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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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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.
Exloratory Data Analysis Untuk Data Belanja Pelanggan dan Pendapatan Bisnis Widi Hastomo; Adhitio Satyo Bayangkari Karno; Sudjiran; Dodi Arif; Eka Sally Moreta
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1547

Abstract

A more quantifiable perspective is assuming the role of mechanistic management in an effort to enhance business based on its capacity to transform data into knowledge and insight. The industry has not completely supported its business strategy also with driven data. Using a transaction dataset taken from one of the Kaggle.com challenges, this experiment attempts to determine consumer spending patterns and Retail Fashion business revenues (H&M Personalized Fashion Recommendations). The results of the experiment are the number of transactions based on customer age, the most sales product and one-time purchased item, and the type of product that generates the highest and smallest income. The approach employed is EDA using the Python language. In order for businesses to generate analytical findings that provide future perspectives and to help identify the gap by delivering analytical results in the form of suggestions that can be perpetuated, the findings of this experiment are intended to support the capabilities of simulation. The challenge in this experiment is the abundance of datasets, which necessitates a suitable operating environment.
Menggunakan Xception, Transfer Learning, dan Permutasi untuk Meningkatkan Klasifikasi Ketidaksempurnaan Permukaan Baja: Using Xception, Transfer Learning, and Permutation to Improve the Classification of Steel Surface Imperfections Setiawati, Popong; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Setiawan, Iwan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1258

Abstract

Kualitas permukaan baja yang diproduksi sangat penting untuk meningkatkan daya saing dalam industri baja. Tingginya tingkat cacat pada permukaan baja merupakan masalah serius yang berdampak pada kualitas keluaran. Pengendalian yang masih dilakukan secara manual dan visual saat ini hanya dapat dilakukan oleh orang-orang dengan bakat dan keahlian tertentu. Pengamatan dengan metode konvensional ini memerlukan waktu yang lama, lamban, dan presisi yang rendah. Saat ini, perkembangan teknik pembelajaran mendalam memungkinkan deteksi cacat permukaan baja secara otomatis dengan tingkat akurasi yang tinggi. Arsitektur Xception digunakan dalam pekerjaan ini untuk menerapkan strategi pembelajaran mendalam. Teknik permutasi dan augmentasi digunakan untuk mengatasi ketidakseimbangan data. Model yang dikembangkan dapat membedakan empat jenis cacat pada permukaan baja. Koleksi 7.095 foto permukaan baja digunakan dalam prosedur pelatihan. Jika dibandingkan dengan tidak menggunakan transfer learning, hasil pengukuran kinerja proses pelatihan dengan menggunakan transfer learning (Imagenet) menunjukkan hasil yang lebih baik. Pelatihan pembelajaran transfer menghasilkan skor akurasi masing-masing sebesar 94,9% dan 97,7% untuk data pelatihan dan validasi. Sedangkan hasil penilaian nilai kerugian untuk data latih dan validasi masing-masing sebesar 19,4% dan 14,4%.
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
Development of Adaptive Lecture Scheduling System using Genetic Algorithm Case Study: Ahmad Dahlan Institute of Technology and Business Ardana, Nandika Bayu; Hastomo, Widi; Arman, Shevti Arbekti
Journal of Computer Science Advancements Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i4.1310

Abstract

Optimal course scheduling is a crucial aspect in supporting the efficiency of the teaching and learning process in higher education. In many institutions, lecture scheduling is still done manually or with static methods that are not adaptive to changing needs and limited resources. This research aims to develop an adaptive lecture scheduling system using genetic algorithms, with a case study at ITB Ahmad Dahlan. Genetic algorithms were chosen because of their ability to solve complex optimization problems with high efficiency, such as managing dynamic variables such as lecturer availability, rooms, and lecture time preferences. In this research, data related to courses, lecturers, time, classroom availability, and curriculum requirements are integrated into the designed system to generate an optimal course schedule. The development process involved several key stages, including requirements analysis, system design, algorithm implementation, and performance evaluation. Genetic algorithm implementation is done by simulating various scheduling scenarios to find the most optimal solution. The results show that the developed system is able to produce a more efficient and clash-free course schedule compared to traditional scheduling methods. In addition, the system also allows higher flexibility in adjusting the schedule to changes that may occur, such as the addition or reduction of classes. Thus, this research makes a significant contribution in improving the quality of educational services at ITB Ahmad Dahlan as well as offering solutions that can be adopted by other educational institutions facing similar challenges.
OPTIMALISASI BANK SAMPAH, KELOMPOK WANITA TANI, DAN POS PEMBINAAN TERPADU DENGAN PERSPEKTIF AL-ISLAM KEMUHAMMADIYAHAN Maratus Soleha; Serly Maeda; Fitriyani Fitriyani; Zahwa Zia Asy-Syifa; Aulia Nurhidayati; Dhea Ananda Putri; Ibadu Rahman; Muhammad Mardani; Yulianti Muthmainnah; Widi Hastomo
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.
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