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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Media Informatika Jurnal Informatika: Jurnal Pengembangan IT SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Fitriyani, Rofi
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Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Peningkatan Pembelajaran Anak melalui Game Edukatif Around the World ASEAN Fitriyani, Rofi; Rahmatullah, Andhyka; Rafi, Muhammad
Media Informatika Vol 24 No 1 (2025)
Publisher : P3M STMIK LIKMI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37595/mediainfo.v24i1.325

Abstract

Around the World ASEAN adalah game edukatif yang dirancang untuk membantu anak-anak menjelajahi budaya dan kekayaan alam di Asia Tenggara. Game ini berfokus pada kurikulum pendidikan dasar dengan pendekatan interaktif. Penelitian ini menggunakan teori SDT dan metode ADDIE, serta menerapkan eksperimen pretest-posttest pada 10 siswa kelas 4 SDN Girimukti 2. Hasilnya menunjukkan peningkatan 20% dalam minat belajar dan keterampilan pemecahan masalah. Integrasi game edukatif terbukti efektif meningkatkan kualitas pendidikan anak di era digital.
Analisis Keamanan WhatsApp di Berbagai Platform: Studi Kasus Serangan dan Perlindungan Data Pengguna Fitriyani, Rofi
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 9 No. 2 (2025): IKRAITH-INFORMATIKA Vol 9 No 2 Juli 2025
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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Abstract

Salah satu aplikasi pesan instan sangat populer di dunia yaitu WhatsApp, yang tersedia di berbagai platform seperti Web, Android, dan iOS, semakin rentan terhadap ancaman keamanan seperti phishing, malware, eksploitasi enkripsi, dan kebocoran data pengguna. Tujuan dari penelitian ini adalah untuk memeriksa keamanan WhatsApp di berbagai platform dengan mengidentifikasi jenis serangan yang paling umum dan mengevaluasi metode yang telah digunakan untuk melindungi data. Penelitian ini menggunakan studi literatur dan analisis forensik digital. Studi menunjukkan bahwa peretas dapat menggunakan celah keamanan WhatsApp terutama melalui rekayasa sosial dan eksploitasi pihak ketiga meskipun WhatsApp menggunakan enkripsi end-to-end dan fitur keamanan lainnya. Oleh karena itu, untuk meminimalkan risiko keamanan, kesadaran pengguna harus ditingkatkan dan mekanisme perlindungan data harus diperkuat
Comparison of Machine Learning Algorithm for Enzyme Production Optimization from Industrial Waste Bastian, Ade; Fitriyani, Rofi; Susandi, Dony; Pangestu, Arki Aji; Mardiana, Ardi; Sujadi, Harun
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8212

Abstract

The manufacture of industrial enzymes from trash provides a sustainable remedy for environmental issues. This work investigates machine learning methods to enhance enzyme production from industrial waste by examining critical factors such as waste type and chemical makeup. Three algorithms—Linear Regression, Decision Tree, and Neural Network—were used to estimate and forecast enzyme production. Evaluation criteria, such as Mean Squared Error (MSE) and Coefficient of Determination (R²), were used to evaluate model performance. The results indicated that the Decision Tree method was the most effective, exhibiting lowest error and enhanced accuracy in selecting ideal production factors such as fermentation temperature and time. This method improves efficiency, lowers operating expenses, and encourages sustainable waste management practices. The results highlight the potential of machine learning to convert trash into useful industrial goods, providing a route to more sustainable biotechnology. Future study may enhance hybrid algorithms, include new waste factors, and facilitate real-time implementation for wider industrial applicability.  
Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas Fitriyani, Rofi; Ayip Luthfi Firmansyah; Al Yaafi Nadiyal Fithri; Larasati Angelica Nurfadillah
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 8 (2024): STIMA 8.0 : Menuju Kesinambungan : Inovasi dan Adaptasi Teknologi untuk Pembangunan Be
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/stima.v8i0.1129

Abstract

In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how to process big data originating from user transactions and activities on e-commerce platforms, as well as how to identify patterns that are useful for customer segmentation. The data used in this research includes purchase history, frequency of visits, length of time spent on the site, and interactions with certain products. The solution method applied in this research is the clustering algorithm, especially K-Means and DBSCAN. K-Means is used to group data into a predetermined number of clusters based on the Euclidean distance between data points. Meanwhile, DBSCAN is used to identify clusters with high density and separate data that is considered noise or outliers. Data preprocessing is carried out to clean and normalize the data before being applied to the clustering algorithm. Validation of clustering results is carried out using metrics such as Silhouette Score and Davies-Bouldin Index. The research results show that by applying the clustering algorithm, customers can be grouped into several segments that have similar characteristics. For example, we found groups of customers with high purchase frequency but low transaction value, as well as other groups with high transaction value but low purchase frequency. This information is very useful for companies to design more effective marketing strategies, such as special offers for customers with high transaction values ​​or loyalty programs for customers with high purchasing frequency. The conclusion of this research is that clustering algorithms can be a very effective tool in e-commerce customer segmentation, allowing companies to understand customer behavior patterns and develop more targeted and effective marketing strategies. Thus, implementing this method is expected to improve business performance and overall customer satisfaction.
Co-Authors Ade Bastian Al Yaafi Nadiyal Fithri Ardi Mardiana Ayip Luthfi Firmansyah Dony Susandi Harun Sujadi Larasati Angelica Nurfadillah M. Rafi Pangestu, Arki Aji Rahmatullah, Andhyka