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All Journal Scientific Journal of Informatics JUSTIFY : Jurnal Sistem Informasi Ibrahimy
Jarot Dwi Prasetyo
Universitas Ibrahimy
Computer Science & IT

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Optimalisasi Sistem Informasi Untuk Efektifitas Program Kerja Pengawasan Tahunan (PKPT) Inspektorat Kabupaten Bondowoso Lukman Fakih Lidimilah; Jarot Dwi Prasetyo; Ahmad Homaidi
Jurnal Ilmiah Informatika Vol. 5 No. 2 (2020): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v5i2.936

Abstract

The preparation of an annual supervisory work program (PKPT) requires a long process in order to get good and accurate results, because the process is done semi-computerized, which still involves a lot of staff in the Inspectorate. This makes the PKPT formulation process less effective and efficient. So that the supervisory work program that should have been able to be carried out quickly became constrained because of the long process in its preparation. Therefore, it is deemed necessary to design an application that can help facilitate the preparation of an annual monitoring work program with the waterfall method, making it easier for the inspectorate to determine audit objects and the implementation of supervision of predetermined objects. Based on the tests carried out, it was found that the application feasibility was 93%. From the results of these tests, this application can help make it easier for the inspectorate to provide an assessment of the level of risk, determine supervision plans and assignments to be more effective and efficient. So that efforts to become Good Governance and Clean Governance can be realized properly.
PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOUR DAN NAÏVE BAYES UNTUK MENDETEKSI PENIPUAN KARTU KREDIT Fauzan Firdaus; Ahmad Homaidi; Jarot Dwi Prasetyo; Hermanto Hermanto; Ach. Zubairi; Lukman Fakih Lidimilah
Jurnal Ilmiah Informatika Vol. 10 No. 2 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/.v10i2.8992

Abstract

Credit card fraud is a serious problem in the financial industry that continues to increase with the development of digital transaction technology. This study aims to compare the performance of the K-Nearest Neighbour (KNN) and Naive Bayes algorithms in detecting credit card fraud by considering various evaluation metrics evaluation metrics, including not only accuracy but also precision, recall, and F1-score. The dataset used was sourced from Kaggle, comprising a total of 10,000 transaction records, which included financial transaction attributes and user behaviour. The research process included data pre-processing, attribute selection, data normalisation, and the application of both algorithms using RapidMiner software. The test results showed that the KNN algorithm produced an accuracy of 98.43%, a precision of 98.53%, and a recall of 99.90%, while Naive Bayes obtained an accuracy 98.20% accuracy, 99.69% precision, and 98.48% recall. Although KNN showed slightly superior performance in detecting fraudulent transactions, the T-Test statistical test showed that the difference in performance between the two algorithms was not statistically significant. KNN has an advantage in recognising complex patterns, but requires greater computational time, while Naive Bayes is more efficient in terms of speed. This study concludes that the selection of a fraud detection algorithm needs to consider the trade-off between accuracy and computational efficiency according to system requirements.
Model Klasifikasi Hybrid Berbasis PSO-KNN untuk Akurasi Diagnosis Penyakit Hepatitis Sunardi Sunardi; Jarot Dwi Prasetyo; Hermanto Hermanto; Ach. Zubairi; Ahmad Homaidi; Irma Yunita; Lukman Fakih Lidimilah
JUSTIFY : Jurnal Sistem Informasi Ibrahimy Vol. 5 No. 1 (2026): JUSTIFY : Jurnal Sistem Informasi Ibrahimy
Publisher : Fakultas Sains dan Teknologi, Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/justify.v5i1.10322

Abstract

Diagnosis dini penyakit hepatitis masih menghadapi tantangan akibat kompleksitas data medis, tingginya variasi hasil laboratorium, serta keberadaan atribut yang tidak relevan sehingga menurunkan kinerja algoritma K-Nearest Neighbor (K-NN). Penelitian ini bertujuan mengembangkan model hibrida Particle Swarm Optimization–K-Nearest Neighbor (PSO-KNN) untuk mengoptimalkan bobot fitur dan parameter K-NN secara simultan guna meningkatkan akurasi klasifikasi penyakit hepatitis. Metode yang digunakan meliputi preprocessing dataset HCV, normalisasi Min-Max, pembagian data latih dan uji sebesar 80:20, serta optimasi menggunakan PSO dengan 20 partikel dan 30 iterasi. Evaluasi dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa PSO-KNN meningkatkan akurasi dari 95,1% menjadi 96,7%, presisi dari 88,9% menjadi 100%, recall dari 61,5% menjadi 69,2%, dan F1-score dari 72,7% menjadi 81,8%. Model juga berhasil menghilangkan false positive dan menurunkan false negative. Bobot fitur menunjukkan bahwa ALT, AST, GGT, dan Bilirubin merupakan biomarker yang paling berpengaruh dalam klasifikasi. Dengan demikian, model PSO-KNN terbukti mampu meningkatkan performa klasifikasi, efisiensi komputasi, serta interpretabilitas diagnosis hepatitis, sehingga berpotensi mendukung pengambilan keputusan klinis secara lebih akurat.
Co-Authors Ach. Zubairi Ahmad Homaidi Fauzan Firdaus Hermanto Hermanto Irma Yunita Lukman Fakih Lidimilah Sunardi Sunardi