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All Journal EKONOMIA Jurnal Informatika dan Teknik Elektro Terapan Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Psychology, Evaluation, and Technology in Educational Research Jurnal Abdimas Mahakam METIK JURNAL Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Didaktik : Jurnal Ilmiah PGSD STKIP Subang Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing BAKTI BANUA : JURNAL PENGABDIAN KEPADA MASYARAKAT Teknika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Bangkit Indonesia Jurtik STMIK Bandung Jurnal Abdimas Lamin Journal of Innovative and Creativity Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Buffer Informatika INOVTEK Polbeng - Seri Informatika JSE Journal of Science and Engineering KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Taghfirul Azhima Yoga Siswa
Universitas Muhammadiyah Kalimantan Timur
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MODEL RFGS-CS UNTUK MENGATASI HIGH DIMENSIONAL DATA STUNTING KOTA SAMARINDA Sari, Lidya; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5997

Abstract

Di Samarinda, Kalimantan Timur, prevalensi stunting terus meningkat, dengan angka mencapai 23,9% pada tahun 2022. Kondisi ini menunjukkan perlunya intervensi lebih efektif untuk mengatasi masalah gizi di wilayah tersebut. Metode klasifikasi data mining dapat memprediksi risiko stunting, namun penelitian sebelumnya menghadapi tantangan  dengan dataset berdimensi tinggi yang dapat mempengaruhi akurasi. Tujuan dari penelitian ini adalah untuk meningkatkan akurasi klasifikasi stunting di Kota Samarinda menggunakan algoritma Random Forest (RF) yang dioptimalkan dengan seleksi fitur Chi-Square dan optimasi parameter Grid Search. Dataset yang digunakan adalah data stunting dari 26 puskesmas di Kota Samarinda tahun 2023 dari Dinas Kesehatan Kota Samarinda. Metode validasi yang digunakan yaitu cross-validation dengan k=10. Hasil penelitian menunjukkan bahwa fitur-fitur seperti BB/U, Tinggi, ZS BB/U, ZS TB/U adalah yang paling signifikan dalam mempengaruhi performa model RF. Model RF dengan seleksi fitur Chi-Square mencapai akurasi sebesar 99.11%, tidak ada peningkatan akurasi setelah penambahan metode optimasi Grid Search. Hasil penelitian ini menunjukkan bahwa model Random Forest (RF), baik dengan maupun tanpa optimasi, efektif dalam mengklasifikasikan data stunting. Keefektifan model ini dalam menangani dataset yang rumit dan kompleks, sehingga diharapkan dapat mendukung kebijakan serta intervensi kesehatan
Penerapan Metode PSO-SMOTE Pada Algoritma Random Forest Untuk Mengatasi Class Imbalance Data Bencana Tanah Longsor Ariyadi, Dedy; Siswa, Taghfirul Azhima Yoga; Rudiman, R
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.574

Abstract

Landslides are natural disasters that frequently occur in Samarinda City, with 45-80 affected areas in 2022-2023. The use of machine learning to classify landslide data faces the challenge of data imbalance, which can lead to bias towards the majority class. This study aims to address this issue by implementing the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and optimization using Particle Swarm Optimization (PSO). The data used comes from BMKG and BPBD Samarinda City, consisting of 11 features and 730 records. The results show that SMOTE successfully balanced the data, improving accuracy from 89.91% to 94.76%, an increase of 4.85%.
Optimasi Algoritma KNN dengan Parameter K dan PSO Untuk Klasifikasi Status Gizi Balita Rochman, Bagus Fathur; Rahim, Abdul; Siswa, Taghfirul Azhima Yoga
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7841

Abstract

The toddler years are a crucial phase that requires constant nutritional monitoring, because rapid growth and development require optimal nutritional intake. Nutritional problems in toddlers can hinder physical growth and can even be fatal. In assessing the nutritional status of toddlers, it is important to use efficient methods. One approach that can be used is machine learning, which can help determine the nutritional status of toddlers. K-Nearest Neighbors (KNN) is an algorithm commonly used in object classification based on nearest neighbors. Even though it is simple, determining the correct K value is very important because it can significantly influence KNN performance. This research emphasizes the importance of choosing the right parameters to increase the accuracy of the KNN model in classifying the nutritional status of toddlers. The test results show that the optimal combination for KNN is at K=4, using the 'distance' weight and distance metric p=1, producing the highest accuracy of 91.15% on the test data. Furthermore, the research applied Particle Swarm Optimization (PSO) to optimize KNN parameters, and it was found that the optimal combination was with K=6, 'distance' weight, and distance metric p=1, achieving a mean accuracy of 93.44% and a test accuracy of 93.98%. PSO is proven to be effective in finding the best parameters that increase model generalization to test data. Test results with a training data ratio of 80% and testing 20% show the best accuracy of 93.98%. .The use of PSO for parameter optimization succeeded in increasing model accuracy by 3.10% compared to the model without optimization
Perbaikan Akurasi Naïve Bayes dengan Chi-Square dan SMOTE Dalam Mengatasi High Dimensional dan Imbalanced Data Banjir Rivaldo, Vito Junivan; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7886

