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All Journal Indonesian Journal of Applied Informatics JURNAL TEKNOLOGI TECHNOSCIENTIA Walisongo Journal of Information Technology
Iin Rohmatika Aulia
Universitas Teknologi Yogyakarta
Computer Science & IT Earth & Planetary Sciences Engineering

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Pendekatan Hybrid: Naïve Bayes dan Decision Tree untuk Prediksi Kerusakan Mesin pada Industri Manufaktur PT X Iin Rohmatika Aulia; Arief Hermawan; Donny Avianto
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.97905

Abstract

Abstrak : Perkembangan teknologi sistem informasi banyak dirasakan di setiap sector ekonomi. PT X merupakan perusahaan manufaktur di bidang percetakan, dimana produktivitas dipengaruhi dari efisiensi mesin. Optimasi produktivitas mesin dapat dilakukan dengan prediktif maintenance. Penelitian ini bertujuan untuk mengembangkan teknik data mining dalam prediktif kerusakan mesin produksi. Fokus utama penelitian adalah untuk mengklasifikasi kerusakan mesin berdasarkan data historis pada PT X. Model klasifikasi yang akan dikembangkan menggunakan algoritma model Naïve Bayes dan Decision Tree. Dalam klasifikasi ada 2 label keputusan yaitu tingkat resiko (tinggi, sedang rendah) dan kegiatan preventif (Ya,Tidak) Evaluasi dilakukan dengan menilai akurasi dan efektivitas setiap model. Hasil uji klasifikasi preventif dengan model Naïve Bayes memiliki nilai akurasi 97,90 %, sedangkan dengan model Decision Tree memiliki nilai akurasi 77%. Hasil uji klasifikasi tingkat resiko dengan model Naïve Bayes nilai akurasi 98% sedangkan dengan model Decision Tree nilai akurasinya 100%. hasil uji menunjukan untuk label preventif dengan 2 kelas lebih baik menggunakan model Naïve Bayes sedangkan label tingkat resiko dengan 3 kelas lebih baik menggunakan model Decision Tree. Hasil uji ini dapat dijadikan acuan Perusahaan X khususnya divisi maintenance dalam melakukan penjadwalan prediktif maintenance. Metode ini juga dapat diterapkan pada Perusahaan lain jika memiliki data historis kerusakan mesin, memiliki mesin dengan jenis operasional yang relevan, dan memiliki tujuan dan klasifikasi yang sesuai.===================================================Abstract :The advancement of information system technology has significantly impacted all economic sectors. PT X, a manufacturing company in the printing industry, experiences productivity fluctuations that are strongly influenced by machine efficiency. Optimizing machine productivity can be achieved through predictive maintenance. This study aims to develop data mining techniques for predicting machine failures in production. The primary focus is to classify machine failures based on historical data from PT X. The classification models employed are the Naïve Bayes algorithm and the Decision Tree algorithm. Two classification labels are used: risk level (high, medium, low) and preventive action (Yes, No). Evaluation was conducted by measuring the accuracy and effectiveness of each model. The classification results for the preventive action label showed that the Naïve Bayes model achieved an accuracy of 97.90%, while the Decision Tree model reached 77%. For the risk level label, the Naïve Bayes model achieved 98% accuracy, and the Decision Tree model achieved 100%. The findings indicate that the Naïve Bayes model is more suitable for binary classifications such as preventive actions, while the Decision Tree model performs better in multi-class classifications such as risk levels. These results can serve as a reference for PT X’s maintenance division in scheduling predictive maintenance. Moreover, the method can be applied to other companies, provided they have historical machine failure data, machines with similar operational characteristics, and compatible classification objectives
A Comparison of Machine Learning Algorithms for Placement Status Classification : Analysis of Model Accuracy in Supporting Accurate Decision-Making IIN ROHMATIKA AULIA; Arief Hermawan; Donny Avianto
JURNAL TEKNOLOGI TECHNOSCIENTIA Vol. 18 No. 1 (2025): Technoscientia Vol 18 No 1 September 2025
Publisher : Direktorat Penelitian & Pengabdian Kepada Masyarakat (LPPM), Universitas AKPRIND Indonesia Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v18i1.5333

Abstract

Fresh graduates face various challenges in competing for employment opportunities, especially due to the intense competition for limited job positions in the labor market. Both individuals as job seekers and organizations as employers must consider multiple complex factors during the recruitment process. These factors include GPA, university background, aptitude test score, number of projects, internship experience, and academic performance at both the secondary (SSC) and higher secondary (HSC) levels. This study aims to classify job placement eligibility using machine learning methods with algorithms such as Naïve Bayes, Deep Learning, Random Forest, and K-Nearest Neighbors (KNN). The experimental results indicate that the Naïve Bayes algorithm achieved the highest accuracy at 81.90%, followed by Deep Learning with 80.20%. These findings suggest that despite its simplicity, Naïve Bayes is highly effective for datasets with relatively independent and well-distributed features. Meanwhile, other algorithms such as KNN and Decision Tree provided competitive results but showed less consistency in generalizing data. For future research, it is recommended to use a larger and more diverse dataset and to consider additional features such as informal work experience, organizational involvement, or personality data to improve prediction accuracy. Furthermore, exploring ensemble learning or hybrid modeling approaches may lead to more optimal classification outcomes.
Implementasi OWASP ZAP untuk Menguji Kerentanan Sistem Informasi Akademik (SIA Universitas Teknologi Yogyakarta Domain: sia.uty.ac.id) IIN ROHMATIKA AULIA; Rianto; EIH UJIANTO
Walisongo Journal of Information Technology Vol. 6 No. 1 (2024): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2024.6.1.25390

Abstract

The security of academic information systems is essential to maintaining the confidentiality, integrity, and availability of academic data. This study aims to identify web application vulnerabilities in an academic information system using the OWASP framework. Vulnerability testing identified three main findings: NGINX server misconfigurations, potential exposure of sensitive files, and the need to strengthen authentication and access controls. The identified misconfigurations and potential file exposure indicate security risks that require further verification to determine their actual exploitability and impact. Recommended mitigation measures include correcting server configurations, strengthening authentication mechanisms, and restricting unauthorized access. These findings highlight the importance of systematic vulnerability assessment and appropriate security controls to improve the protection and reliability of academic information systems.
Co-Authors Donny Avianto EIH Ujianto Rianto Rianto