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All Journal Jurnal Teknologi Informatika dan Komputer Indonesian Community Journal Jurnal Sains Student Research Jurnal TNI Angkatan Udara
Sembada Denrineksa Bimorogo
Universitas Pertahanan Republik Indonesia
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Edukasi Bahaya Judi Online di Madrasah Aliyah Al-Kautsar Nadiza Lediwara; Ahmad Abizar Alghiffari; Regifia Ningrum Nur Aulia; Rendi Hanif Dhaifullah; Sembada Denrineksa Bimorogo; Aulia Khamas Heikmakhtiar; Nick Holson Mangiring Silalahi H.
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7793

Abstract

Kegiatan pengabdian kepada masyarakat yang telah dilaksanakan dalam bentuk edukasi mengenai bahaya judi online ditujukan kepada siswa/i di Madrasah Aliyah Yayasan Al Kautsar yang dihadiri sebanyak 47 orang. Maraknya akses terhadap judi online di kalangan remaja menimbulkan berbagai dampak negatif, baik dari sisi psikologis, sosial, maupun akademik. Tujuan dari kegiatan ini adalah untuk meningkatkan kesadaran remaja akan risiko dan konsekuensi dalam aktivitas perjudian digital. Metode pelaksanaan dilakukan melalui penyampaian materi edukatif, diskusi interaktif, serta refleksi bersama yang mendorong peserta untuk mengidentifikasi dampak nyata dari judi online. Hasil dari kegiatan menunjukkan adanya peningkatan pemahaman peserta terhadap bahaya judi online serta tumbuhnya kesadaran untuk menjauhi aktivitas tersebut. Kegiatan ini juga memberikan ruang bagi peserta untuk mengeksplorasi alternatif aktivitas yang lebih positif dan produktif. Edukasi semacam ini terbukti efektif sebagai langkah preventif dalam menekan potensi kecanduan judi online di kalangan remaja.
Prediction of Academic Performance of Student Cadets at the Republic of Indonesia Defense University Using Random Forest Triananda Marsya Harahap; Muhammad Zidan Hafidz Ervia Nur Hakim; Nadiza Lediwara; Aulia Khamas Heikhmakhtiar; Sembada Denrineksa Bimorogo
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 2 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i2.3795

Abstract

The academic readiness of cadets at the Indonesian Defense University (UNHAN RI) needs proactive monitoring, as cadets carry both academic workloads and military discipline simultaneously. This study aims to build a classification model for the academic performance of UNHAN RI cadets into three categories, Low, Moderate, and High, using the Random Forest algorithm within the CRISP-DM framework. Data were collected through an online questionnaire distributed to 100 cadet respondents from cohorts 5 and 6. After data cleaning, 95 valid records were obtained. The academic performance label was constructed from academic grades using a mean ± standard deviation categorization method, yielding a distribution of 20.00% Low, 57.89% Moderate, and 22.11% High. Six daily-habit variables, namely study hours, assignment load, sleep hours, exercise frequency, academic fatigue level, and perceived task burden, were used as predictors without including the grade itself, to avoid data leakage. Data were split into 80% training and 20% test sets using stratified sampling. A Random Forest model with 100 decision trees achieved 57.89% accuracy on the test set and an average of 55.79% across 5-fold cross-validation. Study hours, exercise frequency, and assignment load were identified as the three most influential features. An experiment removing the least important feature, perceived task burden, produced comparable accuracy and cross-validation mean but a higher cross-validation standard deviation, so the original six features were retained in the final model. The model's accuracy falls into the Poor category under common interpretation guidelines and is close to the majority-class baseline, indicating that daily-habit data alone is not yet sufficient to reliably predict academic performance at this limited sample size. The model is intended as a starting point for the institution to develop a more mature early-detection system for cadets' academic performance.
Co-Authors Ahmad Abizar Alghiffari Allodya Nadra Xaviera Almas Shafwan Alya Hijrianisa Amadea Kenyoning Hapsari Ananda Yessica Margaretha Hutagalung Angelia Melisa Hutapea Army Kanaya Audrey Nur Aisyah Aulia Khamas Heikhmakhtiar Aulia Khamas Heikmakhtiar Aulia Khamas Heikmakhtiar Damaris Easter Nugrahita Christi E.I. Bhiftime Fhatur Robby Tanzil Herris Findi Zhafirah Fira Putri Wulandari Fulkan Kafilah Al Husein Gamelia Putri Anneta Hondor Saragih Khaerul Imam Phatoni Lediwara, Nadiza Mufti Labib Ahmada Muhammad Zidan Hafidz Ervia Nur Hakim Muhmmad Alvito Faros Nashiratul Husni Nick Holson Mangiring Silalahi H. Regifia Ningrum Nur Aulia Rendi Hanif Dhaifullah Ricky Chan Rizal Gian Febriantama Sadiyana Yaqutna Naqiya Salman Alfarisi Shella Athaya Miwazuki Tazky Khumaira Tsany Tiara Damayati Triananda Marsya Harahap Vonna Lestari Dian Subianty Zefanya Seto Gandhara Zerusealtin David Naibaho