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Astri Ayu Purwati
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Jurnal Testing dan Implementasi Sistem Informasi
Published by Lembaga Riset dan Inovasi Al-Matani
ISSN : -     EISSN : 29867991     DOI : 10.55583/jtisi
Core Subject : Science,
Computer Science & IT
Jurnal Testing dan Implementasi Sistem Informasi includes research in the field of Information Technology, Information Systems Engineering, Intelligent Business Systems, and others. Editors invite research lecturers, the reviewer, practitioners, industry, and observers to contribute to this journal. The language used in English. Jurnal Testing dan Implementasi Sistem Informasi is a national scientific journals are open to seeking innovation, creativity and novelty. Either letters, research notes, articles, supplemental articles, or review articles. Jurnal Testing dan Implementasi Sistem Informasi aims to achieve state-of-the-art in theory and application of this field. Jurnal Testing dan Implementasi Sistem Informasi provide platform for scientists and academics across Indonesia to promote, share, and discuss new issues and the development of information systems and information technology. E-ISSN : 2986-7991
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 39 Documents
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HAND POSE CLASSIFICATION USING MEDIAPIPE HANDS AND CNN-LSTM FOR AUGMENTED REALITY BASED INTRAVENOUS INFUSION LEARNING Desnelita, Yenny; Siddik, Muhammad; Lita, Lita; Hajjah, Alyauma; Gustientiedina, Gustientiedina
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2343

Abstract

Intravenous infusion training requires precise hand positioning and coordinated movements; however, conventional training approaches remain subjective and lack consistent real-time feedback. Moreover, existing augmented reality (AR)-based systems are largely limited to visualization and do not provide intelligent, automated skill evaluation. To address this gap, this study proposes an integrated hand pose classification framework that combines MediaPipe-based landmark extraction, CNN-LSTM spatio-temporal modeling, and AR-based feedback for real-time procedural learning. The novelty of this work lies in the seamless integration of lightweight feature representation, hybrid deep learning, and interactive AR feedback within a unified learning system. Experimental results demonstrate that the proposed approach achieves high classification performance, with an accuracy of 94.82% and an AUC of approximately 0.97, indicating strong discriminative capability. The system also operates in real time with low latency, enabling immediate feedback and adaptive learning. This study contributes theoretically to spatio-temporal gesture modeling and practically to the development of intelligent AR-based training systems. The proposed framework offers a scalable and objective solution for improving procedural accuracy, consistency, and accessibility in medical education.
THE INFLUENCE OF MARKETING MIX ON CONSUMER PURCHASE PATTERNS USING THE APRIORI DATA MINING ALGORITHM Umam, Muhammad Isnaini Hadiyul; Kurniawan, Muhammad Ilham; Lubis, Fitriani Surayya; Rizki, Muhammad
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2357

Abstract

The increasing volume of sales transaction data in retail and marketplace environments presents an opportunity to extract valuable insights for decision-making; however, such data are often underutilized. This study aims to analyze consumer purchasing patterns using the Apriori algorithm and to examine the influence of the marketing mix (product, price, place, and promotion) on purchasing decisions that shape these patterns. This research employs a quantitative approach by integrating data mining and statistical analysis. Transaction data are processed using the Apriori algorithm through RapidMiner to generate association rules and identify frequent itemsets. In addition, questionnaire data are analyzed using multiple linear regression to evaluate the effect of marketing mix variables on purchasing decisions. The results show that product, price, place, and promotion simultaneously have a significant effect on purchasing decisions. Partially, product (t = 2.622; p = 0.011), price (t = 4.738; p = 0.000), and place/distribution (t = 2.239; p = 0.029) have a significant positive effect, while promotion does not have a significant effect (t = 1.486; p = 0.143). The Apriori analysis reveals dominant purchasing patterns that can be translated into practical marketing strategies, such as product bundling and layout optimization. This study contributes by integrating association rule mining with marketing mix analysis to provide both predictive patterns and explanatory insights. However, the findings should be interpreted with caution due to data limitations, including a relatively small sample size (n = 148) and a short observation period of three months during peak season, which may limit generalizability. Despite these constraints, the results offer practical implications for optimizing marketing strategies and contribute theoretically to interdisciplinary research in data mining and consumer behavior.
Improving FAQ Retrieval for Academic Regulations Using Semantic Embeddings and LLM Question Augmentation Fajri Profesio Putra; I Gusti Agung Putu Mahendra; Agus Tedyyana; Muhammad Noor
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2176

