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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 432 Documents
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SENTIMENT ANALYSIS OF TWITTER DATA ON KIP-KULIAH USING TEXTBLOB AND GRADIENT BOOSTING Desi Masdin; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.353

Abstract

The Indonesian government aims to position the country among developed nations by 2045, with a primary focus on improving education quality from elementary to higher education levels. One of the key initiatives is the KIP-Kuliah (Indonesia Smart College Card) program, which supports high-achieving students from underprivileged economic backgrounds in accordance with UU No. 12/2012 on Higher Education. This study applies sentiment analysis using TextBlob and the Gradient Boosting algorithm to build a predictive model that identifies public support for the program through Twitter data. The results reveal a significant dominance of negative sentiment, with the model achieving an accuracy of 97%. These findings underscore the importance of sentiment analysis as a feedback tool for policymakers during the implementation of education-related programs. Furthermore, the results suggest that continuous monitoring of public opinion via social media can contribute to more adaptive and responsive policy development. This research highlights the need for future studies to expand the scope of analysis using more advanced natural language processing techniques for deeper understanding and broader coverage of public sentiment.
Explainable AI-Driven TabNet Model Enhanced with Bayesian Optimization for Lung Cancer Prediction and Interpretation Maulana, Ilham
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.354

Abstract

This study aims to develop an accurate and explainable lung cancer risk prediction model using a TabNet approach optimized with Bayesian Optimization and applying Explainable AI (XAI) methods through LIME (Local Interpretable Model-Agnostic Explanations). TabNet was selected for its efficiency in processing tabular data and its ability to produce high-accuracy predictions. In the initial stage, the TabNet model was tested using a dataset that was preprocessed through standardization and split into training and testing sets. The performance evaluation of the model without optimization showed an accuracy of 95.83%, precision of 95.87%, recall of 95.76%, and F1-Score of 95.81%. Subsequently, Bayesian Optimization was applied using the Optuna library to find the best hyperparameter combination for the TabNet model. The optimization results demonstrated a significant improvement, achieving an accuracy of 98.33%, precision of 98.48%, recall of 98.21%, and F1-Score of 98.32%. After optimizing the TabNet model, LIME was implemented to provide interpretability for the generated predictions. LIME was used to identify the most influential features contributing to the predictions, enhancing the model's transparency in the lung cancer risk prediction process. Through the combination of TabNet, Bayesian Optimization, and Explainable AI, this study successfully developed a lung cancer prediction model that is not only accurate but also highly interpretable. This model can assist medical professionals in identifying key risk factors and providing transparent explanations for each prediction made.
TWITTER SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL DISPUTE DECISION USING NAÏVE BAYES AND SVM Aulia Rahman, Ihsan; Ruhyana, Nanang
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.355

Abstract

Public sentiment regarding the 2024 presidential election dispute decision was analyzed through the Twitter platform. The method employed was Naïve Bayes, implemented using RapidMiner software. The dataset consisted of thousands of tweets collected during the presidential election dispute period. Each tweet was classified into three sentiment categories: positive, negative, and neutral. The text mining process involved data cleaning, tokenization, and the application of natural language processing (NLP) techniques for feature extraction. The results of the analysis revealed the distribution of sentiments among Twitter users and changes in sentiment trends over specific periods. This research is expected to provide insights into public perceptions and sentiment patterns related to the presidential election dispute decision
LOW-POWER MULTI-SENSOR EXTENSION FOR WEMOS CONTROLLERS USING MULTIPLEXED ANALOG INPUTS Budiarso, Zuly Budiarso; Sukur, Muji; Nurraharjo, Eddy; saefurrohman
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.358

