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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer SMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTEGER: Journal of Information Technology JURNAL MEDIA INFORMATIKA BUDIDARMA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal RESISTOR (Rekayasa Sistem Komputer) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Manajemen Informatika Jurnal Abdimas Mahakam JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Prima Abdika: Jurnal Pengabdian Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT Jurnal ilmiah teknologi informasi Asia
Farokhah, Lia
Institut Teknologi Dan Bisnis Asia Malang
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Aplikasi Cerdas Prediksi Kelulusan Mahasiswa Berbasis Website Menggunakan Metode Support Vector Machine (SVM) Budi Kurniawan, Fajar; Farokhah, Lia
JURNAL FASILKOM Vol. 15 No. 1 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i1.8767

Abstract

Kelulusan mahasiswa merupakan salah satu indikator penting bagi perguruan tinggi dalam akreditasi dan pemaksimalan kemampuan mahasiswa. Selain itu, penanganan yang terlambat membuat indikator ini sulit dicapai dan berdampak pada berbagai hal. Penelitian ini bertujuan untuk mengembangkan website model prediksi kelulusan mahasiswa menggunakan metode Support Vector Machine (SVM). Data yang digunakan mencakup berbagai fitur, seperti umur, jenis kelamin, status pekerjaan, riwayat cuti, dan Indeks Prestasi Semester (IPS) dari semester 1 hingga 4. Proses analisis meliputi normalisasi data, oversampling untuk mengatasi ketidakseimbangan kelas. Evaluasi model menggunakan teknik cross-validation dan confusion matrix. Hasil evaluasi menunjukkan bahwa model SVM mencapai rata-rata akurasi sebesar 97,21%. Temuan ini menunjukkan bahwa model yang dikembangkan dapat diandalkan untuk memprediksi kelulusan mahasiswa, memberikan kontribusi signifikan terhadap pengelolaan akademik di institusi pendidikan tinggi.
Analysis and Development of Eight Deep Learning Architectures for the Classification of Mushrooms Lia Farokhah; Suastika Yulia Riska
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5498

Abstract

One food item that is easy to find in nature is the mushroom. In terms of form and characteristics, mushrooms are similar. Arranging mushrooms into groups so that poisonous and non-poisonous ones can be separated is important. Real-time analysis of mushrooms is still not used very often. Previous studies focused primarily on performance and accuracy, ignoring architectural computing and a significant amount of data preprocessing. The data set used is more laboratory-conditioned. This will impede the process of widespread implementation. The study suggests changes to eight current architectures: Modified DenseNet201, DenseNet121, VGG16, VGG19, ResNet50, InceptionNetV3, MobileNet, and EfficientNet B1. The development of this architecture took place within the areas of classification and hyperparameter learning. In contrast to the other eight architectures, the MobileNet architecture exhibits the lowest computational performance and highest accuracy, according to the comparison results. When the confusion matrix is used for evaluation, an accuracy of 82.7% is achieved. Modified MobileNet has the best speed because it keeps a lower computation architecture and cuts down on unnecessary preprocessing. This means that many people can use smartphones with more realistic data conditions to make it work.
Early Stroke Disease Prediction Based on Lifestyle Factors Applied with Machine Learning Suastika Yulia Riska; Lia Farokhah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6495

Abstract

Stroke prediction has many supporting features and variables. Some forecasts focus more on health or elements that are already present. Predicting stroke risk by identifying habitual factors provides more advantages for preventive action. In addition, the complexity of features or variables is a concern in predicting stroke risk. In this study, we used a public dataset from Kaggle with 10 features or variables. In this study, we propose to collaborate algorithms and preprocessing in feature selection using Pearson Correlation and Principal Component Analysis (PCA) dimension reduction to unravel the complexity of variables and data processing computing. This aims to predict stroke risk more simply. The results of the experiment show that feature selection using Pearson Correlation between features and labels produces maximum results using 5 features out of 10 provided features. This approach produces the best performance on the Naïve Bayes, Iterative Dichotomiser Tree (ID3), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Logistic Regression with 100% accuracy and reduces features by 50% to support the reduction of the complexity of prediction variables and data processing computing.
Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB Farokhah, Lia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722608

