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All Journal Teosofi: Jurnal Tasawuf dan Pemikiran Islam JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI NUR EL-ISLAM : Jurnal Pendidikan dan Sosial Keagamaan AL-TANZIM : JURNAL MANAJEMEN PENDIDIKAN ISLAM JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Pengabdian Masyarakat Bhinneka Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Jurnal Manajemen Sistem Informasi SOROT: Jurnal Pengabdian Kepada Masyarakat SmartComp Al-Muhith : Jurnal Ilmu Qur'an dan Hadits
Husni Hidayat
Universitas al-Azhar Kairo
Religion Agriculture, Biological Sciences & Forestry Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Algoritma Naive Bayes Berbasis Backward Elimination untuk Prediksi Kesiapan Kerja Pada Siswa SMK hidayat, husni
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 10, No 2 (2021): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v10i2.2492

Abstract

SMK (Sekolah menengah kejuruan) adalah kegiatan bimbingan belajar vokasi formal dalam mengembangkan bidang keilmuan, kemampuan  kepribadian, sikap sosial, pembelajaran, pengembangan karir, perencanaan dan juga bakat siswa dalam memasuki dunia pekerjaan. Berdasarkan data yang ada pada SMK mutu lulusan SMK tidak semua siswanya memenuhi kualifikasi siap bekerja dikarenakan banyak faktor sehingga diperlukan sebuah pengolahan data yang menerapkan metode prediksi dengan teknik data mining. Salah satu teknik data mining adalah teknik klasifikasi yaitu Naïve Bayes, yang mampu menghasilkan nilai akurasi sampai 95.55% dalam memprediksi kesiapan kerja siswa SMK, Namun dari banyaknya faktor yang berpengaruh pada kesiapan kerja siswa SMK maka dibutuhkan penambahan fitur seleksi Backward Elimination yang mampu meningkatkan akurasi menjadi 96.95% dengan mengeliminasi beberapa fitur yang tidak relevan terhadap klasifikasi dan mendapat hasil yang lebih baik daripada menggunakan metode Naïve Bayes saja.Kata kunci : SMK; naïve bayes; backward elimination; prediksi
MANAJEMEN INSIDEN CUSTOMER TELKOM BERBASIS SERVICE DESK MENGGUNAKAN FRAMEWORK ITIL V3 Andira, Fransisca Ayu; Hadian, Nur; Hidayat, Husni
Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Vol. 6 No. 1 (2025): Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Publisher : Information Technology Education Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/jipti.v6i1.2435

Abstract

In today's digital era, information technology (IT) plays an important role as the main support for business processes in various organizations, companies, and government agencies. Disruptions in IT services can have a serious impact on business operations, so effective IT service management is needed to improve service quality and stability. This research aims to identify gaps in IT incident management at PT Telekomunikasi Indonesia Merak Pekalongan City by applying the Information Technology Infrastructure Library (ITIL) version 3 framework which is an industry standard for Information Technology Service Management (ITSM). Research methods include interviews, observations, and literature studies, followed by a gap analysis between existing conditions and ideal conditions in handling IT incidents. The findings show that the current incident management process is reactive, without structured incident logging, and there is no standard escalation and resolution time. Based on the results of the gap analysis, this research produces a draft Standard Operating Procedure (SOP) that covers incident handling, escalation, incident closure, and recapitulation. This SOP aims to improve incident handling efficiency, speed up response time, and ensure better incident documentation. ITIL implementation is expected to improve IT service performance, reduce the impact of disruptions, and provide added value to customers. This research contributes to the development of more effective incident management procedures in organizations, particularly those operating in the telecommunications sector.
Predictive Analysis of Student Academic Performance Using Ensemble Learning Methods: A Case Study on the Portuguese Student Performance Dataset Hakim, Mujibul; Zuliarso, Eri; Hidayat, Husni; Imam, Muhammad Nurul; Sholehudin, Mukti Ahmad
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.492

Abstract

The ability to predict student academic performance at an early stage is crucial for educational institutions to provide timely interventions. This research aims to apply and evaluate the effectiveness of ensemble learning methods in predicting the final grades (G3) of secondary school students using the UCI "Student Performance" public dataset. To prevent data leakage, the models were executed without incorporating historical grade variables (G1 and G2), ensuring the system functions strictly as an Early Warning System. The methodological training process was enhanced by integrating k-fold cross-validation,hyperparameter optimization, and a direct comparison against a baseline model (Linear Regression) to guarantee model robustness and validity. Evaluation results indicate that the XGBoost model achieved the highest performance, yielding an Rsquared ($R^2$) of 0.28. Furthermore, feature importance analysis revealed that accumulated absences and prior class failures are the most significant predictors. As a practical implication, these findings recommend that schools develop proactive early warning dashboards and improve the overall school climate to address the root causes of absenteeism at an early stage.
Co-Authors Ananda, Luthfi Ayu Andira, Fransisca Ayu Arifin, Misdin Baehaqi Chatarina Umbul Wahyuni Darmawi . Effiyaldi, Effiyaldi Eri Zuliarso Fanani, M. Rudi Hadian, Nur Hakim, Mujibul Hambali Hambali M. Syukri Ismail Muhammad Nurul Imam, Muhammad Nurul Nadia, Hasnatun Pradana, Aria Mulya Putri, Elza Eka Rahmadani, Nadya Mayla Putri Rizal, Muhamad Rizaludin Rizaluddin, Muhammad Sholehudin, Mukti Ahmad