Garuda - Garba Rujukan Digital

STRATEGI PEMUNGUTAN PENERIMAAN PAJAK REKLAME KOTA BEKASI Ichsan, Aulia; Siregar, Hermanto; Soetarto, Endriatmo
Jurnal Manajemen Pembangunan Daerah Vol. 10 (2018): Edisi Khusus "Tatakelola Keuangan dan Investasi Daerah"
Publisher : Program Studi Manajemen Pembangunan Daerah. Fakultas Ekonomi dan Manajemen. IPB University

One of the efforts of Local Government in financing regional development is through local taxes. Bekasi City is a cross-trade City and its strategic location has great potential to be able to maximize local revenue through the local tax sector, e.g. advertisement tax. The purpose of this research was to determine the growth rate and the contribution of advertisement tax to local taxes, measure the effectiveness and efficiency of advertisement tax, and determine the strategy in the implementation of revenue advertisement tax collections. Analysis methods of this research includes calculation of growth and contribution ratio as well as effectiveness and efficiency ratio and the analytical hierarchy process (AHP). Results of this research show that the growth rate of advertisement tax in Bekasi City average 20.65% and its contribution to local taxes average of 3.52% with the average effectiveness rate 82.20% (“quite effective”) and the efficiency level 6.31% (“highly efficient”). It is suggested from the AHP results that the Local Government of Bekasi City should implement a strategy of improving the quality and quantity of human resources as well as a strategy of improving technical guidelines on advertisement tax collection.Keywords : advertisement tax, growth and contribution, effectiveness and efficiency, strategy, Bekasi City.

Klasifikasi Risiko Penyakit Jantung Dengan Multilayer Perceptron Irwan Daniel; Limas Ptr, Agus Fahmi; Ichsan, Aulia
Data Sciences Indonesia (DSI) Vol. 4 No. 1 (2024): Article Research Volume 4 Issue 1, June 2024
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v4i1.4667

Penyakit jantung merupakan salah satu penyebab utama kematian di seluruh dunia, dengan deteksi dini yang seringkali menjadi tantangan karena gejala awalnya yang tidak spesifik. Penelitian ini bertujuan untuk mengevaluasi efektivitas model Multilayer Perceptron (MLP) dalam klasifikasi risiko penyakit jantung dengan membandingkan dua fungsi aktivasi, yaitu ReLU dan Tanh. Dataset yang digunakan terdiri dari 1190 entri dengan 11 fitur kesehatan, yang dibagi dalam rasio 80:20 untuk pelatihan dan pengujian. Model MLP dikembangkan dengan tiga lapisan tersembunyi, dan setiap model diterapkan dengan fungsi aktivasi ReLU dan Tanh untuk mengevaluasi performa masing-masing fungsi dalam mengklasifikasikan risiko penyakit jantung. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa model MLP dengan fungsi aktivasi ReLU memperoleh akurasi sebesar 81,51%, presisi 81,77%, dan recall 81,51%, sedangkan model dengan fungsi aktivasi Tanh mencapai akurasi 80,25%, presisi 80,32%, dan recall 80,25%. Perbedaan ini mengindikasikan bahwa ReLU unggul dalam hal akurasi dan metrik evaluasi lainnya, menjadikannya pilihan yang lebih efektif untuk deteksi dini penyakit jantung. Temuan ini memberikan insight berharga tentang bagaimana pemilihan fungsi aktivasi dapat mempengaruhi kinerja model dalam klasifikasi risiko penyakit, serta menggarisbawahi pentingnya pemilihan teknik yang tepat untuk meningkatkan akurasi deteksi dalam aplikasi medis

Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Disiase Classification Jannah, Aulia; Husaini, Abdillah; Ichsan, Aulia; Azhari, Mulkan
Hanif Journal of Information Systems Vol. 1 No. 2 (2024): February Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v1i2.14

Dengue hemorrhagic fever (DHF) is a condition brought on by infection with the dengue virus. DHF is a severe illness with hemorrhagic clinical signs that can result in shock and death. One of the four viral serotypes of the genus Flavivirus is responsible for DHF. DHF symptoms include fever, joint pain, red skin patches, and others that are similar to those of other illnesses. So that there are no errors in illness prediction, strong accuracy and accuracy are required when classifying DHF patients or not. The Fuzzy K-Nearest Neighbor (FKNN) method is used in this study to classify dengue sickness in order to obtain the best classification outcomes. In this investigation, k was searched for eight times, with values of 3,5,7,9,11,13,15, and 20. Each K's accuracy statistics are 75.15, 75.16, 77.58%, 79.51%, 85.01%, 78.14%, and 75.20 percent. K = 13, which has an accuracy score of 85.01%, yields the highest accuracy.

