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Sentiment Analysis of Cigarette Use Based on Opinions from X Using Naive Bayes and SVM Tundo; Eldina, Ratih; Setiawan, Kiki; Fajri, Raisah
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.947

The research employs Naive Bayes and Support Vector Machine (SVM) classification techniques to analyze attitudes toward cigarette consumption based on Twitter user opinions. Twitter, being one of the most popular social media platforms, serves as an excellent source for gauging public sentiment on various issues, including cigarette smoking, referred to here as "X." The diverse array of opinions poses a challenge for accurate sentiment classification. This study evaluates the effectiveness of the Naive Bayes and SVM algorithms in categorizing sentiment as positive, negative, or neutral. Data is collected through web scraping, and preprocessing steps such as text cleaning, tokenization, and stemming are implemented. The performance of the classification is assessed using metrics like accuracy, precision, recall, and F1-score. The results indicate that SVM outperforms Naive Bayes in sentiment analysis related to cigarette use. These findings provide new insights into public opinion and aim to assist policymakers in developing effective tobacco control strategies.

Penerapan IoT dengan Algoritma Fuzzy dan Mikrokontroler ESP32 dalam Monitoring Penyiraman Tundo; Sodik; Setiawan, Kiki; Aula, Raisah Fajri
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.977

Smart farming has become a major focus in the development of modern agricultural technology. In this context, the Internet of Things (IoT) offers innovative solutions to increase productivity and efficiency in crop management. This research introduces an IoT-based plant watering monitoring system that uses a fuzzy algorithm and an ESP32 microcontroller. This system is designed to automatically regulate plant watering based on environmental conditions and plant needs. The ESP32 microcontroller acts as the brain of the system, collecting environmental data such as soil moisture, air temperature, and relative humidity. This data is analyzed using a fuzzy algorithm to determine plant watering needs in real-time. Based on the output of the fuzzy algorithm, the system automatically controls the water pump to water the plants according to the specified needs. The application of fuzzy algorithms allows the system to overcome uncertainty in plant watering decisions, especially in the face of complex variations in environmental conditions. With the adoption of IoT technology, farmers can monitor and control crop watering efficiently through web interfaces or mobile applications, even remotely. This research shows that the integration of IoT with fuzzy algorithms and ESP32 microcontrollers can be an effective solution in managing crop watering, increasing overall agricultural productivity, while reducing water and energy consumption.

Analisis Konfigurasi Tunnel IPv6, Auto Tunnel, dan ISATAP dalam Pembangunan Infrastruktur Jaringan Akbar, Yuma; Setiawan, Kiki; Aula, Raisah Fajri; Aimar, Muqorrobin
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.993

IP (Internet Protocol) is needed to be the address of a device that wants to connect to each other, whether connected locally or to the internet. As technology develops, IP addressing allocation becomes increasingly necessary. The IP address that is often used is IPv4, but IPv4 allocation is increasingly limited. For this reason, a renewable IP version was created, namely IPv6. IPv6 itself has many advantages in terms of security which is equipped with encryption, then in terms of effectiveness in configuration which can use Auto Config, as well as a larger number of allocations compared to IPv4. If IPv4 only uses 32 bits, IPv6 uses 128 bits, for this reason the number of allocations given by IPv6 is much greater. This amount should be able to cover the lack of allocation from IPv4. Therefore, IPv4 to IPv6 migration must be carried out slowly, in-order-to overcome the lack of allocation in IPv4. Fortunately, IPv6 tunneling can run in parallel with IPv4 without disrupting existing infrastructure. Where IPv4 can be used as an Underlay Network (a network that is the foundation of the virtual network above it), and IPv6 will be an Overlay Network (a virtual network that connects users as if they were connected directly, this virtual network must be built on top of the Underlay Network, in this case the IPv6 tunnel will be used as a virtual network).

