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All Journal International Journal of Electrical and Computer Engineering Sainteks Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek Semesta Teknika Suhuf Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal Pilar Nusa Mandiri Indonesian Journal of Information System Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) Justek : Jurnal Sains Dan Teknologi CARADDE: Jurnal Pengabdian Kepada Masyarakat JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi JIKA (Jurnal Informatika) Community Empowerment Journal of Telecommunication, Electronics and Control Engineering (JTECE) Insearch: Information System Research Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jutech: Jurnal Teknologi Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Science and Technology: Jurnal Pengabdian Masyarakat Journal of Informatics and Information Security Prosiding SeNTIK STI&K Sasambo: Jurnal Abdimas (Journal of Community Service) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi Journal of Computer Science Advancements
Widi Hastomo
Institut Teknologi Dan Bisnis Ahmad Dahlan
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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PENDEKATAN BISNIS MODEL CANVAS UNTUK KAMPUS BERKELANJUTAN Widi Hastomo, LM. Rasdi Rere dan Soegijanto
Prosiding Seminar SeNTIK Vol. 4 No. 1 (2020): Prosiding SeNTIK 2020
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

PENDEKATAN BISNIS MODEL CANVAS UNTUK KAMPUS BERKELANJUTAN
CONVOLUTION NEURAL NETWORK ARSITEKTUR MOBILENET-V2 UNTUK MENDETEKSI TUMOR OTAK Widi Hastomo; Soegijanto; Sudjiran
Prosiding Seminar SeNTIK Vol. 5 No. 1 (2021): Prosiding SeNTIK 2021
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Tumor ganas atau kanker adalah penyebab kematian kedua di dunia setelah kardiovaskuler. Negara berkembang tingkat kematian terhadap penyakit tumor mencapai 70%, sedangkan di negara maju tingkat kematian dapat ditekan karena peralatan dan pelayanan kesehatan sudah sangat baik. Diagnosa cepat dan lebih dini tentu akan mampu menekan tingkat kematian penyakit ini. Metoda CNN mampu membaca image dari peralatan CT Scanner untuk memprediksi pasien terhadap penyakit tumor otak. Penelitian ini menggunakan CNN dengan arsitektur MobileNet-V2 untuk mentrainning dan menguji sebanyak 2.870 image tumor otak. Hasil dari penelitian ini diperoleh nilai akurasi trainning dan testing masing-masing sebesar 97% dan 94%. Dengan nilai akurasi untuk tiap klasifikasi yaitu glioma (99%), meningioma (85%), no_tumor (99%) dan pituaty (96%). Akurasi hasil dari penelitian ini adalah sangat baik, dan model yang dihasilkan dapat digunakan untuk mendiagnosa pasien dengan cepat, murah dan akurat
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.
Penyuluhan Kewirausahaan untuk Pemulihan Ekonomi Terdampak Covid-19 di Tegal Alur Jakarta Barat Eko Ahmad; Widi Hastomo; Yoga Rarasto Putra; Ambardi Ambardi
Sasambo: Jurnal Abdimas (Journal of Community Service) Vol. 4 No. 1: February 2022
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/sasambo.v4i1.611

Abstract

Abdimas ini bertujuan meningkatkan pengetahuan tentang kewirausahaan dan mendorong kaum milenial untuk berwirausaha. Kaum milenial dianggap memiliki kreatifitas sehingga mampu menciptakan produk yang inovatif. Maka kegiatan ini dilaksanakan di Komunitas Pemuda Karang taruna dan Pemuda Masjid Al Hudda di Tegal Alur Jakarta Barat dengan jumlah peserta 18 orang. Metode yang dilakukan adalah metode ceramah dan diskusi dengan materi tentang Kewirausahaan, Studi Kelayakan Bisnis, dan Membuat Logo Produk. Hasil capaian kegiatan ini antara lain terbentuk 1 kelompok wirausaha yang terjun dibidang penjualan produk home cleaner. Dari angket, pemahaman materi tentang kewirausahaan sebesar 94%, perubahan persepsi sebesar 89%, motivasi menjadi wirausahawan sebesar 100%, dan kepuasan peserta terhadap kegiatan sebesar 94%. Entrepreneurship Counseling for Economic Recovery Affected by Covid-19 in Tegal Alur, West Jakarta This Abdimas aims to increase knowledge about entrepreneurship and encourage millennials to become entrepreneurs. Millennials are considered to have created so that they are able to create innovative products. So this activity was carried out at the Youth Youth Community and the Al Hudda Mosque Youth Community in Tegal Alur, West Jakarta with 18 participants. The method used is the lecture and discussion method with material on Entrepreneurship, Business Feasibility Studies, and Creating Product Logos. The results of this activity include the formation of 1 group of entrepreneurs who are involved in the sale of home cleaning products. From the questionnaire, the material understanding of entrepreneurship is 94%, the perception change is 89%, the motivation to become an entrepreneur is 100%, and participants' satisfaction with activities is 94%.
A Breakthrough in Viral Pneumonia Detection: Unveiling Insights with ResNet-152 Widi Hastomo; Adhitio Satyo Bayangkari Karno; Nani Kurniawati; Harini Agusta
Journal of Informatic and Information Security Vol. 4 No. 2 (2023): Desember 2023
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/1zcjsb83