Abstract

The increasing demand for low-cost, compact, and energy-efficient IoT systems has highlighted the limitations of microcontroller units (MCUs) such as the WeMOS D1 Mini, which are constrained by a single analog input pin. This is a major challenge for applications that require multiple analog sensors, such as environmental monitoring or smart agriculture. This paper presents a hardware-based solution using the CD74HC4067 16-channel analog multiplexer to expand the analog input capacity of the WeMOS controller. The system architecture allows multiple sensors to share a single analog input managed by digital control lines. The experimental implementation used six Light Dependent Resistor (LDR) sensors to evaluate accuracy, latency, and power efficiency. The results show that sensor reading accuracy remains within ±2% deviation, with an average channel switching latency of 42 milliseconds and only a 5% increase in total power consumption compared to a single sensor system. These results confirm that the multiplexing strategy maintains reliable performance while significantly improving scalability without compromising power constraints. The proposed approach provides a robust, low-power, and low-cost method for multi-sensor deployment on constrained MCUs, enabling broader application in resource-constrained IoT systems. Future developments may include cascading multiple multiplexers and integrating wireless data transmission for remote sensing applications.
IMPLEMENTATION OF RSA ASYMMETRIC CRYPTOGRAPHY USING GPG AND KLEOPATRA FOR SCHOOL DATA SECURITY Pratiwi, Ayu; Tahir, Muhlis; Nawafilillah; Almas Alvaradis, Ach
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.360

Abstract

Digital transformation drives the need for reliable data security systems to protect sensitive information from unauthorized access. This study aims to implement and analyze the use of the RSA cryptographic algorithm based on GPG (GNU Privacy Guard) software and the Kleopatra interface in securing school data. The research method employed is descriptive experimentation with a qualitative approach, conducted at SMK Al-Aziziyah Kwanyar in April 2025. The simulation includes RSA key pair generation, public key exchange, encryption and decryption of school digital data, and digital signature testing. The results indicate that the encryption process using the public key and decryption using the private key are effective and secure. The use of Kleopatra has proven to facilitate key management and cryptographic processes visually. This study emphasizes that the combination of RSA, GPG, and Kleopatra is a practical and efficient solution for data protection in educational environments and can serve as a reference for other institutions in implementing asymmetric cryptography-based digital data security.
Sentiment Analysis on Import Tariff Policy and Gold Price Increase with TF-IDF masripah, siti; Amegia Saputra, Rizal
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.361

Abstract

Changes in global economic policy, such as Donald Trump's import tariff policy in 2025, have generated various public responses recorded through social media such as Twitter. Analysis of this public opinion is important to understand public perception of the dynamics of gold prices as a strategic commodity. This study aims to analyze public sentiment towards the issue of tariff policies and gold using TF-IDF feature extraction. To overcome class imbalance in tweet data, the Synthetic Minority Over-sampling Technique (SMOTE) technique was used. The dataset was obtained from Twitter with the keywords "trump", "tariffs", and "gold", then preprocessing and sentiment labeling (positive, negative, neutral) were carried out. The results of the analysis showed that 88.8% of tweets contained positive sentiment, 6.9% negative, and 4.1% neutral. The model evaluation produced an accuracy of 81.23%, with the highest precision in the positive class (0.81) and a recall of 1.00. These findings indicate that the issue of tariff policies is associated optimistically by the public because it is considered beneficial to gold prices.
IMPLEMENTATION OF SUPPORT VECTOR MACHINE, PARTICLE SWARM OPTIMIZATION, AND NAÏVE BAYES ALGORITHMS IN SENTIMENT ANALYSIS OF PRODUCT REVIEWS: A CASE STUDY OF E-COMMERCE LAZADA Mery Oktaviyanti Puspitaningtyas; Kartika Puspita; Yuris Alkhalifi; Yulita Ayu Wardani
Jurnal Riset Informatika Vol. 7 No. 2 (2025): Maret 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i2.362

Abstract

Sentiment analysis is pivotal in deciphering customer opinions and attitudes towards products on e-commerce platforms such as Lazada. Machine learning algorithms like Support Vector Machine (SVM), SVM with Particle Swarm Optimization (PSO), and Naïve Bayes (NB) are leveraged to automate this process, aiding decision-making in business settings. This study specifically aims to assess the performance of SVM, SVM + PSO, and NB in analyzing sentiment from Lazada product reviews, focusing on key metrics like accuracy and Area Under the Curve (AUC). Using a dataset of Lazada reviews, each algorithm is rigorously trained and evaluated. SVM achieves 72.74% accuracy and an AUC of 0.893, while integrating PSO boosts accuracy significantly to 84.84% with an AUC of 0.898. In contrast, NB achieves 75.34% accuracy and an AUC of 0.663. These results highlight SVM + PSO's superior performance in sentiment classification compared to SVM and NB. The findings suggest that SVM + PSO presents a robust solution for sentiment analysis in e-commerce, surpassing traditional SVM and NB methods in accuracy and AUC metrics. This underscores the potential of optimization techniques like PSO to enhance machine learning algorithms for effective sentiment analysis in practical e-commerce applications.
INTEGRATION SYSTEM OF THREE SECURITY FEATURES IN SMART DUAL MCB WITH AUTOMATIC LOAD BALANCING AND FIRE DETECTION BASED ON ARDUINO UNO (SINTAKS) Ridwan, Achmad; Prabowo, Agung
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.363

