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All Journal Math Didactic: Jurnal Pendidikan Matematika HISTOGRAM: Jurnal Pendidikan Matematika Sinkron : Jurnal dan Penelitian Teknik Informatika JSAI (Journal Scientific and Applied Informatics) bit-Tech G-Tech : Jurnal Teknologi Terapan Jurnal Sisfotek Global Academic Journal of Computer Science Research (AJCSR) Indonesian Community Journal Jurnal Tren Bisnis Global Malcom: Indonesian Journal of Machine Learning and Computer Science Information Technology Education Journal Jurnal Topik Global Jurnal Pendidikan MIPA Sasambo: Jurnal Abdimas (Journal of Community Service)
Ferawati Ferawati
Institut Teknologi Dan Bisnis Bina Sarana Global
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : bit-Tech

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Implementasi Algoritma Regresi Linear Berganda untuk Memprediksi Prestasi Siswa Ferdinan, William Chicho; Noerfikri, Mohamad Rayhan; Panchadri, Pramana Anwas; Ferawati, Ferawati
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2228

Abstract

Prestasi siswa merupakan aspek krusial dalam dunia pendidikan yang dipengaruhi oleh berbagai faktor internal dan eksternal. Namun, memprediksi prestasi siswa dengan akurasi tinggi tetap menjadi tantangan bagi para pendidik dan peneliti. Studi ini bertujuan untuk membangun model prediksi yang mampu mengidentifikasi faktor-faktor utama yang berkontribusi terhadap prestasi siswa menggunakan algoritma regresi linear berganda. Model ini dikembangkan dengan menerapkan pendekatan Cross-Industry Standard Process for Data Mining (CRISP-DM), yang terdiri dari tahapan pemahaman bisnis, pengumpulan data, persiapan data, pemodelan, serta evaluasi. Faktor-faktor yang dianalisis dalam penelitian ini mencakup fasilitas belajar, kebiasaan belajar, partisipasi dalam kursus tambahan, motivasi diri, dan dukungan orang tua. Pengumpulan data dilakukan melalui survei kuesioner kepada 100 siswa Sekolah Menengah Atas (SMA) yang tersebar di beberapa wilayah. Hasil analisis menunjukkan bahwa dukungan orang tua memiliki pengaruh terbesar terhadap prestasi siswa, diikuti oleh motivasi diri, kebiasaan belajar, dan partisipasi dalam kursus tambahan. Namun, evaluasi model regresi menunjukkan nilai R² sebesar -0,34, yang mengindikasikan bahwa model belum optimal dalam menjelaskan variabilitas data target. Temuan ini menunjukkan bahwa regresi linear berganda mungkin bukan metode terbaik untuk prediksi prestasi siswa, dan diperlukan pendekatan yang lebih kompleks seperti machine learning non-linear atau model berbasis AI. Studi ini memberikan wawasan penting bagi pendidik dan pembuat kebijakan dalam memahami faktor utama yang berkontribusi terhadap prestasi siswa. Selain itu, penelitian ini merekomendasikan pengembangan model prediksi yang lebih canggih guna meningkatkan akurasi hasil dan memberikan rekomendasi berbasis data bagi institusi pendidikan.
Analisis Kinerja ESP32-CAM Dalam Mendeteksi Objek Herwandi, Aditya; Ramadhan, Alfian Aditya; Sunggono, Nova Teguh; Ferawati, Ferawati
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2296

Abstract

Deteksi objek merupakan salah satu komponen kunci dalam sistem visi komputer yang banyak digunakan pada berbagai aplikasi seperti pengawasan, otomasi, dan sistem berbasis Internet of Things (IoT). Namun, penerapan algoritma deteksi berbasis deep learning umumnya memerlukan sumber daya komputasi tinggi yang tidak sesuai untuk perangkat berbiaya rendah seperti ESP32-CAM. Penelitian ini bertujuan untuk menganalisis kinerja ESP32-CAM dalam mendeteksi objek menggunakan algoritma YOLOv3, serta mengkaji pengaruh berbagai kondisi lingkungan terhadap hasil deteksi. Studi ini menggunakan pendekatan eksperimental dengan lima skenario pengujian yang mencakup variabel pencahayaan (siang dan malam), gerakan kamera (bergerak dan tidak bergerak), resolusi gambar (800×600 dan 1280×1024 piksel), serta ukuran objek (kecil, sedang, dan besar). Evaluasi dilakukan dengan mengukur tingkat akurasi deteksi, waktu pemrosesan per frame, serta kejernihan citra pada masing-masing skenario. Hasil pengujian menunjukkan bahwa ESP32-CAM mampu mendeteksi objek dengan akurasi tertinggi sebesar 73% pada kondisi siang hari dengan pencahayaan optimal dan kamera statis. Akurasi menurun secara signifikan dalam kondisi gelap, saat kamera bergerak, serta ketika objek berukuran kecil. Penelitian ini menegaskan bahwa keberhasilan deteksi sangat dipengaruhi oleh pengaturan lingkungan dan karakteristik objek. Meskipun ESP32-CAM memiliki keterbatasan dalam daya komputasi dan memori, hasil penelitian ini membuktikan bahwa perangkat ini tetap layak digunakan untuk sistem deteksi objek sederhana dalam lingkungan terkendali. Penelitian ini memberikan kontribusi praktis dalam pengembangan sistem deteksi visual berbasis perangkat ringan, dan menawarkan rekomendasi teknis untuk meningkatkan performa melalui pengaturan lingkungan dan pemilihan parameter yang tepat.
Decision Tree and Reinforcement Learning Approaches in Adapting Math Problems Based on Student Ability Sya’bani, Dwiyana Anugrah; Nalar, M Arief Lukman; Ferawati, Ferawati
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2645

