Article Per Year (5 Year)
p-Index From 2021 - 2026
5.591
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Computatio : Journal of Computer Science and Information Systems JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL REKAYASA TEKNOLOGI INFORMASI Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Abdimas Mahakam Jurnal Mnemonic Jurnal Sains Komputer dan Teknologi Informasi Journal of Computer Networks, Architecture and High Performance Computing Jurnal Cahaya Mandalika NUSANTARA: Jurnal Pengabdian Kepada Masyarakat YASIN: Jurnal Pendidikan dan Sosial Budaya Safari : Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Publikasi Teknik Informatika (JUPTI) Journal of Empowerment and Community Service Kegiatan Positif : Jurnal Hasil Karya Pengabdian Masyarakat Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Pengabdian Masyarakat Nian Tana GEMBIRA (Pengabdian Kepada Masyarakat) Journal of Computer-based Instructional Media Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika INOVTEK Polbeng - Seri Informatika KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Wawan Joko Pranoto
Universitas Muhammadiyah Kalimantan Timur
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

Published : 45 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
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.
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.
Co-Authors A Arbansyah A Halim Abdul Hallim Abdul Rahim AGUS WIDODO Agus Widodo Alam, Aksal Illal Al Any Sawheri Gading Arbansyah Arbansyah Arif Nur Rahman Augie Sugiarto Nunka Aulia Khofifah Syamsuri Bayu Gaung Oktio Putra Damari, Azwar Della Eliyana Saputri Dinda Nur Octaviany Dini Anitasari Evitasari, Yuliana Dilla Faldi Faldi Faldi, Faldi Fitri Damayanti Fitriayana, Fitriayana Gilang Adhmadani Gina Maulidina Gunawan Ariyanto Hallim, Abdul Hasudungan, Rofilde Highness Mailani Putri Highness Mailani Putri Husni Thamrin Ibnu Sabdaniansyah Ika Safitri Windiarti Ilham, Muhammad Fauzan Nur Indra Pradista Indra Pradista Istimaroh Istimaroh Lidya Sari M. Gilang Romadhon M. Gillang Ramadhani Masni Masni Maulidin, Achmad Mawaddah, Suci Melisa Nur Aini Miliani, Dwi Fitri Mohammad Hiqmal Fiqri Mubaraq, Ahmad Ridhani Muhammad Fath Thoriq Muhammad Nur Irvan Muhammad Rifqi Pratama MUTHMAINNAH Naufal Azmi Verdikha Novia Hidayati Ramadhani Nurdin, Andi Pambudi, Faldy Alfareza Rahmad Fardian Ramadhan, Ahmad Kasim Ramadhan, Muhammad Firdaus Ramadhani, Daib Jidan Restu, Anggiq Karisma Aji Reyka Luna Karalo Reza, Andi Ridha Anisa Soldzu Parnga Ridha Anisa Soldzu Parnga Rita Yulfani Rivaldo, Vito Junivan Rofilde Hasudungan Sari, Septa Intan Permata Satria, Bima Siti Muawwanah Sofie Azizah, Jahra Syandy Apriyan Nur Taghfirul Azhima Yoga Siswa Taufiq, Ilham Tri Duwi Pramudito Wahyu Laksana Wahyudi Yulyanto Wisnu Priyo Jatmiko Yaakub, Saleh Yastria, Nurul Marisya Yuliana, Irma