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All Journal International Journal of Applied Mathematics and Computing. International Journal of Information Engineering and Science Journal of Engineering, Electrical and Informatics
Untung Surapati
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Design of a Web-Based Instagram Content Management System to Support Brand Awareness for SR12 Herbal Cosmetics Products Untung Surapati; Agus Tanti Rahayu; Tatinia Arda Rizqi Amalia; Lusi Noviani
International Journal of Information Engineering and Science Vol. 3 No. 1 (2026): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i3.83

Abstract

PT. SR12 Herbal Cosmetics is a company engaged in the field of herbal and skin care. Founded in 2015 by Toni Firmansyah, S. Farm., Apt. and Asrianty Salam, S. Farm. This company has a vision to provide benefits to many people through the herbal and skin care products they produce. SR12 Herbal Cosmetics products are formulated based on research from certified scientists, and have been tested at the Sucofindo Laboratory, are free of mercury and hydroquinone, and have been registered with the Indonesian Food and Drug Supervisory Agency (BPOM RI). SR12 Herbal Cosmetics has several factories in West Java Province and has an extensive distribution network with hundreds of distributors and tens of thousands of partners throughout Indonesia. The goal to be achieved is to produce a management information system model including a management information system for PT SR12 Herbal Cosmetics. The research object chosen is a company in the field of cosmetics and skin care which has its head office in Gunung Sindur, West Java. This selection aims to form a management information system design model that is able to produce relevant and timely information for planning, controlling, decision making and evaluating the performance of activities. For the Web-Based Instagram Content Management Information System Design project to Support SR12 Herbal Cosmetics' Brand Awareness, I used Agile (Scrum) due to the dynamic nature of digital marketing and potential changes to the Instagram API or business needs. This allowed SR12 to get core functionality faster and provide iterative feedback, ensuring the system built was truly relevant to their brand awareness needs.
Implementation of the Naive Bayes Algorithm and Support Vector Machine for Public Sentiment Analysis towards the Ratification of the Job Creation Bill on Twitter Untung Surapati; Sopan Adrianto; Erno Sumantri; Melinius Nopianto
Journal of Engineering, Electrical and Informatics Vol. 2 No. 1 (2022): Februari : Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v2i1.202

Abstract

The test design of the Public Sentiment Analysis on the Ratification of the Job Creation Bill with the RapidMiner Studio application. The initial stage is to collect data in the form of tweets of Twitter users and then put it into a CSV file, the data obtained will be divided into training data and test data. Furthermore, the training data will be labeled consisting of 2 types of labels, namely Positive and Negative labels, then the data will be cleaned from unneeded words such as Mention or Hastag, then the data will go through several stages in the Preprocessing stage to convert raw data into data that is ready to be processed. Furthermore, each word will be weighted with the TF-IDF method. The final result of the comparison with these two test methods, namely the prediction of Public Sentiment Towards the Issue of Determining the Job Creation Bill based on data obtained from Twitter and implemented by the SVM (Support Vector Machine) method, showed an accuracy value of 96.52%. Of the 605 test data, 492 data were predicted as Negative Sentiment and 112 data as Positive Sentiment and the Naive Bayes Method showed an accuracy value of 49.67%. Of the 605 test data, 492 data were predicted as Negative Sentiment and 112 data as Positive Sentiment.
Classification of Favorite Book Borrowing Data at the STIKOM CKI Library Using the Decision Tree Algorithm Yuma Akbar; Untung Surapati; Sutisna Sutisna; Yansen Yansen
Journal of Engineering, Electrical and Informatics Vol. 2 No. 1 (2022): Februari : Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v2i1.3663

Abstract

The library on the STIKOM CKI campus as a means of providing information and has a complete collection of learning media books, but the data processing system for borrowing and returning favorite books in the library is currently still manual, that is, all data collection processes are written on book cards, although it is quite good but the process is rather slow and requires quite a long time because in the process of searching the data must be checked per page one by one so that the data processing is less effective and efficient. To overcome this, it is necessary to develop an application using the decision tree algorithm method which can make it easier to collect borrowing data and return favorite books that are more effective and efficient and display integrated output of student reports that have not returned so that data processing is more accurate and can speed up officer performance. library. Submitting a favorite book lending classification application can make it easier to access loans and returns anywhere and anytime. So that data processing is more accurate and can speed up librarian performance.
Prediction of Credit Sales Value with the Naive Bayes Algorithm on Sujase Cell Jakarta Veri Arinal; Untung Surapati; Sugiyono Sugiyono; Dita Safira
International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i3.110