Abstract

Mathematics learning often faces challenges in adjusting the difficulty level of questions to match individual students' abilities. Traditional methods, which apply a uniform difficulty across all students, are less effective as they fail to account for differences in comprehension and learning speed. This study introduces an adaptive learning system utilizing Decision Tree and Reinforcement Learning approaches to dynamically adjust the difficulty of mathematics questions based on real-time student performance. The Decision Tree model classifies questions into easy, moderate, and difficult categories by analyzing the distribution of correct and incorrect student answers, achieving a classification accuracy of 71.33% and an F1-score of 80.02%. Reinforcement Learning, particularly the Q-Learning algorithm, adjusts the difficulty level of subsequent questions based on continuous student performance feedback, with a success rate of 65.96% and a total reward of 626,885. This dual approach significantly enhances the learning process by providing personalized and adaptive experiences, ensuring each student is challenged at an appropriate level. Implemented as a web-based system, it facilitates real-time adjustments and continuous adaptation to student needs. By continuously analyzing student responses, the system maintains engagement and supports effective mastery of mathematical concepts. This personalized feedback mechanism fosters a dynamic and interactive learning environment that is more responsive to individual needs, improving both student engagement and conceptual understanding.
Classification of Children's Numerical Intelligence Levels Based on Mathematics Learning Activities Using Machine Learning Suryantiko, Sandy; Mulyadi, Gilang Adriana; Ferawati, Ferawati
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2646

Abstract

Numerical ability is a critical aspect of education that impacts both academic achievement and the development of logical thinking skills. However, in Islamic boarding schools (pondok pesantren), the teaching of mathematics often encounters limitations such as a lack of technological integration and diverse learning resources. This study aims to assess students' numerical intelligence levels based on their mathematics learning activities using machine learning techniques, specifically Decision Tree and Random Forest algorithms. Data was collected through questionnaires that captured key variables such as study duration, frequency of practice, learning methods, class engagement, and the speed of problem-solving. After data preprocessing, both models were evaluated through confusion matrices and cross-validation to determine their classification accuracy and stability. The findings revealed that the Decision Tree model achieved an accuracy rate of 86%, while the Random Forest model surpassed it with a 92% accuracy rate, showing more consistent performance. This study highlights the potential of machine learning to enhance educational outcomes, particularly in pesantren settings, by offering deeper insights into students' learning patterns. Beyond classification accuracy, machine learning helps educators identify key factors influencing students' numerical intelligence. The Random Forest model, in particular, revealed that variables such as practice frequency and student engagement are significant predictors of numerical intelligence levels. This information can be used to develop more personalized and effective teaching strategies aimed at improving students' mathematical skills.
Co-Authors Abdul Azis Aep Saepul Agung Kurnia Agustinus Sirumapea Ahmad Yodi Firdaus Ahmad Zaenuri Alifiana, Mufhida Alvira Hanum Salsabiella Andhika Fajar Utama Aprianto Teguh Abdi Ardi Fazar Fauzi Arwin, Achmad Rofiqi Avin Hendrawan Detin Sofia Dewi, Julita Mulya Dinda Fitrah Nur Ramadhan Dudy Ruchyat Edo Rahayu Erlan Ardiansyah Fajar Bara Maulana Ferdinan, William Chicho Fiqih Hana Saputri Fiqih Hana Saputri Fiqih Hana Saputri, Fiqih Hana Hanafri, Muhammad Iqbal Hertaliando, Abdee Wahyu Herwandi, Aditya Hidayati, Rina Ida Ayu Putu Sri Widnyani Isral, Isral Istiqomah, Pami Jarudin Jarudin Kasih, Asmara Firda Laela, Neng Linda Badratul Mayninda Nisa Putri Moh. Sofjan Mulyadi, Gilang Adriana Nalar, M Arief Lukman Nasrulah, Dayu Ovtama Noerfikri, Mohamad Rayhan Nurmaesah, Nunung Pami Istiqomah Panchadri, Pramana Anwas Pausi, Ahmad Petty Aprilia Sari Pramana Anwas Panchadria Purwanto, Ridho Maher Putri, Eka Uliyanti Putrie, Nabila Rahmat Tullah Ramadhan, Alfian Aditya Rayhan Noerfikri, Mohamad Rifqi Abu Dzaki Rio Nurrizkiyadi Rizal Ramadhan Royadi, Refi Nabillah Santoso Santoso Saputra, Ricky Aditya Satria Audria Sakti Satriyo, Azis SOFIA, DETIN Stianingsih, Lilis Sunggono, Nova Teguh Suryantiko, Sandy Sutarman Sutarman Syaipul Ramdhan Sya’bani, Dwiyana Anugrah Tullah, Rahmat Wahyu Abdi Sutrisno