Design and Construction of a Website-Based Bus Ticket Booking Information System Ichsan, Aulia
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 8, No 2 (2024): InfoTekJar Maret
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v8i2.9906

In the business world, generally users and owners of companies need something innovative to help support performance and facilitate the use or search for product data. The presence of this website-based internet will make it easier for all groups to access all forms of information, including utilizing the media to be able to connect the relationship between customers and the company's products, namely one of which is ordering bus tickets. Based on the various conveniences and uses of the internet, a "Utilization of Information Systems in Ordering and Digitizing Bus Tickets Based on Websites" was developed. This system is made with PHP, MySQL and Bootstrap software. This Information System is designed to provide convenience in terms of ticket ordering services and obtaining other information needed by customers

Design of a Web-Based Mail Management System at The Sub- District Office of Tano Tombangan Angkola Ichsan, Aulia
Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 3, No 1 (2022)
Publisher : Al'Adzkiya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55311/aiocsit.v3i1.237

In the process of modernizing the administration, it is necessary to implement an information system that can support all administrative activities in an institution, institution or company. At the CAMAT Tano Tombangan Angkola office, South Tapanuli Regency, there is still a conventional letter archive management method where archive recording is still carried out using books and stationery. This often causes many problems, such as loss of documents, damage or documents that are not recorded. During the practical work, the author tries to develop an application that can record all archives in the agency. Then it will be stored in a database and can be accessed anytime and anywhere. As a result, many benefits are provided to the agency, such as more structured and neat archive data collection.

Klasifikasi Risiko Penyakit Jantung Dengan Multilayer Perceptron Irwan Daniel; Limas Ptr, Agus Fahmi; Ichsan, Aulia
Data Sciences Indonesia (DSI) Vol. 4 No. 1 (2024): Article Research Volume 4 Issue 1, June 2024
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v4i1.4667

Penyakit jantung merupakan salah satu penyebab utama kematian di seluruh dunia, dengan deteksi dini yang seringkali menjadi tantangan karena gejala awalnya yang tidak spesifik. Penelitian ini bertujuan untuk mengevaluasi efektivitas model Multilayer Perceptron (MLP) dalam klasifikasi risiko penyakit jantung dengan membandingkan dua fungsi aktivasi, yaitu ReLU dan Tanh. Dataset yang digunakan terdiri dari 1190 entri dengan 11 fitur kesehatan, yang dibagi dalam rasio 80:20 untuk pelatihan dan pengujian. Model MLP dikembangkan dengan tiga lapisan tersembunyi, dan setiap model diterapkan dengan fungsi aktivasi ReLU dan Tanh untuk mengevaluasi performa masing-masing fungsi dalam mengklasifikasikan risiko penyakit jantung. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa model MLP dengan fungsi aktivasi ReLU memperoleh akurasi sebesar 81,51%, presisi 81,77%, dan recall 81,51%, sedangkan model dengan fungsi aktivasi Tanh mencapai akurasi 80,25%, presisi 80,32%, dan recall 80,25%. Perbedaan ini mengindikasikan bahwa ReLU unggul dalam hal akurasi dan metrik evaluasi lainnya, menjadikannya pilihan yang lebih efektif untuk deteksi dini penyakit jantung. Temuan ini memberikan insight berharga tentang bagaimana pemilihan fungsi aktivasi dapat mempengaruhi kinerja model dalam klasifikasi risiko penyakit, serta menggarisbawahi pentingnya pemilihan teknik yang tepat untuk meningkatkan akurasi deteksi dalam aplikasi medis

Alat Pemberian Pakan Ternak Otomatis Berbasis IoT (Internet of Things) Batubara, Hanafi; Siambaton, Mhd. Zulfasyuri; Ichsan, Aulia
Hello World Jurnal Ilmu Komputer Vol. 4 No. 3 (2025): Edisi Oktober
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/helloworld.v4i3.974

