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All Journal Jurnal Teknologi Informasi dan Komunikasi
Ahmad, Hermansyah Nur
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Computer Science & IT Control & Systems Engineering

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SISTEM MONITORING KENYAMANAN TOILET BERBASIS IoT MENGGUNAKAN PLATFORM BLYNK Parlaungan S., Timbo Faritcan; Novianti, Wieke; Permana, Eka; Ahmad, Hermansyah Nur
Jurnal Teknologi Informasi dan Komunikasi Vol 17 No 2 (2024): October
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/a.v17i2.245

Abstract

The purpose of this research can monitor and control the comfort of IoT-based toilets (Internet Of Things) using the Blynk Platform. In general, the comfort of the toilet is known if the situation is dirty and seen directly, especially if it causes an unpleasant odor. This system aims to collect temperature, gas data and provide access to toilet condition information to cleaners easily and quickly through an application integrated with Blynk. In monitoring comfort in toilets, the research methods used include literature surveys to understand IoT concepts and relevant technologies, as well as a review of temperature and gas sensor technologies that are suitable for data collection. In addition, user needs analysis and system design are also carried out to ensure the availability of accurate and relevant information. From the results obtained, it is hoped that this study can help determine the condition of the toilet in realtime so that toilet comfort is more awake and users feel comfortable when using the toilet. This research can also be a reference for future researchers related to the material used.
Perbandingan Kinerja Algoritma Naïve Bayes dan C4.5 pada Sistem Web Klasifikasi Kelayakan PKH Jupriyanto, Jupriyanto; Apandi, Jamaludin; Wijaya, Anderias Eko; Hermawan, Rian; Siallagan, Timbo Faritcan Parlaungan; Udoyono, Kodar; Ahmad, Hermansyah Nur
Jurnal Teknologi Informasi dan Komunikasi Vol 18 No 1 (2025): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v18i1.287

Abstract

This study discusses the development of a web-based classification system for determining the eligibility of recipients of the Family Hope Program (PKH), by comparing two data mining algorithms: C4.5 and Naïve Bayes. The dataset used includes various attributes relevant to eligibility assessment for social assistance. The C4.5 algorithm is employed to generate an interpretable decision tree, while the Naïve Bayes algorithm is used for probabilistic classification. The results show that Naïve Bayes achieved the highest accuracy at 98%, excelling in processing large datasets more efficiently. Meanwhile, C4.5 achieved an accuracy of 93.33% and offered better interpretability through its decision tree visualization. Both algorithms proved effective in classifying PKH eligibility and can be implemented in social assistance information systems to improve the accuracy and efficiency of the beneficiary selection process. This research concludes that the choice of algorithm should be based on system priorities—whether the focus is on processing speed or result interpretability.
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.
Co-Authors Anderias Eko Wijaya Apandi, Jamaludin Haq, Haris Nizhomul Jupriyanto Kodar Udoyono, Kodar Novianti, Wieke Nugraha, Luthfy Akmal Permana, Eka Rian Hermawan Siallagan, Timbo Faritcan Parlaungan Timbo Faritcan Parlaungan Siallagan