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All Journal ComEngApp : Computer Engineering and Applications Journal IPTEK The Journal for Technology and Science Fountain of Informatics Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi INTEK: Informatika dan Teknologi Informasi Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknologi Informatika dan Komputer Insyst : Journal of Intelligent System and Computation JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Of Computer, Network Security and Information System (IJCONSIST) Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pengabdian Sains dan Humaniora Journal of Data Science Theory and Application Jurnal Riset Multidisiplin Edukasi Journal of Applied Informatics Research
Haq, Dina Zatusiva
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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FETAL HEALTH RISK STATUS IDENTIFICATION SYSTEM BASED ON CARDIOTOCOGRAPHY DATA USING EXTREME GRADIENT BOOSTING WITH ISOLATION FOREST AS OUTLIER DETECTION Sari, Firda Yunita; Rini Novitasari, Dian Candra; Hamid, Abdulloh; Haq, Dina Zatusiva
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1711-1724

Abstract

Premature birth and birth defects contribute significantly to infant mortality, highlighting the need for early identification of fetal health risks. This study uses XGBoost for fetal health classification, integrating IForest for outlier detection to improve model performance. By varying the contamination percentage, learning rate (η), maximum depth, and n_estimator, the best results were achieved at CP = 8%, η = 0.01, max_depth = 7, and n_estimator = 100, which resulted in 100% accuracy, sensitivity, and specificity with a calculation time of 0.36 seconds. IForest effectively reduced the dataset from 2126 to 1956 samples by removing outliers, improving accuracy by 3.76%, and reducing computation time by 0.51 seconds. These findings suggest that IForest improves classification efficiency while maintaining high predictive performance, supporting early identification of fetal health risks to aid timely medical intervention.
Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network Puspitasari, Wahyu Tri; Haq, Dina Zatusiva; Novitasari, Dian Candra Rini
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%.
PREDIKSI HARGA BERAS PREMIUM TAHUN 2024 MENGGUNAKAN METODE GRADIENT BOOSTED TREES REGRESSION Andriyani , Mayrisa; Nurwilda , Siti; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14859

Abstract

Food needs are a special concern among the community. Every year the growth of Indonesian society increases so that the amount of food needed increases, especially rice which is the staple food of Indonesian society. Regarding this, the public needs information regarding forecasting rice prices for future needs. Therefore, this research aims to predict rice prices using the Gradient Boosted Trees Regression method. This method was chosen because of its ability to produce accurate predictions by minimizing errors through an ensemble approach. Evaluation is seen from the R-Squared and Root Mean Square Error (RMSE) values. The results of research using the Gradient Booster Trees Regression model obtained an R-Squared value of 0.9047 and an RMSE value of 0.0473, which indicates that the model has a high level of accuracy in predicting rice prices. The results of the dataset testing are divided into 80 percent training data and 20 percent for testing data. Based on this research, model testing was carried out by displaying decision tree visualization, using a sample of 50 decision trees.
KLASIFIKASI KUALITAS UDARA MENGGUNAKAN METODE EXTREME LEARNING MACHINE (ELM) Jannah, Rachma Raudhatul; Sholahuddin, Muhammad Zulfikar; Haq, Dina Zatusiva; Novitasari, Dian C Rini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 2 (2024): Volume 10 Nomor 2
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i2.3066

Abstract

Air quality is a critical factor affecting both ecological and human well-being. Air pollution is a global epidemic that poses a threat to human health and the environment. High population density resulting from industrial expansion and the increased number of motor vehicles are two primary causes of declining air quality in metropolitan areas. Air pollutants include surface ozone (O3), dust particles (PM 10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO). Researchers have begun exploring the use of Extreme Learning Machine (ELM) to classify air quality. The ELM method assesses air quality as either very good or poor. In this study, we compare datasets to evaluate the effectiveness of hidden node parameters using the split method. Our tests indicate that the split method impacts accuracy, sensitivity, and specificity. The ideal model with a 70:30 split ratio and 15 hidden nodes achieved a 90% success rate.  
Classification of Eye Diseases Using the AlexNet Convolutional Neural Network Model Algorithm Moch Deny Pratama; Royal Fajar Sultoni; Adil Sandy Wardhani; Maulana Hassan Sechuti; Yerezqy Bagus; Dina Zatusiva Haq; Yoga Ari Tofan
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.160

Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.
Detection of Diabetic Retinopathy Using Hybrid InceptionResNetV2-KELM Method Musfiroh, Musfiroh; Novitasari, Dian C Rini; Hakim, Lutfi; Damayanti, Adelia; Haq, Dina Zatusiva; Aisah, Siti Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11967

