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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

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.

Analisa Internet Movie Database (IMDb) Menggunakan Algoritma Machine Learning Super Vector Machine Wardhana, Indra Sari Kusuma
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 9, No 3 (2025)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v9i3.28010

This paper presents an analysis of the Internet Movie Database (IMDb) using the Support Vector Machine (SVM) algorithm for sentiment classification. IMDb, as one of the largest online movie review platforms, offers a vast dataset of user reviews, which can be leveraged to analyze public opinion on movies. The goal of this study is to classify movie reviews as positive or negative using SVM, a machine learning algorithm known for its effectiveness in binary classification tasks. The dataset, consisting of thousands of IMDb reviews, undergoes pre-processing steps such as tokenization, removal of stop words, and text vectorization using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM algorithm is then applied to this processed data to train the model, which is evaluated based on its accuracy, precision, recall, and F1-score. Experimental results indicate that the SVM model performs with high accuracy, proving its reliability in sentiment analysis tasks for large-scale movie review datasets. This paper also discusses the advantages of using SVM over other machine learning algorithms and highlights areas for future improvement, including incorporating more nuanced sentiment categories and optimizing the model's hyperparameters.

Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet Adhitio Satyo Bayangkari Karno; Dodi Arif; Indra Sari Kusuma Wardhana; Eka Sally Moreta
Journal of Informatic and Information Security Vol. 2 No. 1 (2021): Juni 2021
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/abbs9m42

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).

METAVERSE: TRANSFORMASI DUNIA DIGITAL DAN TANTANGAN MASA DEPAN Wardhana, Indra Sari Kusuma; Irawati, Diyah Ruri; Hakim, Abdul
Prosiding Seminar SeNTIK Vol. 8 No. 1 (2024): Prosiding SeNTIK 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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

Metaverse merupakan evolusi teknologi yang menggabungkan elemen realitas virtual (VR), augmented reality (AR), dan lingkungan digital untuk menciptakan ruang virtual yang imersif dan interaktif. Artikel ini membahas potensi metaverse dalam mengubah cara manusia berinteraksi, belajar, dan bekerja, sekaligus menganalisis tantangan yang dihadapi dalam pengembangannya. Dari segi transformasi digital, metaverse menawarkan peluang besar di berbagai sektor, termasuk pendidikan, bisnis, dan hiburan, dengan memungkinkan kolaborasi dan komunikasi tanpa batas geografis. Namun, penerapan teknologi ini tidak terlepas dari tantangan signifikan seperti keterbatasan infrastruktur teknologi, masalah privasi dan keamanan data, serta regulasi yang belum jelas. Artikel ini juga mengeksplorasi peran teknologi blockchain, cloud computing, dan kecerdasan buatan (AI) dalam mengatasi tantangan tersebut, serta pentingnya kolaborasi lintas sektor untuk menciptakan metaverse yang aman, inklusif, dan berkelanjutan. Dengan memadukan analisis teoretis dan studi kasus, artikel ini menawarkan pandangan komprehensif tentang masa depan metaverse dan dampaknya terhadap transformasi dunia digital.

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

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.

Klasifikasi Tingkat Prestasi Mahasiswa Pada Mata Kuliah Penambangan Data Menggunakan Naïve Bayes Putri, Basmallah Ramadhani Aisyah; Mursidan, Almurozy; Wardhana, Indra Sari Kusuma
Indo-MathEdu Intellectuals Journal Vol. 7 No. 1 (2026): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v7i1.5050

This study aims to classify the academic achievement levels of students in Data Mining courses using the Naïve Bayes algorithm and to evaluate the performance of the resulting classification model. This study uses a quantitative approach with academic data from 190 students, including assignment scores, mid-term exam scores, and final exam scores. The classification process was carried out by applying the Naïve Bayes algorithm, while model evaluation was performed using accuracy metrics, classification reports, and confusion matrices. The test results showed that the Naïve Bayes model produced an accuracy rate of 73.68%. Based on the classification report, classes B and B+ showed the best performance with recall values of 1.00 and f1-scores of 0.87 and 0.95, respectively. Confusion matrix analysis showed that most of the data in classes B and B+ were classified correctly. The results of this study indicate that the Naïve Bayes algorithm is quite effective in classifying students' academic achievement levels and has the potential to be used as an academic evaluation tool in learning decision-making.