Abstract

Floods are one of the natural disasters that frequently occur in Indonesia. The city of Samarinda is affected by floods every year, resulting in significant losses. The data used in this study comes from the Regional Disaster Management Agency (BPBD) and the Meteorology, Climatology, and Geophysics Agency (BMKG) for the years 2021-2023 in Samarinda. This data includes 11 attributes and 1095 records. Previous studies on data mining related to floods have been conducted. However, issues arise with high-dimensional data and data imbalance. High dimensionality leads to overfitting and reduced accuracy, while imbalanced data causes overfitting to the majority class and inaccurate representation. This study aims to improve the accuracy of the Naive Bayes algorithm in predicting high-dimensional and imbalanced flood data. The approach involves using the Chi-Square feature selection technique and oversampling with the Synthetic Minority Over-sampling Technique (SMOTE). Chi-Square is used to find optimal features for predicting floods and to enhance the accuracy of the Naive Bayes algorithm in predicting high-dimensional and imbalanced flood data. The validation method used is 10-fold cross-validation, and a confusion matrix model is employed to calculate accuracy values. The results of the study show that Chi-Square can identify four best features: average humidity (rh_avg), rainfall (rr), maximum wind direction (ddd_x), and most frequent wind direction (ddd_car). The use of the Naive Bayes algorithm with SMOTE achieved an accuracy of 71.58%. However, after applying Chi-Square feature selection, the accuracy dropped to 60.82%. This decline is attributed to the reduced number of minority classes after feature selection. Therefore, Chi-Square feature selection is not sufficiently effective in improving the accuracy of Naive Bayes on high-dimensional data.
Optimasi Random Forest dengan Genetic Algorithm dan Recursive Feature Elimination pada High Dimensional Data Stunting Samarinda Satria, Bima; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7883

Abstract

Stunting is a chronic malnutrition problem that disrupts children's growth, with long-term impacts on physical growth, cognitive development, and productivity in adulthood. In Indonesia, the prevalence of stunting is still above the WHO threshold, reaching 24.4% according to the 2021 Indonesian Nutritional Status Study (SSGI), and in Samarinda City, the prevalence reached 24.7% in 2021 with 1,402 toddlers identified as stunted. Addressing this problem requires a more structured data-driven approach to provide targeted interventions. This study uses data from the Samarinda City Health Office, encompassing 150,474 stunting data points, and involves data collection, data cleaning, feature selection, and classification model application. This study aims to improve the accuracy of stunting data classification in Samarinda City in 2023 using the Random Forest algorithm enhanced with Recursive Feature Elimination (RFE) feature selection techniques and Genetic Algorithm (GA) optimization. The feature selection results using RFE show that the most influential features are Weight, ZS TB/U, ZS BB/U, and BB/U. The application of RFE increased the model's average accuracy from 91.91% to 93.64%, while GA optimization further increased the average accuracy to 98.39%. The definite accuracy increased from 94.23% (baseline model) to 97.10% (with RFE) and reached 99.70% (with RFE and GA). The combination of RFE and GA has proven effective in tackling data complexity and improving the reliability of stunting predictions. This study significantly contributes to the development of machine learning techniques for high-dimensional data analysis in health and is expected to be the foundation for more effective intervention programs in addressing stunting issues in Indonesia.
Penerapan Metode GA-TL Pada Algoritma Naive Bayes Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Widyastuti, Dessy; Siswa, Taghfirul Azhima Yoga; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6737

