Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Komunikasi
Haq, Haris Nizhomul
Unknown Affiliation
Computer Science & IT Control & Systems Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
SISTEM INFORMASI TUNJANGAN PENDAPATAN KEPEGAWAI DI KANTOR KECAMATAN MENGGUNAKAN FRAMEWORK CODEIGNITER Selviani, Rima; Haq, Haris Nizhomul; Sobari, Dicky Iskandar
Jurnal Teknologi Informasi dan Komunikasi Vol 17 No 1 (2024): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v17i1.256

Abstract

In the current era of technological advancements, the demand for information has significantly risen across various facets of life. The Employee Income Allowance Information System (TPP) implemented at the Subang Sub-District Office encompasses comprehensive data, including employee profiles, attendance records, TPP reports, and employee assessments. This system facilitates employees in efficiently accessing and reviewing attendance and TPP report data. The implementation is carried out using CodeIgniter, PHP, and MySQL technologies.
Evaluasi Performa Naive Bayes dan CART pada Klasifikasi Kualitas Tahu Nugraha, Luthfy Akmal; Jupriyanto, Jupriyanto; Haq, Haris Nizhomul; Wijaya, Anderias Eko; Ahmad, Hermansyah Nur
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 2 (2025): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i2.328

Abstract

Untuk tetap bersaing di pasar global, produsen tahu harus memastikan kualitas produk yang konsisten. Pabrik Tahu Sumber Barokah, sebagai pemasok tahu bernutrisi tinggi yang telah lama beroperasi, menghadapi tantangan dalam menjaga kualitas sepanjang proses produksi. Penelitian ini membandingkan kinerja algoritma Naïve Bayes dan Classification and Regression Trees (CART) dalam mengklasifikasikan kualitas tahu menggunakan dataset yang dikumpulkan dari pabrik, yang berisi sampel tahu berkualitas tinggi dan rendah. Metodologi penelitian mencakup identifikasi masalah, pengumpulan data, preprocessing, klasifikasi, validasi, evaluasi, dan penarikan kesimpulan. Cross-validation digunakan untuk validasi model, dan confusion matrix digunakan untuk menilai precision, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa Naïve Bayes mencapai akurasi 91%, precision 100%, recall 85%, dan F1-score 92%, sedangkan CART mencapai akurasi 86%, precision 70%, recall 100%, dan F1-score 82%. Hasil ini menunjukkan bahwa Naïve Bayes lebih cocok untuk mengklasifikasikan kualitas tahu dalam konteks ini.
SISTEM REKOMENDASI KEPUTUSAN UPGRADE SMARTPHONE MENGGUNAKAN ALGORITMA C4.5 BERBASIS AI Haq, Haris Nizhomul; Ahmad, Hermansyah Nur; Catur, Ryan; Wijaya, Anderias Eko; Udoyono, Kodar; Permana, Eka; Leander, Daud Elia
Jurnal Teknologi Informasi dan Komunikasi Vol 19 No 1 (2026): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v19i1.395

Abstract

The increasing use of smartphones necessitates rational decision-making about device upgrades. Many users upgrade without considering the actual device condition and usage requirements. This study aims to develop an artificial intelligence-based recommendation system to objectively determine smartphone upgrade decisions. The method used is the C4.5 algorithm for classification based on device specifications and usage patterns. The dataset consists of 100 records, including 80 for training and 20 for testing. The results show that the system successfully generates a representative decision tree model. Performance evaluation using a confusion matrix yields an accuracy of 95.00 percent, categorized as excellent. The system is also integrated with AI Gemini to generate narrative explanations from classification results, improving interpretability. The contribution lies in integrating classification algorithms with generative models to produce accurate and informative recommendations. This system provides a practical solution for users to efficiently determine smartphone upgrade needs.
Co-Authors Ahmad, Hermansyah Nur Anderias Eko Wijaya Catur, Ryan Jupriyanto . Kodar Udoyono, Kodar Leander, Daud Elia Nugraha, Luthfy Akmal Permana, Eka Selviani, Rima Sobari, Dicky Iskandar