Article Per Year (5 Year)
p-Index From 2020 - 2025
2.636
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek Semesta Teknika Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal Pilar Nusa Mandiri Indonesian Journal of Information System JMM (Jurnal Masyarakat Mandiri) Journal of Information Systems and Informatics JIKA (Jurnal Informatika) Insearch: Information System Research Journal RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Hastomo, Widi
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
Prediksi Cacat Lempeng Baja Menggunakan Algoritma Bagging: Pendekatan Machine Learning untuk Peningkatan Kualitas Produksi Digdoyo, Aji; Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Sestri, Elliya; Fitriansyah, Reza
Jurnal Ilmiah Komputasi Vol. 24 No. 1 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 1, Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.1.3654

Abstract

Industri baja memiliki peran krusial dalam berbagai sektor, menjadi faktor kunci dalam memastikan integritas struktural produk akhir. Penelitian ini bertujuan untuk mengatasi masalah ini dengan menerapkan algoritma Bagging dalam prediksi cacat lempeng baja. Hasil model training dengan kurva ROC dengan nilai AUC 99% dab logloss 0,14. Pengukuran precision, recall, dan f1 score untuk 7 jenis cacat baja memperoleh prosentase yang sangat baik (lebih dari 90%). Confusion Matrix menunjukan korelasi yang kuat antara jenis cacat ke 6 dan ke 5. Sedangkan validasi, antara jenis cacat ke 4 dan ke 0 terdapat hubungan yang sangat kuat. Classification report menunjukan nilai precision, recall, dan f1 score terbaik (lebih dari 80%) untuk jenis cacat ke 1, 2, dan 3. Nilai AUC yang cukup baik yaitu 88% dan Logloss yang cukup besar yaitu 3,13. Penelitian selanjutnya dapat fokus untuk meningkatkan nilai logloss yang masih harus diperbaiki untuk proses validasi.
Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

Abstract

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.
PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY Setiawati, Popong; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Sestri, Ellya; Kasoni, Dian; Arif, Dodi; Razi, Fahrul
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6126

Abstract

In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.
Desain Komunikasi Visual Berbasis Segmentasi Pelanggan untuk H&M Terisia, Vany; Hastomo, Widi; Sestri, Elliya; Syamsu, Muhajir; Novitasari, Lyscha; Putra, Yoga Rarasto; Fiqhri, Zul; Sudarwanto, Pantja; Daruningsih, Kukuh
Prosiding Semnastek PROSIDING SEMNASTEK 2025
Publisher : Universitas Muhammadiyah Jakarta

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

Abstract

Penelitian ini bertujuan untuk merancang strategi komunikasi visual berdasarkan segmentasi pelanggan pada industri fashion retail, studi kasus pada H&M Group. Data diambil dari dataset H&M Personalized Fashion Recommendations di Kaggle dan diolah dengan pendekatan RFM (Recency, Frequency, Monetary) serta algoritma K-Means clustering untuk mengidentifikasi tipe pelanggan. Hasil analisis menunjukkan tiga klaster utama: pelanggan bernilai tinggi, sedang, dan rendah. Berdasarkan hasil tersebut, dirancang pendekatan visual yang berbeda untuk setiap segmen, baik dalam desain iklan digital maupun visual merchandising. Penelitian ini memberikan kontribusi dalam pengambilan keputusan pemasaran visual yang berbasis data untuk meningkatkan retensi pelanggan.
OPTIMALISASI BANK SAMPAH, KELOMPOK WANITA TANI, DAN POS PEMBINAAN TERPADU DENGAN PERSPEKTIF AL-ISLAM KEMUHAMMADIYAHAN Soleha, Maratus; Maeda, Serly; Fitriyani, Fitriyani; Asy-Syifa, Zahwa Zia; Nurhidayati, Aulia; Putri, Dhea Ananda; Rahman, Ibadu; Mardani, Muhammad; Muthmainnah, Yulianti; Hastomo, Widi
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i2.29739