Abstract

Diabetic Retinopathy (DR) is a complication of Diabetes Mellitus (DM), both type 1 and type 2 DM. Based on its severity, DR is divided into mild DR, moderate DR, severe DR, and proliferative DR stages. Manual detection is difficult because there is a fairly small difference between normal and DR. The Computer-Aided Diagnosis (CAD) system is a solution for detecting the severity of DR quickly and accurately so that DR sufferers do not get worse, which can cause blindness. This study uses fundus images from the Mesindor dataset consisting of four classes, namely normal, mild DR, moderate DR, and severe DR, with the InceptionResNetV2-KELM hybrid method. InceptionResNetV2 is used as a feature extraction and Kernel Extreme Learning Machine (KELM) as its classification. Several types of kernels are applied as model trials. The results show the highest sensitivity lies in the polynomial kernel experiment with a sensitivity value of 99.88%, an accuracy of 99.88%, and a specificity of 99.96%. The method used is able to detect very well and is quite time-effective compared to conventional CNN.
Enhancing Clickbait Headline Identification Performance Without Preprocessing Through Feature Reduction and Sentiment Analysis Moch Deny Pratama; Anisa Nur Azizah; Misbachul Falach Asy'ari; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi; Dina Zatusiva Haq
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44659

Abstract

This study addresses the challenge of identifying clickbait headlines without relying on conventional text preprocessing, which can be resource-intensive and may degrade contextual integrity. To enhance detection performance, we examine three feature extraction methods: TF-IDF, Word2Vec, and Headline2Vec, an embedding technique designed for short texts like headlines. These features are optimized using feature selection algorithms, including Pearson Correlation Coefficient (PCC), Neighborhood Component Analysis (NCA), and Relief, to reduce dimensionality and enhance relevant signal retention. Sentiment polarity is also integrated as a complementary feature. A comparative evaluation is conducted using several machine learning classifiers, namely Support Vector Classifier (SVC), Random Forest, LightGBM, and XGBoost, across all combinations of feature extraction and selection methods. Results show that the optimal configuration Headline2Vec with Relief and SVC achieves the highest accuracy at 94.40%, outperforming other approaches. This demonstrates the effectiveness of combining semantic vectorization and feature selection for clickbait detection in the absence of traditional preprocessing. The findings support the development of streamlined and scalable classification models capable of maintaining high accuracy while reducing preprocessing overhead, making the proposed method particularly suitable for real-time and large-scale content moderation and news verification systems.
KLASIFIKASI DEPRESI PADA PELAJAR BERSDASARKAN GAYA HIDUP MENGGUNAKAN METODE TREE-BASED Haq, Dina Zatusiva; Bagus, Yerezqy; Maharani, Masti Fatchiyah; Dica Fitrani, Laqma; Pratama, Moch Deny
Journal of Data Science Theory and Application Vol. 5 No. 1 (2026): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/enqnkx98

Abstract

Depresi pada mahasiswa merupakan salah satu masalah kesehatan mental yang berdampak signifikan terhadap kualitas pendidikan dan produktivitas. Penelitian ini bertujuan untuk mengklasifikasikan depresi berdasarkan faktor gaya hidup menggunakan metode tree-based machine learning, yaitu Decision Tree, Random Forest, XGBoost, dan LightGBM. Data yang digunakan adalah Depression Student Dataset dengan 502 sampel yang mencakup atribut demografis, akademik, dan gaya hidup. Proses penelitian meliputi preprocessing data, pembagian data latih dan uji, pembangunan model, serta evaluasi menggunakan metrik akurasi, sensitivitas, dan spesifisitas. Hasil pengujian menunjukkan bahwa XGBoost memberikan performa terbaik dengan akurasi 94%, sensitivitas 100%, dan spesifisitas 87%, diikuti oleh LightGBM dengan akurasi 92%. Temuan ini menegaskan bahwa algoritma berbasis boosting lebih unggul dibandingkan metode pohon tunggal maupun bagging, sehingga dapat dimanfaatkan sebagai alat bantu deteksi dini depresi pada mahasiswa. Penelitian ini berkontribusi pada pengembangan model prediktif berbasis data yang mendukung pencapaian Sustainable Development Goals (SDG) terkait pendidikan berkualitas dan pekerjaan layak.
ANALISIS KUALITAS PERANGKAT LUNAK LEARNING MANAGEMENT SYSTEM BERBASIS WEB DALAM MENDUKUNG SDG’S 4 (QUALITY EDUCATION) MENGGUNAKAN ISO/IEC 25010 BAGUS, YEREZQY; Haq, Dina Zatusiva; Fitrani, Laqma Dica; Maharani, Masti Fatchiyah
Journal of Data Science Theory and Application Vol. 5 No. 1 (2026): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/z69ayx52

