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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknoinfo JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Sains Komputer dan Teknologi Informasi Journal of System and Computer Engineering Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Engineering, Science and Information Technology Insearch: Information System Research Journal Batara Wisnu : Indonesian Journal of Community Services Journal of Artificial Intelligence and Digital Business Journal Of Technology and Information System (J-TIS) Bulletin of Informatics and Data Science Advances in Community Services Research IPSIKOM Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) JURDIASRA
Tonggiroh, Mursalim
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Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : jurnal ilmiah fifo

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Enhancing Liver Disease Classification Using Support Vector Machine with IQR-Based Outlier Handling Soares, Teotino Gomes; Tonggiroh, Mursalim; Erkamim, Moh.; Widarti, Erni
Jurnal Ilmiah FIFO Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i1.010

Abstract

Liver disease is a significant health issue that requires early and accurate diagnosis to prevent serious complications. In this study, we propose an outlier filtering approach using the Interquartile Range (IQR) to enhance the performance of the Support Vector Machine (SVM) algorithm in liver disease classification. A publicly available liver dataset consisting of 1,700 patient records with various clinical attributes was used, and the IQR method was applied to detect and remove extreme values before model training. The SVM model employed the Radial Basis Function (RBF) kernel to capture nonlinear relationships in the data. The classifier was evaluated under two conditions: without and with IQR-based outlier removal. Performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC were used to assess the model. The experimental results showed that the IQR-based preprocessing improved model performance, with the accuracy increasing from 84.41% to 84.74% and the ROC-AUC score rising from 92.08% to 93.28%. Notably, the recall for the negative class improved from 84.31% to 89.76%, indicating enhanced detection of healthy patients. These findings demonstrate that outlier handling using IQR can contribute to more stable and accurate classification outcomes, especially for models that are sensitive to data irregularities such as SVM.
Pengembangan Sistem Informasi Bimbingan Konseling Menggunakan Metode Feature-Driven Development Al-Hakim, Rosyid Ridlo; Yanuardi, Yanuardi; Rumandan, Rhaishudin Jafar; Tonggiroh, Mursalim
Jurnal Ilmiah FIFO Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i2.009

Abstract

Dalam era perkembangan teknologi informasi yang pesat, penerapan sistem digital dalam pendidikan menjadi sangat penting, terutama dalam layanan bimbingan dan konseling yang memainkan peran krusial dalam mendukung perkembangan siswa. Namun, sistem pengelolaan bimbingan konseling manual sering menghadapi kendala seperti keterbatasan dalam manajemen data dan kurangnya integrasi antar fungsi, yang berdampak pada penurunan kualitas layanan. Penelitian ini bertujuan untuk mengembangkan sistem informasi bimbingan konseling berbasis web dengan menggunakan metode Feature-Driven Development (FDD). FDD dipilih karena kemampuannya yang berfokus pada pengembangan berbasis fitur, memungkinkan pembangunan sistem yang komprehensif dan dinamis. Melalui FDD, setiap fitur inti dari sistem, seperti pengelolaan data pelanggaran, penjadwalan bimbingan, penilaian konseling, dan pembuatan laporan, dikembangkan secara iteratif dan bertahap, sehingga meminimalkan risiko dan memastikan integrasi yang baik di setiap tahap. Penerapan pendekatan FDD menghasilkan sistem yang diselesaikan dalam 4 bulan dengan 4 iterasi, sesuai jadwal yang direncanakan. Pengujian usability menunjukkan hasil yang baik dengan rata-rata nilai 90%, mengindikasikan bahwa sistem ini tidak hanya fungsional tetapi juga mudah digunakan dan diterima dengan baik oleh penggunanya.
Co-Authors ., Rusliyawati Al-Hakim, Rosyid Ridlo Alexander, Allan D Ali Nur Hidayat, Muhammad Bumi, Kalbu Pandu Ciptaningrum, Wahyu Dian Pertiwi Dwi Lestari Dwi Saputro, Agung Erni Widarti Fryda Fatmayati Hakim, Jamaludin Handayani, Nurdiana Hasan, Rian Fajry Heriyani, Nofitri Hidayat, Muhammad Ali Nur Jusmawati Jusmawati Jusmawati, Jusmawati khusnul khotimah Limbong, Alfa Natasya Limbong, Josua Josen Alexander lina, Ros Matdoan, Irjii Moh. Erkamim Muhamad Taher Jufri Muhammad Riandi Widiyantoro Murien Nugraheni Ndala, Stefanus Nur Hidayat, M. Ali Nurhasan Nugroho Prakoso, Sukma Dwi Que, Meirina Diah Setiawati Rasna, Rasna Rini Nuraini, Rini Rumandan, Rhaishudin Jafar Sah, Andrian Sandra Dewi Saraswati Sasono, Damar Surya SEJATI, FAJAR RINA Septarini, Ri Sabti Septilia, Tiara Indah Septyana Prasetianingrum Sidiq, Sofian Siti Nurhayati Soares, Teotino Gomes Suharto, Emilda Sulaiman, Husni Tanniewa, Adam M Weu, Wirda Widiyantoro, Muh. Riandi Yakub Yanuardi Yanuardi Yendra Zakaria Zakaria