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INDONESIA
JURNAL TEKNOLOGI DAN OPEN SOURCE
Published by Universitas Islam Kuantan Singingi
ISSN : 26557592     EISSN : 26221659     DOI : 10.36378/jtos
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi dan Open Source menerbitkan naskah ilmiah. yang berkaitan dengan sistem informasi, teknologi informasi dan aplikasi open source secara berkala (2 kali setahun). Jurnal ini dikelola dan diterbitkan oleh Program Studi Teknik Informatika Fakultas Teknik, Universitas Islam Kuantan Singingi. Tujuan penerbitan jurnal ini adalah sebagai wadah komunikasi ilmiah antar akademisi, peneliti dan praktisi dalam menyebarluaskan hasil penelitian.
Arjuna Subject : -
Articles 486 Documents
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Multivariate Analysis for Formulating Data-Driven Recruitment Strategies in the Informatics Engineering Study Program, UNIKS Harianja; Erlinda; Syakiro Rahmin; Nor Ismaiel
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5468

Abstract

This research aims to formulate an effective and data-driven recruitment strategy for the Informatics Engineering Study Program (TI) at Kuantan Singingi Islamic University (UNIKS) through a comprehensive analysis of student data. Expanding upon previous descriptive studies, this research applies a quantitative methodology with a case study approach that integrates cluster analysis, correlation, and association testing (Chi-Squared). Historical student data from 2019 to 2025 were processed and visualized using R Studio. The results confirm a strong positive correlation between the year and the number of new students. Furthermore, cluster analysis successfully grouped districts (kabupaten) and sub-districts (kecamatan) into clusters based on applicant potential, indicating an uneven geographical distribution. Moreover, the Chi-Squared test revealed a significant relationship between the number of students and demographic variables such as admission track and parental income. These findings provide strategic insights to focus promotional efforts on geographical areas with the highest potential and to target audiences based on socio-economic characteristics. Thus, R Studio proves to be a powerful tool to support data-driven strategic decision-making in the academic environment.
Rule-Based SQL Grammar Validator Syahrul Fajar Laqsono; Agung Prasetya; Mohamad Khoirul Ansor
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5474

Abstract

This study addresses the growing need for reliable data access systems by focusing on the validation of SQL queries generated from natural language using a Text-to-SQL approach. The primary objective of this research is to evaluate the effectiveness of a rule-based SQL grammar validator in detecting syntactic errors and improving the overall quality of queries generated by Large Language Models (LLMs), particularly in the context of Indonesian language input. The research methodology follows a structured process, including literature review, dataset construction, system design, implementation, and performance evaluation. Two datasets were developed: one for validating the grammar checker using both valid and invalid SQL queries, and another for evaluating the Text-to-SQL system. The validator was implemented using a rule-based system with grammar defined in EBNF and executed using forward chaining inference. The results indicate that the system achieves high performance, with an accuracy of 0.909, precision of 0.857, recall of 1.000, and F1-score of 0.923. The validator successfully identifies common structural errors such as missing table references and incomplete JOIN clauses. However, some limitations remain in detecting more complex syntax patterns. Overall, the integration of the grammar checker significantly enhances the reliability of SQL query generation. In conclusion, the proposed system demonstrates strong effectiveness in syntax validation and contributes to improving the robustness of Text-to-SQL systems.
Neural Network-Based Exfiltration Schema Identification Vetrick Aringga Dicktiony Racero; Agung Prasetya; Taufiq Agung Cahyono
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5475

Abstract

This study uses the BERT architectural technique to identify schema exfiltration in a neural network-based Text-to-SQL system. The growing usage of Large Language Models (LLM) in Text-to-SQL systems, which may provide a danger of database schema leaking through user prompts, provides the context for this study. This research challenge is how to use a deep learning model to reliably and adaptively identify prompt modifications that could carry out exfiltration techniques. The study employed a deep learning strategy with a feedforward neural network as the classifier and the BERT architecture as the primary encoder. There were 20 classes in all, consisting of 19 exfiltration scheme categories and 1 benign class. The dataset was created using a variety of sources, including WikiSQL, DatabaseAnswers, and educational datasets. It was then subjected to tokenisation, labelling, and normalization processes. The model obtains an accuracy of 0.9462, precision of 0.8425, recall of 0.7483, F1-score of 0.7926, and precise match accuracy of 0.7596, according to the data. Additionally, the study demonstrated that the model outperformed implicit suggestions like role switching and prompt injection in identifying explicit prompts. The study concludes that while there are still issues with enhancing detection capabilities for intricate manipulating patterns, the BERT-based approach can provide good performance in identifying schema exfiltration in Text-to-SQL systems.
Semantic Diversity In The Formation Of Story Questions From The Big Language Model Andra Havid Andra Ramadhon; Agung Cahyono; Taufiq Agung Cahyono
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5490