Abstract

Background: The rapid growth of mobile phone usage has significantly increased the demand for prepaid credit services (mobile airtime), creating large volumes of transaction data that require effective analysis for business decision-making. Sujase Cell, a mobile credit retailer in Jakarta, faces challenges in predicting future sales performance and customer purchasing interest due to the accumulation of transaction records over time and the limitations of manual analysis. Objective: This study aims to identify customer purchasing interest and predict mobile credit sales values by implementing the Naive Bayes algorithm as a data mining approach to support sales forecasting and business development strategies. Methods: The research employed a quantitative predictive approach using a private dataset obtained from Sujase Cell. Data collection was conducted through observation and literature review. The dataset consisted of historical mobile credit sales transactions and sales balance records collected during the study period. The data underwent preprocessing stages, including normalization using the Min-Max Scaler technique, followed by data partitioning into training and testing datasets. The Naive Bayes classification method was then applied to analyze sales patterns and generate predictions. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and confusion matrix-based assessment metrics. Several experimental scenarios involving different training-testing ratios and parameter configurations were conducted to determine the most effective predictive model. Results: The findings indicate that the Naive Bayes method successfully identified sales trends and customer purchasing behavior patterns. The best-performing model was obtained using a 90% training dataset and 10% testing dataset, resulting in the lowest prediction error. Experimental results demonstrated that the generated prediction model was capable of following actual sales patterns and producing reliable forecasting outcomes. The implementation of Naive Bayes provides valuable support for sales planning, inventory management, and marketing decision-making at Sujase Cell, enabling the business to improve operational efficiency and anticipate future market demand more effectively.
Traffic Condition Classification Using IoT on Raden Inten II Road Untung Surapati; Yuma Akbar; Dwi Swasono Rachmad; Hadi Gunawan
International Journal of Applied Mathematics and Computing Vol. 2 No. 4 (2025): October : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i4.121

Abstract

Unmonitored traffic conditions often hinder decision-making processes in traffic management, particularly on secondary roads. Jalan Raden Inten II in East Jakarta is one of the connecting routes with heavy traffic activity at certain times, yet no integrated data-based monitoring system is currently available. This study proposes an Internet of Things (IoT)-based traffic condition classification system to identify Clear, Normal, or Congested states based on vehicle counts and speed categorization. The system is designed using an ESP32 microcontroller, an HB100 sensor to detect vehicle speed, and two AJ-SR04M ultrasonic sensors to detect vehicle presence. Data on vehicle counts and the percentage of slow-moving vehicles are periodically transmitted to the ThingSpeak platform and processed using the Threshold-Based Classification method. The classification results are visualized on a dashboard-based website equipped with charts, traffic condition status, and notifications when consecutive congestion is detected. Testing was conducted using simulation data over a specific period. Qualitative validation was carried out by comparing the classification results with traffic indicators from Google Maps. The results show that the system can classify traffic conditions with a good degree of agreement with external references, although discrepancies occurred at certain times due to the limitations of simulated data. This research demonstrates that a simple IoT approach can provide an affordable and effective solution for monitoring and classifying traffic conditions, with potential for real-world implementation in future studies.
Sentiment Analysis of the Trending Topic #Indonesiagelap on X Using a Naive Bayes Algorithm Based on Particle Swarm Optimization Untung Surapati; Veri Arinal; Tri Wahyudi; Ahmad Fauzan
International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i2.127

Abstract

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.
Development of an IoT-Based Smart Health Monitoring System with Heart Attack Prediction Using the SVM (Support Vector Machine) Algorithm Untung Surapati; Dadang Iskandar Mulyana; Dedi Gunawan; Anggit Purnama
International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v2i3.128

Abstract

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.
Co-Authors Agus Tanti Rahayu Ahmad Fauzan Akbar, Yuma Anggit Purnama Arinal, Veri Dadang Iskandar Mulyana` Dedi Gunawan Dita Safira Dwi Swasono Rachmad Erno Sumantri Hadi Gunawan Lusi Noviani Melinius Nopianto SOPAN ADRIANTO Sugiyono Sugiyono Sutisna Sutisna Tatinia Arda Rizqi Amalia Tri Wahyudi Yansen Yansen