Abstract

The Indonesia Smart Card (KIP) Scholarship Program aims to support students from underprivileged families in pursuing higher education, yet the distribution of recipient data often experiences class imbalance, leading to inaccuracies in scholarship allocation. This imbalance, characterized by disproportionate data between recipient and non-recipient groups, affects classification model performance, causing models to favor the majority class and overlook the minority class, potentially excluding eligible recipients. To address this issue, this study combines the Genetic Algorithm for feature selection and optimization with Tomek Links-Random Undersampling for data balancing. The research process includes data preprocessing, 10-fold cross-validation, and performance evaluation using a confusion matrix. Results indicate that without Tomek Links-Random Undersampling, Naïve Bayes accuracy increased from 65.2% to 66.0% after feature selection and optimization using the Genetic Algorithm, while applying Tomek Links-Random Undersampling improved accuracy from 56% to 63%. This method also enhanced fairness in recipient classification, promoting a more equitable distribution of benefits. The improved model accuracy significantly aids future scholarship selection processes, demonstrating that integrating efficient machine learning approaches optimizes the KIP Scholarship Program by ensuring beneficiaries are appropriately targeted based on predetermined criteria.
Penerapan Metode GA-CBU Pada Algoritma Logistic Regression Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Poernamawan, Ahmad Nugraha; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6747

Abstract

The issue of class imbalance often poses a challenge in data analysis, where the number of instances in the majority class is significantly higher than that in the minority class. This can lead classification models to be biased towards predicting the majority class, resulting in low accuracy in identifying the minority class. This research aims to implement the Logistic Regression (LR) algorithm combined with the Clustering Based Undersampling (CBU) method as an undersampling technique, feature selection, and optimization using Genetic Algorithm (GA) in classifying KIP-College scholarship data at Muhammadiyah University of East Kalimantan. In addition, this research also evaluates the performance of the model with 10-Fold Cross Validation and Confusion Matrix techniques as accuracy metrics and aims to overcome the problem of class imbalance in the data of scholarship recipients (KIP) at Muhammadiyah University of East Kalimantan. The data used consists of 1075 records with 37 features related to the socio-economic factors of scholarship recipients. The results from the application of the CBU method indicate an increase in the accuracy of the Logistic Regression model from 62.51% to 67.68%. Furthermore, the combination of GA and CBU has providing more stable results in classifying minority classes. It is hoped that this research can make a significant contribution to the development of a more accurate and efficient scholarship recipient selection system, as well as serve as a reference for future studies in the fields of data mining and machine learning.
Penerapan Metode GA-NM Pada Algoritma SVM Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Abror, Irfan Fiqry; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6756

Abstract

Class imbalance is a common challenge in data analysis, especially when the number of instances in the majority class significantly exceeds that in the minority class. This imbalance can cause classification models to favor the majority class, resulting in low accuracy in identifying the minority class. In this study, the Support Vector Machine (SVM) method combined with Near Miss and Genetic Algorithm (GA) is used to address the class imbalance problem in the scholarship recipient data of the Kartu Indonesia Pintar (KIP) program at Universitas Muhammadiyah Kalimantan Timur. The dataset consists of 1,075 records with 27 features representing the socio-economic factors of the scholarship recipients. Near Miss was applied to undersample the majority class, producing a more balanced data distribution. Subsequently, the SVM algorithm was utilized as the primary classification model, with feature selection and parameter optimization conducted using GA. The results indicate that the combination of SVM, Near Miss, and GA improved classification performance in identifying the minority class. The initial accuracy obtained without the method was 60.55% and after implementation it increased to 76.88%. This approach not only enhances the overall accuracy of the model but also ensures more stable performance, particularly for the minority class. Therefore, this study is expected to provide a significant contribution to the development of a more accurate and efficient scholarship selection system, as well as serve as a reference for future research in data mining and machine learning.
Penerapan Metode GA-RU Pada Algoritma Random Forest Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Rahman, Febrian Nor; Siswa, Taghfirul Azhima Yoga; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6757

Abstract

Class imbalance is a common challenge in data analysis, where the majority class significantly outnumbers the minority class. This condition causes classification models to lean toward predicting the majority class, resulting in low accuracy in identifying the minority class. This study proposes the application of Genetic Algorithm (GA) combined with Random Undersampling (RU) on the Random Forest algorithm to address class imbalance issues in the dataset of Indonesia Smart Card (KIP) scholarship recipients at Universitas Muhammadiyah Kalimantan Timur. The dataset comprises 1,080 records with 37 features related to the socio-economic factors of the scholarship recipients. After data cleaning, 1,075 records were retained. The results indicate that the Random Undersampling method improved the accuracy of the Random Forest model from 84.27% to 85.06%. Although this improvement appears modest, it is significant as it demonstrates increased model stability in classifying the minority class, which previously had low accuracy. The combination of GA and RU proved effective in enhancing model performance, resulting in more stable classification for the minority class. This study is expected to contribute to the development of more accurate and efficient scholarship selection systems and serve as a reference for research in data mining and machine learning.
KLASIFIKASI KECELAKAAN LALU LINTAS DI KOTA SAMARINDA MENGGUNAKAN ALGORITME K-NEAREST NEIGHBOR Anitasari, Dini; Yulianto, Fendy; Azhima Yoga Siswa, Taghfirul
Jurnal Mnemonic Vol 8 No 1 (2025): Mnemonic Vol. 8 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v8i1.12747