Abstract

Era computer vision merupakan era dimana komputer dilatih untuk bisa melihat, mengidentifikasi dan mengklasifikasi seperti kecerdasan manusia. Algoritma klasifikasi berkembang dari yang paling sederhana seperti K-Nearest Neighbor (KNN) sampai Convolutional Neural Networks. KNN merupakan algoritma klasifikasi yang paling sederhana dalam mengklasifikasikan sebuah gambar kedalam sebuah label. Metode ini mudah dipahami dibandingkan metode lain karena mengklasifikasikan berdasarkan jarak terdekat dengan objek lain (tetangga). Tujuan penelitian ini untuk membuktikan kelemahan metode KNN dan ekstraksi fitur warna RGB dengan karakteristik tertentu. Percobaan pertama dilakukan terhadap dua objek dengan kemiripan bentuk tetapi dengan  warna yang  mencolok di salah satu sisi objek. Percobaan kedua terhadap dua objek yang memiliki perbedaan karakteristik bentuk meskipun memiliki kemiripan warna. Empat objek tersebut adalah bunga coltsfoot, daisy, dandelion dan matahari. Total data dalam dataset adalah 360 data. Dataset memiliki tantangan variasi sudut pandang, penerangan, dan  gangguan dalam latar. Hasil menunjukkan bahwa kolaborasi metode klasifikasi KNN dengan ekstraksi fitur warna RGB memiliki kelemahan terhadap percobaan pertama dengan akurasi 50-60% pada K=5. Percobaan kedua memiliki akurasi sekitar 90-100% pada K=5. Peningkatan akurasi, precision dan recall terjadi ketika menaikkan jumlah K yaitu dari K=1menjadi K=3 dan K=5.Kata kunci: k-nearest neighbour, RGB, kelemahan, kemiripan, bunga  IMPLEMENTATION OF K-NEAREST NEIGHBOR FOR FLOWER CLASSIFICATION WITH EXTRACTION OF RGB COLOR FEATURESThe era of computer vision is an era where computers are trained to be able to see, identify and classify as human intelligence. Classification algorithms develop from the simplest such as K-Nearest Neighbor (KNN) to Convolutional Neural Networks. KNN is the simplest classification algorithm in classifying an image into a label. This method is easier to understand than other methods because it classifies based on the closest distance to other objects (neighbors). The purpose of this research is to prove the weakness of the KNN method and the extraction of RGB color features for specific characteristics. The first  experiment on two objects with similar shape but with sharp color on one side of the object. The second experiment is done on two objects that have different shape characteristics even having a similar colors. The four objects are coltsfoot, daisy, dandelion and sunflower. Total data in the dataset is 360 data. The dataset has the challenge of varying viewpoints, lighting and background noise. The results show that the collaboration of the KNN classification method with RGB color feature extraction has weakness in the first experiment with the level of accuracy about 50-60% at K = 5. The second experiment has an accuracy of around 90-100% at K = 5. Increased accuracy, precision and recall occur when increasing the amount of K, from K = 1 to K = 3 and K = 5.  Keywords: k-nearest neighbour, RGB, weakness, similar, flower
Perancangan dan Pembuatan Website Majelis Ulama Indonesia Kota Batu Malang Farokhah, Lia; Noercholis, Achmad; Ahda, Fadhli Almu’iini; Sulistyo, Danang Arbian; Rofiq, Muhammad
Prima Abdika: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): Volume 4 Nomor 1 Tahun 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar Universitas Flores Ende

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/abdika.v4i1.3746

Abstract

Community service is one of the activities required for lecturers at Institut Teknologi dan Bisnis ASIA Malang every semester. This activity is carried out in groups to distribute knowledge to the community. This activity is in partnership with the Batu City MUI in creating a digital website for distribution of information to the wider community. Problems arise when a partner's website is hacked or damaged by a hacker. The service team wanted to teach how to recover or mitigate after damage, but the technical team could not provide information regarding the website and suggested creating a new website. In the initial stage, this service will create a new website. The method of this service approach is to carry out discussions in group discussion forums (FGD). The results of the discussion were realized in the form of a website for the Batu City MUI. Evaluations were carried out regarding design and functionality requirements. The partners are satisfied but it must be developed further. In ongoing collaboration this website will continue to be developed. After that, training in mitigating data when exposed to hackers will be carried out in the next service.
Co-Authors Achmad Noercholis Adriani Kala'lembang Ahda, Fadli Almu'ini Budi Kurniawan, Fajar Fadhli Almu’iini Ahda Fauji, Fahrul Fitria, Vivi Aida Fransiska Sisilia Mukti Gandes Nawang Sari Lilis Widayanti Lukman Hakim Machrus Machrus Machrus Machrus Maghfirah Maghfirah Muhammad Rofiq Muhammad Rofiq Nur Lailatul Aqromi Rendy Aditya Rina Dewi Indahsari Rio Jumardi, Rio Siti Nurul Afiyah Suastika Yulia Riska Sulistyo, Danang Arbian Titania Dwi Andini Yudistira Arya Sapoetra