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All Journal TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora QISTINA: Jurnal Multidisiplin Indonesia Indonesian Journal of Education and Development Research
Rahmadhani, Alfi
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Politik Identitas dan Polarisasi Dalam Pemilu Rahmadhani, Alfi; Tarigan, Febrinata Silvianna Br; Gorat, Loveyanni Marito Benedikta; Sinaga, Novi Novani; Zai, Frans Pratamarifai Doya
Indonesian Journal of Education and Development Research Vol 3, No 1 (2025): Januari 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/ijedr.v3i1.4671

Abstract

Penelitian ini bertujuan untuk mengindetifikasi definisi dan dampak politik identitas dan polarisasi dalam pemilu. Penelitian ini menggunakan metode penelitian deskriptif. Politik Identitas merupakan fenomena yang semakin menonjol dalam konteks pemilihan umum (Pemilu), terutama di negara-negara dengan masyarakat yang beragam. Polarisasi dalam konteks pemilu merujuk pada meningkatnya perbedaan tajam antara kelompok-kelompok masyarakat yang memiliki pandangan politik berbeda. Etnis dan agama adalah bagian dari identitas yang seringkali menjadi alat atau dipolitisasi oleh sekelompok orang untuk kepentingannya. Politik identitas dan polarisasi dalam pemilu adalah tantangan yang serius bagi banyak negara. Menciptakan kesadaran akan perlunya toleransi, dialog, dan pendekatan inklusif dalam politik sangat penting untuk menjaga stabilitas sosial dan mendukung proses demokrasi yang sehat. Dengan demikian, meskipun politik identitas akan tetap ada dalam dinamika pemilihan umum, penting bagi semua pihak untuk berkomitmen pada prinsip-prinsip demokrasi dan persatuan bangsa demi masa depan yang lebih baik.
Interactive Teaching Media Design for Past Tense Lesson Material Using VB.NET Syahputra, Fahmy; Putri, Tansa Trisna Astono; Lubis, Khodijah May Nuri; Rahmadhani, Alfi; Fattah, M
QISTINA: Jurnal Multidisiplin Indonesia Vol 3, No 2 (2024): December 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/qistina.v3i2.4228

Abstract

The development of information technology has influenced the use of various types of media, as a tool in the learning process. So teachers are expected to be able to use these tools or equipment effectively classroom learning. In this article, we design teaching media or applications for English subjects. This teaching media is designed using the Microsoft Visual Studio 2022/VB.Net programming language with Past Tense subject matter. The aim of this teaching media design is to increase students' understanding through a visual and interactive approach. This application provides a menu including simple past tense, past continuous tense, past perfect tense, past perfect continuous tense, and is equipped with quiz questions. With this, it is hoped that students will be more involved in the learning process so that they can master the use of past tense effectively and have fun.
Tinjauan Literatur Sistematis (2019–2025) Kinerja Decision Tree dan Neural Network (Deep Learning) serta Perbandingannya dengan Naive Bayes dan SVM Syahputra, Fahmy; Sabrina, Elsa; Br Tarigan, Febrinata Silvianna; Sarumaha, Matius Irvan; Rahmadhani, Alfi; Simanjorang, Sandha Calista; Gorat, Loveyanni Marito Benedikta
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 6, No 4 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/trilogi.v6i4.13429

Abstract

This study presents a Systematic Literature Review (2019–2025) comparing the performance of Decision Tree and Neural Network (Deep Learning) models, alongside their relative performance against Naive Bayes and Support Vector Machine (SVM). The review synthesizes empirical findings across multiple application domains—including healthcare, education, industry, and finance—focusing on commonly reported classification metrics such as accuracy, precision, recall, and F1-score. The synthesis indicates that Decision Trees are frequently preferred for structured/tabular data due to their high interpretability and transparent decision rules, which are valuable for accountable decision-making. In contrast, Neural Networks/Deep Learning tend to outperform on unstructured data (e.g., medical images and text) and complex non-linear patterns, albeit often with reduced explainability. In several studies, Naive Bayes remains competitive as a lightweight baseline, while SVM continues to be effective for high-dimensional feature spaces and specific classification settings. Overall, the review highlights that algorithm selection should be driven by data characteristics, problem complexity, interpretability requirements, and computational constraints, since no single algorithm consistently dominates across all scenarios.
Co-Authors Br Tarigan, Febrinata Silvianna Fattah, M Gorat, Loveyanni Marito Benedikta Lubis, Khodijah May Nuri Putri, Tansa Trisna Astono SABRINA, ELSA Sarumaha, Matius Irvan Simanjorang, Sandha Calista Sinaga, Novi Novani Syahputra, Fahmy Tarigan, Febrinata Silvianna Br Zai, Frans Pratamarifai Doya