Analisis Tingkat Kepuasan Pelanggan Terhadap Pengguna Jasa Layanan Grab Menggunakan Metode C4.5 Permatasari, Veren Nita; Aula, Raisah Fajri; Akbar, Yuma; Hidayat, Aditya Zakaria
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.1002

Technological advances have brought significant changes in various aspects of life, including transportation. Grab as one of the pioneers of online transportation services in Indonesia is the main choice for many people. However, competition among online transportation service companies forces each service provider to compete in improving the quality of their services. This study aims to analyze the level of customer satisfaction with the use of Grab services using the C4.5 method. This method was chosen because of its ability to form a decision tree model that helps identify the main factors that influence customer satisfaction. The data for this study were obtained from a survey of Grab customers who used Grab driver services within a certain period of time. The survey covered various aspects of user experience, such as Ease of Use of the Application, Service Availability, Waiting Time, Price, Security. The data was analyzed using the C4.5 algorithm to gain an in-depth understanding of the factors that influence the level of customer satisfaction. The analysis shows that the C4.5 method is effective in identifying factors that influence customer satisfaction. The results of the rapidminer test show the accuracy of the C4.5 algorithm from 100 respondent data obtained, which is 94.00%. These results are expected to provide valuable input for the Grab company and drivers in an effort to improve the quality of their services.

Penerimaan Aplikasi Loklok Menggunakan Metode Technology Acceptance Model (TAM) oleh Masyarakat Jakarta Adzani, Adinda Mutiara; Aula, Raisah Fajri
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i3.1004

Nowadays, the development of communication and information technology has brought major chages in various aspect of life, one of them is in entertainment aspects. For example video streaming technology. Video streaming technology has been part of modern society. Loklok is of video streaming technology application which is popular in Indonesia. The purpose of this research is to analyze the acceptance video streaming technology on Loklok application and find out whether it can be generally acceptance by the people of Jakarta. This research was done for loklok application users. Loklok is an application or platform that provides video streaming services with many varieties of genres such Korean dramas, western films and animations. The conceptual model of technology acceptance assessment is based on the Technology Acceptance Model (TAM), which is a research model that analyzes the factors that influence the acceptance of computer technology. The statistical analysis used is the partial least squares structural equation model. This research uses quantitative methods with data collection techniques in the form of filling out online questionnaires through Google Forms with the number of respondents determined by the Slovin formula and sampling techniques using simple random sampling. This study uses PLS-SEM data analys with SmartPLS tools with a sample size of 100. The results of this study indicate that the variables Perceived Usefulness -> Attitude of use, Perceived ease of use -> Attitude of use, Attitude of use - > Behavioral Intention Of Use and Behavioral Intention Of Use -> Actually System Use are positive which means that the LokLok application is well received by the Jakarta Community.

Penerapan IoT dalam Sistem Monitoring Suhu dan Kelembapan pada Lahan Bawah Tanah (Basement) Masjid Al-Barkah Tundo; Azhar, Anisah Nurul; Setiawan, Kiki; Aula, Raisah Fajri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3199

Underground areas, commonly referred to as basements, are often used for essential functions such as parking and electrical distribution spaces. However, unstable temperature and humidity levels due to poor air circulation can affect comfort and safety. Therefore, a system capable of automatically monitoring and controlling temperature and humidity is needed to optimize comfort and energy efficiency. This research employs an Internet of Things (IoT) approach using a DHT11 sensor to detect temperature and humidity in the basement. The data collected by the sensor is processed using a NodeMCU ESP32 microcontroller and then displayed in real-time on a web-based application via the cloud. The system also automatically controls the fan/blower to maintain ideal conditions in the basement. The results of this research show that the implemented IoT system demonstrates high effectiveness in monitoring temperature and humidity in real-time, providing accurate data, enabling energy savings by automatically regulating the fan/blower, and improving air quality and user comfort in the basement.

Proposed Marketing Strategy to Increase Brand Awareness of Design Studio in Bandung (Case Study: Quartet Studio, 2019) Aula, Raisah Fajri
Jurnal sosial dan sains Vol. 5 No. 5 (2025): Jurnal Sosial dan Sains
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsosains.v5i5.32206

In the competitive creative industry, brand awareness plays a crucial role in determining business success. This study addresses the issue of low brand awareness at Quartet Studio, a graphic design studio based in Bandung, despite offering quality services at affordable prices. The objective of this research is to propose a marketing strategy to enhance brand awareness and market penetration. A mixed-method approach was employed, using quantitative questionnaires to assess brand recognition and qualitative analysis of social media and competitor strategies. Analytical tools applied include STP, Marketing Mix, SWOT-TOWS Matrix, Brand Awareness Pyramid, and Porter’s Generic Strategies. The results suggest a marketing strategy focused on the Cimahi and Bandung areas, targeting females aged 17–34. The studio is positioned as an affordable design service with fixed pricing and structured promotions. The findings imply that an integrated digital and offline marketing strategy is essential to strengthen the competitiveness of local creative businesses. This proposed strategy can serve as a reference for other SMEs in similar sectors.