Abstract

Abstrak: Pengelolaan sampah dan ketahanan pangan berbasis komunitas masih menghadapi tantangan seperti rendahnya kesadaran masyarakat, minimnya pemanfaatan teknologi, serta kurangnya integrasi nilai-nilai Islam dalam praktik lingkungan. Program ini bertujuan untuk meningkatkan pemahaman dan keterampilan masyarakat dalam mengelola sampah serta pertanian berkelanjutan melalui pendekatan berbasis Al-Islam dan Kemuhammadiyahan (AIK). Metode yang digunakan yaitu Participatory Action Research, serta implementasi sistem digital pada bank sampah dan Kelompok Wanita Tani (KWT). Program ini melibatkan 85 peserta dari RW 08 Cirendeu, Ciputat Timur, yang terdiri dari pengurus bank sampah dan anggota KWT. Evaluasi keberhasilan dilakukan melalui survei pre-test dan post-test, wawancara mendalam, serta Focus Group Discussion (FGD). Hasil evaluasi menunjukkan peningkatan kesadaran dan partisipasi masyarakat sebesar 70%, peningkatan pendapatan anggota KWT sebesar 25%, serta penurunan volume sampah tidak terkelola sebesar 35%. Selain itu, 80% anggota KWT mulai menggunakan pupuk organik dan 75% peserta memahami konsep AIK dalam pengelolaan lingkungan. Dengan strategi keberlanjutan yang mencakup kemitraan dengan lembaga Muhammadiyah dan sistem insentif digital, program ini diharapkan dapat terus berjalan secara mandiri dan memberikan dampak positif jangka panjang bagi masyaraka.Abstract: Community-based waste management and food security continue to face challenges such as low public awareness, limited use of technology, and lack of integration of Islamic values into environmental practices. This program aims to enhance community understanding and skills in waste management and sustainable agriculture through an Al-Islam and Kemuhammadiyahan (AIK)-based approach. The methodology employed is Participatory Action Research (PAR), combined with the implementation of a digital system for the waste bank and Women's Farming Group (KWT). The program involved 85 participants from RW 08 Cirendeu, Ciputat Timur, including waste bank administrators and KWT members. Success was evaluated through pre-test and post-test surveys, in-depth interviews, and Focus Group Discussions (FGD). The evaluation results showed a 70% increase in community awareness and participation, a 25% rise in KWT members' income, and a 35% reduction in unmanaged waste volume. Additionally, 80% of KWT members adopted organic fertilizers, and 75% of participants gained a deeper understanding of AIK concepts in environmental management. With sustainability strategies that include partnerships with Muhammadiyah institutions and a digital incentive system, this program is expected to continue independently and create a lasting positive impact on the community.
OPTIMASI CONVOLUTION NEURAL NETWORK UNTUK DETEKSI COVID-19 Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Bakti, Indra
RADIAL : Jurnal Peradaban Sains, Rekayasa dan Teknologi Vol. 10 No. 2 (2022): RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37971/radial.v10i2.299

Abstract

Abstrak: Optimasi Convolution Neural Network Untuk Deteksi Covid-19. Kondisi pandemi seperti sekarang ini diperlukan sebuah algoritma pembelajaran mesin untuk mendeteksi covid-19 secara otomatis berdasarkan pada gambar rontgen dada guna memudahkan dalam mambantu pengambil keputusan. Penelitian ini ingin membandingkan arsitektur CNN AlexNet dan MobileNetV2 untuk mendeteksi (a) covid-19, (b) lung opacity, (c) normal, (d) viral pneumonia. Data himpunan rontgen dada yang digunakan sejumlah 4000 yang berasal dari kaggle.com, 0.8 data dibagi untuk pelatihan sedangkan 0.2 nya digunakan untuk pengujian. Optimizer yang digunakan yaitu keras SGD momentum, dengan nilai learning rate 0.005 dan momentum 0.9, serta epoch 50. Ukuran gambar untuk input yaitu 224x224 serta ukuran batch 32. Hasil optimasi dari kedua algoritma tersebut yaitu, MobileNetV2 lebih baik untuk mendeteksi covid-19 dengan nilai akurasi presisi mencapai 99%. Penelitian selanjutnya dapat membandingkan algoritma CNN yang lainnya serta data himpunan yang lebih banyak. Kata kunci: CNN; AlexNet; MobileNetV2; Covid-19 Abstract: Convolution Neural Network Optimization for Covid-19 Detection. In the current pandemic conditions, a machine learning algorithm is needed to detect COVID-19 automatically based on chest X-ray images to make it easier to assist decision makers. Aim study be disposed for compare the architecture of CNN AlexNet and MobileNetV2 to detect (a) covid-19, (b) lung opacity, (c) normal, (d) viral pneumonia. The data set of chest X-rays used are 4000 from kaggle.com, 0.8 of the data is shared for training while 0.2 is used for testing. The optimizer used is hard SGD momentum, with a value of leaning rate 0.005 and momentum 0.9, and epoch 50. The image size for the input is 224x224 and the batch size is 32. The optimization results from the two algorithms are, MobileNetV2 is better for detecting covid-19 with an accuracy value The precision reaches 99%. Future research can compare other CNN algorithms and larger data sets. Keywords: CNN; AlexNet; MobileNetV2; Covid-19
Co-Authors Adhitio Satyo Bayangkari Karno Ambardi, Ambardi Arif, Dody Aryo Nur Utomo Asy-Syifa, Zahwa Zia Azis, Nur Bakti, Indra Bayangkari Karno, Adhitio Satyo Chufran, Indra Bakti Daruningsih, Kukuh Deon Strydom Deswandi, Arief Digdoyo, Aji Dodi Arif Fahrul Razi Fahrul Razi Fiqhri, Zul Fitriyani Fitriyani Handayani, Sri Setya Holmes Rolandy Kapuy Hudaa, Syihaabul Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Ire Puspa Wardhani Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Karno, Adhitio Satyo Bayangkari Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti Lussiana ETP Lyscha Novitasari Maeda, Serly Masriyanda, Masriyanda Muhammad Mardani, Muhammad Nisfiani, Ervina Nurhidayati, Aulia Putra, Yoga Rarasto Putri, Dhea Ananda Rahman, Ibadu Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Saputro, Ahmad Eko Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Soleha, Maratus Sudarto Usuli Sudarwanto, Pantja Sundoro, Aries Surawan, Tri Sutarno Sutarno Syamsu, Muhajir Vany Terisia Yayat Sujatna, Yayat Yulianti Muthmainnah, Yulianti Yuningsih, Nia YUSUF, DIANA