Permasalahan dalam dunia peternakan salah satunya adalah keterlambatan dalam pemberian pakan ternak yang berdampak pada produktivitas dan kesehatan hewan. Penelitian ini bertujuan untuk merancang dan membangun sebuah alat pemberi pakan ternak otomatis berbasis Internet of Things (IoT) yang dapat mengatur jadwal pemberian pakan secara otomatis serta memantau kondisi pakan melalui aplikasi smartphone. Sistem ini menggunakan mikrokontroler ESP32 sebagai pusat kendali, motor servo untuk mekanisme pemberian pakan, serta sensor ultrasonik untuk mendeteksi ketinggian pakan dalam wadah. Platform Blynk digunakan sebagai antarmuka pengguna untuk mengatur jadwal dan mengontrol alat dari jarak jauh. Metode yang digunakan dalam penelitian ini adalah rekayasa perangkat keras dan lunak, mulai dari perancangan sistem, implementasi, hingga pengujian. Hasil pengujian menunjukkan bahwa sistem mampu memberikan pakan sesuai jadwal yang ditentukan, serta memberikan notifikasi kepada pengguna apabila volume pakan mulai menipis. Dengan demikian, alat ini dapat membantu peternak dalam menghemat waktu, meningkatkan efisiensi, dan memastikan pemberian pakan dilakukan secara konsisten. Sistem ini juga memiliki potensi untuk dikembangkan lebih lanjut dengan fitur tambahan seperti integrasi kamera atau sensor suhu untuk pemantauan lingkungan kandang.

COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS IN THE CLASSIFICATION OF DYSPEPSIA DISEASE Zahra, Fathima; Ichsan, Aulia; Riyadi, Sugeng
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.6874

Functional dyspepsia remains a prevalent gastrointestinal disorder globally, with a higher burden in low- and middle-income countries such as Indonesia. Diagnostic challenges are exacerbated by limited healthcare infrastructure and a low ratio of gastroenterologists. Machine learning approaches offer a promising solution to enhance diagnostic consistency and accuracy in resource-limited settings. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in differentiating dyspepsia from gastroenteritis using Indonesian clinical data. A quantitative experimental method was applied using patient medical records, including gastrointestinal disease categories, vital signs, and symptom profiles. Data preprocessing was carried out by handling missing values through imputation and Min-Max scaling normalization. The dataset was divided into 80% training data and 20% testing data using stratified random sampling. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Random Forest demonstrated superior performance on all evaluation metrics compared to SVM. RF achieved 86.5% accuracy, 86.0% precision, 85.0% recall, and 85.5% F1-score, while SVM achieved 83.2% accuracy, 83.0% precision, 81.0% recall, and 82.0% F1-score. The 3.3 percentage point improvement in accuracy and 4.0 percentage point improvement in recall are clinically significant. Random Forest proved more effective in dyspepsia classification, showing better handling of complex clinical data interactions and more reliable diagnostic performance. These findings support the implementation of an RF-based decision support system in Indonesian healthcare facilities to improve diagnostic consistency and patient outcomes.

Enhancing Multi-Layer Perceptron Performance with K-Means Clustering Pardede, Doughlas; Ichsan, Aulia; Riyadi, Sugeng
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3600

Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications

Analysis of Logistic Regression Regularization in Wild Elephant Classification with VGG-16 Feature Extraction Ichsan, Aulia; Riyadi, Sugeng; Pardede, Doughlas
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3789

The research article explores the intersection of image-based wildlife classification and logistic regression regularization, focusing on the classification of wild elephant species. It begins by highlighting the significance of ecological research in biodiversity monitoring and conservation and introduces Convolutional Neural Networks (CNNs) as potent tools for feature extraction from images. The VGG-16 model is particularly emphasized for its ability to capture hierarchical representations of visual features crucial for classification tasks. The integration of VGG-16 feature extraction with logistic regression regularization is proposed as a compelling approach, offering a balance between sophisticated feature representation and efficient classification algorithms. The literature review delves into image-based wildlife classification, emphasizing the role of CNNs, especially VGG-16, in extracting discriminative features. It discusses the fusion of VGG-16 features with logistic regression and the challenges in this field, such as dataset annotation and environmental variability. The method section outlines the dataset acquisition, feature extraction using the VGG-16 architecture, and model configuration using logistic regression with lasso and ridge regularization. The process of finding the optimal regularization parameter (lambda) and model evaluation through cross-validation is detailed. Results showcase the optimal lambda values for lasso and ridge regularization and compare the performance of logistic lasso and logistic ridge models. Misclassification analysis reveals factors influencing classification accuracy, including feature variability and contextual complexity. The discussion reflects on the implications of the findings, emphasizing the importance of lambda selection and addressing challenges in wildlife classification. It suggests avenues for further research, such as advanced modeling techniques and feature engineering approaches. In conclusion, the study contributes to advancing wildlife classification efforts by leveraging state-of-the-art techniques and sheds light on opportunities to enhance classification accuracy in wildlife conservation.

Title

Found 13 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 13 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 13 Documents
Search