Abstract

The development of Large Language Models (LLMs) has opened new opportunities for the automatic generation of math word problems (MWPs). However, many existing approaches still produce repetitive and template-based problems due to limited variation in context, narrative structure, and semantic relationships. This limitation reduces the effectiveness of such problems in assessing students’ conceptual understanding. This study aims to develop a semantic diversity pipeline based on context-aware generation to produce more varied, meaningful, and curriculum-aligned math word problems for Indonesian elementary education. The proposed method involves building an Indonesian MWP dataset, fine-tuning an LLM using Low-Rank Adaptation (LoRA), and designing a generation pipeline consisting of context retrieval, prompt diversification, semantic evaluation, and a solvability filtering mechanism. Evaluation was conducted using automated metrics, including Self-BLEU, Jaccard Similarity, and Cosine Similarity based on Sentence-BERT, as well as qualitative assessments from mathematics teachers. The results show that the proposed approach successfully improves lexical, semantic, and contextual diversity in generated problems. The Self-BLEU score of 4.79 indicates low repetition, the Jaccard Similarity score of 0.194 reflects high vocabulary variation, and the Cosine Similarity score of 0.424 demonstrates balanced semantic diversity while maintaining mathematical consistency. Teacher evaluations further confirm that the generated problems are relevant to the curriculum, appropriately challenging, and more natural compared to conventional methods. Overall, this research contributes to the development of more adaptive and diverse LLM-based math word problem generation systems for mathematics learning in Indonesia.
Analysis of Facebook Professional User Acceptance for Digital Content Monetization with UTAUT 3 Noli Pitnawati; Dedy Setiawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5496

Abstract

This study aims to analyze the factors influencing the acceptance and use of Facebook Professional as a means of monetizing digital content among the people of Sungai Penuh City. The main problem in this study is the low utilization of this feature despite its significant economic potential. The study used a quantitative approach with the Unified Theory of Acceptance and Use of Technology 3 model expanded with knowledge variables. Data were collected from 195 respondents and analyzed using Structural Equation Modeling-Partial Least Squares. The results showed that perceived usefulness, hedonic motivation, and personal innovation significantly influenced usage intention, while habits, knowledge, and usage intention significantly influenced actual usage behavior. Technical and social factors did not show a dominant influence. This study concluded that personal, psychological, and user experience factors played a greater role in driving the adoption of Facebook Professional than technical factors, so optimizing user education and experience is important in increasing its utilization as a digital economic medium.
Web Developer Implementation Design Using Prototyping Method at PT. Freeport Indonesia - IACB Special Community Affairs Division (Institutional Agreements & Capacity Building) Nelson Magal
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5499

Abstract

The advancement of information technology has encouraged PT Freeport Indonesia, particularly the Institutional Agreement & Capacity Building (IACB) sub-division, to adopt a more efficient and transparent digital system. Previously, data and reporting processes were managed manually, causing inefficiencies in coordination and documentation. This study aims to design and implement a web-based information system to optimize data, document, and communication management using the Prototyping method. The system was developed iteratively through requirement analysis, design, prototype development, and user evaluation. The results show that the system improves reporting efficiency by 65% and reduces data errors by 80%. It also provides an internal communication forum, real-time dashboard, and e-learning module to support collaboration and community capacity building. The implementation of web development technology enhances the effectiveness, transparency, and accountability of institutional activities within IACB.
Red Onion Price Prediction in Bandar Lampung Using Long Short-Term Memory Naufal Arby Danuartha
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5503