Leveraging Machine Learning to Analyze User Conversion in Mobile Pharmacy Apps Using Behavioral and Demographic Data Lestari, Sri; Setiawan, Kiki; Aula, Raisah Fajri
International Journal for Applied Information Management Vol. 4 No. 3 (2024): Regular Issue: September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i3.86

This study explores the use of machine learning techniques to predict user conversion in a mobile pharmacy app based on user behavior and demographic data. The analysis was conducted using two classification models: Logistic Regression and Random Forest. Key features such as time spent on the product page, adding items to the cart, and user demographics (age, gender, device type) were evaluated to determine their impact on conversion rates. Both models demonstrated strong performance, with the Logistic Regression model achieving an Area Under the Curve (AUC) of 0.88 and the Random Forest model achieving an AUC of 0.87. These results indicate that both models effectively distinguish between users who convert and those who do not, with Logistic Regression showing a slightly better overall performance. Feature importance analysis revealed that factors such as adding items to the cart and the time spent on the product page are the most significant predictors of conversion. Furthermore, demographic features like age group and device type also contributed to the model’s predictive power, although they had a smaller impact compared to user engagement features. The findings suggest that machine learning models, particularly Logistic Regression, can be utilized to predict user behavior and optimize user engagement strategies in mobile apps. The study also highlights the importance of user engagement in driving conversions and the potential for targeted marketing based on demographic data. Future work should focus on hyperparameter tuning, exploring additional algorithms, and incorporating real-time data to further enhance model accuracy and adaptability.

Support Vector Machine-Based Sentiment Analysis of Customer Reviews for Android Smartphone Products on Shopee Marketplace Hutauruk, Lucas Namora; Lestari, Sri; Aula, Raisah Fajri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.321

The rapid expansion of e-commerce in Indonesia has resulted in a surge of unstructured online reviews, especially on platforms such as Shopee. These reviews offer valuable insights into customer satisfaction, product complaints, and purchasing behavior but remain largely underutilized due to their volume and informal language style. This study applies Support Vector Machine (SVM) with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to classify reviews of Android smartphones into positive, negative, and neutral categories. Using a dataset of 300 manually annotated reviews from Samsung, Xiaomi, and Oppo official stores, the model achieved an accuracy of 76.67% and demonstrated stable results through 5-fold cross-validation. The negative class showed the highest performance (F1 = 0.86), while the neutral class performed weakest (F1 = 0.62), reflecting challenges posed by mixed opinions and underrepresented samples. Compared with Naïve Bayes and Logistic Regression, the SVM model consistently outperformed both baselines, confirming its suitability for high-dimensional text data and informal Indonesian expressions. The findings highlight SVM’s potential to support automated sentiment monitoring in e-commerce, enabling businesses to identify emerging issues, improve customer service strategies, and leverage positive reviews for marketing. Future research should consider larger and more balanced datasets, techniques for handling imbalanced classes, and integration with deep learning models such as LSTM or BERT to improve performance and generalization.

Support Vector Machine-Based Sentiment Analysis of Customer Reviews for Android Smartphone Products on Shopee Marketplace Hutauruk, Lucas Namora; Lestari, Sri; Aula, Raisah Fajri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.321

The rapid expansion of e-commerce in Indonesia has resulted in a surge of unstructured online reviews, especially on platforms such as Shopee. These reviews offer valuable insights into customer satisfaction, product complaints, and purchasing behavior but remain largely underutilized due to their volume and informal language style. This study applies Support Vector Machine (SVM) with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to classify reviews of Android smartphones into positive, negative, and neutral categories. Using a dataset of 300 manually annotated reviews from Samsung, Xiaomi, and Oppo official stores, the model achieved an accuracy of 76.67% and demonstrated stable results through 5-fold cross-validation. The negative class showed the highest performance (F1 = 0.86), while the neutral class performed weakest (F1 = 0.62), reflecting challenges posed by mixed opinions and underrepresented samples. Compared with Naïve Bayes and Logistic Regression, the SVM model consistently outperformed both baselines, confirming its suitability for high-dimensional text data and informal Indonesian expressions. The findings highlight SVM’s potential to support automated sentiment monitoring in e-commerce, enabling businesses to identify emerging issues, improve customer service strategies, and leverage positive reviews for marketing. Future research should consider larger and more balanced datasets, techniques for handling imbalanced classes, and integration with deep learning models such as LSTM or BERT to improve performance and generalization.

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