Abstract

Academic regulations in higher education are often documented in lengthy and formal handbooks, making it difficult for students to find relevant information using everyday language. This study developed a semantic FAQ retrieval system for academic regulations using IndoSBERT and question augmentation. The FAQ corpus was constructed from official academic and internship documents, resulting in 92 FAQ entries across 33 topical categories. Seed questions were generated from category–keyword pairs and expanded using simple rule-based augmentation and FLAN-T5-based paraphrasing. The dataset was evaluated using an 80:10:10 train–validation–test split. IndoSBERT was fine-tuned with Multiple Negatives Ranking Loss under three configurations: baseline, baseline with simple augmentation, and baseline with simple plus LLM-based augmentation. Retrieval performance was measured using Recall@1, Recall@3, Recall@5, and Mean Reciprocal Rank. The best result was achieved by the simple plus LLM augmentation configuration, with Recall@1 of 0.7848, Recall@5 of 0.8987, and MRR of 0.8396. These findings show that LLM-based question augmentation improves semantic retrieval robustness while keeping answers grounded in curated academic regulations.
Phishing Detection Model on Social Media Enhanced With CNN and BERT Nurliana Nasution; Wenni Syafitri; Feldiansyah Feldiansyah
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2194

Abstract

Phishing on social media has become an increasingly serious cyber threat because attackers exploit persuasive language, conversational context, and dynamic interaction patterns to deceive users. This study proposes a hybrid CNN-BERT model for detecting phishing content in Indonesian social media text by combining BERT’s contextual semantic representation with CNN’s ability to capture locally relevant textual patterns. The dataset was preprocessed to remove noise, normalize writing variations, and prepare the text for deep learning analysis; class proportions were also examined to support fairer evaluation. Model performance was assessed under multiple data-splitting scenarios and cross-validation to examine robustness and consistency. The experimental results indicate that the proposed hybrid model achieves strong and stable performance across accuracy, precision, recall, and F1-score, and outperforms the baseline model when the BERT backbone is frozen. However, when BERT is fully fine-tuned, the performance gain from the CNN layer becomes marginal, suggesting that strong contextual representations are already highly effective for this task. These findings indicate that integrating CNN and BERT is effective for phishing detection on social media, although domain adaptation challenges, overfitting risk, and real-world deployment constraints remain important considerations. The novelty of this work lies in systematically comparing frozen versus fully fine-tuned IndoBERT backbones with and without a CNN head for Indonesian short-message phishing detection.
Optimized Land Surface Low Point Detection Using the D8 Algorithm in a Geographic Information System (GIS) Framework Khairul Muttaqin; Novianda Novianda; Ahmad Ihsan; Dea Ayuni Putri; Cut Alna Fadhilla; Chichi Rizka Gunawan; Chicha Rizka Gunawan; Jefril Rahmadoni
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2205

Abstract

Hydrological analysis in urban areas often suffers from inaccuracies in Digital Elevation Model (DEM) interpretation, especially in detecting micro-depressions and small-scale surface flow patterns. Previous studies typically relied solely on the automatic D8 algorithm in GIS without manual verification, resulting in flow directions that do not fully represent actual surface conditions. This study aims to compare manual D8-based flow direction calculations with automatic ArcGIS processing using DEMNAS data for Langsa City. The DEM (8.1 m resolution) underwent sink filling, hydrological conditioning, slope and aspect processing, followed by field validation using GPS measurements. The results show that the manual method identified 23 flow paths, whereas ArcGIS detected only 11. The differences stem mainly from micro-topographic variations that the automatic algorithm failed to capture in flat areas or anthropogenically modified surfaces. Field validation confirmed that 8 of the 11 ArcGIS-derived paths matched the actual drainage patterns, while the additional manual paths better represented subtle elevation gradients.This research contributes by offering a systematic comparison between manual and automatic D8 approaches, highlighting the importance of manual verification in low-slope urban terrains. The findings are valuable for micro-scale flood mitigation planning and urban surface hydrology analysis.
An Expert System for Early Detection of Mental Health Conditions Using Certainty Factor and DASS-42 Indri Rahmayuni; Yance Sonatha; Tsalsabila Jilhan Haura; Fazrol Rozi
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2214

Abstract

Mental health problems such as depression, anxiety, and stress continue to increase in many countries, while access to professional services is still limited. Many digital screening systems use fixed scoring methods and do not consider uncertainty in user responses. This study developed a web-based expert system by combining the Depression Anxiety Stress Scales (DASS-42) and the Certainty Factor (CF) method to represent uncertainty in overlapping emotional symptoms and provide more flexible screening results. The knowledge base was prepared through consultation with a licensed clinical psychologist and converted into 42 production rules based on the DASS-42 items. Each rule was assigned a confidence value according to expert judgment. The system uses forward chaining to combine active rules and calculate confidence scores for depression, anxiety, and stress at the same time. System evaluation was conducted using 50 community cases aged 18–35 years and compared with independent expert assessment. The overall accuracy reached 86% (43 of 50 cases). The accuracy for each category was 88.2% for depression, 82.3% for anxiety, and 87.5% for stress. Most classification errors occurred between anxiety and stress, which may be related to overlapping symptoms in the DASS-42 instrument. The findings indicate that the proposed system can support early mental health screening through interpretable confidence-based results. However, this study used a limited dataset and only one expert in knowledge development. The system is intended as a screening support tool and not as a replacement for clinical diagnosis.
Analysis of User Reviews for The Mytelkomsel App Using Naïve Bayes and Random Forest Methods M. Rudi Sanjaya; Annisa Khoiriah; Rahmat Izwan Heroza; Bayu Wijaya Putra
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2220

