based Model Context Protocol (MCP) server and command-line interface that provides LLM oriented tools for browser-based web scraping, REST API requests, Google Custom Search, smart routing over natural language commands, and robust file downloading. MCP-Web-Curl is designed around strict character limits, explicit truncation metadata, and resource blocking so that external calls remain token efficient and predictable for agent planning. Using a design science approach, we elicit requirements from real world agentic coder usage, design a modular architecture, implement the server with Puppeteer and the official MCP SDK, and evaluate it qualitatively through documentation-reading, API inspection, and download scenarios, complemented by independent marketplace reviews on MCPlane, Glama, and related MCP catalogs. The resulting architecture positions MCP-Web-Curl as a reusable blueprint for generic web/API access layers in agentic coding environments.
Accuracy Evaluation of 2D MediaPipe-Based Pose Estimation for Archery Posture Detection Using N-MPJPE Prasetya, Muhammad Andhika Bayu; Harits Ar Rosyid; M. Zainal Arifin
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.49778

Abstract

Archery requires high consistency and precise body posture, where small deviations can affect stability and accuracy. Recently, 2D human pose estimation has become an effective approach for analyzing sports techniques through automatic joint detection. This study proposes a 2D pose estimation system based on the MediaPipe framework to detect eight fundamental phases of archery technique and evaluate accuracy using the Normalized Mean Per Joint Position Error (N-MPJPE) metric. The dataset consists of annotated images representing the eight phases, which serve as ground-truth references. Accuracy is measured by calculating the normalized Euclidean distance between predicted joint positions and ground-truth coordinates across all phases. Experimental results show an average N-MPJPE of 0.71, indicating low joint-position deviation after scale normalization. Compared with prior studies reporting N-MPJPE values between 0.6 and 1.2, the proposed system demonstrates competitive accuracy for real-time 2D pose estimation. These results indicate that the system can reliably capture posture variations across archery phases and provide quantitative feedback on body alignment, making it a practical tool to support athletes and coaches in improving training quality and shooting performance.
Interactive Mobile IoT Application for Monitoring Etawa Crossbreed (PE) Goat Growth in Rural Communal Farms Wardhana, Ariq Cahya; Amrulloh, Arif; Adhitama, Rifki
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.50110

Abstract

The rapid development of communication and information technology has encouraged the adoption of the Internet of Things (IoT) in the agricultural sector, particularly in livestock management. This study presents the development of an interactive mobile IoT application for monitoring the growth of Etawa Crossbreed (PE) goats in rural communal farms. The proposed system integrates a load cell–based digital weighing scale with an IoT module to automatically record livestock weight data and store it in cloud storage, which can be accessed through a mobile application. The application also supports the recording of feed and health history to enable structured growth monitoring. The system was deployed and tested in a communal farm environment involving 20 rural farmers, where the IoT scale measured livestock weight and transmitted data wirelessly to the mobile application. A design thinking approach was employed to ensure usability and suitability for rural users. The usability evaluation using the System Usability Scale (SUS) produced an average score of 83.5, classified as excellent (Grade B), indicating high user acceptance. In addition, technical performance evaluation showed that all 20 monitoring records were successfully transmitted, resulting in a transmission success rate of 100%, with complete and consistent data storage achieving 100% data integrity. Observational latency results indicated that RFID reading required an average of 2.5 seconds, while the weighing and data synchronization process took an average of 7.5 seconds before appearing in the mobile application. These results demonstrate that the proposed system is not only user-friendly but also reliable and capable of supporting near real-time livestock monitoring in communal farming environments.
Starlink-Based IoT Network Performance Evaluation for Water Quality Monitoring in Remote Environments Amron, Kasyful; Kartikasari, Dany Primanita; Atarian, Tiara Calista Kusumawardani; Efendi, Archie Vian Nizam; Nugraha, Rayhan Egar Sadtya; Devy, Maritza Aliyya
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: 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.v19i1.50142

Abstract

Continuous water quality monitoring in remote and infrastructure-limited regions is constrained by the lack of reliable communication networks to support real-time IoT data transmission. Starlink offers a promising alternative due to its independence from terrestrial infrastructure, yet empirical evidence on its reliability as an end-to-end IoT communication backbone remains limited. This study therefore presents the design, implementation, and empirical network performance evaluation of a Starlink-based IoT system for real-time water quality monitoring. The proposed system integrates an ESP32 microcontroller with pH, total dissolved solids (TDS), and turbidity sensors, transmitting sensor data via a Starlink satellite link to a backend platform using the MQTT protocol with AES-128-GCM application-layer encryption. Received data are processed in Node-RED, stored in InfluxDB, and visualized through a Grafana real-time dashboard. Network performance was evaluated through five independent test iterations under both TCP and UDP transmission modes, measuring latency, jitter, packet loss, and throughput as key indicators of satellite link reliability for continuous IoT data transmission. The results demonstrate stable and reliable satellite connectivity, with latency consistently within 33–36 ms, jitter below 10 ms, zero packet loss across all configurations, and UDP throughput reaching up to 32.8 Mbps. TCP throughput was constrained to approximately 3.4–4.1 Mbps due to congestion control behavior over high-latency satellite links, a finding with direct implications for transport protocol selection in satellite-based IoT deployments. These results confirm that Starlink-based connectivity provides communication quality well in excess of the demands of periodic MQTT-based sensor transmission, demonstrating its feasibility as a reliable communication backbone for IoT-based water quality monitoring in environments where terrestrial infrastructure is unavailable or unreliable.

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