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INTEGER: Journal of Information Technology
Published by Institut Teknologi Adhi Tama Surabaya
ISSN : 2579566X     EISSN : 2579566X     DOI : -
Core Subject : Science,
Computer Science & IT
This journal contains articles from the results of scientific research on problems in the field of Informatics, Information Systems, Computer Systems, Multimedia, Network and other research results related to these fields.
Arjuna Subject : -
Articles 313 Documents
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Perbandingan Seleksi Fitur Sequential, Chi-Square, dan Embedded Pada Klasifikasi Penyakit Kanker Payudara Menggunakan Algoritma Random Forest Auliya, Yudha Alif; Furqon, Muhammad ‘Ariful; Wibiyanto, Nico
INTEGER: Journal of Information Technology Vol 11, No 1 (2026): Maret
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2026.v11i1.7984

Abstract

Cancer is typically linked to malignant tumors that can metastasize to extensive body tissues. Breast cancer arises from the uncontrolled proliferation of breast cells, resulting in the formation of benign and malignant tumors. Breast cancer presents various indicators, including small, round, and soft lumps associated with benign breast conditions and non-cancerous growths. In contrast, malignant breast cancer presents as asymmetrical, irregular, painful, and various other manifestations. If untreated, the tumor may metastasize and present a fatal risk. This study intends to evaluate the efficacy of Sequential Feature Selection, Chi-Square, and Embedded methods in classifying breast cancer, alongside implementing hyperparameter optimization via grid search on the random forest algorithm. This study utilizes the Wisconsin Breast Cancer dataset from the UCI Machine Learning Repository, comprising 569 data entries, 30 attributes, and 1 class label. The performance of the model is assessed using a Confusion matrix, which quantifies accuracy, precision, recall, and F1-score. The test results were derived from twenty testing schemes employing a combination of data splitting, cross-validation, and hyperparameter tuning via grid search. The optimal performance outcomes were achieved using the random forest model, which was subjected to hyperparameter tuning alongside SFS feature selection. The integration of 20 features yielded an accuracy of 97.37%, precision of 95.83%, recall of 97.87%, and an F1 score of 96.84%. The employed prediction model demonstrates effective performance in identifying both positive and negative classes. The model accurately predicted the true negative class in 66 instances. The model accurately identified the true positive class in 46 instances. One instance involved the model predicting a false positive class, while another instance involved the model predicting a false negative class. These results demonstrate that the model exhibits a high degree of accuracy with negligible prediction errors.
Rancang Bangun Sistem Informasi Akademik Mahasiswa Berbasis Website Misdiyanto, Misdiyanto; Nourdy, Ahmad Fadli Muhaimin; Aprilia, Ira
INTEGER: Journal of Information Technology Vol 11, No 1 (2026): Maret
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2026.v11i1.8003

Abstract

This study aims to design and develop a web-based academic information system at Universitas Panca Marga Probolinggo to facilitate the management of student academic data. The research method used is the waterfall model software development methodology, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The results of this study indicate that the developed academic information system can improve efficiency and accuracy in managing academic data, as well as make it easier for students and lecturers to access academic information.Keywords : academic information system, website, Panca Marga University. 
Analisis Sentimen Kepuasan Pengguna Aplikasi Mobile Banking Bank ABC Menggunakan Algoritma K-Nearest Neighbor Prasetya, Bima Aji; Meilani, Budanis Dwi
INTEGER: Journal of Information Technology Vol 11, No 1 (2026): Maret
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2026.v11i1.8752

Abstract

Aplikasi mobile banking kini menjadi layanan penting dalam mendukung aktivitas transaksi digital. Untuk mengetahui tingkat kepuasan pengguna, analisis sentimen terhadap ulasan pengguna dapat memberikan informasi yang berharga bagi pengembangan aplikasi. Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pengguna aplikasi mobile banking Bank ABC dengan menggunakan metode K-Nearest Neighbor (KNN). Data sebanyak 1000 ulasan dikumpulkan dari Google Play Store dan selanjutnya melalui tahap pre-processing, yang meliputi cleaning, case folding, tokenizing, stopword removal, normalisasi, dan stemming. Setelah itu, pembobotan kata menggunakan metode TF-IDF digunakan untuk mengubah teks menjadi fitur numerik yang dapat diproses oleh model. Proses klasifikasi dilakukan menggunakan algoritma KNN, sedangkan evaluasi performa dilakukan melalui confusion matrix pada tiga skenario jumlah data, yaitu 250, 500, dan 1000 data. Hasil evaluasi menunjukkan bahwa model memperoleh akurasi sebesar 91% pada 250 data, 88% pada 500 data, dan 89% pada 1000 data, dengan rata-rata akurasi sebesar 87,5%. Temuan ini menunjukkan bahwa metode KNN cukup efektif dalam mengolah dan mengklasifikasikan ulasan pengguna aplikasi mobile banking. Sistem yang dibangun juga dilengkapi antarmuka berbasis web yang memungkinkan pengguna melakukan analisis sentimen dan melihat hasil evaluasi secara interaktif.

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