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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Teknoka JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Sisfo: Jurnal Ilmiah Sistem Informasi JIKA Journal Of Artificial Intelligence And Software Engineering Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Solusi Bersama : Jurnal Pengabdian Dan Kesejahteraan Masyarakat Indonesian Journal of Innovation Multidisipliner Research
Iis Afrianty
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : bulletin of computer science research

 1 
Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine Alwaliyanto Alwaliyanto; Siska Kurnia Gusti; Iis Afrianty; Fadhilah Syafria
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.612

Abstract

Stroke is one of the leading causes of death and disability worldwide, making it essential to develop classification models that can assist in early and accurate diagnosis. This study aims to implement the Support Vector Machine (SVM) algorithm with three types of kernels linear, polynomial, and Radial Basis Function (RBF) to classify stroke disease data. The Adaptive Synthetic Sampling (ADASYN) method is employed to address the class imbalance problem, while model training and evaluation are carried out using 5-Fold Cross-Validation to ensure stable and reliable results. The findings indicate that ADASYN successfully improves the model’s sensitivity to stroke cases (the minority class), as reflected by an increase in recall and F1-score, despite a slight decrease in overall accuracy a common trade-off in handling imbalanced data. The linear kernel (after ADASYN) achieved the best performance after imbalance handling, with an average AUC-ROC of 0.8333, recall of 0.7827, and F1-score of 0.2181 for the stroke class. Although the F1-score remains relatively low, it improved compared to the pre-ADASYN results, indicating better detection of stroke cases. The implementation was conducted using Google Colab, which also contributed to efficient data processing and visualization. Overall, the results demonstrate that the combination of SVM and ADASYN is effective in enhancing the model’s sensitivity to minority classes and is well-suited for medical data classification tasks, particularly in the early diagnosis of stroke using machine learning approaches.
Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation Nada Tsawaabul Khair; Iis Afrianty; Fadhilah Syafria; Elvia Budianita; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.637

Abstract

Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
Perbandingan Teknik Penyeimbang Kelas Pada Multi-Layer Perceptron (MLP) Berbasis Backpropagation Untuk Klasifikasi Diabetes Mellitus Robby Azhar; Siska Kurnia Gusti; Iis Afrianty; Elvia Budianita
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.804

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected early; therefore, early diagnosis is highly important. One of the methods that can be applied for early diagnosis is the classification technique in data mining. However, the classification process often faces challenges due to class imbalance, which can reduce model performance. This study aims to analyze the effect of class balancing techniques on the performance of the Backpropagation Neural Network (BPNN) in classifying DM cases. BPNN is a form of Multi-Layer Perceptron (MLP) with a simple structure and the ability to solve complex problems with good accuracy. The dataset used in this study is the Pima Indians Diabetes Dataset, consisting of 768 instances, including 500 non-diabetic and 268 diabetic cases. The research was conducted using three scenarios: without balancing, Synthetic Minority Over-sampling Technique (SMOTE), and Random Under Sampling (RUS). The BPNN model was designed with two architectural variations (one hidden layer and two hidden layers), three learning rate values (0.1, 0.01, and 0.001), and a varying number of neurons. The dataset was divided using the 10-Fold Cross Validation technique. The results show that applying SMOTE achieved the best performance, with an average accuracy of 90.89%, precision of 91.22%, recall of 90.89%, and F1-score of 90.89% on the BPNN architecture with one hidden layer. Furthermore, the single hidden layer architecture proved more stable than the two hidden layers, especially when the dataset size decreased due to RUS. Therefore, the combination of SMOTE and BPNN with one hidden layer provides better performance in classifying Diabetes Mellitus cases.
Penerapan Seleksi Fitur Information Gain dan Metode Backpropagation Neural Network Untuk Klasifikasi Atrisi Karyawan Dinyah Fithara; Elvia Budianita; Iis Afrianty; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.922

