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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal on Mathematics Education
Alfa Saleh, Alfa
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Computer Science & IT Education Engineering Mathematics Social Sciences Other

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Integrating traditional food and technology in statistical learning: A learning trajectory Ramadhani, Rahmi; Prahmana, Rully Charitas Indra; Soeharto; Saleh, Alfa
Journal on Mathematics Education Vol. 15 No. 4 (2024): Journal on Mathematics Education
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jme.v15i4.pp1277-1310

Abstract

In the 21st century, understanding variability and developing statistical investigation skills are crucial for enhancing students' data literacy. However, these essential skills are often overlooked, limiting students' growth in numeracy whereby statistical problems are frequently disconnected from real-world or cultural contexts, reducing student engagement. To address this issue, this study integrates the culturally relevant context of Lemang Batok, which enhances students' ability to understand, apply, and analyze data through appropriate statistical concepts. The research uses an ethno-flipped classroom model that promotes flexible, collaborative learning, aiming to design a learning trajectory for teaching descriptive statistics in this context to improve numeracy skills. Utilizing design research methodology, specifically a validation study, the research followed three phases: preliminary design, experimental design, and retrospective analysis. The subjects were junior high school students from Medan and Binjai Cities, North Sumatera-Indonesia. The results indicated that the learning trajectory developed through tiered discussions significantly improved students' numeracy skills in descriptive statistics, as evidenced by increased critical thinking and enhanced abilities to analyze variability.
Penggunaan Teknik Unsupervised Discretization pada Metode Naive Bayes dalam Menentukan Jurusan Siswa Madrasah Aliyah Saleh, Alfa; Nasari, Fina
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 3: Juni 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (238.548 KB) | DOI: 10.25126/jtiik.201853705

Abstract

Pemilihan jurusan bagi siswa merupakan langkah positif yang dilakukan untuk memfokuskan siswa sesuai dengan potensi yang dimiliki, hal ini dianggap penting karena dengan adanya jurusan, siswa diharapkan mampu mengembangkan kemampuan akademis sesuai bidang yang dikuasai. Pada penelitian sebelumnya, telah dilakukan pengujian dengan metode Naive Bayes yang bertujuan untuk mengkasifikasikan jurusan siswa bedasarkan kriteria yang menunjang dengan studi kasus pada siswa Madrasah Aliyah Swasta PAB 6 Helvetia, dan didapatkan hasil pengujian dari 100 data siswa dengan tingkat keakuratan 90%. pada penelitian ini, dilakukan optimalisasi metode yang digunakan sebelumnya dengan menerapkan teknik Unsupervised Discretization yang akan mentransformasikan kriteria numerik/kontinyu menjadi kriteria kategorikal dan mengeliminasi satu kriteria yang dianggap tidak mempengaruhi keakuratan hasil pengujian, dengan begitu keakurasian hasil klasifikasi dapat meningkat. Dari 120 data siswa yang diuji, terbukti bahwa hasil klasifikasi penerapan teknik unsupervised discretization pada metode naive bayes naik dari 90% menjadi 92.8%. AbstractSelection of majors for students is a positive step that is done to focus students in accordance with their potential, it is considered important because with the majors, students are expected to develop academic ability according to the controlled field. In previous research, Naive Bayes method has been tested to classify the students department based on the supportive criterias (case study on Madrasah Aliyah PAB 6 Helvetia), and the test result of 100 students data, the classification accuracy is about 90% . in this study, optimizaton is done with a method used earlier by applying Unsupervised Discretization techniques that would transform numerical / continuous criteria into categorical criteria and eliminating one criterion that is considered not affect the accuracy of test results. thus the accuracy of classification results could increase. 120 students data is tested, it is evident that the results of the classification of the application of unsupervised discretization techniques on the Naive Bayes method rose from 90% to 92.8%.
Co-Authors Fina Nasari Rahmi Ramadhani Rully Charitas Indra Prahmana Soeharto