Pelatihan Implementasi Mikrokontroler Berbasis Internet of Things (IoT) untuk Monitoring Berbasis Lingkungan dan Kualitas Air di PT. Netsource Global Technologies Ramadhani Aisyah Putri, Basmallah; Sari Kusuma Wardhana, Indra; Mursidan, Almurozy
Smart Dedication: Jurnal Pengabdian Masyarakat Vol. 3 No. 2 (2026): Smart Dedication : Jurnal Pengabdian Masyarakat
Publisher : SMART SCIENTI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70427/smartdedication.v3i2.315

Perkembangan teknologi Internet of Things (IoT) meningkatkan kebutuhan industri terhadap sistem monitoring lingkungan yang akurat dan terintegrasi. Namun, kompetensi engineer dalam implementasi mikrokontroler dan integrasi sensor masih perlu ditingkatkan untuk mendukung pengembangan solusi berbasis IoT. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan kompetensi engineer PT. Netsource Global Technologies melalui pelatihan implementasi mikrokontroler berbasis Arduino/ESP32 untuk monitoring suhu, kelembaban, tingkat keasaman (pH) air, dan tinggi permukaan air. Metode yang digunakan meliputi workshop, praktikum (hands-on training), pendampingan, serta evaluasi menggunakan kuesioner skala Likert. Kegiatan dilaksanakan pada 15 Desember 2025 dengan melibatkan enam engineer sebagai peserta. Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan peserta dalam pemrograman mikrokontroler, integrasi sensor, serta pengembangan sistem monitoring berbasis IoT. Selain itu, dihasilkan prototipe sistem monitoring yang mampu melakukan pengukuran parameter lingkungan secara real-time. Evaluasi kegiatan memperoleh nilai rata-rata 4,54 dengan kategori sangat baik, yang menunjukkan tingkat kepuasan peserta yang tinggi terhadap materi, praktikum, dan manfaat kegiatan. Berdasarkan hasil evaluasi, kegiatan dinilai bermanfaat dan sesuai dengan kebutuhan peserta serta mendukung kolaborasi antara perguruan tinggi dan industri.

Optimasi Load Balancing Trafik Jaringan LTE Melalui Implementasi Antena Pro Sectoral 1800 Mhz & 2100 Mhz Almurozy Mursidan; Basmallah Ramadhani Aisyah Putri; Indra Sari Kusuma Wardhana
BEES: Bulletin of Electrical and Electronics Engineering Vol 7 No 1 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v7i1.10473

This study aims to analyze the effectiveness of implementing a sectoral pro antenna on LTE 1800 MHz and 2100 MHz frequencies in improving LTE network performance at the Kota Wisata site, Bogor, West Java. A quantitative comparative method was employed by comparing network performance before and after the implementation of the pro antenna. Data were collected from the operator’s network monitoring system during the pre-implementation period (Mei 11–12, 2026) and the post-implementation period (Mei 18–19, 2026). The analyzed parameters included Main Site Payload, Cluster Payload, and RSRP Coverage. The results indicate that the implementation of the sectoral pro antenna significantly improved LTE network performance. The Main Site Payload increased from 335.42 GB to 726.13 GB, representing an improvement of 116.48%, while the Cluster Payload increased from 6,308.82 GB to 6,431.93 GB, representing an increase of 1.95%. In addition, network coverage quality improved, as indicated by the increase in the percentage of RSRP values greater than -100 dBm from 99.09% to 99.85%, as well as an overall RSRP coverage improvement of 2.11%. These results demonstrate that the implementation of the sectoral pro antenna on LTE 1800 MHz and 2100 MHz frequencies is effective in distributing traffic more evenly, reducing the potential for network congestion, improving signal coverage quality, and enhancing overall LTE network performance.

Implementation of the Internet of Things for Real-Time Monitoring and Control of Environmental Conditions Indra Sari Kusuma Wardhana; Almurozy Mursidan
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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

The development of the Internet of Things (IoT) enables environmental conditions to be monitored and controlled in real time through the integration of sensors, microcontrollers, wireless communication, monitoring interfaces, and actuators. This study aims to implement an IoT-based system for real-time environmental monitoring and automatic control based on measured environmental conditions, particularly temperature and humidity. The developed system integrates DHT11, MQ-2, and MQ-6 sensors with Arduino Uno R3, NodeMCU ESP8266, wireless communication, a monitoring dashboard, and relay-controlled actuators. Sensor measurements are processed and transmitted to the monitoring system, while predefined temperature thresholds are used to determine actuator activation. Experimental evaluation was conducted through sensor accuracy, communication reliability, monitoring, and automatic control tests. The results showed average temperature and humidity measurement errors of 0.78% and 1.46%, respectively, indicating relatively small deviations from reference measurements under the tested conditions. Communication testing achieved a 100% success rate for 100 transmitted packets with no packet loss, while the average communication delay was 412 ms. Automatic control testing achieved 100% success across 10 trials using a 30 °C temperature threshold. These findings demonstrate that the integrated IoT architecture can support responsive environmental monitoring and automated control within the tested operating conditions.

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