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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) SITEKIN: Jurnal Sains, Teknologi dan Industri Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science SISFO Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) JAST : Jurnal Aplikasi Sains dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science Jurnal Abdi Masyarakat Indonesia Bulletin of Information Technology (BIT) ALMUJTAMAE: Jurnal Pengabdian Masyarakat Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Kohesi: Jurnal Sains dan Teknologi Journal of Information System and Application Development
Lailil Muflikhah
Fakultas Ilmu Komputer, Universita Brawijaya, Malang, Indonesia
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Journal : journal of information technology and computer science

 1 
Classifying Application User Comments Using the Improved K-Nearest Neighbor and BM25F Weighting Methods Indriati, Indriati; Rahman, Muh Arif; Arwani, Issa; Muflikhah, Lailil
Journal of Information Technology and Computer Science Vol. 9 No. 2: August 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.92622

Abstract

Mobile JKN is an application developed by an Indonesian state-owned health insurance company, BPJS Kesehatan. The application is developed to provide easier access and more optimal service for its participants. The application allows users to access various information related to national health insurance programs anywhere, anytime. Despite the benefits this application offers, Mobile JKN received low ratings from the users. However, the ratings are sometimes irrelevant to the comments. We proposed an application to automatically review the comments based on ratings and comments, both negative and positive, using the Improved K-Nearest Neighbor (IKNN) classification algorithm with the BM25F weighing method. The initial stage of the algorithm involved document preprocessing, the document consisting of comments and rating were preprocessed to obtain the term. After that, BM25F weighing was applied to find the similarity between documents. The document was then classified using IKNN based on its BM25F weight. The test result showed that the highest accuracy rate was 0.925, obtained from parameter k=20 on IKNN with k1=1.2, Bs=0.75, and BM25F weight of 2 and 5. The result indicates that this method manages to classify the document properly
High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis Muflikhah, Lailil; Haryanto, Dimas Joko
Journal of Information Technology and Computer Science Vol. 3 No. 2: November 2018
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.242 KB) | DOI: 10.25126/jitecs.20183260

Abstract

Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda, c value, epsilon and iteration). As general, the performance for polynomial kernel of 88.75% is slightly higher than RBF kernel of 83.25%.
Enhancing Brain Tumor MRI Classification Performance Using EfficientNetV2-B3 with an Efficient Channel Attention Module Navira Rahma Salsabila; Lailil Muflikhah; Edita Rosana Widasari
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103846

Abstract

Early identification of brain tumors using magnetic resonance imaging helps doctors make quick and informed decisions about treatment. Although recent deep learning approaches achieve high accuracy, many rely on complex architectures that increase computational cost and limit interpretability. In order to overcome these constraints, this work proposes a system for four-class brain tumor classification utilizing a public MRI dataset of 3,264 images that is built on EfficientNetV2-B3 and an Efficient Channel Attention (ECA) module used after feature extraction and Grad-CAM. The ECA module enhances cross-channel feature representation with minimal computational overhead. Experimental results indicate consistent performance gains over the baseline model, with accuracy increasing from 97.58% to 99.09% and macro-averaged F1-score from 97.51% to 99.08%. Despite the strong baseline, these gains are achieved without increasing architectural complexity. Grad-CAM visualizations support model interpretability by highlighting tumor-relevant regions that contribute most to the classification decisions. Overall, the proposed framework provides a balanced trade-off between classification accuracy, computational efficiency, and interpretability within the evaluated setting.
Hyperparameter Optimization of Extreme Gradient Boosting Using Particle Swarm Optimization For Diabetic Nephropathy Prediction Argaputri, Maulida Khairunisa; Lailil Muflikhah; Prasetio, Barlian Henryranu
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103859