Abstract

Shallots are a food commodity with fluctuating prices and influence people's needs. Unpredictable price changes can make it difficult for consumers, traders, and related parties to make decisions. This study aims to predict shallot prices in Bandar Lampung using the Long Short-Term Memory (LSTM) method. The data used are historical shallot price data from 2020 to 2025. The research stages include data preprocessing, normalization using Min-Max Scaler, dividing training data and test data with a 70:30 ratio, generating sequence data using a sliding window, parameter tuning experiments, LSTM model training, and model evaluation. Based on the experimental results, the best parameters were obtained at a lookback of 14, the number of neurons 50, the tanh activation function, a batch size of 48, a learning rate of 0.001, a maximum epoch of 150, and a patience of 15. The model produced a Mean Absolute Percentage Error (MAPE) value of 2.8035% with an accuracy of 97.1965% on the test data. These results indicate that the LSTM method is capable of predicting shallot prices in Bandar Lampung effectively and can follow price change patterns based on historical data. Furthermore, a comparison was conducted with the Gated Recurrent Unit (GRU) method using the same dataset and training parameters to evaluate the performance of the proposed model. The test results showed that the LSTM model performed better than the GRU.
Benchmarking IndoBERT and Multilingual BERT for Indonesian Financial News Sentiment Classification Matius Ivan Bimasena; I Kadek Yogi Prayoga; I Gusti Agung Putu Mahendra; Purnama Sidik
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5583

Abstract

Financial news sentiment classification was important for understanding market narratives and investor perception, but Indonesian financial news remained challenging because it contained domain-specific terminology, numerical expressions, and imbalanced sentiment categories. This study benchmarked two transformer-based models, IndoBERT and Multilingual BERT, for classifying Indonesian financial news sentiment into negative, neutral, and positive classes. The dataset consisted of economic and financial news articles from Kontan, CNBC, and Bisnis.com during the first quarter of 2026. After preprocessing, 3,366 articles were used, consisting of 3,070 neutral, 184 negative, and 112 positive articles. The dataset was divided into training, validation, and testing sets using stratified splitting. Class weighting was applied to reduce the effect of class imbalance. The results showed that IndoBERT achieved the best overall performance, with 0.94 accuracy and 0.71 macro F1-score, while Multilingual BERT achieved 0.93 accuracy and 0.70 macro F1-score. These findings indicated that IndoBERT was more suitable for Indonesian financial news sentiment classification, although Multilingual BERT remained competitive, especially in detecting positive sentiment.
Developing a Digital Platform for Interior and Renovation Services Using the Business Model Canvas (BMC) Approach Lailatur Rahmi; Ferizka Tiara Devani; Laras Ayu Anastasya; Dzaky Hanifah Ahmad
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.5584

Abstract

This research was motivated by the ongoing difficulty of searching for and managing interior design and renovation services, which are typically conducted through personal recommendations and social media. This situation makes it difficult for clients to find suitable service providers, compare service quality and prices, monitor project progress, and obtain guaranteed transaction security and structured work documentation. Furthermore, interior designers and renovation contractors also experience limitations in promoting portfolios, managing projects, and building professional reputations objectively. These impacts include low efficiency in the service search process, lack of project transparency, and limited collaboration opportunities between clients and service providers. To address these issues, the web-based DesignIn platform was developed, integrating interior design and renovation services into one centralized system. Platform development began with a Business Model Canvas (BMC) analysis to generate a business model tailored to user needs. The development results indicate that DesignIn is able to facilitate the search for verified partners, project management, communication, digital contracts, progress monitoring, and an integrated payment system. Thus, this platform can improve the efficiency, transparency, security, and quality of interior design and renovation services.
Comparison of KNN and Logistic Regression Algorithms in Classifying Food Product Healthiness Based on Nutritional Information Iidris Fikri; Ahmad Homaidi; Syarif Aminul Khoiri
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5626

Abstract

This research was conducted to analyze and compare the effectiveness of the K-Nearest Neighbor (KNN) and Logistic Regression algorithms in identifying the health category of food products using nutritional information. The dataset employed in this study was collected from Kaggle in CSV format and consisted of several nutritional attributes, including energy, fat, protein, sugar, and sodium content. The research methodology followed a data mining process that included preprocessing, data normalization, model training, and performance evaluation through a confusion matrix. Furthermore, a web-based classification application was created using the PHP programming language to assist in testing and simulating the product classification process. The experimental results indicated that the K-Nearest Neighbor algorithm achieved an accuracy value of 84.25%, with a precision of 0.82 and a recall of 0.78. Meanwhile, Logistic Regression produced an accuracy of 82.88%, a precision of 0.81, and a recall of 0.83. Based on these findings, the K-Nearest Neighbor method demonstrated slightly better performance in classifying the healthiness of food products.

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