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All Journal Jurnal Sains Informatika Terapan (JSIT) Jutech: Jurnal Teknologi Informasi Journal of Innovative and Creativity
Diana Kemala Odang
Universitas Alifah Padang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Identifikasi Siswa yang Beresiko Terlibat Tawuran Menggunakan Metode Algoritma Decision Tree pada Siswa SMP N 18 Padang Dinda Permata Sukma; Munadi Fauzan; Eriwandi Eriwandi; Diana Kemala Odang; Irvandori Aplana
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i2.2771

Abstract

Permasalahan sosial yang kerap terjadi di lingkungan pendidikan adalah tawuran. maraknya tawuran yang terjadi di kota padang salah satunya pada SMPN 18 Kota Padang. Guru kerap menemukan senjata tajam di dalam tas siswa ketika melalukan razia di kelas. Pada penelitian ini menerapkan metode decision tree untuk mengidentifikasi resiko penyebab siswa melakukan tawuran khususnya pada siswa kelas IX. Dengan metode ini, sekolah diharapkan dapat mengambil tindakan pencegahan lebih awal. Pengambilan data dilakukan dengan cara wawancara dengan guru BK dan mengumpulkan data sebanyak 100 data siswa. Data yang sudah di dapat selanjutnya di olah. Variabel yang dibutuhkan untuk mengidientifikasi resiko siswa yang terlibat tawuran diantaranya pelanggaran siswa, bermasalah dengan teman, hasil nilai rapor dan siswa tinggal dengan siapa. Hasil dari pengujian menggunakan metode Decision Tree ini apakah siswa beresiko terlibat tawuran atau tidak. Tujuan dari penelitian ini adalah membantu para guru dalam mengantisipasi siswa yang terlibat tawuran sebelum terjadi tawuran antar pelajar. Faktor - faktor yang menyebabkan resiko terjadinya tawuran antara lain pelanggaran yang dilakukan , permasalahan dengan teman, hasil nilai rapor siswa dan siswa tinggal dengan siapa. dari hasil penelitian ini variabel pelanggaran mendapatkan nilai Gain tertinggi yaitu 0.740344.
Klasterisasi Wilayah Rawan Tindak Kriminal Menggunakan Algoritma K-Means . (Studi Kasus Di Wilayah Hukum Pengadilan Negeri Painan) Sando Eka Putra; Sintia Sintia; Diana Kemala Odang
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.6737

Abstract

Meningkatnya jumlah kasus kriminal setiap tahun di wilayah hukum Pengadilan Negeri Painan menjadi perhatian penting bagi aparat penegak hukum. Oleh karena itu, diperlukan penyediaan informasi yang bermanfaat bagi masyarakat mengenai daerah-daerah yang tergolong rawan tindak kriminal. Salah satu metode yang dapat digunakan untuk mengidentifikasi daerah rawan tersebut adalah klasterisasi dengan algoritma K-Means. Penelitian ini bertujuan untuk mengelompokkan daerah berdasarkan tingkat kerawanan tindak kriminal di wilayah hukum Pengadilan Negeri Painan menggunakan algoritma K-Means. Metode ini termasuk dalam teknik data mining yang berfungsi untuk memisahkan atau mengelompokkan data ke dalam beberapa kelompok (cluster) berdasarkan kesamaan karakteristik tertentu. Hasil penelitian menunjukkan bahwa penerapan algoritma K-Means dengan bantuan aplikasi RapidMiner 5.3 mampu mengelompokkan wilayah ke dalam beberapa kategori tingkat kerawanan, yaitu rawan, kurang rawan, dan aman. Informasi yang dihasilkan dari penelitian ini diharapkan dapat membantu lembaga penegak hukum dalam melakukan pemetaan daerah rawan kejahatan serta mendukung pengambilan keputusan yang lebih efektif dalam upaya pencegahan tindak kriminal
Implementasi Algoritma Winnoming Untuk Penilaian Otomatis Jawaban Esai Pada Universitas Alifa Padang Berbasis Web Sintia; Diana Kemala Odang
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1170

Abstract

The development of information technology has driven a transformation in the learning evaluation system, particularly through the implementation of web-based online exams. However, the assessment of essay answers remains a challenge because it is generally done manually, which is time-consuming and potentially subjectivist. This study aims to implement the Winnowing Algorithm in a web-based automated essay answer assessment system at Alifa University, Padang. The Winnowing Algorithm is used to measure the level of text similarity between student answers and answer keys using document fingerprinting techniques. The research method used is an experiment with preprocessing stages, n-gram formation, hash calculation using rolling hash, window formation, and fingerprint selection. The data used are student essay answers and answer keys determined by the lecturer. The results show that the system is able to calculate the level of similarity automatically with similarity values ​​ranging from 50% to 90%. Examples of manual calculations produce hash values ​​of 3192 and 3262 for the two sets of texts tested. Thus, the Winnowing Algorithm can be an effective solution in the development of a web-based automated essay answer assessment system.
Prediksi Tingkat Pemahaman Siswa SMP Pada Pembelajaran Digital Menggunakan Algoritma Machine Learning Munadi Fauzan; Dinda Permata Sukma; Eriwandi Eriwandi; Diana Kemala Odang
Jurnal Teknologi Informasi (JUTECH) Vol. 7 No. 1 (2026): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v7i1.3543

Abstract

With the proliferation of digital learning platforms—including Learning Management Systems (LMS)—data on student learning activities has been generated that is useful for supporting the evaluation of student learning. A total of 200 data points were analyzed, with key variables including login frequency, study duration, quiz scores, assignment scores, timeliness in submitting assignments, attendance, and engagement during learning. These variables can be analyzed using machine learning to predict students’ comprehension levels early on, enabling teachers to identify which students require additional support. This study focuses on the application of machine learning to predict students’ comprehension levels at SMP Negeri 18 Padang as an effort to support more accurate decision-making in the learning process. This study utilized three algorithms: Random Forest, Decision Tree, and Naïve Bayes. The results of the testing revealed that the Decision Tree algorithm achieved the highest accuracy rate at 88.52%, followed by the Random Forest algorithm at 83.61%, and the Naïve Bayes algorithm at 62.30%. Of the three algorithms, the Decision Tree algorithm achieved the highest accuracy. This algorithm can be utilized as an early warning system for teachers to identify which students have high, moderate, or low levels of understanding, allowing teachers to provide appropriate support.
Implementasi Algoritma Winnoming Untuk Penilaian Otomatis Jawaban Esai Pada Universitas Alifa Padang Berbasis Web Sintia; Diana Kemala Odang
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1170

Abstract

The development of information technology has driven a transformation in the learning evaluation system, particularly through the implementation of web-based online exams. However, the assessment of essay answers remains a challenge because it is generally done manually, which is time-consuming and potentially subjectivist. This study aims to implement the Winnowing Algorithm in a web-based automated essay answer assessment system at Alifa University, Padang. The Winnowing Algorithm is used to measure the level of text similarity between student answers and answer keys using document fingerprinting techniques. The research method used is an experiment with preprocessing stages, n-gram formation, hash calculation using rolling hash, window formation, and fingerprint selection. The data used are student essay answers and answer keys determined by the lecturer. The results show that the system is able to calculate the level of similarity automatically with similarity values ​​ranging from 50% to 90%. Examples of manual calculations produce hash values ​​of 3192 and 3262 for the two sets of texts tested. Thus, the Winnowing Algorithm can be an effective solution in the development of a web-based automated essay answer assessment system.
Co-Authors Dinda Permata Sukma Eriwandi Eriwandi Irvandori Aplana Munadi Fauzan Sando Eka Putra Sintia Sintia Sintia