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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Jurnal Pengabdian Pada Masyarakat Journal of Information Technology
Endang Wahyudi
Universitas Bina Sarana Informatika
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Pelatihan E-Marketing Untuk Meningkatkan Peluang Usaha Melalui Media Sosial Pada Koperasi Wanita Atsiri Citayam Lydia Salvina Helling; Adi Supriyatna; Indra Riyana Rahadjeng; Endang Wahyudi
Jurnal Pengabdian Pada Masyarakat Vol 7 No 2 (2022): Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Mathla'ul Anwar Banten

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.963 KB) | DOI: 10.30653/002.202272.53

Abstract

E-MARKETING TRAINING TO INCREASE BUSINESS OPPORTUNITIES THROUGH SOCIAL MEDIA AT ATSIRI CITAYAM WOMEN’S COOPERATIVE. The Global Pandemic event that occurred in March 2020 had a major impact on all sectors of human life, especially the economic sector. Indonesia is one of the countries affected by the incident so its people are trying to find a way out amid the uncertainty of the world economy. The Ministry of Communication and Information Technology even urges micro, small and medium enterprises (MSMEs) to utilize digital technology in the marketing process. The Director of Information and Communication for the Economy and Maritime Affairs of the Ministry of Communication and Information said that more than 18% of MSMEs in Indonesia have entered the digital economy market, of which 37% of 60% are social media users. Members of the Citayam Bojonggede Atsiri Women's Cooperative are one form of MSMEs that are trying to hone their skills in marketing their products online. Social media is the most popular means of doing marketing with a wide range of people. In the Community Service Program conducted by the lecturers of the Bina Sarana Informatics University, training on the use of social media was given as a way to increase business opportunities amidst the ongoing Covid-19 pandemic. The training includes a brief presentation about e-marketing, explaining the importance of e-marketing in the midst of the current pandemic, providing several e-marketing options that are currently booming, and giving examples of how to promote on Facebook social media.
DESIGN OF KOST RENT INFORMATION SYSTEM Lydia Salvina Helling; Hasanudin Hasanudin; Endang Wahyudi; A.A.Gede Ajusta
Jurnal Riset Informatika Vol. 3 No. 1 (2020): December 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i1.42

Abstract

Rumah Kost Hj. Gaby is one of the many boarding houses for rent around industrial areas and offices in the Tg. Priok area, North Jakarta. This boarding house still uses a board in front of the house to indicate that the house is rented out for boarding. This method is less effective in promoting the boarding house because prospective tenants must accidentally pass and see the promotion board. The down payment for boarding and monthly rent still has to meet directly with the owner of the boarding house so that it requires special time. The boarding rental information system that is designed is expected to help prospective tenants or tenants to make more practical order and payment transactions, in addition to helping boarding owners tidy up their boarding room rental management The methods used in this research are: interviews, observation, and literacy studies for data collection. Meanwhile, the model used for software development uses the Rapid Application Development Model (RAD). The results of this study are expected to assist boarding owners in promoting their boarding houses and also provide a more effective way of conducting transactions related to the boarding houses.
Komparasi Performa Algoritma Kompresi Data Lossless Menggunakan Rasio Kompresi Dan Penghematan Ruang Aswar Hanif; Endang Wahyudi; Harna Adianto; Lilik Martanto
J-INTECH ( Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.863

Abstract

Data growth is a sizeable challenge. The goal of data compression is to reduce the size of data needed to still represent useful information. Data compression can be used to increase the efficiency of data storage, transmission and protection. Lossless algorithms can precisely reconstruct the original data from the compressed data. Lossless compression is often used for data that needs to be stored or transmitted accurately. Several lossless compression methods and algorithms include the Lempel–Ziv–Markov chain algorithm (LZMA), Prediction by partial matching (PPM), Burrows-Wheeler block sorting text compression algorithm and Huffman coding (BZip2), and Deflate. Even though all compression systems are based on the same principles, there should still be differences in performance. Because of that, a general guide is needed to help determine the most appropriate data compression algorithm to use. This study aims to determine the data compression algorithm that has the best performance, based on a comparison using the Compression Ratio and Space Saving values. The research phase begins with determining the compression algorithm used, data preparation, performance testing, to then be discussed and conclusions drawn. The results show that the compression ratio and space savings that can be achieved specifically will depend on the data used. Although the range of average values of compression performance is not that big, in general LZMA2 shows the best results with a compression ratio of 1.457 and a space saving of 15.00%. Hopefully, the results of this test can be used as an overview in helping to choose a lossless data compression algorithm.
Pemilihan Model Churn pada Data Tidak Seimbang Berdasarkan ROC AUC dan Recall Aswar Hanif; Harna Adianto; Lilik Martanto; Endang Wahyudi
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9821

Abstract

Abstrak - Customer churn adalah sebuah keadaan di mana pelanggan menghentikan hubungan bisnis dengan sebuah usaha. Kemampuan untuk memprediksi customer churn merupakan salah satu faktor penting dalam perencanaan bisnis. Umumnya data customer churn tidak seimbang, dan menjadi tantangan signifikan dalam pembelajaran mesin. Untuk mengatasi masalah ini, pendekatan yang paling sering digunakan adalah oversampling. Metode yang populer adalah SMOTE, yang bisa meningkatkan peforma model, namun juga bisa menyebabkan overfitting. Telah banyak penelitian dilakukan dengan menggunakan oversampling dalam menghadapi data tidak seimbang. Tetapi masih sedikit penelitian yang fokus pada pemilihan model klasifikasi berdasarkan metrik yang sesuai, tanpa menggunakan oversampling. Penelitian ini menguji model-model klasifikasi dalam memprediksi customer churn terhadap data tidak seimbang, baik dengan maupun tanpa menggunakan SMOTE, untuk perbandingan hasil cross-validation dan performa pengujian. Kemudian model-model ini dievaluasi menggunakan metrik Balanced Accuracy. Kebaruan terletak pada fokus bahwa pemilihan model berdasarkan kombinasi ROC AUC dan Recall, bisa menemukan model prediksi customer churn terbaik tanpa harus menggunakan oversampling. Diharapkan hasil ini dapat berkontribusi dalam memperluas wawasan dari asumsi bahwa data tidak seimbang selalu harus diatasi menggunakan oversampling.Kata kunci : Pemilihan model; Data tidak seimbang; Tanpa oversampling; ROC AUC; Recall; Abstract - Customer churn refers to the phenomenon in which a customer ends their relationship with a company. Being able to predict customer churn is crucial for business planning. However, customer churn data is often imbalance, making it a major challenge for machine learning. One way to tackle this issue is oversampling. A widely used approach is SMOTE, which can boost model performance but also risks overfitting. There have been many studies using oversampling to address imbalanced data. However, there's a lack of research on selecting a classification model based on suitable metrics without relying on oversampling. This study evaluates classification models for predicting customer churn on imbalanced datasets, comparing performance with and without the application of SMOTE using cross-validation and test results. Subsequently, the models are evaluated using the Balanced Accuracy metric. This study introduces a novel approach in which model selection based on a combination of ROC AUC and Recall identifies the optimal customer churn prediction model without the need for oversampling. These results may broaden understanding beyond the prevailing assumption that imbalanced data must always be addressed using oversampling.Keywords: Model selection; Data imbalance; Without oversampling; ROC AUC; Recall
Co-Authors A.A.Gede Ajusta Adi Supriyatna Aswar Hanif Harna Adianto Hasanudin Hasanudin Indra Riyana Rahadjeng Lilik Martanto Lydia Salvina Helling