Abstract

While sentiment analysis of local application reviews predominantly utilizes native Indonesian data, these datasets frequently suffer from colloquial ambiguities and informal structures that degrade classifier performance. This study addresses this gap by implementing a language-filtering mechanism to separate and analyze English and Indonesian user opinions from the MyTelkomsel application, specifically justifying the inclusion of English reviews due to their superior grammatical structure and syntactic consistency, which inherently enhances feature extraction. A systematic methodology was employed, encompassing data collection from the Google Play Store, comprehensive pre-processing (case folding, tokenization, stopword removal, and stemming), and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Evaluated using Naïve Bayes and Random Forest algorithms on 25,000 customer feedbacks, the models were compared across accuracy, precision, recall, and F1-score. The empirical results demonstrated that Random Forest outperformed Naïve Bayes, achieving a higher accuracy of 86.85% compared to 86.36%. This superiority stems from Random Forest’s robust capability to mitigate class imbalance and minimize error distribution across sentiment categories. Ultimately, this approach provides precise, actionable insights into service quality, enabling Telkomsel to effectively distinguish user satisfaction, target operational improvements, and mitigate customer churn.
Design and Evaluation of a Decision Support System for Classifying Tourism Site Crowding and Recommending Governance Responses in Bunaken National Park Aditya Kalua; Mochamad Agung Wibowo; Luther Alexander Latumakulita
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2232

Abstract

Effective governance of marine protected areas (MPAs) requires reliable mechanisms to translate multidimensional ecological and social data into coordinated institutional action. Despite widespread adoption of carrying capacity frameworks, a significant "implementation gap" persists between theoretical conservation thresholds and operational decision-making at the site level. This study addresses that gap by designing, implementing, and evaluating a Decision Support System (DSS) artifact tailored for Bunaken National Park (BNP), Indonesia. Grounded in Design Science Research (DSR) principles, the artifact employs a deterministic, rule-based classification engine that processes four normalized input dimensions visitor density, social carrying capacity, infrastructure load, and governance readiness to compute a Composite Crowding Index (CCI). The CCI is mapped through an explicit IF-THEN rule engine to four crowding categories (Low, Moderate, High, Extreme), each linked to a validated governance action package. A deterministic rule-based approach was chosen over probabilistic or machine-learning alternatives to ensure full decision traceability, which is a non-negotiable requirement for public-sector governance. System robustness was evaluated through structured scenario testing across 140 logic-coverage cases, assessed against four criteria: output consistency (100%), expert rule alignment (97.8%), decision traceability (100%), and processing efficiency (<1.15 seconds per scenario). The artifact successfully automates the mapping of site-level crowding status to discrete, auditable governance actions. The theoretical contribution lies in formalizing subjective management reasoning into a transparent, reproducible DSS that bridges sustainability science and institutional practice in high-pressure marine tourism environments.
Design and Evaluation of an AI-Assisted Digital KPI Information System for Employee Performance Monitoring and Recommendation Asnefi Asnefi; Essy Malays Sari Sakti; Dayanti Dayanti; Irwan Syarif
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2342

Abstract

Manual and fragmented KPI-based evaluation often weakens employee performance monitoring by causing delays, input errors, and inconsistent interpretation. This study addresses that problem by designing and evaluating an AI-assisted digital KPI information system that integrates competency assessment, digital KPI records, performance scoring, dashboard-based monitoring, and recommendation support in a single environment. The study contributes by transforming the conventional competency–KPI–performance framework into an operational decision-support artifact with embedded AI classification. Using a prototype-based approach, the system was evaluated with a structured dataset of 340 employee-year records from 2021–2025. Three machine learning models were compared for classifying employee performance into High, Moderate, and Low categories. The results show that the system is functionally feasible and usable for KPI-based performance monitoring, while Random Forest achieved the best classification performance with 0.9853 accuracy and 0.9852 F1-score. The findings indicate that the proposed system can improve the structure of digital KPI monitoring and provide AI-assisted support for managerial review and follow-up actions. The study contributes theoretically by extending KPI-based performance management into an intelligent information system context and practically by offering a feasible model for organizations operating under limited implementation conditions.

Page 4 of 4 | Total Record : 39

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