Abstract

Sustainable Development Goals (SDGs) Goal 4 emphasizes the importance of inclusive and equitable quality education. The utilization of information technology, particularly web-based Learning Management Systems (LMS), plays a significant role in supporting sustainable and accessible education. This study aims to analyze the software quality of a web-based LMS in supporting SDGs 4 (Quality Education) using the ISO/IEC 25010 standard. A quantitative research method with a survey approach was employed. Data were collected through questionnaires distributed to 40 LMS users consisting of students and lecturers. The software quality aspects analyzed include functional suitability, usability, reliability, and maintainability. The results indicate that the LMS demonstrates good overall software quality, with functional suitability and usability achieving very good ratings. However, reliability and maintainability aspects still require improvement to ensure long-term system sustainability. This study is expected to serve as a reference for the development and evaluation of sustainable educational software systems.
PERBANDINGAN LSTM DAN PROPHET DALAM PREDIKSI TREN BAJU LEBARAN 2026 BERBASIS GOOGLE TRENDS Maharani, Masti Fatchiyah; Laqma Dica Fitrani; Yerezqy Bagus; Dina Zatusiva Haq
Journal of Data Science Theory and Application Vol. 5 No. 1 (2026): JASTA
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/tgeq5784

Abstract

Tren busana Lebaran di Indonesia menunjukkan pola musiman yang kuat dan berulang setiap tahun, sehingga prediksi yang akurat menjadi penting bagi perencanaan produksi dan strategi bisnis industri fesyen. Penelitian ini bertujuan membandingkan kinerja metode Prophet dan Long Short-Term Memory (LSTM) dalam memprediksi tren busana Lebaran tahun 2026 menggunakan data Google Trends periode 2018–2025. Model dilatih menggunakan data hingga Desember 2024 dan dievaluasi pada periode pengujian tahun 2025 menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil menunjukkan bahwa LSTM memiliki akurasi numerik yang lebih baik, sedangkan Prophet lebih konsisten dalam menangkap pola musiman tahunan. Temuan ini menunjukkan bahwa pemilihan metode prediksi perlu disesuaikan dengan tujuan analisis. Penelitian ini berkontribusi dalam mendukung pengambilan keputusan berbasis data pada industri fesyen serta selaras dengan Sustainable Development Goals (SDGs) 8 terkait pertumbuhan ekonomi yang inklusif dan berkelanjutan.
Co-Authors Abdillah, Rifqi Abdulloh Hamid Addien Haniefardy Adil Sandy Wardhani Adinda Putri Pratiwi Adzan, M. Sailul Agus Wibowo Andriyani , Mayrisa Angga Lisdiyanto Anisa Nur Azizah Bagus, Yerezqy Binti Kholifah C. R. Novitasari, Dian Chastine Fatichah Damayanti, Adelia Dian Candra Rini Novitasari Dica Fitrani, Laqma DIMAS NOVIAN ADITIA SYAHPUTRA Fitrani, Laqma Dica Hartanto, Salsabila Ikhwan Abdillah Jannah, Rachma Raudhatul Laqma Dica Fitrani Laqma Dica Fitrani Laqma Fitrani Lutfi Hakim M Adamu Islam Mashuri Maharani, Masti Fatchiyah Maulana Hassan Sechuti Misbachul Falach Asy'ari Moch Deny Pratama Moch Deny Pratama Muhammad Syaifulloh Fattah Muhtadin Billah, Muhammad Musfiroh Musfiroh Musfiroh Musfiroh, Musfiroh Novitasari, Dian C Rini Nurul Fuad Nurwilda , Siti Pratama, Moch Deny Puspitasari, Wahyu Tri Putri Wulandari Ramadanti , Alvin Nuralif Rasyid Al-Hadi, Daud Rifqi Abdillah Royal Fajar Sultoni Sari, Firda Yunita Septaro Travian Gadha Sholahuddin, Muhammad Zulfikar Siti Nur Aisah Sri Wahyuni Vinza Hedi Satria Winarti Wulandari, A’isyah Yerezqy Bagus Yerezqy Bagus Yerezqy Bagus Yoga Ari Tofan