Abstract

Kecelakaan lalu lintas di Kota Samarinda terus meningkat seiring dengan tingginya volume kendaraan dan berbagai faktor risiko, seperti kondisi jalan, cuaca, serta kelalaian pengemudi. Peningkatan angka kecelakaan ini berdampak pada kerugian material dan korban jiwa, sehingga diperlukan metode prediksi yang efektif untuk mengklasifikasikan tingkat keparahan kecelakaan. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kecelakaan lalu lintas di Kota Samarinda menggunakan algoritme K-Nearest Neighbor (KNN), yang dikenal sebagai metode berbasis jarak yang efektif dalam klasifikasi data. Data yang digunakan diperoleh dari Kepolisian Sektor Samarinda Kota dan mencakup faktor-faktor seperti kondisi cahaya, kelas jalan, tipe jalan, dan batas kecepatan. Model KNN diimplementasikan dengan pembagian data latih dan uji menggunakan validasi silang K-Fold untuk memastikan keakuratan prediksi. Hasil pengujian menunjukkan bahwa dengan pemilihan parameter yang optimal, model KNN mampu mencapai akurasi sebesar 92,54%. Kesimpulan dari penelitian ini adalah bahwa metode KNN dapat digunakan secara efektif untuk mengklasifikasikan tingkat kecelakaan lalu lintas, sehingga hasilnya dapat menjadi referensi bagi otoritas terkait dalam meningkatkan keselamatan jalan raya di Kota Samarinda
Co-Authors Abdul Rahim Abdul Rahim Abror, Irfan Fiqry Agustya Nanda Pratiwi Akbar, Zakaria Ihza Albab, Muhammad Ulil Anis Siti Nurrohkayati Anitasari, Dini Anton Prafanto Anton Saputra Arbansyah Arbansyah Ari Ahmad Dhani Ariyadi, Dedy Asnur Karima Aspianur Bahrudin, Faizal Betris Dea Maretta, Nanda Damari, Azwar Darmawan Setiya Budi Dewi, Catur Kumala Dzul Rachman, Dzul Ekawati Ekawati Enriko Chiesa Sipahutar FAUZI Fendy Yulianto Fendy Yulianto Gubtha Mahendra Putra Haryadi, Rina Mashitoh Haryadi, Rina Masithoh Hasudungan, Rofilde Heri Abijono Hery Kurniawan Hidayati Ramadhani, Novia Istimaroh Istimaroh Joko Pranoto, Wawan Jubaidi Khanisa Octavia Khatimah, Khusnul lia, Alvina Lidya Sari Mardiana Mardiana Muhammad Fadly Ramadhani Muhammad Najeri Al Syahrin Muhammad Norhalimi Muhammad Rhosyid Akhmad Muhammad Wildan Hadinata Naufal Azmi Verdikha Pambudi, Faldy Alfareza Paula Mariana Kustiawan Pitoyo Pitoyo Pitoyo, Pitoyo Poernamawan, Ahmad Nugraha Prihandoko . Putri, Azzahra Namira Raenald Syaputra Rahmad Fahrozi, Mu. Aldi Rahman, Febrian Nor Ramadhani, Daib Jidan Renaldi Panji Wibowo Restu, Anggiq Karisma Aji Rivaldo, Vito Junivan Rizky Aspiah Rochman, Bagus Fathur Rudiman, R Rudiman, Rudiman Salsabila, Cindy Azra Santi Yatnikasari Satria, Bima Sidiq, Reza June Siti Muawwanah Taufiq, Ilham Taufiqurrahman Taufiqurrahman Wahyu Hidayat Wawan Joko Pranoto Wawan Joko Pranoto Wawan Joko Pranoto Widyastuti, Dessy Yoga Priantama