Abstract

Diabetic Nephropathy (DN) is a critical complication with a mortality rate 20-40 times higher than in non-diabetic nephropathy patients, necessitating precise machine learning models to determine whether a patient has nephropathy. Extreme Gradient Boosting (XGBoost) has emerged as a prominent machine learning model for medical diagnostics, with several studies validating its superiority in medical classification. Nevertheless, a significant limitation of XGBoost lies in the complexity of manual hyperparameter tuning. To address this limitation, an automated optimization algorithm is requisite to systematically identify the optimal hyperparameter configuration. This study focuses on optimizing Extreme Gradient Boosting (XGBoost) hyperparameters using Particle Swarm Optimization (PSO), with the F1-Score as its fitness function. To evaluate its effectiveness, the performance of this hybrid XGBoost-PSO model was compared against the baseline XGBoost model. The results showed that the hybrid model outperformed the baseline model, achieving a consistent improvement of 0.02 (2%) across all evaluation metrics. Notably, the F1-Score increased from 0.91 to 0.93, while the Recall metric improved from 0.93 to 0.95. Furthermore, the PSO algorithm efficiently identified the Global Best (GBest) hyperparameters at the 9th iteration. In conclusion, the XGBoost-PSO model provides a robust medical diagnostic tool that maintains a stable performance to enhance clinical judgment.
Predicting Heart Disease with Enhanced Genetic Algorithms: The Role of Latin Hypercube Sampling and Hamming Distance-Based Diversity Sifaunnufus Ms, Fi Imanur; Amila Fadhila Rahmaniati; Maulida Khairunisa Argaputri; Yonathan Fanuel Mulyadi; Lailil Muflikhah
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102946

