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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Abdimas Ilmiah Citra Bakti (JAICB)
Muis, Saludin
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences

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Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara Pramudita, Rully; Muis, Saludin; Safitri, Nadya; Shafirawati, Fitri
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1928

Abstract

Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of classification algorithms in Machine Learning. Classification algorithms that will be evaluated include Support Vector Machine (SVM), Neural Network, Logistic Regression, Decision Tree, and K-Nearest Neighbors (K-NN). Evaluation of the performance of these algorithms will be carried out using the confusion matrix, Receiver Operating Characteristic (ROC) Curve, and Area Under Curva (AUC). The result of this research is a comparative analysis of the optimization of classification algorithms using the bagging technique. After carrying out the evaluation process using the confusion matrix and ROC curve, it was found that the algorithm optimization using the bagging technique only had an effect on the Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms. . The accuracy of the DT algorithm increased by 0.6% while the accuracy of KNN increased by 1.3%. The AUC value for the DT algorithm increased by 1.4% and the KNN algorithm increased by 0.3%.
Model Prediksi Kepadatan Pariwisata Jawa Barat Menggunakan Metode Long Short-Term Memory with Temporal Attention Nadya Safitri; Pramudita, Rully; Muis, Saludin; Shafirawati, Fitri; Anggoro, Muhammad Seno
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2086

Abstract

This study aims to apply the Long Short-Term Memory Networks (LSTM) with Temporal Attention method in predicting tourism density in West Java tourist destinations. The problem faced is the uncertainty in estimating tourist density at various locations and times, which makes the management of tourism resources and facilities difficult. Therefore, this study is important to provide a tool that can help make more effective decisions in the tourism sector in West Java. The urgency of this study lies in the need for accurate and real-time tourist density predictions to support the management and development of tourist destinations in West Java. With the right prediction model, related parties can regulate capacity, optimize services, and avoid negative impacts such as excess capacity and crowds that have the potential to endanger visitors and the environment. The purpose of this study is to develop a tourism density prediction model that combines the distinctive features of LSTM with a temporal attention mechanism. This model aims to provide accurate and dynamic tourist density estimates, taking into account the temporal patterns of tourist visits in West Java. The model evaluation methods used in this study are RMSE and MAE, and the results of the model testing are that it has an RMSE value of 32208867.139 and an MAE value of 5099.219, and it is hoped that there will be a dataset with a long period after the covid mass where the dataset is free from abnormal events so that a more appropriate model is obtained.
PENINGKATAN LAYANAN PUBLIK KOTA BEKASI MELALUI PELATIHAN SIX SIGMA DAN DIGITALISASI MENUJU SMART CITY Alfian, Ari Nurul; Priyadi, Wiwit; Muis, Saludin; Akmal, Muhamad; Ramadhan, Firdan Margian
Jurnal Abdimas Ilmiah Citra Bakti Vol. 6 No. 3 (2025)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v6i3.5332

Abstract

Layanan publik yang efektif dan efisien merupakan elemen kunci dalam tata kelola pemerintahan modern. Namun, berbagai kendala seperti waktu tunggu yang lama, kurangnya transparansi prosedur, dan keterbatasan sistem digital masih menjadi tantangan dalam penyelenggaraan layanan publik di Kecamatan Bekasi Barat, Kota Bekasi. Untuk menjawab permasalahan ini, Universitas Bina Insani melaksanakan Program Pengabdian kepada Masyarakat (PkM) bertajuk "Peningkatan Kualitas Layanan Publik melalui Pelatihan Six Sigma dan Pengembangan Sistem Informasi Menuju Smart City". Metode yang digunakan dalam kegiatan ini adalah pelatihan dan pendampingan berbasis Lean Six Sigma, serta survei evaluasi kepuasan layanan publik dengan melibatkan 25 responden dari kalangan Aparatur Sipil Negara (ASN) Kecamatan Bekasi Barat. Hasil survei menunjukkan bahwa 100% responden menyatakan integrasi sistem informasi antar instansi dapat meningkatkan kualitas layanan publik, sementara 100% responden mendukung pelatihan bagi petugas layanan publik agar lebih kompeten dalam memberikan pelayanan. Selain itu, aspek kecepatan layanan (88%) dan ketersediaan informasi (87%) menjadi perhatian utama yang perlu ditingkatkan. Berdasarkan temuan tersebut, kegiatan PkM ini merekomendasikan peningkatan kapasitas SDM melalui pelatihan rutin, integrasi sistem informasi digital yang lebih user-friendly, serta optimalisasi transparansi informasi layanan publik. Implementasi strategi ini diharapkan dapat meningkatkan efisiensi layanan, mempercepat proses administrasi, serta meningkatkan kepuasan masyarakat terhadap layanan publik di Kecamatan Bekasi Barat.
Co-Authors Akmal, Muhamad Alfian, Ari Nurul Anggoro, Muhammad Seno Nadya Safitri Nadya Safitri, Nadya Priyadi, Wiwit Ramadhan, Firdan Margian Rully Pramudita Shafirawati, Fitri