Abstract

Viral pneumonia is one of the most serious health issues. The key problem in providing early detection and rapid mitigation through the use of chest X-ray imaging has become the ability to identify accurately. The ResNet-152 convolutional neural network approach will be used in this study to predict viral pneumonia. The input dataset was obtained from Kaggle.com. The accuracy findings from this investigation obtained a substantial value, namely 0.99, indicating that the model used performed admirably. The model used can efficiently distinguish between the viral pneumonia dataset and other datasets. It is intended that the findings of this study will be used to inform early decisions in related medical sectors.
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

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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.
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
OPTIMIZATION OF FASHION RETAIL CUSTOMER DATA MANAGEMENT THROUGH EXPLORATORY DATA ANALYSIS AND RECENCY, FREQUENCY, MONETARY Yusuf, Yusuf; Basri, Lody Saladin; Hastomo, Widi; Wardhani, Ire Puspa
Prosiding Seminar SeNTIK Vol. 8 No. 1 (2024): Prosiding SeNTIK 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

This study was conducted to analyze revenue patterns, product segmentation, and customer retention in the H&M retail business using Kaggle competition data "H&M Personalized Fashion Recommendations." The urgency of this study lies in the need to understand revenue fluctuations and customer behavior in order to optimize business strategies. The data used includes transactions, articles, and customer profiles from 2018 to 2020. The analysis methods applied include exploratory data analysis (EDA) analysis and customer segmentation using the RFM (recency, frequency, and monetary) model to identify customer groups based on purchasing behavior. The results of the study show that the highest revenue occurs in the middle of the year, with a sharp decline in growth in mid-2018. Low-recency customers contribute more to revenue, while product segmentation shows the need for stock adjustments, especially for baby/children and divided products. This study successfully identified key factors that influence revenue and customer retention and provided strategic recommendations for inventory improvement and market segmentation. These results are important for H&M to improve operational efficiency and improve marketing strategies in the future.
Co-Authors Adhitio Bayangkari Satyo Karno Adhitio Satyo Adhitio Satyo Bayangkari Karno Adhitio Satyo Bayangkari Karno Adhitio Satyo Bayangkari Karno Agita Tunjungsari Ahmad Eko Saputro Ahmad Eko Saputro Ahmad Eko Saputro Aji Digdoyo Aji Digdoyo Ambardi Ambardi Ambardi Ambardi Ambardi, Ambardi Aminudin Ardana, Nandika Bayu Arif, Dody Aryo Nur Utomo Aulia Nurhidayati Azis, Nur Bakti, Indra Basri, Lody Saladin Bayangkari Karno, Adhitio Satyo Chufran, Indra Bakti Daruningsih, Kukuh Deon Strydom Deswandi, Arief Dhea Ananda Putri Diana Yusuf Digdoyo, Aji Dodi Arif Dodi Arif Dody Arif Eka Sally Moreta Eka Sally Moreta Eko Ahmad Eko Ahmad Eko Hadiyanto Elliya Sestri Eva Karla, Eva Fahrul Razi Fahrul Razi Faqihudin Faqihudin Fiedha Nasution Fiqhri, Zul Fitriyani Fitriyani Handayani, Sri Setya Harini Agusta Holmes Rolandy Kapuy Hudaa, Syihaabul Ibadu Rahman Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Ire Puspa Wardhani Iwan Setiawan Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Karno, Adhitio Satyo Bayangkari Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti LM Rasdi Rere LM Rasdi Rere Lussiana ETP Lyscha Novitasari Maratus Soleha Masriyanda, Masriyanda Meika Syahbana Rusli Muhammad Mardani Nada Kamilia Nada Kamilia Nada Kamilia Nani Kurniawati Nia Yuningsih Nia Yuningsih Nisfiani, Ervina Nur Aini Putra, Yoga Rarasto Putri , Basmallah Ramadhani Aisyah Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Reza Fitriansyah Rudy Yulianto Rudy Yulianto Saputro, Ahmad Eko Serly Maeda Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Soegijanto Soegijanto Stevianus Stevianus Sudarto Usuli Sudarwanto, Pantja Sudjiran sudjiran Sukardi, Sukardi Sundoro, Aries Surawan, Tri Sutarno Sutarno Sutarno Sutarno Sutarno Syamsu, Muhajir Syihaabul Hudaa Tri Surawan Tri Surawan Vany Terisia Wardhana , Indra Sari Kusuma Yayat Sujatna Yayat Sujatna Yayat Sujatna, Yayat Yoga Rarasto Putra Yoga Rarasto Putra Yoga Rarastro Putra Yulianti Muthmainnah Yuningsih, Nia Yusuf Yusuf YUSUF, DIANA Zahwa Zia Asy-Syifa