Abstract

Heart disease remains a predominant cause of mortality globally and in Indonesia, impacting both older and younger demographics due to pervasive unhealthy lifestyles, heredity, and lack of awareness of heart disease. This study addresses the critical problem of necessitating early detection methods to mitigate severe complications and fatalities associated with heart disease. With that, it is crucial to develop a robust and highly accurate prediction model for heart disease by integrating Artificial Neural networks (ANN) with Genetic Algorithms (GA). The model starts by constructing an ANN model utilizing the Keras Framework for streamlined training, followed by hyperparameter optimization through GA. As a result, this research found that the integrated ANN and GA model attains superior predictive accuracy, with the optimal configuration achieving an accuracy of 85.33%, precision of 92.55%,  recall of 81.32%, and f1-score of 86.57% via Latin Hypercube Sampling (LHS). These show that the combination of ANN and GA can significantly increase prediction accuracy and model efficiency, as a solution for more effective heart disease identification at an early stage.
Co-Authors A. Bachtiar , Fitra Abdurrachman Bachtiar, Fitra Achmad Jafar Al Kadafi, Achmad Jafar Addin Sahirah, Rafifa Adinugroho, Sigit Adnawirya Pratama, Cendikia Agung Setiyoaji Agus Ardiansyah Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Nur Royyan Ahmad Wildan Attabi' Akbar Grahadhuita Al Kautsar, Prima Daffa Aldi Bagus Sasmita Aldino Caturrahmanto Amalia Rahmawati Amila Fadhila Rahmaniati Anif Fatma Chawa Anis Zubair, Anis Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiza Dwi Septian Argaputri, Maulida Khairunisa Arief Andy Soebroto Arum Sari, Yuita Aryo Pinandito Ashidiq, Muhammad Fihan Aulia Herdhyanti Bachtiar, Harsya Baharudin B. Baharum Baihaqi, Galih Restu Bajsair, Fath' Hani Sarli Barlian Henryranu Prasetio Bayu Laksana Yudha Bayu Rahayudi Bening Herwijayanti Bintang, Tulistyana Irfany Brillian Ghulam Ash Shidiq Budi Darma Setiawan Candra Dewi Candra Dewi Daneswara Jauhari Daneswara Jauhari, Daneswara Darma Setiawan, Budi Daud, Nathan Dewi, Buana Dhimas Wida Syahputra Dian Eka Ratnawati Dian Eka Ratnawati Dimas Firmanda Al Riza Dimas Joko Hariyanto Dimas Joko Haryanto Duwi Purnama Sidik Edita Rosana Widasari Edy Santoso Edy Santoso Eni Hartika Harahap Eva Agustina Ompusunggu Fajrin Intan Safitri Faris Dinar Wahyu Gunawan Fatimah Az-Zahra, Adinda Feri Eko Herman Firdaus, Nada Fitra A. Bachtiar Fitra Abdurrachman Bachtiar Fitrotuzzakiyah, Shafira Puspa Gessia Faradiksi Putri Gumelar, Dimas HANA RATNAWATI Hanggar Wahyu Agi Prayogo Haris, Asmuni Haryanto, Dimas Joko Hinandy Nur Anisa Hoar, Wilhelmina Sonya Ichsan Achmad Fauzi Iftinan, Salsa Nabila Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indriati Indriati Indriati Indriati Indriati Ismatul Khasanah Issa Arwani Kartika Sari Kautsar, Ahmad Izzan Khairunnisa, Alifah Kirana, Nareswara Lintang Ksatria Bhuana Kukuh Bhaskara Kukuh Haryobismoko Kurnianingtyas, Diva Laily Putri Rizby Langgeng Setyono, Langgeng Luqyana, Wanda Athira Luthfi Afrizal Ardhani M. Ali Fauzi M. Tanzil Furqon, M. Tanzil Marine Putri Dewi Yuliana Marji . Marji Marji Maulana, Muhammad Taufik Maulida Khairunisa Argaputri Maulidiya, Afifulail Maya Nur Muh Arif Rahman Muh Hamim Fajar Muh. Arif Rahman Muhammad Abduh Muhammad Fajri Muhammad Ferian Rizky Akbari Muhammad Ibnu Sa'ad Muhammad Rafif Al Aziz MUHAMMAD SYAFIQ Muhammad Tanzil Furqon Muhammad Wafiq Mukhrodi, Dillah Lyra Nabil Auliya, Muhammad Hanif Nashi Widodo Navira Rahma Salsabila Nisa, Lisa N. Noor Fatyanosa, Tirana Novanto Yudistira Nurfansepta, Amira Ghina Nurhidayati Desiani Nurul Badriyah Nurul Dyah Mentari Nurul Hidayat Nurul Hidayat Olivia Bonita Puji Indah Lestari Puspita Sari Putra Pandu Adikara Putri, Rania Aprilia Dwi Setya Rachmad Indrianto Rachmatika, Isnayni Sugma Radeetha Radeetha Rafifah Nawawi, Danisha Ramadhan, Galang Gilang Randi Pratama Nugraha Randy Cahya Wihandika Ratih Kartika Dewi Rekyan Regarsari Mardhi Putri Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rendi Cahya Wihandika Retno Damayanti Rheza Raditya Andrianto Ria Ine Pristiyanti Rihana Sofie Nabela Rika Raudhotul Rizqiyah Riski Darmawan Riski Darmawan Riyanarto Sarno Rizal Setya Perdana Rizal Setya Perdana Robbiyatul Munawarah Rowan Rowan Rusydi Hanan, Muhammad Satrio Hadi Wijoyo Setiana, Maya Setya Perdana, Rizal Setyawan, Frederikus Setyo Tri Wahyudi Shalsadilla, Shafatyra Reditha Sholeh, Mahrus Sifaunnufus Ms, Fi Imanur Sukma, Lintang Cahyaning Supraptoa Supraptoa Surya Dermawan Susanto, Dominicus Christian Bagus Sutrisna, Naufal Putra Sutrisno Sutrisno Sutrisno, Sutrisno Syafruddin Agustian Putra Syarif Hidayatulloh Tahtri Nadia Utami Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Tri Fadilah, Ghina Utaminingrum, Fitri Vianti Mala Anggraeni Kusuma Vidya Capristyan Pamungkas Wahyu Rizki Ferdiansyah Wahyudi, Moh. Imron Wardana, Dzaky Ahmadin Berkah Warut, Gregorius Batara De Wibowo, Dhimas Bagus Bimasena Wiguna, Riady Wijaya, Nicholas Yobel Leonardo Tampubolon Yogi Suwandy Yonathan Fanuel Mulyadi Yudhonugroho, Wirkananda Bagus Yulian Ekananta Yunita, W. Lisa Zakiyyah, Rizka Husnun Zanna Annisa Nur Azizah Fareza