Abstract

Employee attrition management is a critical challenge for organizations as it involves costs, time, and the risk of decision-making errors. This problem requires a data-driven business strategy to achieve more accurate predictions of employees who are potentially at risk of termination. This study applies the Information Gain feature selection method and the Backpropagation Neural Network (BPNN) algorithm in the employee attrition classification process with the aim of increasing the accuracy and efficiency of the prediction model. BPNN is chosen due to its simpler architecture, faster training time, and greater stability for small to medium sized datasets.  With the assistance of Information Gain feature selection, BPNN is able to achieve optimal performance without requiring a complex architecture. The dataset used consist of 35 attributes and 1.470 employee records covering various factor such as age, income level, and employment status. The research stages include feature selection based on information gain values with specific thresholds, data partitioning using k-fold cross validation, and model training using BPNN with variations of learning rates and hidden neuron counts. The results show that the combination of Information Gain and BPNN improves classification accuracy compared to models without feature selection, achieving the highest average accuracy of 87.28% when using 25 selected attributes, with a BPNN configuration of learning rate 0.001, 35 hidden neurons, and 50 epochs. The attributes with the highest Information Gain score include JobLevel, OverTime, MaritalStatus, and MonthlyIncome. This study demonstrates that the proposed approach successfully enhances the prediction performance of employee attrition and can serve as a foundation for developing data-driven models that support employee retention efforts.
Sistem Prediksi Produksi Kelapa Sawit Berbasis Gradio Menggunakan Algoritma Regresi Linear Berganda Irfan Jamal Matondang; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.994

Abstract

The instability of oil palm production often leads to discrepancies between production targets and actual outputs, thereby necessitating an accurate prediction model to support operational planning. This study aims to develop an oil palm production prediction model and to identify the most influential variables affecting production outcomes as a basis for data-driven decision-making. The model was developed using the Multiple Linear Regression method based on historical data from 2020–2024, consisting of 60 monthly observations with variables including number of trees, land area, rainfall, number of fruit bunches, and plant age. The research stages included data preprocessing, variable selection through testing several feature combinations, model development, and performance evaluation using the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The results indicate that the combination of number of trees, land area, number of fruit bunches, and plant age produced the best performance, with an R² value of 0.85 on the training data and 0.81 on the testing data. The MAE values were 125,307 kg and 176,984 kg, the MSE values were 28,870,838,455 kg² and 52,809,954,662 kg², and the RMSE values were 169,914 kg and 229,804 kg, respectively. Based on the regression coefficients, the number of fruit bunches was identified as the most dominant variable, with a coefficient value of 637,720 kg. The model was subsequently implemented using the Python Gradio library in the form of an interactive interface to support production planning effectiveness and minimize the risk of inaccurate decision-making in oil palm plantation management.
Co-Authors Agung Surya Maulana Alwaliyanto Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Aqshol Al Fahrozi Arif Mudi Priyatno Boni Iqbal Darmila Dewi Nasien Dinyah Fithara efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Ekawati Saputri Ekawati Saputri, Saputri Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvia Budianita Erni Rouza, Erni Evodius Nasus Fadhilah Syafria Faizah Husniah Fakhri Fakhri Febi Yanto Felian Nabila Fioni Nikmatul Fajar Fitra Lestari Fitri Insani Fitri Insani Fratiwi Rahayu grace tedy tulak Gusti, Siska Kurnia Habibollah Haron Ihlal Hanafi Harahap Ikhsanul Hamdi Irfan Jamal Matondang Ismail marzuki Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Karina Julita Lestari Handayani M. Aufaa Rahman Mawadda Warohma Mentari Aulia Putri Muhammad Fikry Muhammad Fikry Muhammad Habib Nazlis Muhammad Irsyad Nabilla Alya Firana Nada Tsawaabul Khair Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Nurafni Syahfitri Nurul Fatiara Purnama, Nilam Puspa Melani Almahmuda Qurrata A'yuni Rahayu Anggraini Rahmad Abdillah Rahmad Kurniawan Rahmat Al Hafiz Ramadhani Herfin Ravi Umbara Reski Mai Candra Restu Kharrisa Andini Riyan Wibowo Saputra Robby Azhar Sahrul Poalahi Salu Said Nurfan Hidayad Tillah Salmiyati Salmiyati Sephia Pratista Sonya Aulia Febriana SURYA ADITYA GD Surya Agustian Suswantia Andriani Suwanto Sanjaya Syaiful Azhar Ummy Agustina Putri Yusra Yusra