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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Muhammad Said Hasibuan
Institute Business and Informatics Darmajaya
Computer Science & IT Decision Sciences, Operations Research & Management

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Interactive Learning Media for Early Childhood Education through Android-Based Educational Games using GDLC Method Lendy Rahmadi; M. Junius Effendi; Muhammad Said Hasibuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10129

Abstract

The rapid development of technology has influenced various aspects of life, including education. One of the innovations in the field of education is the use of technology-based learning media, such as interactive multimedia, which can enhance students' interest in learning. This research aims to develop an Android-based educational game called Edufun Kids Game, using the Game Development Life Cycle method. (GDLC). This game is designed to help early childhood children recognize letters, numbers, animals, and fruits interactively, creating a fun learning atmosphere and making it easier for educators to deliver the material. The research was conducted at the Tunas Ilmu Early Childhood Education in Pagar Alam City, where the previous learning media was still conventional. The implementation of the Edufun Kids Game shows positive results in increasing children's learning motivation and facilitating a more effective teaching and learning process, as evidenced by beta testing results where 85% of respondents rated the game as helpful for preschoolers in recognizing numbers, letters, animals, and fruits. The use of Android technology allows this application to be easily accessed on mobile devices, providing flexibility for users. The development of Android-based educational games can be an innovative solution to improve the quality of early childhood education
TOPSIS and WP for BLT Decision Support in Srimulyo Muhamad Abror; Muhammad Said Hasibuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10606

Abstract

Srimulyo Village, situated in Anak Ratu Aji District, Central Lampung Regency, faces difficulties in ensuring an accurate and efficient process for determining recipients of Direct Cash Assistance (BLT). The current manual system often results in errors and perceived inequities. This research explores the application of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method alongside the Weighted Product (WP) method to enhance decision-making in selecting BLT recipients. TOPSIS is employed to identify optimal alternatives based on their closeness to a positive ideal solution, while WP emphasizes the importance of criteria through assigned weights. The findings indicate that integrating these two methods yields recommendations that are more objective, transparent, and efficient. The TOPSIS approach enables the system to rank alternatives by assessing their proximity to the ideal solution, facilitating data-driven decision-making. Meanwhile, the WP method ensures that each criterion's importance is appropriately weighted, thereby increasing the reliability of the results. This dual-method approach not only minimizes human error but also promotes fairness in the selection process. The proposed integrated system offers a practical solution to improve the accuracy of social assistance distribution in Srimulyo Village. By adopting these decision-support methods, local authorities can establish a more equitable, reliable, and efficient mechanism for BLT allocation, ensuring that aid reaches the individuals who need it most.
Sentiment Analysis of Free Nutritious Meal Programme on Social Media X using Linear Regression and Random Forest Algorithms Idi Hermansyah; Muhammad Said Hasibuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10633

Abstract

This study analyzes public sentiment towards the Free Nutritional Food Program on social media platform X using Linear Regression and Random Forest algorithms. By collecting data from Twitter and employing sentiment analysis methods based on natural language processing, this research aims to measure societal perceptions and compare the effectiveness of both algorithms in sentiment classification. The results indicate that Random Forest outperforms Linear Regression with an accuracy of 0.85 and a recall of 0.97, compared to Linear Regression, which achieves an accuracy of 0.83 and a recall of 0.91. While Linear Regression excels in precision with a score of 0.86, whereas Random Forest records 0.85, overall, Random Forest achieves a higher F1-Score of 0.90 compared to Linear Regression's score of 0.88. These findings provide important insights for governments and policymakers in responding to public opinion and designing more effective interventions to enhance the program
Sentiment Analysis of Mobile Banking Reviews Using Machine Learning Models Sri Rahayu; Muhammad Said Hasibuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10677

Abstract

With the increasing use of mobile banking applications in Indonesia, understanding user reviews and feedback has become increasingly important for banks to enhance the services and performance of the applications they offer. This research aims to analyze user sentiment towards the mobile banking applications BCA, BNI, Brimo, and Byond by BSI, and to compare the effectiveness of the Random Forest, K-Nearest Neighbor (KNN), Naive Bayes, and Support Vector Machine (SVM) algorithms. The data used consists of user reviews obtained from Google Play Store through web scraping techniques, with 4,000 samples of reviews divided into training data (80%) and testing data (20%). The pre-processing process is conducted to prepare the data, which includes stopword removal and tokenization, using the Bag of Words (BoW) method.Based on the labeling results that can be seen in the visualization stage, it is known that the Byond by BSI Mobile application has a positive sentiment with 540 more reviews and a negative sentiment with 528 fewer reviews compared to other mobile banking applications. In the form of a comparative matrix graph, the Random Forest algorithm has a higher accuracy value of 0.58 for the BCA application and 0.74 for the Brimo application, while Naive Bayes has an accuracy value of 0.71, which is greater for the BNI mobile banking application, and Support Vector Machine has an accuracy value of 0.74, which is higher for the Byond by BSI mobile banking application. From the explanations above, it means that the Random Forest algorithm is capable of classifying efficiently and effectively compared to the other three algorithms. With the results of this research, it is hoped to provide important insights for mobile banking application developers to improve service quality based on user feedback, as well as to recommend the use of Random Forest for more accurate and reliable sentiment analysis.
Co-Authors Idi Hermansyah Lendy Rahmadi M. Junius Effendi Muhamad Abror Sri Rahayu