Abstract

The increasing use of electronic devices in Indonesian households has significantly strained traditional electrical systems, with electricity consumption growing 4.5% annually and 78% of urban homes utilizing over 10 electronic devices. This situation poses substantial fire risks, as electrical short circuits cause 62.8% of urban fires, with MCB overloads accounting for 27% of incidents. This research introduces SINTAKS (Sistem Integrasi Tiga Keamanan Smart Dual MCB), an innovative integrated system combining three essential safety features: energy monitoring, automatic load balancing, and early fire detection. Unlike conventional systems requiring separate components, SINTAKS provides a comprehensive solution using Arduino Uno as the main controller, integrated with ACS712 current sensors, DS18B20 temperature sensors, MQ-2 smoke detectors, and relay modules. The system demonstrates remarkable performance with 97.8% current measurement accuracy, load balance improvement from 62.7% to 91.3%, and fire detection response time of 2.9-4.7 seconds. Field testing in real household installations confirmed system reliability with 94.8% success rate across various operational scenarios. SINTAKS achieves 4.2% energy savings while maintaining cost-effectiveness at IDR 875,000, making it accessible for widespread residential implementation. This autonomous system operates independently without IoT dependence, ensuring reliable protection even in offline environments. The research successfully addresses critical gaps in household electrical safety through practical, affordable, and integrated technology.
Wattpad User Satisfaction Analysis Using System Usability Scale (SUS) Method Dellia, Prita; Pratiwi, Ayu; Nawafilillah; Adela Sapitri, Tania; Izzaturrahmah, Annis
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.364

Abstract

Wattpad is an app that you can use to read, write, share, and comment on stories from various genres, such as romance, horror, fantasy, and more. This app is in high demand, especially by young people, because of its ease of use and provides many interesting stories for free. In this study, the author wants to find out how easy and convenient the Wattpad application is to be used by its users. For this reason, the System Usability Scale (SUS) method is used, which is a measurement tool commonly used to assess the usability of a system or application. Researchers collected data by distributing a questionnaire via Google Forms to 50 people who used Wattpad. The results of the questionnaire showed that the average SUS score obtained was 72.2. This value is included in the "C" category with the label "Good". This means that in general, Wattpad is considered quite good and comfortable to use. However, the results of the study also show that there are still some things that can be improved, such as feature clarity, fixes if the app crashes, and the number of ads that appear in the free version. If these shortcomings are addressed, then Wattpad can become even better and more fun for its users to use.
EVALUATION OF TWO-FACTOR AUTHENTICATION METHOD IN IMPROVING AUTHENTICATION SECURITY ON SSL VPN (Case Study of PT. Kanmo Group) Akbar Prasetyo, Muhamad; Chalik Azhar, Nur
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.367

Abstract

The increasing use of remote access in corporate environments through Virtual Private Networks (VPNs) requires additional security measures to protect corporate data and systems from cyber threats. In Indonesia, several data leakage cases involving large companies have revealed vulnerabilities in existing security systems, highlighting the importance of stronger data protection. At PT Kanmo Group, the VPN used is still vulnerable to security threats such as brute force attacks and credential theft, because it has not implemented 2FA in increasing its security. To overcome this problem, this research aims to improve SSL VPN security by implementing 2FA as an additional layer in the user authentication process. The research methodology includes problem identification, literature study, implementation, and simulation of system testing using an experimental approach. The results showed that the implementation of 2FA significantly improved VPN access security, reduced the risk of credential leakage, and provided a basis for recommendations for companies in strengthening their security systems. This research is expected to be a reference for the development of a more reliable remote access security system in the corporate environment.

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